diff --git a/INSTALL.md b/INSTALL.md
deleted file mode 100644
index c6dc2f62..00000000
--- a/INSTALL.md
+++ /dev/null
@@ -1,50 +0,0 @@
-Installing SoundCast
-====================
-
-- Complete documentation is always at http://soundcast.readthedocs.io
-
-Requirements:
---------------
-
-Hardware:
-
-- 8 CPUs
-- 32 GB RAM
-- 120 GB disk space **per model run**
-
-Software:
-
-- Windows 7+, 64-bit only
-- Python 2.7 and pip (tested only with Anaconda Python - strongly recommended)
-- Emme 4.2.3+
-
-
-Basic setup instructions:
--------------------------
-
-1. Install 64-bit Anaconda Python 2.7 from http://www.continuum.io/downloads
-
-2. Find your Python install folder in File Explorer:
-   - Go to Anaconda2\Lib\site-packages
-   - Delete the folder 'ply'
-   - (This is for Emme Python compatibility)
-
-3. Install Emme 4.2.3+
-   - NO to Emme Python as the System Python
-   - Make sure you can open Emme Desktop and that your license is validated
-
-4. Run Emme Desktop:
-   - Go to Tools - App Options - Modeller.
-   - Change Python Path to point to your Anaconda install folder, and click "Install Modeller Package".
-   - Close Emme.
-
-5. Clone SoundCast or grab latest zipfile with either:
-   - git clone http://github.com/psrc/soundcast.git
-   - or download and unzip: https://github.com/psrc/soundcast/archive/master.zip
-
-6. Open a Command Terminal (CMD DOS box):
-   - cd into the SoundCast folder
-   - Run 'install.bat' to install all software dependencies
-   - Check output for errors.
-
-You're ready to run SoundCast.
diff --git a/daysim_configuration_template.properties b/daysim_configuration_template.properties
index 5c644b4d..924b7588 100644
--- a/daysim_configuration_template.properties
+++ b/daysim_configuration_template.properties
@@ -9,7 +9,7 @@ NodeDistanceReaderType = HDF5
 
 # general path settings
 BasePath = inputs
-OutputSubpath = ..\outputs
+OutputSubpath = ..\outputs\daysim
 WorkingDirectory =..\working
 WorkingSubpath = ..\working
 EstimationSubpath = ..\estimation
@@ -22,7 +22,7 @@ NProcessors = 24
 NBatches = 96
 
 # PSRC and HDF5
-ShouldRunInputTester = true
+ShouldRunInputTester = false
 HDF5Filename = hh_and_persons.h5
 HDF5Path = daysim_outputs.h5
 ReadHDF5 = true
@@ -46,10 +46,10 @@ RawParcelNodePath = parcel_nodes_2014.txt
 RawParcelNodeDelimiter = 32 
 
 # value of time
-VotVeryLowLow = 12 
-VotLowMedium = 24 
-VotMediumHigh = 30 
-VotHighVeryHigh = 40
+VotVeryLowLow = 13.07 
+VotLowMedium = 26.14 
+VotMediumHigh = 32.67 
+VotHighVeryHigh = 43.56
 
 
 # global settings
@@ -109,7 +109,7 @@ ShouldSynchronizeRandomSeed= true
 # internal-external worker fractions for living and working
 IxxiPath = psrc_worker_ixxifractions.dat 
 IxxiDelimiter = 9 
-IxxiFirstLineIsHeader = false 
+IxxiFirstLineIsHeader = False
 
 # zone list, district lookup
 ImportZones = true 
@@ -163,12 +163,12 @@ OutputSamplingWeightsPath = sampling_weights.dat
 
 # Model Coefficients and which models to run
 WorkLocationModelSampleSize = 30 
-WorkLocationModelCoefficients= coefficients/WorkLocationModel_psrcper1_Calib3.F12 
+WorkLocationModelCoefficients= coefficients/WorkLocationModel.F12 
 ShouldRunWorkLocationModel= true 
 IncludeWorkLocationModel= true 
 
 SchoolLocationModelSampleSize = 30 
-SchoolLocationModelCoefficients = coefficients/SchoolLocationModel_psrcper1_Calib2.F12 
+SchoolLocationModelCoefficients = coefficients/SchoolLocationModel.F12 
 ShouldRunSchoolLocationModel = true 
 IncludeSchoolLocationModel = true 
 
@@ -176,17 +176,17 @@ PayToParkAtWorkplaceModelCoefficients = coefficients/PayToParkAtWorkplaceModel_p
 ShouldRunPayToParkAtWorkplaceModel = $RUN_ALL 
 IncludePayToParkAtWorkplaceModel = true 
 
-TransitPassOwnershipModelCoefficients = coefficients/HTransitPassOwnershipModel_psrcper1.f12 
+TransitPassOwnershipModelCoefficients = coefficients/TransitPassOwnershipModel.f12 
 ShouldRunTransitPassOwnershipModel = $RUN_ALL 
 IncludeTransitPassOwnershipModel = true 
 
 AutoOwnershipModelCoefficients = coefficients/AutoOwnershipModel_psrcper1.f12 
 ShouldRunAutoOwnershipModel = $RUN_ALL 
 
-IndividualPersonDayPatternModelCoefficients = coefficients/IndividualPersonDayPatternModel_psrcper1.f12 
+IndividualPersonDayPatternModelCoefficients = coefficients/IndividualPersonDayPatternModel.f12 
 ShouldRunIndividualPersonDayPatternModel = $RUN_ALL 
 
-PersonExactNumberOfToursModelCoefficients = coefficients/PersonExactNumberOfToursModel_psrcper1.F12 
+PersonExactNumberOfToursModelCoefficients = coefficients/PersonExactNumberOfToursModel.F12 
 ShouldRunPersonExactNumberOfToursModel = $RUN_ALL 
 
 WorkTourDestinationModelSampleSize = 20 
@@ -194,50 +194,50 @@ WorkTourDestinationModelCoefficients = coefficients/WorkTourDestinationModel_psr
 ShouldRunWorkTourDestinationModel = $RUN_ALL 
 
 OtherTourDestinationModelSampleSize = 20 
-OtherTourDestinationModelCoefficients = coefficients/OtherTourDestinationModel_psrcper1.F12 
+OtherTourDestinationModelCoefficients = coefficients/OtherTourDestinationModel.F12 
 ShouldRunOtherTourDestinationModel = $RUN_ALL 
 
-WorkBasedSubtourGenerationModelCoefficients = coefficients/WorkbasedSubtourGenerationCoefficients_SACOG-v1.5.F12 
+WorkBasedSubtourGenerationModelCoefficients = coefficients/WorkBasedSubtourGenerationModel.F12 
 ShouldRunWorkBasedSubtourGenerationModel = $RUN_ALL 
 
-WorkTourModeModelCoefficients = coefficients/WorkTourModeModel_psrcper1.F12 
+WorkTourModeModelCoefficients = coefficients/WorkTourModeModel.F12 
 ShouldRunWorkTourModeModel = $RUN_ALL 
 
-SchoolTourModeModelCoefficients = coefficients/SchoolTourModeModel_psrcper1.F12 
+SchoolTourModeModelCoefficients = coefficients/SchoolTourModeModel.F12 
 ShouldRunSchoolTourModeModel = $RUN_ALL 
 
-WorkBasedSubtourModeModelCoefficients = coefficients/WorkBasedSubtourModeCoefficients_SACOG-v1.5.F12 
+WorkBasedSubtourModeModelCoefficients = coefficients/WorkBasedSubtourModeModel.F12 
 ShouldRunWorkBasedSubtourModeModel = $RUN_ALL 
 
-EscortTourModeModelCoefficients = coefficients/EscortTourModeModel_psrcper1.F12 
+EscortTourModeModelCoefficients = coefficients/EscortTourModeModel.F12 
 ShouldRunEscortTourModeModel = $RUN_ALL 
 
-OtherHomeBasedTourModeModelCoefficients = coefficients/OtherHomeBasedTourModeModel_psrcper1.F12 
+OtherHomeBasedTourModeModelCoefficients = coefficients/OtherHomeBasedTourModeModel.F12 
 ShouldRunOtherHomeBasedTourModeModel = $RUN_ALL 
 
-WorkTourTimeModelCoefficients = coefficients/WorkTourTimeModel_psrcper1.F12 
+WorkTourTimeModelCoefficients = coefficients/WorkTourTimeModel.F12 
 ShouldRunWorkTourTimeModel = $RUN_ALL 
 
-SchoolTourTimeModelCoefficients = coefficients/SchoolTourTimeModel_psrcper1.F12 
+SchoolTourTimeModelCoefficients = coefficients/SchoolTourTimeModel.F12 
 ShouldRunSchoolTourTimeModel = $RUN_ALL 
 
-OtherHomeBasedTourTimeModelCoefficients = coefficients/OtherHomeBasedTourTimeModel_psrcper1.F12 
+OtherHomeBasedTourTimeModelCoefficients = coefficients/OtherHomeBasedTourTimeModel.F12 
 ShouldRunOtherHomeBasedTourTimeModel = $RUN_ALL 
 
-WorkBasedSubtourTimeModelCoefficients = coefficients/WorkbasedSubtourTimeCoefficients_SACOG-v1.5.F12 
+WorkBasedSubtourTimeModelCoefficients = coefficients/WorkBasedSubtourTimeModel.F12 
 ShouldRunWorkBasedSubtourTimeModel = $RUN_ALL 
 
-IntermediateStopGenerationModelCoefficients = coefficients/IntermediateStopGenerationModel_psrcper1.F12 
+IntermediateStopGenerationModelCoefficients = coefficients/IntermediateStopGenerationModel.F12
 ShouldRunIntermediateStopGenerationModel = $RUN_ALL 
 
 IntermediateStopLocationModelSampleSize = 20 
 IntermediateStopLocationModelCoefficients = coefficients/IntermediateStopLocationModel_psrcper1.F12 
 ShouldRunIntermediateStopLocationModel = $RUN_ALL 
 
-TripModeModelCoefficients = coefficients/TripModeModel_psrcper1.f12 
+TripModeModelCoefficients = coefficients/TripModeModel.f12 
 ShouldRunTripModeModel = $RUN_ALL 
 
-TripTimeModelCoefficients = coefficients/TripTimeModel_psrcper1.f12 
+TripTimeModelCoefficients = coefficients/TripTimeModel.f12 
 ShouldRunTripTimeModel = $RUN_ALL 
 
 # Path Impedance Parameters
@@ -247,9 +247,10 @@ PathImpedance_TransitInVehicleTimeWeight = 1.0
 PathImpedance_TransitFirstWaitTimeWeight = 2.0 
 PathImpedance_TransitTransferWaitTimeWeight = 2.0 
 PathImpedance_TransitNumberBoardingsWeight = 8.0 
+PathImpedance_TransitNumberBoardingsWeight_Rail = 8.0
 PathImpedance_TransitDriveAccessTimeWeight = 2.0 
 PathImpedance_TransitWalkAccessTimeWeight = 2.0 
-PathImpedance_WalkTimeWeight = 2.5 
+PathImpedance_WalkTimeWeight = 5.0 
 PathImpedance_BikeTimeWeight = 2.5 
 PathImpedance_WalkMinutesPerMile = 20.0 
 PathImpedance_TransitWalkAccessDistanceLimit = 1.0 
@@ -258,14 +259,14 @@ PathImpedance_TransitSingleBoardingLimit = 1.1
 PathImpedance_AutoTolledPathConstant = 0.0 
 PathImpedance_AvailablePathUpperTimeLimit = 200.0 
 PathImpedance_TransitLocalBusPathConstant = 0.00 
-PathImpedance_TransitPremiumBusPathConstant = 0.0 
-PathImpedance_TransitLightRailPathConstant = 0.05 
+PathImpedance_TransitPremiumBusPathConstant = 0.0
+PathImpedance_TransitLightRailPathConstant = 0.035  
 PathImpedance_TransitCommuterRailPathConstant = 0.0 
 PathImpedance_TransitFerryPathConstant = 0.0 
 PathImpedance_TransitUsePathTypeSpecificTime = true 
 
 PathImpedance_TransitPremiumBusTimeAdditiveWeight = 0.00 
-PathImpedance_TransitLightRailTimeAdditiveWeight = -0.10
+PathImpedance_TransitLightRailTimeAdditiveWeight = 0.00
 PathImpedance_TransitCommuterRailTimeAdditiveWeight = -0.25
 PathImpedance_TransitFerryTimeAdditiveWeight = -0.40
 PathImpedance_BikeUseTypeSpecificDistanceFractions = false 
diff --git a/docs/input_checklist.csv b/docs/input_checklist.csv
new file mode 100644
index 00000000..5125f04a
--- /dev/null
+++ b/docs/input_checklist.csv
@@ -0,0 +1,64 @@
+exists,update from base year,folder,file,description
+,y,4k,auto.h5,input for truck model
+,y,4k,transit.h5,input for truck model
+,y,4k,trips_auto_4k.h5,input for truck model
+,y,4k,trips_transit_4k.h5,input for truck model
+,y,bikes,bike_count_data.csv,observed bike count values intersected with network
+,y,bikes,edges_0.txt,network attribute table
+,y,bikes,emme_attr.in,link bike facilities and slope factors 
+,,etc,daysim_outputs_seed_trips.h5,
+,,etc,survey.h5,base year survey
+,y,Fares,am_fares_farebox.in,
+,y,Fares,am_fares_monthly_pass.in,
+,y,Fares,md_fares_farebox.in,
+,y,Fares,md_fares_monthly_pass.in,
+,y,Fares,transit_fare_zones.grt,
+,,landuse,distribute_jblm_jobs.csv,
+,,landuse,parcels_military.csv,
+,y,landuse,hh_and_persons.h5,
+,y,landuse,parcels_urbansim.txt,
+,y,networks,x_roadway.in,"where x is time period (e.g., am)"
+,y,networks,x_transit.in,"where x is time period (e.g., am)"
+,y,networks,x_turns.in,"where x is time period (e.g., am)"
+,y,networks,edges_y.*,"where y is 0 through 4, representing time period am through ni, several files associated with shapefile"
+,y,networks,junctions.*,junction shapefile and related files
+,?,networks,modes.txt,Emme mode string definitions
+,?,networks,pnr_lot_capacities.in,
+,y,networks,sc_headways.csv,
+,?,networks,vehicles.txt,
+,?,networks/rdly,x_rdly.txt,"where x is time period (e.g., am)"
+,,seed_skims,x.h5,"where x is 12-hour time segment (e.g., 5to6.h5, 6to7.h5)"
+,,shadow_pricing,shadow_prices.txt,
+,,short_distance_files,node_index_2014.txt,
+,,short_distance_files,node_to_node_distance_2014.h5,
+,,short_distance_files,parcel_nodes_2014.txt,
+,,.,transit_stops.csv,
+,,tolls,x_roadway_tolls.in,"where x is time period (e.g., am)"
+,,tolls,ferry_vehicle_fares.in,
+,,trucks,medium_trucks_ie.in,
+,,trucks,medium_trucks_ei.in,
+,,trucks,medium_trucks_ee.in,
+,,trucks,heavy_trucks_ie.in,
+,,trucks,heavy_trucks_ei.in,
+,,trucks,heavy_trucks_ee.in,
+,,trucks,matrix_calc_spec.txt,
+,y,trucks,tazdata.in,
+,?,trucks,agshar.in,
+,?,trucks,minshar.in,
+,?,trucks,prodshar.in,
+,?,trucks,equipshar.in,
+,?,trucks,tcushar.in,
+,?,trucks,whlsshar.in,
+,,trucks,const.in,
+,y,trucks,special_gen_medium_trucks.in,
+,y,trucks,special_gen_heavy_trucks.in,
+,n,supplemental/generation/rates,hh_triprates.in,
+,n,supplemental/generation/rates,nonhh_triprates.in,
+,n,supplemental/generation/ensembles,puma00.ens,
+,y,supplemental/generation/landuse,tazdata.in,
+,,supplemental/generation,externals.csv,
+,,supplemental/generation/pums,pumshhxc_income-collegestudents.in,
+,,supplemental/generation/pums,pumshhxc_income-k12students.in,
+,,supplemental/generation/pums,pumshhxc_income-size-workers.in,
+,,supplemental/generation/pums,pumshhxc_income-size-workers-vehicles.in,
+,y,emme_configuration.py,spg_general demand dictionary,
diff --git a/emme_configuration.py b/emme_configuration.py
index b49c764f..d29d2f52 100644
--- a/emme_configuration.py
+++ b/emme_configuration.py
@@ -1,4 +1,6 @@
-##################################### NETWORK IMPORTER ####################################
+from input_configuration import *
+##################################### NETWORK IMPORTER ####################################
+import_shape = False    # use network shape
 project = 'Projects/LoadTripTables/LoadTripTables.emp'
 network_summary_project = 'Projects/LoadTripTables/LoadTripTables.emp'
 tod_networks = ['am', 'md', 'pm', 'ev', 'ni']
@@ -16,13 +18,14 @@
 mode_file = 'modes.txt'
 transit_vehicle_file = 'vehicles.txt' 
 base_net_name = '_roadway.in'
+shape_name = '_link_shape.txt'
 turns_name = '_turns.in'
 transit_name = '_transit.in'
-shape_name = '_link_shape_1002.txt'
 no_toll_modes = ['s', 'h', 'i', 'j']
 unit_of_length = 'mi'    # units of miles in Emme
+rdly_factor = .25
 coord_unit_length = 0.0001894    # network links measured in feet, converted to miles (1/5280)
-headway_file = 'sc_headways.csv'
+headway_file = ''.join(['sc_headways_', scenario_name, '.csv'])
 
 ################################### SKIMS AND PATHS ####################################
 log_file_name = 'skims_log.txt'
@@ -72,18 +75,26 @@
 transit_node_attributes = {'headway_fraction' : {'name' : '@hdwfr', 'init_value': .5}, 
                            'wait_time_perception' :  {'name' : '@wait', 'init_value': 2},
                            'in_vehicle_time' :  {'name' : '@invt', 'init_value': 1}}
-transit_node_constants = {'am':{'4943':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
+transit_node_constants = {'2014':{'4943':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
                           '4944':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
                           '4945':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
                           '4952':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
                           '4960':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
                           '4961':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}},
-                          'pm':{'4943':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
-                          '4944':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
-                          '4945':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
-                          '4952':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
-                          '4960':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
-                          '4961':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}}}
+                          '2025':{'5165':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
+                          '5166':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '5167':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
+                          '5168':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '5670':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '5671':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}},
+                          '2040':{'0041':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
+                          '0042':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '0043':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}, 
+                          '0044':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '0055':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '0056':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '0057':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'},
+                          '0058':{'@hdwfr': '.1', '@wait' : '1', '@invt' : '.70'}}}
 
 transit_network_tod_dict = {'5to6' : 'am', '6to7' : 'am', '7to8' : 'am', '8to9' : 'am',
                             '9to10' : 'md', '10to14' : 'md', '14to15' : 'md',
@@ -134,26 +145,29 @@
 taz_data_loc = '/supplemental/generation/landuse/tazdata.in'
 pums_data_loc = '/supplemental/generation/pums/' 
 externals_loc = '/supplemental/generation/externals.csv'
+special_gen_trips = 'inputs/supplemental/generation/special_generators.csv'
+airport_zone_list = [983] # zone numbers for airport special generator
 # Special generator zones and demand (dictionary key is TAZ, value is demand)
-spg_general = {3110: 1682,    
-               631: 7567, 
-               438: 14013}    
-spg_airport = {983: 101838}
+# spg_general = {3110: 1682,    
+#                631: 7567, 
+#                438: 14013}    
+# spg_airport = {983: 101838}
 
 # Using one AM and one PM time period to represent AM and PM skims
 am_skim_file_loc = 'inputs/7to8.h5'
 pm_skim_file_loc = 'inputs/17to18.h5'
-trip_table_loc = 'outputs/prod_att.csv'
+trip_table_loc = 'outputs/supplemental/prod_att.csv'
 output_dir = 'outputs/supplemental/'
 ext_spg_dir = 'outputs/supplemental/ext_spg'
 gq_directory = 'outputs/supplemental/group_quarters'
-gq_trips_loc = 'outputs/gq_prod_att.csv'
+gq_trips_loc = 'outputs/supplemental/gq_prod_att.csv'
 supplemental_project = 'projects/supplementals/supplementals.emp'
 # Iterations for fratar process in trip distribution
 bal_iters = 5
 # Define gravity model coefficients
 autoop = 16.75    # Auto operation costs (in hundreds of cents per mile?)
 avotda = 0.0303    # VOT
+airport_control_total = {'2014' : 101838, '2020' : 130475, '2025' : 149027, '2030' : 170216, '2035' : 189617, '2040' : 211228} 
 
 # Change modes for toll links
 toll_modes_dict = {'asehdimjvutbpfl' : 'aedmvutbpfl', 'asehdimjvutbpwl' :	'aedmvutbpwl', 'ahdimjbp' : 'admbp'}
\ No newline at end of file
diff --git a/input_configuration.py b/input_configuration.py
index 04f412cf..354dc3ce 100644
--- a/input_configuration.py
+++ b/input_configuration.py
@@ -1,3 +1,4 @@
+import os
 from input_configuration_simple import *
 
 # This file contains model input parameters imported by SoundCast scripts.   
@@ -15,22 +16,31 @@
 if not(use_simple_configuration):
     
     # Scenario and input paths
-    base_year = '2014'  # This should always be 2010 unless the base year changes
+    base_year = '2014'  # This should always be 2014 unless the base year changes
     scenario_name = '2014'
+    model_year = '2014'
     daysim_code = 'R:/SoundCast/daysim_2016' 
     master_project = 'LoadTripTables'
-    main_inputs_folder =  'R:/SoundCast/Inputs/'
-    base_inputs = main_inputs_folder + scenario_name
+    main_inputs_folder =  'R:/SoundCast/Inputs'
+    base_inputs = os.path.join(main_inputs_folder, base_year)
+    scenario_inputs = os.path.join(main_inputs_folder, scenario_name)
     # For Overriding the simple configuration, when you want to run things in more detail:
     
-    ###### Only update parking for future-year analysis!######
-    run_update_parking = False
-    ##########################################################
-    
     ###### Only Convert 2010 hhinc if using 2010 base year!######
     run_convert_hhinc_2000_2010 = False
     #############################################################
     
+    ###### Distance-based pricing######
+    add_distance_pricing = False
+    # rate below includes 3.5 cent carbon tax
+    distance_rate_dict = {'am' : 13.5, 'md' : 8.5, 'pm' : 13.5, 'ev' : 8.5, 'ni' : 8.5}
+    # HOT Lanes
+    add_hot_lane_tolls = False
+    hot_rate_dict = {'am' : 35, 'md' : 10, 'pm' : 35, 'ev' : 10, 'ni' : 10}
+    # in the junctions shapefile in the inputs/networks folder, this is the minimum scene_node value where facility type = 99
+    min_hov_node = 199203
+    ###################################
+    
     ######Set up:######
     run_accessibility_calcs = True
     run_copy_daysim_code = True
@@ -38,6 +48,7 @@
     run_setup_emme_bank_folders = True
     run_copy_large_inputs = True
     run_import_networks = True
+    run_daysim_zone_inputs = True
     ###################
 
     ###### Only one of the following should be Tru!!!!!!######
@@ -56,14 +67,17 @@
     run_supplemental_trips = True
     run_daysim = True
     ###########################
+
+    ###### Additional skims for Benefit Cost:######
+    should_run_reliability_skims = True
+    ###########################
     
     #Summaries to run:######
-    run_accessibility_summary = False
+    run_accessibility_summary = True
     run_network_summary = True
+    run_grouped_summary = True
     run_soundcast_summary = True
-    run_create_daily_bank = True
-    run_ben_cost = False
-    run_truck_summary = False
+    run_truck_summary = True
     run_landuse_summary = False
     ########################
 
@@ -93,7 +107,7 @@
 else:
 
     create_no_toll_network = False
-    run_ben_cost = False
+
     min_pop_sample_convergence_test = 10
     
     if run_setup:
@@ -199,7 +213,6 @@
 ###########################################################################################################################################################
 # These files generally do not change and don't need to be toggled here usually
 master_project = 'LoadTripTables'
-run_tableau_db = False
 parcel_decay_file = 'inputs/buffered_parcels.txt' #File with parcel data to be compared to
 # run daysim and assignment in feedback until convergence
 
@@ -213,15 +226,11 @@
 # Please add to this list as you find files that are missing.
 commonly_missing_files = ['buffered_parcels.txt', 'tazdata.in']
 
-
-run_tableau_db = False
-
 # Calibration Summary Configuration
-h5_results_file = 'outputs/daysim_outputs.h5'
+h5_results_file = 'outputs/daysim/daysim_outputs.h5'
 h5_results_name = 'DaysimOutputs'
 h5_comparison_file = 'scripts/summarize/inputs/calibration/survey.h5'
 h5_comparison_name = 'Survey'
 guidefile = 'scripts/summarize/inputs/calibration/CatVarDict.xlsx'
 districtfile = 'scripts/summarize/inputs/calibration/TAZ_TAD_County.csv'
-report_output_location = 'outputs'
-bc_outputs_file = 'outputs/BenefitCost.xlsx'
\ No newline at end of file
+report_output_location = 'outputs/daysim'
\ No newline at end of file
diff --git a/input_configuration_simple.py b/input_configuration_simple.py
index c1eb9842..df5acde0 100644
--- a/input_configuration_simple.py
+++ b/input_configuration_simple.py
@@ -9,7 +9,8 @@
 daysim_code = 'R:/SoundCast/daysim_2016' 
 main_inputs_folder =  'R:/SoundCast/Inputs/'
 master_project = 'LoadTripTables'
-base_inputs = main_inputs_folder + scenario_name
+base_inputs = main_inputs_folder + base_year
+scenario_inputs = main_inputs_folder + scenario_name
 
 run_setup = False
 run_daysim = False
diff --git a/inputs/ObservedBoardings.xlsx b/inputs/ObservedBoardings.xlsx
deleted file mode 100644
index 15235a83..00000000
Binary files a/inputs/ObservedBoardings.xlsx and /dev/null differ
diff --git a/inputs/TransitRouteKey.xlsx b/inputs/TransitRouteKey.xlsx
deleted file mode 100644
index 9d5f4166..00000000
Binary files a/inputs/TransitRouteKey.xlsx and /dev/null differ
diff --git a/inputs/accessibility/PugetSoundNetwork.h5 b/inputs/accessibility/PugetSoundNetwork.h5
deleted file mode 100644
index 806622b3..00000000
Binary files a/inputs/accessibility/PugetSoundNetwork.h5 and /dev/null differ
diff --git a/inputs/accessibility/PugetSoundNetworkAddons.h5 b/inputs/accessibility/PugetSoundNetworkAddons.h5
deleted file mode 100644
index f08bab56..00000000
Binary files a/inputs/accessibility/PugetSoundNetworkAddons.h5 and /dev/null differ
diff --git a/inputs/accessibility/enlisted_personnel.csv b/inputs/accessibility/enlisted_personnel.csv
new file mode 100644
index 00000000..641ac947
--- /dev/null
+++ b/inputs/accessibility/enlisted_personnel.csv
@@ -0,0 +1,781 @@
+w_geocode,Base,Zone,ParcelID,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,688623,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Sand Point,105,788128,10,10,9,9,8,8,7,7,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Fort Lawton,331,788128,0,1,1,2,2,3,4,4,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3033E+14,Pier 91,390,1161163,60,57,54,51,48,45,42,39,36,33,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.30611E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.30611E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3062E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.3061E+14,Everett,2255,103640,268,265,262,259,256,254,251,248,245,242,239,238,236,235,233,232,230,229,227,226,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224,224
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,Camp Murray,3061,462809,0,0,1,1,2,2,2,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM North Fort,3070,196,28,31,34,37,40,44,47,50,53,56,59,59,59,59,59,59,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM McChord,3348,653,61,61,62,62,62,63,63,63,63,64,64,63,63,62,61,61,60,59,58,58,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Madigan,3349,3019,37,41,45,50,54,58,62,66,71,75,79,79,79,79,79,79,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Maingate,3352,3022,19,21,23,25,27,30,32,34,36,38,40,40,40,40,40,40,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM Central,3353,1,211,234,258,281,304,328,351,374,397,421,444,443,443,442,441,441,440,439,438,438,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437,437
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM South,3355,3023,22,24,27,29,31,34,36,38,40,43,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45,45
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30531E+14,JBLM East,3356,3024,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Bangor_North,3496,275679,18,18,18,17,17,17,17,17,16,16,16,16,16,15,15,15,15,15,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30359E+14,Keyport,3508,282840,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,Bangor_South,3518,282597,82,81,80,79,78,78,77,76,75,74,73,72,72,71,70,70,69,68,67,67,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30531E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30531E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30531E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30531E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30531E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30531E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
+5.30351E+14,PSNS,3626,369920,176,168,161,153,146,138,130,123,115,108,100,100,101,101,102,102,102,103,103,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104
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diff --git a/inputs/accessibility/transit_stops_2014.csv b/inputs/accessibility/transit_stops_2014.csv
deleted file mode 100644
index eadcaaa8..00000000
--- a/inputs/accessibility/transit_stops_2014.csv
+++ /dev/null
@@ -1,6137 +0,0 @@
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diff --git a/inputs/coefficients/EscortTourModeModel_psrcper1.F12 b/inputs/coefficients/EscortTourModeModel.F12
similarity index 100%
rename from inputs/coefficients/EscortTourModeModel_psrcper1.F12
rename to inputs/coefficients/EscortTourModeModel.F12
diff --git a/inputs/coefficients/IndividualPersonDayPatternModel.F12 b/inputs/coefficients/IndividualPersonDayPatternModel.F12
new file mode 100644
index 00000000..47b0f858
--- /dev/null
+++ b/inputs/coefficients/IndividualPersonDayPatternModel.F12
@@ -0,0 +1,6464 @@
+pattern020115.ALO   (SACOG2000 v020115)                                        
+Created by ALOGIT version 4                             16:22:40 on 12 Nov 16
+END
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diff --git a/inputs/coefficients/IntermediateStopGenerationModel.F12 b/inputs/coefficients/IntermediateStopGenerationModel.F12
new file mode 100644
index 00000000..3921f766
--- /dev/null
+++ b/inputs/coefficients/IntermediateStopGenerationModel.F12
@@ -0,0 +1,669 @@
+Stop frequency and purpose sfreq1702.ALO                                       
+Created by ALOGIT version 4                             11:30:34 on 10 May 17
+END
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diff --git a/inputs/coefficients/IntermediateStopGenerationModel_psrcper1.F12 b/inputs/coefficients/IntermediateStopGenerationModel_psrcper1.F12
deleted file mode 100644
index 637ae48b..00000000
--- a/inputs/coefficients/IntermediateStopGenerationModel_psrcper1.F12
+++ /dev/null
@@ -1,669 +0,0 @@
-Stop frequency and purpose sfreq1702.ALO                                       
-Created by ALOGIT version 4                             10:08:22 on  7 Apr 12
-END
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diff --git a/inputs/coefficients/IntermediateStopLocationModel_psrcper1.F12 b/inputs/coefficients/IntermediateStopLocationModel_psrcper1.F12
index 81b23e41..de0f8366 100644
--- a/inputs/coefficients/IntermediateStopLocationModel_psrcper1.F12
+++ b/inputs/coefficients/IntermediateStopLocationModel_psrcper1.F12
@@ -3,12 +3,12 @@
 END
    1 Beta00001  T    1.00000000000       .000000000000    
    2 Beta00002  F    1.82314136732       .431364040329    
-   3 Beta00003  F   -25.7470509278       .515644422253    
-   4 Beta00004  F    3.0000000000        1.29370699099    
+   3 Beta00003  F   -140.7470509278       .515644422253    
+   4 Beta00004  F    5.0000000000        1.29370699099    
    5 Beta00005  F   -1.99457840161       .745286155651    
-   6 Beta00006  F   -9.26105010594       3.27122553894    
+   6 Beta00006  F   -10.26105010594       3.27122553894    
    7 Beta00007  F    .919780617878E-01   .116539442778E-01
-   8 Beta00008  F    .078116338084       .143935312712E-01
+   8 Beta00008  F    5.00000000000       .143935312712E-01
    9 Beta00009  F    .432079541092       .220322036336    
   10 Beta00010  F   -1.38710826379       .390194235553    
   11 Beta00011  F    .671491641973       .584778303193    
diff --git a/inputs/coefficients/New_Not_Used/IntermediateStopGenerationModel.F12 b/inputs/coefficients/New_Not_Used/IntermediateStopGenerationModel.F12
new file mode 100644
index 00000000..9f70d266
--- /dev/null
+++ b/inputs/coefficients/New_Not_Used/IntermediateStopGenerationModel.F12
@@ -0,0 +1,669 @@
+Stop frequency and purpose sfreq1702.ALO                                       
+Created by ALOGIT version 4                             21:09:06 on  6 Nov 16
+END
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+  -1474  -1432  -9530  -1946   -817  -1430  -1005     72      0  -4389
+    446    502     26      0    320    177   -239  -3841   1196   1144
+    -33   1010   -763   -572  -6361  -1022  -1652   -585   -690  -2008
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+     40   -776   -104   -369  -1475  -5356  -5450  -2632  -3703 -38258
+  -3335   -802 -32661  -4366  -1291  -1992 -27450  -1892  -1123  -2037
+ -31787  -1335  -1248  -2211 -32369  -1057   -967  -1499 -27439   -987
+   -739  -1310 -36290  -1416   -166   -514      3     45   1192  -3164
+    379     93      0    -42    441    666  -2943      0    394    543
+   -625     26   1488   1679  -9327   1428   -373    400     51   -849
+  -1622   3673   -542  -2186   -512   -287    -78    886  -2064   1319
+  -2109  -6376  14193  -4141    869  -1622     17   -132    340    209
+  -1249  -1153   -850    612  -1057   -398   4862   7063   7853   1834
+   1819   -653   -768  15650  11728
diff --git a/inputs/coefficients/New_Not_Used/PersonExactNumberOfToursModel.F12 b/inputs/coefficients/New_Not_Used/PersonExactNumberOfToursModel.F12
new file mode 100644
index 00000000..058b7c07
--- /dev/null
+++ b/inputs/coefficients/New_Not_Used/PersonExactNumberOfToursModel.F12
@@ -0,0 +1,4638 @@
+SACOG pattern model n tours by purpose                                         
+Created by ALOGIT version 4                             22:01:31 on  6 Nov 16
+END
+   1  Purpose   T    .000000000000       .000000000000    
+ 101   WT-FTW   F   -.595419491440       .166117766317    
+ 102   WT-PTW   T   -.200000000000       .000000000000    
+ 103   WT-RET   T    .000000000000       .000000000000    
+ 104   WT-NWA   T    .000000000000       .000000000000    
+ 105   WT-UNI   T    .000000000000       .000000000000    
+ 106   WT-DAS   T    .000000000000       .000000000000    
+ 109  WT-VLINC  F    .403829783459       .258765077143    
+ 110  WT-LOINC  T    .000000000000       .000000000000    
+ 111  WT-HIINC  T    .000000000000       .000000000000    
+ 112 WT-CARSPD  T    .000000000000       .000000000000    
+ 113 WT-ONLYAD  T    .000000000000       .000000000000    
+ 114 WT-ONLYWK  T    .000000000000       .000000000000    
+ 116 WT-FEMNOC  T    .000000000000       .000000000000    
+ 117 WT-FADCU5  T    .000000000000       .000000000000    
+ 118 WT-FAD515  T    .000000000000       .000000000000    
+ 119 WT-MADCU5  T    .000000000000       .000000000000    
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+ 122 WT-AG2635  F    .131396395056E-01   .152883014316    
+ 123 WT-AG5165  T    .000000000000       .000000000000    
+ 124 WT-WAHOME  T    .000000000000       .000000000000    
+ 125 WT-MIXDEN  T    .000000000000       .000000000000    
+ 126 WT-INTDEN  T    .000000000000       .000000000000    
+ 127 WTAGGLOG2  F    .792276236133       .102993259779    
+ 129 WTAGGLOG3  F    .482387186361       .435768759634    
+ 131 WT-NWORKT  T    .000000000000       .000000000000    
+ 132 WT-NSCHOT  F   -1.03094897552       .702771735869    
+ 133 WT-NESCOT  F    .403616995001       .212161361508    
+ 134 WT-NPBUST  T    .000000000000       .000000000000    
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+ 136 WT-NMEALT  T    .000000000000       .000000000000    
+ 137 WT-NSRECT  T    .000000000000       .000000000000    
+ 141 WT-IWORKS  F    .105930862150       .777921743394E-01
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+ 143 WT-IESCOS  F   -.160565604879       .117311154306    
+ 144 WT-IPBUSS  F    .179059327686       .105992036906    
+ 145 WT-ISHOPS  F    .168907088303       .934798982293E-01
+ 146 WT-IMEALS  T    .000000000000       .000000000000    
+ 147 WT-ISRECS  F   -.210764416083       .165488270357    
+ 152 WC-2Tours  F   -3.44458950731       .182238961624    
+ 153 WC-3Tours  F   -6.23235966358       .501386973250    
+ 201   ST-FTW   T    .000000000000       .000000000000    
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+ 208   ST-CU5   F   -.668685786955       .746116737473    
+ 209  ST-VLINC  T    .000000000000       .000000000000    
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+ 212 ST-CARSPD  F   -.595527568986       .140920099344    
+ 213 ST-ONLYAD  T    .000000000000       .000000000000    
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+ 216 ST-FEMNOC  T    .000000000000       .000000000000    
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+ 227 S-AGGLOG2  F    .448066598132       .864558835449E-01
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+ 252 SC-2Tours  F   -3.86659417154       .247612216134    
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diff --git a/inputs/coefficients/New_Not_Used/WorkBasedSubtourGenerationModel.F12 b/inputs/coefficients/New_Not_Used/WorkBasedSubtourGenerationModel.F12
new file mode 100644
index 00000000..128818fb
--- /dev/null
+++ b/inputs/coefficients/New_Not_Used/WorkBasedSubtourGenerationModel.F12
@@ -0,0 +1,32 @@
+work based tour frequency and purpose wbfreq0501.ALO                           
+Created by ALOGIT version 4                             21:39:55 on  6 Nov 16
+END
+   1 Beta00001  F   -1.06382007968       .370500101643    
+   3 Beta00003  F   -2.22833261833       1.02512113042    
+   4 Beta00004  F   -3.68904126435       .816920967494    
+   6 Beta00006  F   -2.14118599531       .449499017430    
+   8 Beta00008  F   -2.14560875433       .441872609039    
+  10 Beta00010  F   -.734009862713       .356479174177    
+  13 Beta00013  F   -1.75561798085       .429220839362    
+  15 Beta00015  F    1.10997350555       .247065122845    
+  16 Beta00016  F    .450103870523       .210995760527    
+  18 Beta00018  F    .594349062632       .421653118901    
+  22 Beta00022  F   -.418535219130       .312125790598    
+  23 Beta00023  F   -.135615437481       .181310469627    
+  32 Beta00032  F   -.196652926963E-01   .465505027187E-01
+  39 Beta00039  F    .686499966992E-01   .188213259534    
+  50 Theta0050  F    1.26977275612       .185993147374    
+  -1
+    1062  -.531009842162E+03  -.100225059631E+04  -.500118004513E+03
+  17   021:39:55 on  6 Nov 16
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diff --git a/inputs/coefficients/OtherHomeBasedTourModeModel.F12 b/inputs/coefficients/OtherHomeBasedTourModeModel.F12
new file mode 100644
index 00000000..d1ab4904
--- /dev/null
+++ b/inputs/coefficients/OtherHomeBasedTourModeModel.F12
@@ -0,0 +1,161 @@
+Other HB tour mode choice                                                      
+Created by ALOGIT version 4                             10:51:21 on  6 Apr 17
+END
+   1  costutil  F    .461846060850       .743264873766E-01
+   2  timeutil  F    .659611637063       .357174532657E-01
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+  21 wt-nocars  F    1.59176056618       .182769557926    
+  30  s3-const  F   -1.78022496899       .159066664518    
+  31  sr-hhcu5  F    .514123397716       .824788117381E-01
+  32  sr-hh515  F    .224818703896       .647250203599E-01
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+  35 sr-lndist  F    .152322573764       .337807471997E-01
+  38 s3-onephh  F   -2.79241793207       .182688234076    
+  39 s3-twophh  F   -2.81617825169       .130705252962    
+  40  s2-const  F   -2.00944544285       .158784983104    
+  41 sr-nocars  F   -1.48012105583       .206320394147    
+  43 sr-carsltw F   -.892899546287E-01   .137321579832    
+  48 s2-onephh  F   -1.43280251293       .133715087486    
+  50  da-const  F   -.461309408531       .112940823683    
+  52 da-carsltd F   -.790578280547       .915458357506E-01
+  54 inc0to25k  F    .742427734873E-02   .123737072552    
+  60  bi-const  F   -4.44108483963       .360616471386    
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+ 128 tr-dempdn  F    .406644874942E-04   .585560624976E-05
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+ 132 da-othsptr F    .209692204221       .498851467098E-01
+ 133 sr-escsptr T    .000000000000       .000000000000    
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+ 138 sr-socrec  F    .467984103652       .915827776613E-01
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+ 161 bi-class1  T    .000000000000       .000000000000    
+ 162 bi-class2  T    .000000000000       .000000000000    
+ 163 bi-gauntl  T    .000000000000       .000000000000    
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+ 169 bi-dmixed  T    .000000000000       .000000000000    
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+ 178 wk-dempdn  F   -.619572537396E-05   .464771480534E-04
+ 201 distcont1  T   -4.50000000000       .000000000000    
+ 202 distcont2  T   -4.50000000000       .000000000000    
+ 203 distcont3  T    .000000000000       .000000000000    
+ 204 distcont4  T    .000000000000       .000000000000    
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+ 206 distcont6  T    .000000000000       .000000000000    
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+ 208 distcont8  T    .000000000000       .000000000000    
+ 209 distcont9  T    .000000000000       .000000000000   
+ 210 distcon10  T    .00000000000        .000000000000    
+ 211 distcon11  T    .00000000000        .000000000000
+  99  modenest  F    .876150145659       .460275599070E-01
+  -1
+    8060  -.109300824315E+05  -.133185283795E+05  -.766886425667E+04
+   8   010:51:21 on  6 Apr 17
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diff --git a/inputs/coefficients/OtherHomeBasedTourTimeModel.F12 b/inputs/coefficients/OtherHomeBasedTourTimeModel.F12
new file mode 100644
index 00000000..23d1ec24
--- /dev/null
+++ b/inputs/coefficients/OtherHomeBasedTourTimeModel.F12
@@ -0,0 +1,548 @@
+tod other hb tours                                                             
+Created by ALOGIT version 4                              8:27:34 on 24 Oct 16
+END
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diff --git a/inputs/coefficients/OtherTourDestinationModel.F12 b/inputs/coefficients/OtherTourDestinationModel.F12
new file mode 100644
index 00000000..0ec6ec1b
--- /dev/null
+++ b/inputs/coefficients/OtherTourDestinationModel.F12
@@ -0,0 +1,613 @@
+Tour Destination (SACOG2000 v0402)                                             
+Created by ALOGIT version 4                             11:04:21 on 31 Oct 16
+END
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+  10164  16644  19563  10754   5585  19468  12617  18096  18040  15553
+  24033      0  -2052   7506  14863   -624  10894      0  12476  29770
+      0   5365  14139      0  14227      0      0      0      0  -3131
+      0    768  -1405    493    903      0   1351   2578   2013   -656
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+      0      0      0    151   -181   -704   -580  -1003    288   -817
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+    521 -17253 -13668  17139      0   2603      0    424   -448    802
+    400    673    762   1206   1423    789    436   1385    913   1316
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+   -204   1261      0   -563  -1559    379   -497   -666     15  -2890
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+   -425    -76     33    215   -483   -554  -4483      0      0      0
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+   -427   -297  -3166  -2638   -279  -1274   3273  -3976 -14043    822
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+  15728  18475  10182   5276  18387  11917  17103  17050  14705  22698
+      0  -1929   7095  14031   -585  10280      0  11794  28083      0
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+      0  -1489      0   -854    218   -577    906      0   -543   -787
+   -127    551     26    219  -2073  -1893      0      0      0    409
+   -386    411    113   -419      0   -327   -129     63   -234   -496
+   -372  -3142      0      0      0   -467    249    522    187   -279
+   2932  -7724     71  -1591  -1401   -502   -263  -2197  -3633   -833
+    251  -3084   -951  -6752  -3944   3230      0  23847      0   3369
+  -4345   7393   4139   6062   6704  10980  12890   7095   3679  12841
+   8320  11943  11907  10264  15845      0  -1342   4969   9779   -402
+   7175      0   8237  19590      0   3539   9309      0   9373      0
+      0      0   8626  16833      0  28997      0   -224      0   -393
+   -657   -505   1327      0    146  -2363    383   -458    587  -1145
+  -2646  -2630      0      0      0     -9   -578    589    485   -633
+      0   -531     11    257    559     41   -548  -4730      0      0
+      0   -768    586    962   1050    107   4439 -11752    109  -2225
+  -1981   -214     54  -3345  -2895   -171   -608   1204  -5138    350
+  -1732 -15061      0  36091      0   5071  -6570  11175   6295   9158
+  10145  16618  19510  10738   5566  19404  12575  18043  17983  15522
+  23964      0  -2019   7503  14795   -622  10855      0  12452  29662
+      0   5368  14090      0  14188      0      0      0  13049   4770
+      0  47976  31396      0  -1212      0   -842   -999     -2   1811
+      0   -385  -1849    831     72   -219  -1043  -3390  -3029      0
+      0      0    388   -647    713    572   -753      0   -637   -109
+    184    338   -516   -669  -5607      0      0      0   -863    600
+   1016   1010   -245   5194 -13827    133  -2710  -2427   -420   -283
+  -3952   -601    482   1772    978  -3570   -270   7793 -33165      0
+  42529      0   5992  -7744  13172   7407  10797  11965  19591  22992
+  12659   6563  22872  14827  21272  21213  18296  28244      0  -2385
+   8849  17452   -723  12800      0  14683  34945      0   6324  16602
+      0  16720      0      0      0  15384  30247      0  59811  38075
+  71499      0      0      0      0      0      0      0      0      0
+      0      0      0      0      0      0      0      0      0      0
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+      0      0      0      0      0      0      0      0      0      0
+      0      0      0      0      0      0      0      0      0      0
+      0     76      0    245   -388     77    562      0   -345   -134
+    438   -340   -926   -575   -838   -652      0      0      0    -43
+   -171    144    197   -206      0   -173     13     64    230    -27
+   -207  -1528      0      0      0   -236    211    270    361      2
+   1394  -3779     27   -707   -641    -57    -17  -1097    525    456
+    635   1078    -27   1388    138 -12231      0  11587      0   1641
+  -2115   3578   2014   2942   3264   5338   6267   3444   1791   6221
+   4030   5785   5772   4977   7688      0   -656   2398   4766   -197
+   3495      0   3994   9520      0   1725   4524      0   4558      0
+      0      0   4195   9366      0  20030  12071  19982  32501      0
diff --git a/inputs/coefficients/PersonExactNumberOfToursModel_psrcper1.F12 b/inputs/coefficients/PersonExactNumberOfToursModel.F12
similarity index 93%
rename from inputs/coefficients/PersonExactNumberOfToursModel_psrcper1.F12
rename to inputs/coefficients/PersonExactNumberOfToursModel.F12
index 51c2b49c..c6dfef07 100644
--- a/inputs/coefficients/PersonExactNumberOfToursModel_psrcper1.F12
+++ b/inputs/coefficients/PersonExactNumberOfToursModel.F12
@@ -1,14 +1,14 @@
 SACOG pattern model n tours by purpose                                         
-Created by ALOGIT version 4                             13:12:48 on 31 Oct 13
+Created by ALOGIT version 4                             20:09:22 on 10 May 17
 END
    1  Purpose   T    .000000000000       .000000000000    
- 101   WT-FTW   F   -.407539205797       .143003841966    
+ 101   WT-FTW   F   -.697439641202       .170547210124    
  102   WT-PTW   T   -.200000000000       .000000000000    
  103   WT-RET   T    .000000000000       .000000000000    
  104   WT-NWA   T    .000000000000       .000000000000    
  105   WT-UNI   T    .000000000000       .000000000000    
  106   WT-DAS   T    .000000000000       .000000000000    
- 109  WT-VLINC  F    .592323243932       .310797049553    
+ 109  WT-VLINC  F    .357843027752       .260091068221    
  110  WT-LOINC  T    .000000000000       .000000000000    
  111  WT-HIINC  T    .000000000000       .000000000000    
  112 WT-CARSPD  T    .000000000000       .000000000000    
@@ -20,40 +20,40 @@
  119 WT-MADCU5  T    .000000000000       .000000000000    
  120 WT-MAD515  T    .000000000000       .000000000000    
  121 WT-AG1825  T    .000000000000       .000000000000    
- 122 WT-AG2635  F   -.220279675211       .188014372347    
+ 122 WT-AG2635  F    .522988815684E-01   .153388544093    
  123 WT-AG5165  T    .000000000000       .000000000000    
  124 WT-WAHOME  T    .000000000000       .000000000000    
  125 WT-MIXDEN  T    .000000000000       .000000000000    
  126 WT-INTDEN  T    .000000000000       .000000000000    
- 127 WTAGGLOG2  F    .102650488614       .366361729744E-01
- 129 WTAGGLOG3  F    .528604823693E-01   .194958377753    
+ 127 WTAGGLOG2  F    .872263681443       .103217328131    
+ 129 WTAGGLOG3  F    .645808519342       .432538066978    
  131 WT-NWORKT  T    .000000000000       .000000000000    
- 132 WT-NSCHOT  F   -1.33014744163       1.00209246031    
- 133 WT-NESCOT  F    .877676708277E-01   .146505766218    
+ 132 WT-NSCHOT  F   -1.13604823013       .704993371519    
+ 133 WT-NESCOT  F    .387973075912       .211832008485    
  134 WT-NPBUST  T    .000000000000       .000000000000    
  135 WT-NSHOPT  T    .000000000000       .000000000000    
  136 WT-NMEALT  T    .000000000000       .000000000000    
  137 WT-NSRECT  T    .000000000000       .000000000000    
- 141 WT-IWORKS  F    .350052499667       .624011528381E-01
+ 141 WT-IWORKS  F    .958843476619E-01   .780997647204E-01
  142 WT-ISCHOS  T    .000000000000       .000000000000    
- 143 WT-IESCOS  F    .155263206721       .102730721692    
- 144 WT-IPBUSS  F    .193652398110       .108567137566    
- 145 WT-ISHOPS  F   -.104486311259       .122415989039    
+ 143 WT-IESCOS  F   -.190229824757       .118739467843    
+ 144 WT-IPBUSS  F    .172460300439       .106461133727    
+ 145 WT-ISHOPS  F    .160717510000       .933973928137E-01
  146 WT-IMEALS  T    .000000000000       .000000000000    
- 147 WT-ISRECS  F    .383520855052E-01   .235632237301    
- 152 WC-2Tours  F   -3.11451911428       .143515923865    
- 153 WC-3Tours  F   -6.37034639273       .409299495180    
+ 147 WT-ISRECS  F   -.221677227968       .165998464997    
+ 152 WC-2Tours  F   -3.29143877673       .180795980710    
+ 153 WC-3Tours  F   -6.17641342081       .479839678711    
  201   ST-FTW   T    .000000000000       .000000000000    
  202   ST-PTW   T    .000000000000       .000000000000    
  204   ST-NWA   T    .000000000000       .000000000000    
- 205   ST-UNI   F    1.38281106133       .393048672364    
- 206   ST-DAS   F    1.30880197544       .327395815649    
+ 205   ST-UNI   F    2.32811047895       .263110820113    
+ 206   ST-DAS   F   -.474248620690       .456578112813    
  207   ST-PAS   T    .000000000000       .000000000000    
- 208   ST-CU5   F   -1.55339312837       1.03953628998    
+ 208   ST-CU5   F   -.599369188850       .755677959915    
  209  ST-VLINC  T    .000000000000       .000000000000    
- 210  ST-LOINC  F    .679194642755       .377422261780    
+ 210  ST-LOINC  F   -.354130067719       .541076799593    
  211  ST-HIINC  T    .000000000000       .000000000000    
- 212 ST-CARSPD  F    .378700705096       .324943332579    
+ 212 ST-CARSPD  F   -.552008032182       .141091622846    
  213 ST-ONLYAD  T    .000000000000       .000000000000    
  214 ST-ONLYWK  T    .000000000000       .000000000000    
  216 ST-FEMNOC  T    .000000000000       .000000000000    
@@ -67,68 +67,68 @@
  224 ST-WAHOME  T    .000000000000       .000000000000    
  225 ST-MIXDEN  T    .000000000000       .000000000000    
  226 ST-INTDEN  T    .000000000000       .000000000000    
- 227 S-AGGLOG2  F    .153415903312       .200477023805    
- 229 S-AGGLOG3  F    .555494084520       .545681904411    
- 231 ST-NWORKT  F   -.300889702547       .747479876874    
+ 227 S-AGGLOG2  F    .435531040585       .885152843415E-01
+ 229 S-AGGLOG3  F   -.507622949882E-01   .191486477433    
+ 231 ST-NWORKT  F   -.579677483390       .330322248789    
  232 ST-NSCHOT  T    .000000000000       .000000000000    
  233 ST-NESCOT  T    .000000000000       .000000000000    
  234 ST-NPBUST  T    .000000000000       .000000000000    
  235 ST-NSHOPT  T    .000000000000       .000000000000    
  236 ST-NMEALT  T    .000000000000       .000000000000    
  237 ST-NSRECT  T    .000000000000       .000000000000    
- 241 ST-IWORKS  F    .698040068963       .469882361509    
+ 241 ST-IWORKS  F   -.611034540147       .161607519728    
  242 ST-ISCHOS  T    .000000000000       .000000000000    
  243 ST-IESCOS  T    .000000000000       .000000000000    
  244 ST-IPBUSS  T    .000000000000       .000000000000    
  245 ST-ISHOPS  T    .000000000000       .000000000000    
  246 ST-IMEALS  T    .000000000000       .000000000000    
  247 ST-ISRECS  T    .000000000000       .000000000000    
- 252 SC-2Tours  F   -4.93459728768       .364068090484    
- 253 SC-3Tours  F   -8.03517142880       .738839692791    
+ 252 SC-2Tours  F   -3.95272537043       .269906917562    
+ 253 SC-3Tours  F   -5.29358690094       .318481831760    
  301   ET-FTW   T    .000000000000       .000000000000    
- 302   ET-PTW   F    .946334843137       .151860901975    
+ 302   ET-PTW   F    .718377593909       .224738945777    
  303   ET-RET   T    .000000000000       .000000000000    
- 304   ET-NWA   F    .906286647945       .139195901735    
+ 304   ET-NWA   F    .566317806469       .208481425943    
  305   ET-UNI   T    .000000000000       .000000000000    
  306   ET-DAS   T    .000000000000       .000000000000    
- 307   ET-PAS   F    .701266711617       .269501949292    
- 308   ET-CU5   F    1.17208615980       .203856541958    
- 309  ET-VLINC  F   -.253772892245       .275526945160    
+ 307   ET-PAS   F   -.472021329457       .655891555065    
+ 308   ET-CU5   F   -1.33714215126       .509634328869    
+ 309  ET-VLINC  F    .262490803452       .301960463249    
  310  ET-LOINC  T    .000000000000       .000000000000    
  311  ET-HIINC  T    .000000000000       .000000000000    
- 312 ET-CARSPD  F   -.308158385211       .133325371807    
+ 312 ET-CARSPD  F   -.370178624163       .195063705505    
  313 ET-ONLYAD  T    .000000000000       .000000000000    
  314 ET-ONLYWK  T    .000000000000       .000000000000    
  316 ET-FEMNOC  T    .000000000000       .000000000000    
- 317 ET-FADCU5  F    .274344291452       .160653719953    
- 318 ET-FAD515  F    1.19411206724       .127444253255    
- 319 ET-MADCU5  F    .738890773167       .242212736269    
- 320 ET-MAD515  F    .787921808992       .174556496488    
- 321 ET-AG1825  F   -.174545324326       .350268037846    
- 322 ET-AG2635  F    .472120995832E-01   .171765682939    
- 323 ET-AG5165  F   -.312859063315       .149732932374    
- 324 ET-WAHOME  F   -.205931458017       .739013399251    
+ 317 ET-FADCU5  F    .500013872000       .231509918156    
+ 318 ET-FAD515  F    .904451780297       .195972660245    
+ 319 ET-MADCU5  F   -.654162869331E-01   .401048232421    
+ 320 ET-MAD515  F    .855651357287       .297834417777    
+ 321 ET-AG1825  F   -.670277339537       .614292485238    
+ 322 ET-AG2635  F   -.494109964571       .235700205489    
+ 323 ET-AG5165  F   -.613256065652       .287798517089    
+ 324 ET-WAHOME  F    1.05980594363       1.44683208792    
  325 ET-MIXDEN  T    .000000000000       .000000000000    
  326 ET-INTDEN  T    .000000000000       .000000000000    
- 327 E-AGGLOG2  F   -.100531824184E-02   .488132584887E-01
- 329 E-AGGLOG3  F   -.291874223791E-01   .676213650763E-01
- 331 ET-NWORKT  F   -.723110555534       .136452958108    
- 332 ET-NSCHOT  F   -1.52968512721       .268128339353    
+ 327 E-AGGLOG2  F   -.127977632380E-01   .601760164209E-01
+ 329 E-AGGLOG3  F   -.240650231592E-01   .105056933629    
+ 331 ET-NWORKT  F   -1.16002423296       .237659413586    
+ 332 ET-NSCHOT  F   -.896482470138       .411224160892    
  333 ET-NESCOT  T    .000000000000       .000000000000    
  334 ET-NPBUST  T    .000000000000       .000000000000    
  335 ET-NSHOPT  T    .000000000000       .000000000000    
  336 ET-NMEALT  T    .000000000000       .000000000000    
  337 ET-NSRECT  T    .000000000000       .000000000000    
- 341 ET-IWORKS  F    .102995903346       .168192419270    
+ 341 ET-IWORKS  F   -1.11548521380       .683859173797    
  342 ET-ISCHOS  T    .000000000000       .000000000000    
- 343 ET-IESCOS  F    .100923165190       .623767748243E-01
+ 343 ET-IESCOS  F    .320693359022E-01   .108767385788    
  344 ET-IPBUSS  T    .000000000000       .000000000000    
  345 ET-ISHOPS  T    .000000000000       .000000000000    
  346 ET-IMEALS  T    .000000000000       .000000000000    
  347 ET-ISRECS  T    .000000000000       .000000000000    
- 352 EC-2Tours  F   -2.58701564513       .558330632557    
- 353 EC-3Tours  F   -3.68520862124       .744128261552    
- 401   PT-FTW   F   -.200064224537E-01   .176841761781    
+ 352 EC-2Tours  F   -1.06472249890       .623040953589    
+ 353 EC-3Tours  F   -2.75255637210       .993038077992    
+ 401   PT-FTW   F   -.593374335672       .232644061513    
  402   PT-PTW   T    .000000000000       .000000000000    
  403   PT-RET   T    .000000000000       .000000000000    
  404   PT-NWA   T    .000000000000       .000000000000    
@@ -136,15 +136,15 @@
  406   PT-DAS   T    .000000000000       .000000000000    
  407   PT-PAS   T    .000000000000       .000000000000    
  408   PT-CU5   T    .000000000000       .000000000000    
- 409  PT-VLINC  F    .185407632245       .303976541107    
+ 409  PT-VLINC  F    .179468267869       .202208428204    
  410  PT-LOINC  T    .000000000000       .000000000000    
  411  PT-HIINC  T    .000000000000       .000000000000    
- 412 PT-CARSPD  F    .201275698576       .157409477916    
+ 412 PT-CARSPD  F    .170771467636       .153611238937    
  413 PT-ONLYAD  T    .000000000000       .000000000000    
  414 PT-ONLYWK  T    .000000000000       .000000000000    
  416 PT-FEMNOC  T    .000000000000       .000000000000    
- 417 PT-FADCU5  F    .305091636557       .270278226369    
- 418 PT-FAD515  F    .233746281685E-01   .213269288348    
+ 417 PT-FADCU5  F    .282150810054       .327808032061    
+ 418 PT-FAD515  F    .341324547234       .370809424669    
  419 PT-MADCU5  T    .000000000000       .000000000000    
  420 PT-MAD515  T    .000000000000       .000000000000    
  421 PT-AG1825  T    .000000000000       .000000000000    
@@ -153,82 +153,82 @@
  424 PT-WAHOME  T    .000000000000       .000000000000    
  425 PT-MIXDEN  T    .000000000000       .000000000000    
  426 PT-INTDEN  T    .000000000000       .000000000000    
- 427 P-AGGLOG2  F    .617553783012E-01   .615866831033E-01
+ 427 P-AGGLOG2  F    .213146182391E-01   .461753222526E-01
  429 P-AGGLOG3  T    .900000000000E-01   .000000000000    
- 431 PT-NWORKT  F    .528985987960E-02   .161414147674    
- 432 PT-NSCHOT  F   -.170253881544       .185911343217    
- 433 PT-NESCOT  F    .176072344595       .981932160352E-01
+ 431 PT-NWORKT  F   -.429861002878       .249738096938    
+ 432 PT-NSCHOT  F   -.747829438778       .300200422597    
+ 433 PT-NESCOT  F   -.380234505542       .206299908226    
  434 PT-NPBUST  T    .000000000000       .000000000000    
  435 PT-NSHOPT  T    .000000000000       .000000000000    
  436 PT-NMEALT  T    .000000000000       .000000000000    
  437 PT-NSRECT  T    .000000000000       .000000000000    
  441 PT-IWORKS  T    .000000000000       .000000000000    
  443 PT-IESCOS  T    .000000000000       .000000000000    
- 444 PT-IPBUSS  F    .206937185908       .653201822437E-01
+ 444 PT-IPBUSS  F    .195314257914       .743499645501E-01
  445 PT-ISHOPS  T    .000000000000       .000000000000    
  446 PT-IMEALS  T    .000000000000       .000000000000    
  447 PT-ISRECS  T    .000000000000       .000000000000    
- 452 PC-2Tours  F   -3.24264532034       .761927981853    
- 453 PC-3Tours  F   -6.12675011359       .266850757923    
+ 452 PC-2Tours  F   -2.24981554080       .493475253751    
+ 453 PC-3Tours  F   -4.72325617999       .215491941002    
  501   HT-FTW   T    .000000000000       .000000000000    
- 502   HT-PTW   F    .227532849563       .245924694790    
- 503   HT-RET   F   -.695138258212       .257496124278    
+ 502   HT-PTW   F    .218584470951E-01   .284262738570    
+ 503   HT-RET   F    .371597827307E-01   .225891583364    
  504   HT-NWA   T    .200000000000       .000000000000    
  505   HT-UNI   T    .000000000000       .000000000000    
  506   HT-DAS   T    .000000000000       .000000000000    
- 507   HT-PAS   F   -1.71388359870       1.03649144914    
+ 507   HT-PAS   F    .321405388000E-01   .625818993754    
  508   HT-CU5   T    .000000000000       .000000000000    
  509  HT-VLINC  T    .000000000000       .000000000000    
  510  HT-LOINC  T    .000000000000       .000000000000    
- 511  HT-HIINC  F    .366546381354E-01   .190246614212    
+ 511  HT-HIINC  F    .164688443389       .188382808865    
  512 HT-CARSPD  T    .000000000000       .000000000000    
- 513 HT-ONLYAD  F    .756227680628E-02   .229851545106    
+ 513 HT-ONLYAD  F   -.264726229987E-01   .208121691519    
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  516 HT-FEMNOC  T    .000000000000       .000000000000    
- 517 HT-FADCU5  F   -.554308172356       .377672085569    
- 518 HT-FAD515  F    .177127345601       .273085666239    
+ 517 HT-FADCU5  F   -1.25170386081       .613647685496    
+ 518 HT-FAD515  F    .261995589564       .466997830408    
  519 HT-MADCU5  T    .000000000000       .000000000000    
- 520 HT-MAD515  F    .110159540306       .421792594514    
+ 520 HT-MAD515  F    1.04678765993       .478554761641    
  521 HT-AG1825  T    .000000000000       .000000000000    
  522 HT-AG2635  T    .000000000000       .000000000000    
- 523 HT-AG5165  F   -.999304983584E-01   .203228305251    
+ 523 HT-AG5165  F    .387868420598E-01   .232923296543    
  524 HT-WAHOME  T    .000000000000       .000000000000    
  525 HT-MIXDEN  T    .000000000000       .000000000000    
  526 HT-INTDEN  T    .000000000000       .000000000000    
- 527 H-AGGLOG2  F    .156765353750       .112471499197    
- 529 H-AGGLOG3  F    .366467374134       .346691757254    
- 531 HT-NWORKT  F   -1.36285173840       .311945811456    
+ 527 H-AGGLOG2  F    .842797199801E-01   .732350650015E-01
+ 529 H-AGGLOG3  F   -.112629556927       .282612382443    
+ 531 HT-NWORKT  F   -.552600438066       .247269605714    
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  533 HT-NESCOT  T    .000000000000       .000000000000    
- 534 HT-NPBUST  F   -.124817757499       .215725103159    
+ 534 HT-NPBUST  F    .225348241645       .163719783023    
  535 HT-NSHOPT  T    .000000000000       .000000000000    
  536 HT-NMEALT  T    .000000000000       .000000000000    
  537 HT-NSRECT  T    .000000000000       .000000000000    
  541 HT-IWORKS  T    .000000000000       .000000000000    
- 543 HT-IESCOS  F   -.566753026177       .426746547136    
- 544 HT-IPBUSS  F    .184116940117       .141433635306    
- 545 HT-ISHOPS  F   -.410065439184       .221915764967    
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+ 544 HT-IPBUSS  F    .541633101283E-01   .110344332944    
+ 545 HT-ISHOPS  F    .264721615269       .838649338075E-01
  546 HT-IMEALS  T    .000000000000       .000000000000    
- 547 HT-ISRECS  F    .129625182744       .204947944660    
- 552 HC-2Tours  F   -3.82778420360       1.09561279379    
- 553 HC-3Tours  F   -7.99512068277       3.43958324392    
+ 547 HT-ISRECS  F   -.212444686576       .259080939296    
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+ 553 HC-3Tours  F   -4.83171914012       2.22950414543    
  601   MT-FTW   T    .000000000000       .000000000000    
  602   MT-PTW   T    .000000000000       .000000000000    
  603   MT-RET   T    .000000000000       .000000000000    
- 604   MT-NWA   F    .107561723597       .344101976070    
+ 604   MT-NWA   F   -1.09494770164       .475723297651    
  605   MT-UNI   T    .000000000000       .000000000000    
  606   MT-DAS   T    .000000000000       .000000000000    
  607   MT-PAS   T   -3.00000000000       .000000000000    
  608   MT-CU5   T   -3.00000000000       .000000000000    
  609  MT-VLINC  T    .000000000000       .000000000000    
  610  MT-LOINC  T    .000000000000       .000000000000    
- 611  MT-HIINC  F    .604098763707       .333580118353    
+ 611  MT-HIINC  F    .266659120803       .358016635318    
  612 MT-CARSPD  T    .000000000000       .000000000000    
- 613 MT-ONLYAD  F    .461933960977       .360123669499    
+ 613 MT-ONLYAD  F    .569359715996       .378095079063    
  614 MT-ONLYWK  T    .000000000000       .000000000000    
  616 MT-FEMNOC  T    .000000000000       .000000000000    
- 617 MT-FADCU5  F   -.482751771256       1.07098775054    
- 618 MT-FAD515  F   -.101105845023       .778839238831    
+ 617 MT-FADCU5  F    1.15172493359       .884458691943    
+ 618 MT-FAD515  F    .873745354168       .872042536465    
  619 MT-MADCU5  T    .000000000000       .000000000000    
  620 MT-MAD515  T    .000000000000       .000000000000    
  621 MT-AG1825  T    .000000000000       .000000000000    
@@ -252,27 +252,27 @@
  645 MT-ISHOPS  T    .000000000000       .000000000000    
  646 MT-IMEALS  T    .000000000000       .000000000000    
  647 MT-ISRECS  T    .000000000000       .000000000000    
- 652 MC-2Tours  F   -2.88911568402       .287143700717    
+ 652 MC-2Tours  F   -2.38631822660       .302241713299    
  653 MC-3Tours  T   -20.0000000000       .000000000000    
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  703   RT-RET   T    .000000000000       .000000000000    
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- 706   RT-DAS   F    .790147531918E-01   .353323063884    
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  707   RT-PAS   T    .000000000000       .000000000000    
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  709  RT-VLINC  T    .000000000000       .000000000000    
  710  RT-LOINC  T    .000000000000       .000000000000    
  711  RT-HIINC  T    .000000000000       .000000000000    
  712 RT-CARSPD  T    .000000000000       .000000000000    
- 713 RT-ONLYAD  F   -.205475371812       .151991333890    
+ 713 RT-ONLYAD  F    .201443335495       .128785470542    
  714 RT-ONLYWK  T    .000000000000       .000000000000    
  716 RT-FEMNOC  T    .000000000000       .000000000000    
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- 718 RT-FAD515  F   -.896256844773       .278715131139    
+ 717 RT-FADCU5  F   -.123790788573       .259124577203    
+ 718 RT-FAD515  F    .326393486536       .261697605025    
  719 RT-MADCU5  T    .000000000000       .000000000000    
- 720 RT-MAD515  F   -.641936219745       .263333377681    
+ 720 RT-MAD515  F   -.328048187482       .435806033149    
  721 RT-AG1825  T    .000000000000       .000000000000    
  722 RT-AG2635  T    .000000000000       .000000000000    
  723 RT-AG5165  T    .000000000000       .000000000000    
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  727 R-AGGLOG2  T    .000000000000       .000000000000    
  729 R-AGGLOG3  T    .000000000000       .000000000000    
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- 732 RT-NSCHOT  F   -.618998051613       .200593797037    
+ 731 RT-NWORKT  F   -.469139073713       .129130692794    
+ 732 RT-NSCHOT  F   -.735574481742       .187390932073    
  733 RT-NESCOT  T    .000000000000       .000000000000    
  734 RT-NPBUST  T    .000000000000       .000000000000    
  735 RT-NSHOPT  T    .000000000000       .000000000000    
- 736 RT-NMEALT  F    .170080646681       .199851404409    
+ 736 RT-NMEALT  F    .563948188371E-01   .198357372318    
  737 RT-NSRECT  T    .000000000000       .000000000000    
- 741 RT-IWORKS  F   -.606049331435       .281705141963    
+ 741 RT-IWORKS  F    .177167168388       .136273108066    
  743 RT-IESCOS  T    .000000000000       .000000000000    
  744 RT-IPBUSS  T    .000000000000       .000000000000    
  745 RT-ISHOPS  T    .000000000000       .000000000000    
- 746 RT-IMEALS  F    .127872084626       .146855682830    
- 747 RT-ISRECS  F    .839335709562E-01   .124465220624    
- 752 RC-2Tours  F   -2.13914260357       .107391777476    
- 753 RC-3Tours  F   -4.40147472027       .196669930776    
+ 746 RT-IMEALS  F   -.778129695724E-01   .124980421318    
+ 747 RT-ISRECS  F    .971125807422E-01   .100097898242    
+ 752 RC-2Tours  F   -1.81306276691       .955792966590E-01
+ 753 RC-3Tours  F   -4.02191538153       .180446716482    
   -1
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+   16602  -.582340551820E+04  -.182391612165E+05  -.518544436923E+04
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diff --git a/inputs/coefficients/SchoolLocationModel.F12 b/inputs/coefficients/SchoolLocationModel.F12
new file mode 100644
index 00000000..b24be8f5
--- /dev/null
+++ b/inputs/coefficients/SchoolLocationModel.F12
@@ -0,0 +1,243 @@
+SchoolLocation   (SACOG2000 MNL v1201, NL v1301)                               
+Created by ALOGIT version 4                             15:25:25 on  7 Dec 16
+END
+   1 Beta00001  T    1.00000000000       .000000000000    
+   2 Beta00002  F    3.00000000000      .570961166914    
+   3 Beta00003  F    3.50000000000       .347561448620    
+   4 Beta00004  T    1.00000000000       .000000000000    
+   5 Beta00005  T    3.00000000000       .000000000000    
+   6 Beta00006  F   .0000000000000      .734848357861E-01
+   7 Beta00007  F   -18.0468944469       9.98465194432    
+   8 Beta00008  F   -6.08389365858       1.07281756864    
+   9 Beta00009  F   -2.68810350083       .478306799467    
+  10 Beta00010  F   -5.90901494887       2.90390397406    
+  11 Beta00011  F   -6.29842125002       .554081266989    
+  12 Beta00012  F   -2.49534959539       .301652154598    
+  13 Beta00013  F   -6.12426781036       .244062542739    
+  14 Beta00014  F   -4.49744162559       .133375419797    
+  15 Beta00015  F   -4.34266562553       .222867491503    
+  16 Beta00016  F   -2.04910482167       .263807571908    
+  17 Beta00017  F    .908434805383E-01   .154757817240    
+  18 Beta00018  T    .000000000000       .000000000000    
+  19 Beta00019  F    .209982354505       .374538212082E-01
+  20 Beta00020  T    .000000000000       .000000000000    
+  21 Beta00021  T    .000000000000       .000000000000    
+  30 Beta00030  T    .000000000000       .000000000000    
+  31 Beta00031  T    .000000000000       .000000000000    
+  32 Beta00032  T    .000000000000       .000000000000    
+  33 Beta00033  T    .000000000000       .000000000000    
+  34 Beta00034  T    .000000000000       .000000000000    
+  35 Beta00035  T    .000000000000       .000000000000    
+  36 Beta00036  T    .000000000000       .000000000000    
+  37 Beta00037  T    .000000000000       .000000000000    
+  38 Beta00038  T    .000000000000       .000000000000    
+  50 Beta00050  T    .000000000000       .000000000000    
+  51 Beta00051  T   -5.00000000000       .000000000000    
+  52 Beta00052  T    .000000000000       .000000000000    
+  60  LSM_060   F    .345666377192       .375427775809E-02
+  61  Gamma61   F    52.7803605227       449.521116783    
+  62  Gamma62   T   -30.0000000000       .000000000000    
+  63  Gamma63   T   -30.0000000000       .000000000000    
+  64  Gamma64   T   -5.00000000000       .000000000000    
+  65  Gamma65   T   -5.00000000000       .000000000000    
+  66  Gamma66   F    49.9170353263       449.521694159    
+  67  Gamma67   T    .000000000000       .000000000000    
+  68  Gamma68   T   -30.0000000000       .000000000000    
+  69  Gamma69   F   -5.05797140664       2.52585019791    
+  70  Gamma70   T   -30.0000000000       .000000000000    
+  71  Gamma71   T   -30.0000000000       .000000000000    
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+  73  Gamma73   F   -3.79587828332       .331572192057    
+  74  Gamma74   T   -30.0000000000       .000000000000    
+  75  Gamma75   T   -30.0000000000       .000000000000    
+  76  Gamma76   F   -20.6809955294       .775766293183    
+  77  Gamma77   T   -30.0000000000       .000000000000    
+  78  Gamma78   T    .000000000000       .000000000000    
+  79  Gamma79   F   -19.8053448433       .320349769228    
+  80  Gamma80   T   -30.0000000000       .000000000000    
+  81  Gamma81   T   -30.0000000000       .000000000000    
+  82  Gamma82   T   -30.0000000000       .000000000000    
+  83  Gamma83   F   -3.90066622139       .283882132940    
+  84  Gamma84   F   -16.1482512484       .470801203667    
+  98 Gamma0098  T    .000000000000       .000000000000    
+  99    Nest    F    1.20551334005       .516539667970E-01
+  -1
+    3736  -.886663087869E+04  -.125193778943E+05  -.793905672389E+04
+  24   015:25:25 on  7 Dec 16
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diff --git a/inputs/coefficients/SchoolTourModeModel.F12 b/inputs/coefficients/SchoolTourModeModel.F12
new file mode 100644
index 00000000..09787d9f
--- /dev/null
+++ b/inputs/coefficients/SchoolTourModeModel.F12
@@ -0,0 +1,135 @@
+School tour mode choice                                                        
+Created by ALOGIT version 4                             11:29:54 on  6 Apr 17
+END
+   1  costutil  F    1.63662149705       .195013615205    
+   2  timeutil  F    .912894595304       .835249730602E-01
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+  18 sb-univtr  F   -5.26936239483       .683548354914    
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+ 129 tr-dmixed  F    .774623764796       .286444559231    
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+  -1
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+  11   011:29:54 on  6 Apr 17
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diff --git a/inputs/coefficients/SchoolTourTimeModel.F12 b/inputs/coefficients/SchoolTourTimeModel.F12
new file mode 100644
index 00000000..4e15ec63
--- /dev/null
+++ b/inputs/coefficients/SchoolTourTimeModel.F12
@@ -0,0 +1,258 @@
+tod school tours                                                               
+Created by ALOGIT version 4                             18:42:27 on 23 Oct 16
+END
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diff --git a/inputs/coefficients/TransitPassOwnershipModel.F12 b/inputs/coefficients/TransitPassOwnershipModel.F12
new file mode 100644
index 00000000..0c0d49f1
--- /dev/null
+++ b/inputs/coefficients/TransitPassOwnershipModel.F12
@@ -0,0 +1,61 @@
+Transit pass ownership                                                         
+Created by ALOGIT version 4                             16:46:52 on 10 Oct 16
+END
+   1 Pass_Cons  F   -1.59977559552       .550343864288    
+   2  PartTime  F   -.628761541600       .955099932599E-01
+   3  OtherWkr  F   -1.15286383216       .399950646766    
+   4  UnivStud  F    1.48604204080       .297385206477    
+   5  RetiredA  F   -46.7983602105       44.1851495691    
+   6  NonWorkA  F   -2.48156369849       .831209447308    
+   7  HiScStud  F    4.96083577789       1.63994383799    
+   8  ChildU16  T   -2.40810000000       .000000000000    
+   9 LogHHIncm  F   -.709196083248E-01   .383550115348E-01
+  10 MsgIncome  F   -.884061380247       .448344425993    
+  11 MsgUWParc  F    .561731101537       .166623445388    
+  12 MsgUSParc  F   -2.08669466526       .241012673058    
+  13 HomeTDist1 F   -5.34735697026       .599181112363    
+  14 HomeTDist2 F    .262398368335E-01   .280504936121    
+  15 HomeTDistX F   -.513369831133       .121222914231    
+  22  UWNoPath  F   -.547369542311E-01   .111695443191    
+  23  UWGTDiff  F    .511473159411E-02   .194080704134E-02
+  25  USNoPath  F   -.349546909989       .263723006323    
+  26 HAgLSWork  F    .267852874840       .283671267306E-01
+  27 HAgLSStud  F   -.106196695955       .198623197126    
+  28 HAgLSNonW  F    .417545730905       .926942755848E-01
+  29 HAgLSReti  F    4.49869031066       4.28269734948    
+  30 WAgLSWork  F    .412386831795       .167489630357E-01
+  31 SAGLSScho  F    .309089419922E-01   .555901555886E-01
+  32 CostChange T    .000000000000       .000000000000    
+  -1
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+  17   016:46:52 on 10 Oct 16
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diff --git a/inputs/coefficients/TripModeModel.F12 b/inputs/coefficients/TripModeModel.F12
new file mode 100644
index 00000000..6738f440
--- /dev/null
+++ b/inputs/coefficients/TripModeModel.F12
@@ -0,0 +1,279 @@
+Trip level mode choice                                                         
+Created by ALOGIT version 4                             17:04:56 on 10 Nov 16
+END
+   1  costutil  F    1.20442477367       .107113589678    
+   2  timeutil  F    .814083305455       .347145902668E-01
+  18  sb-const  F   -2.97750709075       .341102330686    
+  20  wt-const  F   -4.29559147537       .269517360754    
+  22 wt-carsltd F   -.113631485053       .101101652248    
+  30  s3-const  F   -.870623852710       .306590251917    
+  32  sr-hh515  F   -.233901487148       .302120715830E-01
+  34  sr-hhnwa  F    .120001296544       .349686780803E-01
+  36 s3-onephh  F   -.863594903599       .112603760459    
+  37 s3-twophh  F   -.553892923503       .845333258508E-01
+  38 s2-onephh  F   -.690856470421       .832951573718E-01
+  40  s2-const  F   -1.06026195793       .281603025335    
+  41 sr-nocars  F   -1.47222390872       .139328795103    
+  50  da-const  F   -1.25394974358       .263274726030    
+  52 da-carsltd F   -.412093007805       .614933082052E-01
+  54 da-incu25  F    .454547102842E-02   .969755505889E-01
+  55 da-inc2545 F    .429714605723E-01   .729144383279E-01
+  59 da-ch1617  F   -.964298767073       .300795493826    
+  60  bi-const  F   -3.21328279666       .278068617443    
+  61  bi-male   F    .547019771896       .136264509949    
+  62 bi-ageu35  F    .608468098082       .159796023597    
+  65  bi-ointd  F   -.420638579039E-03   .120426682270E-02
+  72 wk-ageu35  F    .310507931956       .761411294844E-01
+  75  wk-ointd  F    .552523274093E-02   .589737343401E-03
+  78 wk-dmixed  F   -.327366380998       .153339734074    
+ 100  mainmode  F    3.79995894901       .236620222395    
+ 102 mm-onlyob  F    1.33149899137       .703259100486E-01
+ 103 mm-onlyrt  F    1.28535585405       .741476285201E-01
+ 104 mm-frstob  F    .144922670387       .739212805893E-01
+ 105 mm-frstrt  F    .272183536309       .633335653124E-01
+ 106 mm-lastob  F    .633555660318       .781693489387E-01
+ 107 mm-lastrt  F    .501174087640       .659391405061E-01
+ 115 s3-wttour  F   -1.09047302707       .309826643142    
+ 116 s3-sbtour  F    2.15849727582       .337484949579    
+ 118 s2-wttour  F   -.247133156811       .276261770970    
+ 119 s2-sbtour  F    2.34935505900       .327331961241    
+ 120 s2-s3tour  F    3.32016826460       .250950936340    
+ 121 da-dttour  T    .000000000000       .000000000000    
+ 122 da-wttour  F   -2.09191596702       .329330454670    
+ 124 da-s3tour  F    2.01523238830       .239578331033    
+ 125 da-s2tour  F    2.58206844468       .249326001983    
+ 127 bi-wttour  F   -1.07324034975       .236787392658    
+ 128 bi-sbtour  F   -.373150642947       .527657721335    
+ 130 bi-s2tour  F    .158756446327E-02   .259730896071    
+ 131 bi-datour  F   -.510344611976       .306227604945    
+ 141 wk-wktour  F    .983910184127       .864824310408E-01
+ 142 wk-sctour  F    .299624797234       .938594142697E-01
+ 147 bi-wbtour  F   -.463978661218       .785497642217    
+ 149 sr-wktour  F   -1.84171393744       .124254085662    
+ 150 sr-sctour  F   -1.03074763747       .130494318560    
+ 152 sr-estour  F   -1.52299219180       .125329036466    
+ 153 sr-shtour  F    .583655230864       .130676458937    
+ 154 sr-metour  F    .422759427147       .154119205139    
+ 155 sr-srtour  F    .112410352097       .114834819186    
+ 161 sr-escwkam F   -2.15053460618       .226405894530    
+ 162 sr-wkescpm F   -1.73739379610       .209546817951    
+ 163 sr-hmescam F    2.46079070852       .214447987552    
+ 164 sr-hmescmd F   -1.65571112144       .172384737222    
+ 165 sr-hmescpm F   -1.54561719024       .153230315399    
+ 166 sr-hmescev F   -1.44529857767       .287557451400    
+ 167 sr-eschmam F   -2.85650757105       .218756581344    
+ 168 sr-eschmmd F   -1.29740497263       .239442703661    
+ 169 sr-eschmpm F    .354005343260       .164876918535    
+ 170 sr-eschmev F   -.837865898745       .213753479252    
+  99 Theta0099  F    .865473476511       .346096734472E-01
+  -1
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+  14   017:04:56 on 10 Nov 16
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diff --git a/inputs/coefficients/TripTimeModel.F12 b/inputs/coefficients/TripTimeModel.F12
new file mode 100644
index 00000000..663c6ba4
--- /dev/null
+++ b/inputs/coefficients/TripTimeModel.F12
@@ -0,0 +1,334 @@
+MOD 25 TOD intermediate stops                                                  
+Created by ALOGIT version 4                             13:42:51 on  3 Nov 16
+END
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diff --git a/inputs/coefficients/WorkBasedSubtourGenerationModel.F12 b/inputs/coefficients/WorkBasedSubtourGenerationModel.F12
new file mode 100644
index 00000000..6ed41c9e
--- /dev/null
+++ b/inputs/coefficients/WorkBasedSubtourGenerationModel.F12
@@ -0,0 +1,32 @@
+work based tour frequency and purpose wbfreq0501.ALO                           
+Created by ALOGIT version 4                             11:09:24 on 10 May 17
+END
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diff --git a/inputs/coefficients/WorkBasedSubtourModeModel_psrcper1.F12 b/inputs/coefficients/WorkBasedSubtourModeModel.F12
similarity index 100%
rename from inputs/coefficients/WorkBasedSubtourModeModel_psrcper1.F12
rename to inputs/coefficients/WorkBasedSubtourModeModel.F12
diff --git a/inputs/coefficients/WorkBasedSubtourTimeModel.F12 b/inputs/coefficients/WorkBasedSubtourTimeModel.F12
new file mode 100644
index 00000000..fe2b5395
--- /dev/null
+++ b/inputs/coefficients/WorkBasedSubtourTimeModel.F12
@@ -0,0 +1,179 @@
+tod workbased subtour                                                          
+Created by ALOGIT version 4                             10:39:27 on 24 Oct 16
+END
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diff --git a/inputs/coefficients/WorkLocationModel.F12 b/inputs/coefficients/WorkLocationModel.F12
new file mode 100644
index 00000000..b4581d5d
--- /dev/null
+++ b/inputs/coefficients/WorkLocationModel.F12
@@ -0,0 +1,377 @@
+Tour Destination                                                               
+Created by ALOGIT version 4                             15:25:46 on 17 Nov 16
+END
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diff --git a/inputs/coefficients/WorkTourModeModel.F12 b/inputs/coefficients/WorkTourModeModel.F12
new file mode 100644
index 00000000..1645965e
--- /dev/null
+++ b/inputs/coefficients/WorkTourModeModel.F12
@@ -0,0 +1,121 @@
+Work tour mode choice                                                          
+Created by ALOGIT version 4                             15:44:55 on  1 May 17
+END
+   1  costutil  F    .913987315438       .119978654393    
+   2  timeutil  F    .494905334037       .352259138343E-01
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+  99  modenest  F    .557856817597       .412038641952E-01
+  -1
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+  15   015:44:55 on  1 May 17
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diff --git a/inputs/coefficients/WorkTourTimeModel.F12 b/inputs/coefficients/WorkTourTimeModel.F12
new file mode 100644
index 00000000..619e8ce5
--- /dev/null
+++ b/inputs/coefficients/WorkTourTimeModel.F12
@@ -0,0 +1,351 @@
+tod work tours                                                                 
+Created by ALOGIT version 4                             22:41:39 on 21 Oct 16
+END
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+    261    164      0     70      0      0      0      0   -588   -151
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+      0    338    763   -524   -726     20   -763  -1230  29754  50609
+  58151  60972      0  -2053 -20636 -32003 -36928 -34709   2892   2168
+  -1443      0   1882   2263    846    607  -1659      0   -131    543
+     77    401    620    792   -440   -222    296    182    401    541
+      1      1   1995   1593   -667   -758     12    -30    647    411
+   -902   -203   -375   -810      0  -2605      0      0      0      0
+  -1929     70      0   -750 -37719     59      0  -2488   2702   1014
+  -3315
diff --git a/inputs/coefficients/WorkbasedSubtourGenerationCoefficients_SACOG-v1.5.F12 b/inputs/coefficients/WorkbasedSubtourGenerationCoefficients_SACOG-v1.5.F12
deleted file mode 100644
index 3ba59e7f..00000000
--- a/inputs/coefficients/WorkbasedSubtourGenerationCoefficients_SACOG-v1.5.F12
+++ /dev/null
@@ -1,32 +0,0 @@
-work based tour frequency and purpose wbfreq0501.ALO                           
-Created by ALOGIT version 4                             12:39:34 on  6 Apr 12
-END
-   1 Beta00001  F    1.226014052955       .230710861441    
-   3 Beta00003  F    0.58949163188       .619604842912    
-   4 Beta00004  F    0.13633704997       .499140793754    
-   6 Beta00006  F    0.30036927295       .235261602792    
-   8 Beta00008  F    0.39617773946       .243715805154    
-  10 Beta00010  F    0.571469703452       .221813898300    
-  13 Beta00013  F    0.12974858328       .264124077795    
-  15 Beta00015  F    5.57466202513       .139256633046    
-  16 Beta00016  F    1.377063261524       .111028836990    
-  18 Beta00018  F    1.735525734682       .163490879176    
-  22 Beta00022  F    .289006440718E-01   .449871733599    
-  23 Beta00023  F    .283050733130E-01   .116767868120    
-  32 Beta00032  F   -.550200492856E-01   .264673368684E-01
-  39 Beta00039  F    .118883573823       .901918825112E-01
-  50 Theta0050  F    1.33662480750       .989512009706E-01
-  -1
-    2386  -.156632394409E+04  -.229667942815E+04  -.137608319294E+04
-  16   012:39:34 on  6 Apr 12
-  30077  40018  18598  80903  34889  44178  77336  34568  43319  83650
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-  22502  19975   4884  -5000    865   6500  88224  35199  45638  89877
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-  -1452    -88  20609  19373  26065  30333   5312  17905 -64075 -17862
- -30496  -1471  -5383  11030   4193
diff --git a/inputs/coefficients/HTransitPassOwnershipModel_psrcper1.F12 b/inputs/coefficients/old/HTransitPassOwnershipModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/HTransitPassOwnershipModel_psrcper1.F12
rename to inputs/coefficients/old/HTransitPassOwnershipModel_psrcper1.F12
diff --git a/inputs/coefficients/IndividualPersonDayPatternModel_psrcper1.F12 b/inputs/coefficients/old/IndividualPersonDayPatternModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/IndividualPersonDayPatternModel_psrcper1.F12
rename to inputs/coefficients/old/IndividualPersonDayPatternModel_psrcper1.F12
diff --git a/inputs/coefficients/OtherHomeBasedTourModeModel_psrcper1.F12 b/inputs/coefficients/old/OtherHomeBasedTourModeModel_psrcper1.F12
similarity index 97%
rename from inputs/coefficients/OtherHomeBasedTourModeModel_psrcper1.F12
rename to inputs/coefficients/old/OtherHomeBasedTourModeModel_psrcper1.F12
index cf8edae1..0b751f01 100644
--- a/inputs/coefficients/OtherHomeBasedTourModeModel_psrcper1.F12
+++ b/inputs/coefficients/old/OtherHomeBasedTourModeModel_psrcper1.F12
@@ -47,11 +47,11 @@
  178 wk-dempdn  F   -.234459082958E-05   .464232969057E-04
  201 distcont1  T    .000000000000       .000000000000    
  202 distcont2  T    .000000000000       .000000000000    
- 203 distcont3  T    .000000000000       .000000000000    
- 204 distcont4  T    .000000000000       .000000000000    
- 205 distcont5  T    .000000000000       .000000000000    
- 206 distcont6  T    .000000000000       .000000000000    
- 207 distcont7  T    .000000000000       .000000000000    
+ 203 distcont3  T    .500000000000       .000000000000    
+ 204 distcont4  T    .500000000000       .000000000000    
+ 205 distcont5  T    .500000000000       .000000000000    
+ 206 distcont6  T    .500000000000       .000000000000    
+ 207 distcont7  T    .500000000000       .000000000000    
  208 distcont8  T    .000000000000       .000000000000    
  209 distcont9  T    .000000000000       .000000000000    
  210 distcon10  T    .000000000000       .000000000000    
diff --git a/inputs/coefficients/OtherHomeBasedTourTimeModel_psrcper1.F12 b/inputs/coefficients/old/OtherHomeBasedTourTimeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/OtherHomeBasedTourTimeModel_psrcper1.F12
rename to inputs/coefficients/old/OtherHomeBasedTourTimeModel_psrcper1.F12
diff --git a/inputs/coefficients/OtherTourDestinationModel_psrcper1.F12 b/inputs/coefficients/old/OtherTourDestinationModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/OtherTourDestinationModel_psrcper1.F12
rename to inputs/coefficients/old/OtherTourDestinationModel_psrcper1.F12
diff --git a/inputs/coefficients/SchoolLocationModel_psrcper1_Calib2.F12 b/inputs/coefficients/old/SchoolLocationModel_psrcper1_Calib2.F12
similarity index 100%
rename from inputs/coefficients/SchoolLocationModel_psrcper1_Calib2.F12
rename to inputs/coefficients/old/SchoolLocationModel_psrcper1_Calib2.F12
diff --git a/inputs/coefficients/SchoolTourModeModel_psrcper1.F12 b/inputs/coefficients/old/SchoolTourModeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/SchoolTourModeModel_psrcper1.F12
rename to inputs/coefficients/old/SchoolTourModeModel_psrcper1.F12
diff --git a/inputs/coefficients/SchoolTourTimeModel_psrcper1.F12 b/inputs/coefficients/old/SchoolTourTimeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/SchoolTourTimeModel_psrcper1.F12
rename to inputs/coefficients/old/SchoolTourTimeModel_psrcper1.F12
diff --git a/inputs/coefficients/TransitPassOwnershipCoefficients_SACOG-v1.5.F12 b/inputs/coefficients/old/TransitPassOwnershipCoefficients_SACOG-v1.5.F12
similarity index 100%
rename from inputs/coefficients/TransitPassOwnershipCoefficients_SACOG-v1.5.F12
rename to inputs/coefficients/old/TransitPassOwnershipCoefficients_SACOG-v1.5.F12
diff --git a/inputs/coefficients/TripModeModel_psrcper1.F12 b/inputs/coefficients/old/TripModeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/TripModeModel_psrcper1.F12
rename to inputs/coefficients/old/TripModeModel_psrcper1.F12
diff --git a/inputs/coefficients/TripTimeModel_psrcper1.F12 b/inputs/coefficients/old/TripTimeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/TripTimeModel_psrcper1.F12
rename to inputs/coefficients/old/TripTimeModel_psrcper1.F12
diff --git a/inputs/coefficients/WorkBasedSubtourGenerationModel_psrcper1.F12 b/inputs/coefficients/old/WorkBasedSubtourGenerationModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/WorkBasedSubtourGenerationModel_psrcper1.F12
rename to inputs/coefficients/old/WorkBasedSubtourGenerationModel_psrcper1.F12
diff --git a/inputs/coefficients/WorkBasedSubtourModeCoefficients_SACOG-v1.5.F12 b/inputs/coefficients/old/WorkBasedSubtourModeCoefficients_SACOG-v1.5.F12
similarity index 100%
rename from inputs/coefficients/WorkBasedSubtourModeCoefficients_SACOG-v1.5.F12
rename to inputs/coefficients/old/WorkBasedSubtourModeCoefficients_SACOG-v1.5.F12
diff --git a/inputs/coefficients/old/WorkBasedSubtourModeModel_psrcper1.F12 b/inputs/coefficients/old/WorkBasedSubtourModeModel_psrcper1.F12
new file mode 100644
index 00000000..34717ca8
--- /dev/null
+++ b/inputs/coefficients/old/WorkBasedSubtourModeModel_psrcper1.F12
@@ -0,0 +1,37 @@
+work-based subtour mode choice                                                 
+Created by ALOGIT version 4                             13:19:05 on 25 Sep 16
+END
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+   2  timeutil  F    .861836144761       .164742484383    
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+  40  s2-const  F   -4.09650302056       .771197857924    
+  50  da-const  F   -3.48043680515       .771734676954    
+  54 da-incu25  F    .975106248661       .897392554431    
+  55 da-inc2550 F    .146716430635       .363618834453    
+  58 da-dtowork F    2.12759699035       .590922896554    
+  59 da-stowork F    1.74975968113       .615867961817    
+  60  bi-const  F   -6.37760511056       1.03992794538    
+  61  bi-male   F   -.402217695370       .980381068216    
+  69 bi-btowork F    2.90443825653       1.00067132879    
+  79 wk-wtowork T    5.00000000000       .000000000000    
+  88 sr-dtowork F    .535432858414       .402774301814    
+  89 sr-stowork F    1.18262094931       .541700297308    
+  99  modenest  F    .889593189059       .177685142273    
+  -1
+     685  -.726708588249E+03  -.117789730981E+04  -.470201094335E+03
+  12   013:19:05 on 25 Sep 16
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diff --git a/inputs/coefficients/WorkBasedSubtourTimeModel_psrcper1.F12 b/inputs/coefficients/old/WorkBasedSubtourTimeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/WorkBasedSubtourTimeModel_psrcper1.F12
rename to inputs/coefficients/old/WorkBasedSubtourTimeModel_psrcper1.F12
diff --git a/inputs/coefficients/WorkLocationCoefficients_SACOG-v1.5.F12 b/inputs/coefficients/old/WorkLocationCoefficients_SACOG-v1.5.F12
similarity index 100%
rename from inputs/coefficients/WorkLocationCoefficients_SACOG-v1.5.F12
rename to inputs/coefficients/old/WorkLocationCoefficients_SACOG-v1.5.F12
diff --git a/inputs/coefficients/WorkLocationModel_psrcper1_Calib3.F12 b/inputs/coefficients/old/WorkLocationModel_psrcper1_Calib3.F12
similarity index 100%
rename from inputs/coefficients/WorkLocationModel_psrcper1_Calib3.F12
rename to inputs/coefficients/old/WorkLocationModel_psrcper1_Calib3.F12
diff --git a/inputs/coefficients/WorkTourModeModel_psrcper1.F12 b/inputs/coefficients/old/WorkTourModeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/WorkTourModeModel_psrcper1.F12
rename to inputs/coefficients/old/WorkTourModeModel_psrcper1.F12
diff --git a/inputs/coefficients/WorkTourTimeModel_psrcper1.F12 b/inputs/coefficients/old/WorkTourTimeModel_psrcper1.F12
similarity index 100%
rename from inputs/coefficients/WorkTourTimeModel_psrcper1.F12
rename to inputs/coefficients/old/WorkTourTimeModel_psrcper1.F12
diff --git a/inputs/coefficients/WorkbasedSubtourTimeCoefficients_SACOG-v1.5.F12 b/inputs/coefficients/old/WorkbasedSubtourTimeCoefficients_SACOG-v1.5.F12
similarity index 100%
rename from inputs/coefficients/WorkbasedSubtourTimeCoefficients_SACOG-v1.5.F12
rename to inputs/coefficients/old/WorkbasedSubtourTimeCoefficients_SACOG-v1.5.F12
diff --git a/inputs/coefficients/time_of_day_not_calibrated/OtherHomeBasedTourTimeModel.F12 b/inputs/coefficients/time_of_day_not_calibrated/OtherHomeBasedTourTimeModel.F12
new file mode 100644
index 00000000..23d1ec24
--- /dev/null
+++ b/inputs/coefficients/time_of_day_not_calibrated/OtherHomeBasedTourTimeModel.F12
@@ -0,0 +1,548 @@
+tod other hb tours                                                             
+Created by ALOGIT version 4                              8:27:34 on 24 Oct 16
+END
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+  -1
+    9565  -.480242442642E+05  -.611463066653E+05  -.454215794815E+05
+  11   0 8:27:34 on 24 Oct 16
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diff --git a/inputs/coefficients/time_of_day_not_calibrated/SchoolTourTimeModel.F12 b/inputs/coefficients/time_of_day_not_calibrated/SchoolTourTimeModel.F12
new file mode 100644
index 00000000..4e15ec63
--- /dev/null
+++ b/inputs/coefficients/time_of_day_not_calibrated/SchoolTourTimeModel.F12
@@ -0,0 +1,258 @@
+tod school tours                                                               
+Created by ALOGIT version 4                             18:42:27 on 23 Oct 16
+END
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diff --git a/inputs/coefficients/time_of_day_not_calibrated/TripTimeModel.F12 b/inputs/coefficients/time_of_day_not_calibrated/TripTimeModel.F12
new file mode 100644
index 00000000..663c6ba4
--- /dev/null
+++ b/inputs/coefficients/time_of_day_not_calibrated/TripTimeModel.F12
@@ -0,0 +1,334 @@
+MOD 25 TOD intermediate stops                                                  
+Created by ALOGIT version 4                             13:42:51 on  3 Nov 16
+END
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diff --git a/inputs/coefficients/time_of_day_not_calibrated/WorkBasedSubtourTimeModel.F12 b/inputs/coefficients/time_of_day_not_calibrated/WorkBasedSubtourTimeModel.F12
new file mode 100644
index 00000000..fe2b5395
--- /dev/null
+++ b/inputs/coefficients/time_of_day_not_calibrated/WorkBasedSubtourTimeModel.F12
@@ -0,0 +1,179 @@
+tod workbased subtour                                                          
+Created by ALOGIT version 4                             10:39:27 on 24 Oct 16
+END
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diff --git a/inputs/coefficients/time_of_day_not_calibrated/WorkTourTimeModel.F12 b/inputs/coefficients/time_of_day_not_calibrated/WorkTourTimeModel.F12
new file mode 100644
index 00000000..619e8ce5
--- /dev/null
+++ b/inputs/coefficients/time_of_day_not_calibrated/WorkTourTimeModel.F12
@@ -0,0 +1,351 @@
+tod work tours                                                                 
+Created by ALOGIT version 4                             22:41:39 on 21 Oct 16
+END
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diff --git a/inputs/p_r_nodes.csv b/inputs/p_r_nodes.csv
deleted file mode 100644
index c3fc7c41..00000000
--- a/inputs/p_r_nodes.csv
+++ /dev/null
@@ -1,157 +0,0 @@
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diff --git a/inputs/psrc_roster.csv b/inputs/psrc_roster.csv
index 965c3d92..2244baef 100644
--- a/inputs/psrc_roster.csv
+++ b/inputs/psrc_roster.csv
@@ -1,649 +1,579 @@
 #variable,mode,path-type,vot-group,start-minute,end-minute,length,file-type, name, field, transpose, blend-variable,blend-path-type,factor,scaling
-time,bike,full-network,all,0,1439,maxzone,hdf5,7to8.h5/Skims/mfbkpt,1,FALSE,distance,null,null,TRUE
 distance,bike,full-network,all,0,1439,maxzone,hdf5,7to8.h5/Skims/mfbkpt,1,FALSE,distance,null,0.15,TRUE
-distance,hov2,full-network,very-low,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl1d,1,FALSE,distance,null,null,TRUE
-distance,hov2,full-network,low,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl1d,1,FALSE,distance,null,null,TRUE
-distance,hov2,full-network,medium,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl2d,1,FALSE,distance,null,null,TRUE
+time,bike,full-network,all,0,1439,maxzone,hdf5,7to8.h5/Skims/mfbkpt,1,FALSE,distance,null,null,TRUE
 distance,hov2,full-network,high,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl2d,1,FALSE,distance,null,null,TRUE
 distance,hov2,full-network,high,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl3d,1,FALSE,distance,null,null,TRUE
+distance,hov2,full-network,low,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl1d,1,FALSE,distance,null,null,TRUE
+distance,hov2,full-network,medium,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl2d,1,FALSE,distance,null,null,TRUE
 distance,hov2,full-network,very-high,0,1439,maxzone,hdf5,7to8.h5/Skims/h2tl3d,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,300,359,maxzone,hdf5,5to6.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,medium,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-toll,hov2,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
-ivtime,hov2,full-network,medium,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
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-toll,sov,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
-ivtime,sov,full-network,medium,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
-ivtime,sov,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
-toll,sov,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
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-distance,walk,full-network,all,0,1439,maxzone,hdf5,5to6.h5/Skims/walkt,1,FALSE,distance,null,0.1,TRUE
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 fare,transit,local-bus,all,0,1439,maxzone,hdf5,9to10.h5/Skims/mfmfarbx,1,FALSE,null,null,null,TRUE
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-ivtime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
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-ivtime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
-comtime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
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-ivtime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
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-ivtime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
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-ivtime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
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-xwaittime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/twtwa,1,FALSE,null,null,null,TRUE
-ivtime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
-comtime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
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-comtime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
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 fare,transit,light-rail,all,0,1439,maxzone,hdf5,9to10.h5/Skims/mfmfarbx,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+distance,walk,full-network,all,0,1439,maxzone,hdf5,5to6.h5/Skims/walkt,1,FALSE,distance,null,0.05,TRUE
+time,walk,full-network,all,0,1439,maxzone,hdf5,5to6.h5/Skims/walkt,1,FALSE,distance,null,null,TRUE
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 comtime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
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 lrttime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,300,359,maxzone,hdf5,5to6.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,300,359,maxzone,hdf5,5to6.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,300,359,maxzone,hdf5,5to6.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,360,419,maxzone,hdf5,6to7.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,360,419,maxzone,hdf5,6to7.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,360,419,maxzone,hdf5,6to7.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,360,419,maxzone,hdf5,6to7.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,360,419,maxzone,hdf5,6to7.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,360,419,maxzone,hdf5,6to7.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,360,419,maxzone,hdf5,6to7.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,420,479,maxzone,hdf5,7to8.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,420,479,maxzone,hdf5,7to8.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,420,479,maxzone,hdf5,7to8.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,420,479,maxzone,hdf5,7to8.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,420,479,maxzone,hdf5,7to8.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,420,479,maxzone,hdf5,7to8.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,420,479,maxzone,hdf5,7to8.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,480,539,maxzone,hdf5,8to9.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,480,539,maxzone,hdf5,8to9.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,480,539,maxzone,hdf5,8to9.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,480,539,maxzone,hdf5,8to9.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,480,539,maxzone,hdf5,8to9.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,480,539,maxzone,hdf5,8to9.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,480,539,maxzone,hdf5,8to9.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,540,599,maxzone,hdf5,9to10.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,540,599,maxzone,hdf5,9to10.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,540,599,maxzone,hdf5,9to10.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,540,599,maxzone,hdf5,9to10.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,540,599,maxzone,hdf5,9to10.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,540,599,maxzone,hdf5,9to10.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,540,599,maxzone,hdf5,9to10.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,600,839,maxzone,hdf5,10to14.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,600,839,maxzone,hdf5,10to14.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,600,839,maxzone,hdf5,10to14.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,600,839,maxzone,hdf5,10to14.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,600,839,maxzone,hdf5,10to14.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,600,839,maxzone,hdf5,10to14.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,600,839,maxzone,hdf5,10to14.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,840,899,maxzone,hdf5,14to15.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,840,899,maxzone,hdf5,14to15.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,840,899,maxzone,hdf5,14to15.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,840,899,maxzone,hdf5,14to15.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,840,899,maxzone,hdf5,14to15.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,840,899,maxzone,hdf5,14to15.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,840,899,maxzone,hdf5,14to15.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,900,959,maxzone,hdf5,15to16.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,900,959,maxzone,hdf5,15to16.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,900,959,maxzone,hdf5,15to16.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,900,959,maxzone,hdf5,15to16.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,900,959,maxzone,hdf5,15to16.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,900,959,maxzone,hdf5,15to16.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,900,959,maxzone,hdf5,15to16.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,960,1019,maxzone,hdf5,16to17.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,960,1019,maxzone,hdf5,16to17.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,960,1019,maxzone,hdf5,16to17.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,1020,1079,maxzone,hdf5,17to18.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
-ivtime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
-iwaittime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/twtwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,1020,1079,maxzone,hdf5,17to18.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
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+toll,hov3,full-network,low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/h3tl2t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/h3tl2c,1,FALSE,distance,null,null,TRUE
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+toll,hov3,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
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+toll,sov,full-network,low,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,1080,1199,maxzone,hdf5,18to20.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwac,1,FALSE,null,null,null,TRUE
 comtime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwrc,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwaf,1,FALSE,null,null,null,TRUE
 ferrtime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwrf,1,FALSE,null,null,null,TRUE
-premtime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwa,1,FALSE,null,null,null,TRUE
+ivtime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwr,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/iwtwa,1,FALSE,null,null,null,TRUE
+iwaittime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/iwtwr,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwar,1,FALSE,null,null,null,TRUE
 lrttime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwrr,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ndbwa,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ndbwr,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwap,1,FALSE,null,null,null,TRUE
+premtime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/ivtwrp,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/xfrwa,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,1080,1319,maxzone,hdf5,18to20.h5/Skims/xfrwr,1,FALSE,null,null,null,TRUE
+ivtime,hov2,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
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+toll,hov2,full-network,medium,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl2c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-high,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-high,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl3c,1,FALSE,distance,null,null,TRUE
+ivtime,hov2,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1t,1,FALSE,distance,null,null,TRUE
+toll,hov2,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/h2tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,hov3,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/h3tl3t,1,FALSE,distance,null,null,TRUE
+toll,hov3,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/h3tl3c,1,FALSE,distance,null,null,TRUE
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+toll,hov3,full-network,low,1200,299,maxzone,hdf5,20to5.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
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+toll,hov3,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/h3tl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,low,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,low,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,medium,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl2t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,medium,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl2c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl3t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-high,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl3c,1,FALSE,distance,null,null,TRUE
+ivtime,sov,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl1t,1,FALSE,distance,null,null,TRUE
+toll,sov,full-network,very-low,1200,299,maxzone,hdf5,20to5.h5/Skims/svtl1c,1,FALSE,distance,null,null,TRUE
+comtime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+comtime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+ferrtime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+ferrtime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+ivtime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
 ivtime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+iwaittime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
 iwaittime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
-xwaittime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+lrttime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+lrttime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+nboard,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
 nboard,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+premtime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
 premtime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
-lrttime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
-comtime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
-ferrtime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+xwaittime,transit,local-bus,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
+xwaittime,transit,light-rail,all,1320,299,maxzone,null,null,1,FALSE,null,null,null,TRUE
diff --git a/inputs/psrc_worker_ixxifractions.dat b/inputs/psrc_worker_ixxifractions.dat
deleted file mode 100644
index 2ac69f5f..00000000
--- a/inputs/psrc_worker_ixxifractions.dat
+++ /dev/null
@@ -1,3850 +0,0 @@
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diff --git a/inputs/skim_params/bike_assignment.json b/inputs/skim_params/bike_assignment.json
index 53fce11a..8fdfadd3 100644
--- a/inputs/skim_params/bike_assignment.json
+++ b/inputs/skim_params/bike_assignment.json
@@ -2,7 +2,8 @@
     "modes": [
         "k",
         "l",
-        "q"
+        "q",
+        "f"
     ],
     "demand": "mfbike",
     "waiting_time": {
diff --git a/inputs/skim_params/bike_skim_setup.json b/inputs/skim_params/bike_skim_setup.json
index 325c7a98..5b87b5a4 100644
--- a/inputs/skim_params/bike_skim_setup.json
+++ b/inputs/skim_params/bike_skim_setup.json
@@ -3,7 +3,8 @@
         "modes": [
             "k",
             "l",
-            "q"
+            "q",
+            "f"
         ],        
 	"actual_aux_transit_times": "mfbkat"
     },
diff --git a/inputs/skim_params/bike_walk_assignment.json b/inputs/skim_params/bike_walk_assignment.json
index f487bc09..676fd4e3 100644
--- a/inputs/skim_params/bike_walk_assignment.json
+++ b/inputs/skim_params/bike_walk_assignment.json
@@ -1,8 +1,9 @@
 {
     "modes": [
         "k",
-        "l"
-,	"q"   ],
+        "l",
+	    "q",
+        "f"   ],
     "demand": "mfbike",
     "waiting_time": {
         "headway_fraction": 1,
diff --git a/inputs/skim_params/demand_matrix_dictionary.json b/inputs/skim_params/demand_matrix_dictionary.json
index c7517300..3c5ae6bd 100644
--- a/inputs/skim_params/demand_matrix_dictionary.json
+++ b/inputs/skim_params/demand_matrix_dictionary.json
@@ -10,15 +10,24 @@
 (2,1,1):bike
 (2,2,1):bike
 (2,3,1):bike
+(3,1,0):svtl1
 (3,1,1):svtl1
+(3,2,0):svtl2
 (3,2,1):svtl2
+(3,3,0):svtl3
 (3,3,1):svtl3
 (4,1,1):h2tl1
+(4,1,0):h2tl1
 (4,2,1):h2tl2
+(4,2,0):h2tl2
 (4,3,1):h2tl3
+(4,3,0):h2tl3
 (5,1,1):h3tl1
+(5,1,0):h3tl1
 (5,2,1):h3tl2
+(5,2,0):h3tl2
 (5,3,1):h3tl3
+(5,3,0):h3tl3
 (6,1,0):trnst
 (6,2,0):trnst
 (6,3,0):trnst
diff --git a/inputs/skim_params/emme_bank_dimensions.json b/inputs/skim_params/emme_bank_dimensions.json
index 7ad2c945..b05b4c28 100644
--- a/inputs/skim_params/emme_bank_dimensions.json
+++ b/inputs/skim_params/emme_bank_dimensions.json
@@ -10,7 +10,7 @@
   "functions": 99,
   "destination_matrices": 125,
   "transit_segments": 640000,
-  "full_matrices": 100,
+  "full_matrices": 125,
   "scenarios": 10,
   "origin_matrices": 125,
   "transit_vehicles": 37
diff --git a/inputs/skim_params/extended_transit_assignment.json b/inputs/skim_params/extended_transit_assignment.json
index 62dbc7a5..94c75ea5 100644
--- a/inputs/skim_params/extended_transit_assignment.json
+++ b/inputs/skim_params/extended_transit_assignment.json
@@ -46,7 +46,7 @@
         "choices_at_regular_nodes": "OPTIMAL_STRATEGY"
     },
     "flow_distribution_between_lines": {
-        "consider_total_impedance": false
+        "consider_total_impedance": true
     },
     "connector_to_connector_path_prohibition": null,
     "od_results": {
diff --git a/inputs/skim_params/extended_transit_assignment_lr.json b/inputs/skim_params/extended_transit_assignment_lr.json
index de33cf21..e883f92d 100644
--- a/inputs/skim_params/extended_transit_assignment_lr.json
+++ b/inputs/skim_params/extended_transit_assignment_lr.json
@@ -36,7 +36,7 @@
     },
     "in_vehicle_cost": null,
     "aux_transit_time": {
-        "perception_factor": 2
+        "perception_factor": 2.0
     },
     "aux_transit_cost": null,
     "flow_distribution_at_origins": {
@@ -47,7 +47,7 @@
         "choices_at_regular_nodes": "OPTIMAL_STRATEGY"
     },
     "flow_distribution_between_lines": {
-        "consider_total_impedance": false
+        "consider_total_impedance": true
     },
     "connector_to_connector_path_prohibition": null,
     "od_results": {
diff --git a/inputs/skim_params/transit_skim_aggregate_matrix_names.json b/inputs/skim_params/transit_skim_aggregate_matrix_names.json
index c8306978..ad86ff43 100644
--- a/inputs/skim_params/transit_skim_aggregate_matrix_names.json
+++ b/inputs/skim_params/transit_skim_aggregate_matrix_names.json
@@ -1 +1 @@
-{"ndbwa": "ABs All Modes","ndbwr": "ABs Light Rail", "twtwa": "Total Wait Time All Modes","twtwr": "Total Wait Time Light Rail", "ivtwa": "Actual IVTs All Modes", "ivtwr": "Actual IVTs Light Rail", "iwtwa": "First Wait Time All Modes", "iwtwr": "First Wait Time Light Rail", "xfrwa": "Transfer Time All Modes", "xfrwr": "Transfer Time Light Rail"}
\ No newline at end of file
+{"ndbwa": "ABs All Modes","ndbwr": "ABs Light Rail", "twtwa": "Total Wait Time All Modes","twtwr": "Total Wait Time Light Rail", "ivtwa": "Actual IVTs All Modes", "ivtwr": "Actual IVTs Light Rail", "iwtwa": "First Wait Time All Modes", "iwtwr": "First Wait Time Light Rail", "xfrwa": "Transfer Time All Modes", "xfrwr": "Transfer Time Light Rail", "auxwa" :  "Walk Access Transit All Modes", "auxwr" :  "Walk Access Light Rail All Modes"}
\ No newline at end of file
diff --git a/inputs/skim_params/transit_skim_setup.json b/inputs/skim_params/transit_skim_setup.json
index 9c3cfe27..50ba30b0 100644
--- a/inputs/skim_params/transit_skim_setup.json
+++ b/inputs/skim_params/transit_skim_setup.json
@@ -52,9 +52,12 @@
             "c",
             "f",
             "p",
-            "r"
+            "r",
+            "w",
+            "x"
         ],
         "actual_in_vehicle_times": "mfivtwa",
+        "actual_aux_transit_times": "mfauxwa",
         "avg_boardings": "mfndbwa"
     },
     "type": "EXTENDED_TRANSIT_MATRIX_RESULTS",
diff --git a/inputs/skim_params/transit_skim_setup_lr.json b/inputs/skim_params/transit_skim_setup_lr.json
index 08c351b0..0187cc21 100644
--- a/inputs/skim_params/transit_skim_setup_lr.json
+++ b/inputs/skim_params/transit_skim_setup_lr.json
@@ -52,9 +52,12 @@
             "c",
             "f",
             "p",
-            "r"
+            "r",
+            "w",
+            "x"
         ],
         "actual_in_vehicle_times": "mfivtwr",
+        "actual_aux_transit_times": "mfauxwr",
         "avg_boardings": "mfndbwr"
     },
     "type": "EXTENDED_TRANSIT_MATRIX_RESULTS",
diff --git a/inputs/skim_params/user_classes.json b/inputs/skim_params/user_classes.json
index 1e47d9e9..eea31084 100644
--- a/inputs/skim_params/user_classes.json
+++ b/inputs/skim_params/user_classes.json
@@ -1,16 +1,16 @@
 {"Highway": [
-{"Name": "svtl1", "Description": "SOV Toll Income Level 1","Value of Time": 1200,"Mode": "e","Toll": "@toll1","Time": "@timau","Distance": "@dist"}, 
-{"Name": "svtl2", "Description": "SOV Toll Income Level 2","Value of Time": 2400,"Mode": "e","Toll": "@toll1","Time": "@timau","Distance": "@dist"}, 
-{"Name": "svtl3", "Description": "SOV Toll Income Level 3","Value of Time": 4000,"Mode": "e","Toll": "@toll1","Time": "@timau","Distance": "@dist"}, 
-{"Name": "h2tl1", "Description": "HOV 2 Toll Income Level 1","Value of Time": 2000,"Mode": "d","Toll": "@toll2","Time": "@timau","Distance": "@dist"}, 
-{"Name": "h2tl2", "Description": "HOV 2 Toll Income Level 2","Value of Time": 3000,"Mode": "d","Toll": "@toll2","Time": "@timau","Distance": "@dist"}, 
-{"Name": "h2tl3", "Description": "HOV 2 Toll Income Level 3","Value of Time": 5000,"Mode": "d","Toll": "@toll2","Time": "@timau","Distance": "@dist"},  
-{"Name": "h3tl1", "Description": "HOV 3 Toll Income Level 1","Value of Time": 2400,"Mode": "m","Toll": "@toll3","Time": "@timau","Distance": "@dist"}, 
-{"Name": "h3tl2", "Description": "HOV 3 Toll Income Level 2","Value of Time": 3500,"Mode": "m","Toll": "@toll3","Time": "@timau","Distance": "@dist"}, 
-{"Name": "h3tl3", "Description": "HOV 3 Toll Income Level 3","Value of Time": 5500,"Mode": "m","Toll": "@toll3","Time": "@timau","Distance": "@dist"},    
-{"Name": "lttrk", "Description": "Light Trucks","atrr_Name": "svtl1","Value of Time": 4000,"Mode": "v","Toll": "@trkc1","Time": "@timau","Distance": "@dist"}, 
-{"Name": "metrk", "Description": "Medium Trucks","atrr_Name": "svtl1","Value of Time": 4500,"Mode": "u","Toll": "@trkc2","Time": "@timau","Distance": "@dist"}, 
-{"Name": "hvtrk", "Description": "Heavy Trucks","atrr_Name": "svtl1","Value of Time": 5000,"Mode": "t","Toll": "@trkc3","Time": "@timau","Distance": "@dist"}
+{"Name": "svtl1", "Description": "SOV Toll Income Level 1","Value of Time": 1307,"Mode": "e","Toll": "@toll1","Time": "@timau","Distance": "@dist"}, 
+{"Name": "svtl2", "Description": "SOV Toll Income Level 2","Value of Time": 2614,"Mode": "e","Toll": "@toll1","Time": "@timau","Distance": "@dist"}, 
+{"Name": "svtl3", "Description": "SOV Toll Income Level 3","Value of Time": 4356,"Mode": "e","Toll": "@toll1","Time": "@timau","Distance": "@dist"}, 
+{"Name": "h2tl1", "Description": "HOV 2 Toll Income Level 1","Value of Time": 2178,"Mode": "d","Toll": "@toll2","Time": "@timau","Distance": "@dist"}, 
+{"Name": "h2tl2", "Description": "HOV 2 Toll Income Level 2","Value of Time": 3267,"Mode": "d","Toll": "@toll2","Time": "@timau","Distance": "@dist"}, 
+{"Name": "h2tl3", "Description": "HOV 2 Toll Income Level 3","Value of Time": 5445,"Mode": "d","Toll": "@toll2","Time": "@timau","Distance": "@dist"},  
+{"Name": "h3tl1", "Description": "HOV 3 Toll Income Level 1","Value of Time": 2614,"Mode": "m","Toll": "@toll3","Time": "@timau","Distance": "@dist"}, 
+{"Name": "h3tl2", "Description": "HOV 3 Toll Income Level 2","Value of Time": 3811,"Mode": "m","Toll": "@toll3","Time": "@timau","Distance": "@dist"}, 
+{"Name": "h3tl3", "Description": "HOV 3 Toll Income Level 3","Value of Time": 5989,"Mode": "m","Toll": "@toll3","Time": "@timau","Distance": "@dist"},    
+{"Name": "lttrk", "Description": "Light Trucks","atrr_Name": "svtl1","Value of Time": 4356,"Mode": "v","Toll": "@trkc1","Time": "@timau","Distance": "@dist"}, 
+{"Name": "metrk", "Description": "Medium Trucks","atrr_Name": "svtl1","Value of Time": 4900,"Mode": "u","Toll": "@trkc2","Time": "@timau","Distance": "@dist"}, 
+{"Name": "hvtrk", "Description": "Heavy Trucks","atrr_Name": "svtl1","Value of Time": 5445,"Mode": "t","Toll": "@trkc3","Time": "@timau","Distance": "@dist"}
 ],
 "Walk": [
 {"Name": "walk", "Description": "Walk Trips","Mode": "w","Time": "@timau","Distance": "@dist"} 
diff --git a/inputs/soundcast.yml b/inputs/soundcast.yml
new file mode 100644
index 00000000..24ec1f58
--- /dev/null
+++ b/inputs/soundcast.yml
@@ -0,0 +1,432 @@
+name: soundcast
+channels:
+- !!python/unicode
+  'udst'
+- !!python/unicode
+  'conda-forge'
+- !!python/unicode
+  'synthicity'
+- !!python/unicode
+  'defaults'
+dependencies:
+- !!python/unicode
+  'click-plugins=1.0.3=py27_0'
+- !!python/unicode
+  'cligj=0.4.0=py27_0'
+- !!python/unicode
+  'descartes=1.1.0=py27_0'
+- !!python/unicode
+  'expat=2.1.0=vc9_2'
+- !!python/unicode
+  'fiona=1.7.6=np111py27_0'
+- !!python/unicode
+  'freexl=1.0.2=vc9_1'
+- !!python/unicode
+  'geopandas=0.2.1=py27_3'
+- !!python/unicode
+  'geos=3.5.1=vc9_1'
+- !!python/unicode
+  'h5py=2.7.0=np111py27_0'
+- !!python/unicode
+  'hdf4=4.2.12=vc9_0'
+- !!python/unicode
+  'hdf5=1.8.17=vc9_10'
+- !!python/unicode
+  'jpeg=9b=vc9_0'
+- !!python/unicode
+  'kealib=1.4.6=vc9_3'
+- !!python/unicode
+  'krb5=1.14.2=vc9_0'
+- !!python/unicode
+  'libiconv=1.14=vc9_4'
+- !!python/unicode
+  'libnetcdf=4.4.1.1=vc9_4'
+- !!python/unicode
+  'libpng=1.6.28=vc9_0'
+- !!python/unicode
+  'libpq=9.5.4=vc9_4'
+- !!python/unicode
+  'libspatialindex=1.8.5=vc9_1'
+- !!python/unicode
+  'libspatialite=4.3.0a=vc9_14'
+- !!python/unicode
+  'libtiff=4.0.6=vc9_7'
+- !!python/unicode
+  'libxml2=2.9.3=vc9_9'
+- !!python/unicode
+  'munch=2.1.1=py27_0'
+- !!python/unicode
+  'openjpeg=2.1.2=vc9_2'
+- !!python/unicode
+  'pcre=8.39=vc9_0'
+- !!python/unicode
+  'pillow=3.2.0=py27_1'
+- !!python/unicode
+  'proj4=4.9.3=vc9_3'
+- !!python/unicode
+  'psycopg2=2.6.2=py27_1'
+- !!python/unicode
+  'pyproj=1.9.5.1=py27_0'
+- !!python/unicode
+  'pyqt=4.11.4=py27_2'
+- !!python/unicode
+  'pysal=1.13.0=py27_0'
+- !!python/unicode
+  'pytables=3.4.2=np111py27_0'
+- !!python/unicode
+  'qt=4.8.7=vc9_6'
+- !!python/unicode
+  'rtree=0.8.3=py27_0'
+- !!python/unicode
+  'shapely=1.5.17=np111py27_2'
+- !!python/unicode
+  'sqlite=3.13.0=vc9_1'
+- !!python/unicode
+  'vc=9=0'
+- !!python/unicode
+  'xerces-c=3.1.4=vc9_3'
+- !!python/unicode
+  '_nb_ext_conf=0.2.0=py27_0'
+- !!python/unicode
+  'alabaster=0.7.8=py27_0'
+- !!python/unicode
+  'anaconda-client=1.4.0=py27_0'
+- !!python/unicode
+  'anaconda=custom=py27_0'
+- !!python/unicode
+  'anaconda-navigator=1.2.1=py27_0'
+- !!python/unicode
+  'argcomplete=1.0.0=py27_1'
+- !!python/unicode
+  'astropy=1.2.1=np111py27_0'
+- !!python/unicode
+  'babel=2.3.3=py27_0'
+- !!python/unicode
+  'backports=1.0=py27_0'
+- !!python/unicode
+  'backports_abc=0.4=py27_0'
+- !!python/unicode
+  'beautifulsoup4=4.4.1=py27_0'
+- !!python/unicode
+  'bitarray=0.8.1=py27_1'
+- !!python/unicode
+  'boto=2.40.0=py27_0'
+- !!python/unicode
+  'bottleneck=1.0.0=np111py27_1'
+- !!python/unicode
+  'bzip2=1.0.6=vc9_3'
+- !!python/unicode
+  'cdecimal=2.3=py27_2'
+- !!python/unicode
+  'cffi=1.6.0=py27_0'
+- !!python/unicode
+  'chest=0.2.3=py27_0'
+- !!python/unicode
+  'click=6.6=py27_0'
+- !!python/unicode
+  'cloudpickle=0.2.1=py27_0'
+- !!python/unicode
+  'clyent=1.2.2=py27_0'
+- !!python/unicode
+  'colorama=0.3.7=py27_0'
+- !!python/unicode
+  'comtypes=1.1.2=py27_0'
+- !!python/unicode
+  'configobj=5.0.6=py27_0'
+- !!python/unicode
+  'configparser=3.5.0b2=py27_1'
+- !!python/unicode
+  'console_shortcut=0.1.1=py27_1'
+- !!python/unicode
+  'contextlib2=0.5.3=py27_0'
+- !!python/unicode
+  'cryptography=1.4=py27_0'
+- !!python/unicode
+  'curl=7.49.0=vc9_0'
+- !!python/unicode
+  'cycler=0.10.0=py27_0'
+- !!python/unicode
+  'cython=0.24=py27_0'
+- !!python/unicode
+  'cytoolz=0.8.0=py27_0'
+- !!python/unicode
+  'dask=0.10.0=py27_0'
+- !!python/unicode
+  'datashape=0.5.2=py27_0'
+- !!python/unicode
+  'decorator=4.0.10=py27_0'
+- !!python/unicode
+  'dill=0.2.5=py27_0'
+- !!python/unicode
+  'docutils=0.12=py27_2'
+- !!python/unicode
+  'entrypoints=0.2.2=py27_0'
+- !!python/unicode
+  'enum34=1.1.6=py27_0'
+- !!python/unicode
+  'et_xmlfile=1.0.1=py27_0'
+- !!python/unicode
+  'fastcache=1.0.2=py27_1'
+- !!python/unicode
+  'flask=0.11.1=py27_0'
+- !!python/unicode
+  'flask-cors=2.1.2=py27_0'
+- !!python/unicode
+  'freetype=2.5.5=vc9_2'
+- !!python/unicode
+  'funcsigs=1.0.2=py27_0'
+- !!python/unicode
+  'functools32=3.2.3.2=py27_0'
+- !!python/unicode
+  'futures=3.0.5=py27_0'
+- !!python/unicode
+  'get_terminal_size=1.0.0=py27_0'
+- !!python/unicode
+  'gevent=1.1.1=py27_0'
+- !!python/unicode
+  'greenlet=0.4.10=py27_0'
+- !!python/unicode
+  'grin=1.2.1=py27_3'
+- !!python/unicode
+  'heapdict=1.0.0=py27_1'
+- !!python/unicode
+  'icu=57.1=vc9_0'
+- !!python/unicode
+  'idna=2.1=py27_0'
+- !!python/unicode
+  'imagesize=0.7.1=py27_0'
+- !!python/unicode
+  'ipaddress=1.0.16=py27_0'
+- !!python/unicode
+  'ipykernel=4.3.1=py27_0'
+- !!python/unicode
+  'ipython=4.2.0=py27_0'
+- !!python/unicode
+  'ipython_genutils=0.1.0=py27_0'
+- !!python/unicode
+  'ipywidgets=4.1.1=py27_0'
+- !!python/unicode
+  'itsdangerous=0.24=py27_0'
+- !!python/unicode
+  'jdcal=1.2=py27_1'
+- !!python/unicode
+  'jedi=0.9.0=py27_1'
+- !!python/unicode
+  'jinja2=2.8=py27_1'
+- !!python/unicode
+  'jsonschema=2.5.1=py27_0'
+- !!python/unicode
+  'jupyter=1.0.0=py27_3'
+- !!python/unicode
+  'jupyter_client=4.3.0=py27_0'
+- !!python/unicode
+  'jupyter_console=4.1.1=py27_0'
+- !!python/unicode
+  'jupyter_core=4.1.0=py27_0'
+- !!python/unicode
+  'llvmlite=0.11.0=py27_0'
+- !!python/unicode
+  'locket=0.2.0=py27_1'
+- !!python/unicode
+  'lxml=3.6.0=py27_0'
+- !!python/unicode
+  'markupsafe=0.23=py27_2'
+- !!python/unicode
+  'matplotlib=1.5.1=np111py27_0'
+- !!python/unicode
+  'menuinst=1.4.1=py27_0'
+- !!python/unicode
+  'mistune=0.7.2=py27_0'
+- !!python/unicode
+  'mkl=11.3.3=1'
+- !!python/unicode
+  'mkl-service=1.1.2=py27_2'
+- !!python/unicode
+  'mock=2.0.0=py27_0'
+- !!python/unicode
+  'mpmath=0.19=py27_1'
+- !!python/unicode
+  'multipledispatch=0.4.8=py27_0'
+- !!python/unicode
+  'nb_anacondacloud=1.1.0=py27_0'
+- !!python/unicode
+  'nb_conda=1.1.0=py27_0'
+- !!python/unicode
+  'nb_conda_kernels=1.0.3=py27_0'
+- !!python/unicode
+  'nbconvert=4.2.0=py27_0'
+- !!python/unicode
+  'nbformat=4.0.1=py27_0'
+- !!python/unicode
+  'nbpresent=3.0.2=py27_0'
+- !!python/unicode
+  'networkx=1.11=py27_0'
+- !!python/unicode
+  'nltk=3.2.1=py27_0'
+- !!python/unicode
+  'nose=1.3.7=py27_1'
+- !!python/unicode
+  'notebook=4.2.1=py27_0'
+- !!python/unicode
+  'numba=0.26.0=np111py27_0'
+- !!python/unicode
+  'numexpr=2.6.0=np111py27_0'
+- !!python/unicode
+  'numpy=1.11.0=py27_2'
+- !!python/unicode
+  'odo=0.5.0=py27_0'
+- !!python/unicode
+  'openpyxl=2.3.2=py27_0'
+- !!python/unicode
+  'openssl=1.0.2h=vc9_0'
+- !!python/unicode
+  'pandas=0.18.1=np111py27_0'
+- !!python/unicode
+  'partd=0.3.4=py27_0'
+- !!python/unicode
+  'patch=2.5.9=1'
+- !!python/unicode
+  'path.py=8.2.1=py27_0'
+- !!python/unicode
+  'pathlib2=2.1.0=py27_0'
+- !!python/unicode
+  'patsy=0.4.1=py27_0'
+- !!python/unicode
+  'pbr=1.10.0=py27_0'
+- !!python/unicode
+  'pep8=1.7.0=py27_0'
+- !!python/unicode
+  'pickleshare=0.7.2=py27_0'
+- !!python/unicode
+  'pip=8.1.2=py27_0'
+- !!python/unicode
+  'psutil=4.3.0=py27_0'
+- !!python/unicode
+  'py=1.4.31=py27_0'
+- !!python/unicode
+  'pyasn1=0.1.9=py27_0'
+- !!python/unicode
+  'pycosat=0.6.1=py27_1'
+- !!python/unicode
+  'pycparser=2.14=py27_1'
+- !!python/unicode
+  'pycrypto=2.6.1=py27_4'
+- !!python/unicode
+  'pycurl=7.43.0=py27_0'
+- !!python/unicode
+  'pyflakes=1.2.3=py27_0'
+- !!python/unicode
+  'pygments=2.1.3=py27_0'
+- !!python/unicode
+  'pyopenssl=16.2.0=py27_0'
+- !!python/unicode
+  'pyparsing=2.1.4=py27_0'
+- !!python/unicode
+  'pyreadline=2.1=py27_0'
+- !!python/unicode
+  'pytest=2.9.2=py27_0'
+- !!python/unicode
+  'python=2.7.11=5'
+- !!python/unicode
+  'python-dateutil=2.5.3=py27_0'
+- !!python/unicode
+  'pytz=2016.4=py27_0'
+- !!python/unicode
+  'pywin32=220=py27_1'
+- !!python/unicode
+  'pyyaml=3.11=py27_4'
+- !!python/unicode
+  'pyzmq=15.2.0=py27_0'
+- !!python/unicode
+  'qtconsole=4.2.1=py27_0'
+- !!python/unicode
+  'qtpy=1.0.2=py27_0'
+- !!python/unicode
+  'requests=2.13.0=py27_0'
+- !!python/unicode
+  'rope=0.9.4=py27_1'
+- !!python/unicode
+  'ruamel_yaml=0.11.14=py27_1'
+- !!python/unicode
+  'scikit-image=0.12.3=np111py27_1'
+- !!python/unicode
+  'scikit-learn=0.17.1=np111py27_1'
+- !!python/unicode
+  'scipy=0.17.1=np111py27_1'
+- !!python/unicode
+  'setuptools=23.0.0=py27_0'
+- !!python/unicode
+  'simplegeneric=0.8.1=py27_1'
+- !!python/unicode
+  'singledispatch=3.4.0.3=py27_0'
+- !!python/unicode
+  'sip=4.18=py27_0'
+- !!python/unicode
+  'six=1.10.0=py27_0'
+- !!python/unicode
+  'snowballstemmer=1.2.1=py27_0'
+- !!python/unicode
+  'sockjs-tornado=1.0.3=py27_0'
+- !!python/unicode
+  'sphinx=1.4.1=py27_0'
+- !!python/unicode
+  'sphinx_rtd_theme=0.1.9=py27_0'
+- !!python/unicode
+  'spyder=2.3.9=py27_0'
+- !!python/unicode
+  'sqlalchemy=1.0.13=py27_0'
+- !!python/unicode
+  'ssl_match_hostname=3.4.0.2=py27_1'
+- !!python/unicode
+  'statsmodels=0.6.1=np111py27_1'
+- !!python/unicode
+  'sympy=1.0=py27_0'
+- !!python/unicode
+  'tk=8.5.18=vc9_0'
+- !!python/unicode
+  'toolz=0.8.0=py27_0'
+- !!python/unicode
+  'tornado=4.3=py27_1'
+- !!python/unicode
+  'traitlets=4.2.1=py27_0'
+- !!python/unicode
+  'unicodecsv=0.14.1=py27_0'
+- !!python/unicode
+  'vs2008_runtime=9.00.30729.1=2'
+- !!python/unicode
+  'werkzeug=0.11.10=py27_0'
+- !!python/unicode
+  'wheel=0.29.0=py27_0'
+- !!python/unicode
+  'xlsxwriter=0.9.2=py27_0'
+- !!python/unicode
+  'xlwings=0.7.2=py27_0'
+- !!python/unicode
+  'xlwt=1.1.2=py27_0'
+- !!python/unicode
+  'zlib=1.2.8=vc9_3'
+- !!python/unicode
+  'brewer2mpl=1.4=py27_0'
+- !!python/unicode
+  'osmnet=0.1.4=py27_0'
+- !!python/unicode
+  'pandana=0.3.0=py27_0'
+- pip:
+  - backports-abc==0.4
+  - backports.shutil-get-terminal-size==1.0.0
+  - backports.ssl-match-hostname==3.4.0.2
+  - et-xmlfile==1.0.1
+  - geojson==1.3.5
+  - geopy==1.11.0
+  - ipython-genutils==0.1.0
+  - jupyter-client==4.3.0
+  - jupyter-console==4.1.1
+  - jupyter-core==4.1.0
+  - nb-anacondacloud==1.1.0
+  - nb-conda==1.1.0
+  - nb-conda-kernels==1.0.3
+  - sphinx-rtd-theme==0.1.9
+  - tables==3.4.2
+  - xlrd==1.0.0
+prefix: C:\Anaconda
+
diff --git a/inputs/supplemental/parameters.json b/inputs/supplemental/parameters.json
new file mode 100644
index 00000000..d989e68f
--- /dev/null
+++ b/inputs/supplemental/parameters.json
@@ -0,0 +1 @@
+{"hbo": {"vot": {"mvotda": 0.0303, "mvots3": 0.0223, "mvots2": 0.0246}, "modechoice": {"asccs3": -0.5327, "asccs2": -0.3985, "asccwk": 1.896, "trwovt": -0.03, "trwcos": -0.0015, "walktm": -0.0923, "biketm": -0.0342, "ascctw": -2.00, "asccbk": -3.4304, "autcos": -0.0015, "trwivt": -0.005, "autivt": -0.015}, "global": {"autoop": 16.75}}, "hbw3": {"vot": {"avot4v": 0.0142, "avots2": 0.0157, "avots3": 0.0125}, "modechoice": {"asccs3": -4.7987, "asccs2": -3.0284, "asccwk": 0.7207, "trwovt": -0.05, "trwcos": -0.0018, "walktm": -0.0885, "biketm": -0.048, "ascctw": -1.934, "asccbk": -2.7077, "autcos": -0.0018, "trwivt": -0.025, "autivt": -0.025}, "global": {"autoop": 16.75}}, "hbw2": {"vot": {"avots3": 0.0125, "avots2": 0.0157, "avot3v": 0.0184}, "modechoice": {"asccs3": -4.6849, "asccs2": -3.0411, "asccwk": 0.9008, "trwovt": -0.05, "trwcos": -0.0023, "walktm": -0.1104, "biketm": -0.0521, "ascctw": -1.9304, "asccbk": -2.714, "autcos": -0.0023, "trwivt": -0.025, "autivt": -0.025}, "global": {"autoop": 16.75}}, "hbw1": {"vot": {"avots3": 0.0125, "avots2": 0.0157, "avot1v": 0.0496}, "modechoice": {"asccs3": -4.3741, "asccs2": -2.2303, "asccwk": -0.6436, "trwovt": -0.05, "trwcos": -0.004, "walktm": -0.0318, "biketm": -0.0258, "ascctw": -0.6911, "asccbk": -4.7651, "autcos": -0.004, "trwivt": -0.025, "autivt": -0.025}, "global": {"autoop": 16.75}}, "nhb": {"vot": {"mvotda": 0.0303, "mvots3": 0.0223, "mvots2": 0.0246}, "modechoice": {"asccs3": -1.0424, "asccs2": -0.7953, "asccwk": 2.2288, "trwovt": -0.05, "trwcos": -0.0025, "walktm": -0.1333, "biketm": -0.0273, "ascctw": -2.268, "asccbk": -4.357, "autcos": -0.0025, "trwivt": -0.025, "autivt": -0.025}, "global": {"autoop": 16.75}}}
\ No newline at end of file
diff --git a/inputs/supplemental/supplemental_matrices_dict.txt b/inputs/supplemental/supplemental_matrices_dict.txt
new file mode 100644
index 00000000..c8160c1f
--- /dev/null
+++ b/inputs/supplemental/supplemental_matrices_dict.txt
@@ -0,0 +1,990 @@
+{
+  "Scalar_Matrices": [
+  {
+    "Name": "autoop",
+    "Description": "auto operating cost (cents/mile)"
+  },
+  {
+    "Name": "asccs2",
+    "Description": "asc for shared ride 2"
+  },
+  {
+    "Name": "asccs3",
+    "Description": "asc for shared ride 3+"
+  },
+  {
+    "Name": "ascctw",
+    "Description": "asc for transit walk"
+  },
+  {
+    "Name": "asccbk",
+    "Description": "asc for bike"
+  },
+  {
+    "Name": "asccwk",
+    "Description": "asc for walk"
+  },
+  {
+    "Name": "autivt",
+    "Description": "auto iv-time coeff"
+  },
+  {
+    "Name": "trwivt",
+    "Description": "tw iv-time coeff"
+  },
+  {
+    "Name": "biketm",
+    "Description": "bike time coeff"
+  },
+  {
+    "Name": "walktm",
+    "Description": "walk time coeff"
+  },
+  {
+    "Name": "autovt",
+    "Description": "auto ov-time coeff"
+  },
+  {
+    "Name": "trwovt",
+    "Description": "tw ov-time coeff"
+  },
+  {
+    "Name": "trwaux",
+    "Description": "tw aux-time coeff"
+  },
+  {
+    "Name": "trwiwt",
+    "Description": "tw iw-time coeff"
+  },
+  {
+    "Name": "trwxfr",
+    "Description": "tw xfr-time coeff"
+  },
+  {
+    "Name": "trwbrd",
+    "Description": "tw brd-time coeff"
+  },
+  {
+    "Name": "trwnbd",
+    "Description": "tw nbd coeff"
+  },
+  {
+    "Name": "autcos",
+    "Description": "auto cost coefficient"
+  },
+  {
+    "Name": "trwcos",
+    "Description": "tw cost coeff"
+  },
+  {
+    "Name": "bdsovo",
+    "Description": "cbd coeff for sov (mo)"
+  },
+  {
+    "Name": "bdsovd",
+    "Description": "cbd coeff for sov (md)"
+  },
+  {
+    "Name": "bdsr2o",
+    "Description": "cbd coeff for sr2 (mo)"
+  },
+  {
+    "Name": "bdsr2d",
+    "Description": "cbd coeff for sr2 (md)"
+  },
+  {
+    "Name": "bdsr3o",
+    "Description": "cbd coeff for sr3 (mo)"
+  },
+  {
+    "Name": "bdsr3d",
+    "Description": "cbd coeff for sr3 (md)"
+  },
+  {
+    "Name": "bdtrwo",
+    "Description": "cbd coeff for tw (mo)"
+  },
+  {
+    "Name": "bdtrwd",
+    "Description": "cbd coeff for tw (md)"
+  },
+  {
+    "Name": "bdbiko",
+    "Description": "cbd coeff for bike (mo)"
+  },
+  {
+    "Name": "bdbikd",
+    "Description": "cbd coeff for bike (md)"
+  },
+  {
+    "Name": "bdwlko",
+    "Description": "cbd coeff for walk (mo)"
+  },
+  {
+    "Name": "bdwlkd",
+    "Description": "cbd coeff for walk (md)"
+  },
+  {
+    "Name": "uidsvo",
+    "Description": "uf coeff for sov (mo) - int density"
+  },
+  {
+    "Name": "uidsvd",
+    "Description": "uf coeff for sov (md) - int density"
+  },
+  {
+    "Name": "umxsvo",
+    "Description": "uf coeff for sov (mo) - lu mix"
+  },
+  {
+    "Name": "umxsvd",
+    "Description": "uf coeff for sov (md) - lu mix"
+  },
+  {
+    "Name": "unmsvo",
+    "Description": "uf coeff for sov (mo) - non motorized"
+  },
+  {
+    "Name": "unmsvd",
+    "Description": "uf coeff for sov (md) - non motorized"
+  },
+  {
+    "Name": "uresvo",
+    "Description": "uf coeff for sov (mo) - retail far"
+  },
+  {
+    "Name": "uresvd",
+    "Description": "uf coeff for sov (md) - retail far"
+  },
+  {
+    "Name": "uids2o",
+    "Description": "uf coeff for sr2 (mo) - int density"
+  },
+  {
+    "Name": "uids2d",
+    "Description": "uf coeff for sr2 (md) - int density"
+  },
+  {
+    "Name": "umxs2o",
+    "Description": "uf coeff for sr2 (mo) - lu mix"
+  },
+  {
+    "Name": "umxs2d",
+    "Description": "uf coeff for sr2 (md) - lu mix"
+  },
+  {
+    "Name": "unms2o",
+    "Description": "uf coeff for sr2 (mo) - non motorized"
+  },
+  {
+    "Name": "unms2d",
+    "Description": "uf coeff for sr2 (md) - non motorized"
+  },
+  {
+    "Name": "ures2o",
+    "Description": "uf coeff for sr2 (mo) - retail far"
+  },
+  {
+    "Name": "ures2d",
+    "Description": "uf coeff for sr2 (md) - retail far"
+  },
+  {
+    "Name": "uids3o",
+    "Description": "uf coeff for sr3 (mo) - int density"
+  },
+  {
+    "Name": "uids3d",
+    "Description": "uf coeff for sr3 (md) - int density"
+  },
+  {
+    "Name": "umxs3o",
+    "Description": "uf coeff for sr3 (mo) - lu mix"
+  },
+  {
+    "Name": "umxs3d",
+    "Description": "uf coeff for sr3 (md) - lu mix"
+  },
+  {
+    "Name": "unms3o",
+    "Description": "uf coeff for sr3 (mo) - non motorized"
+  },
+  {
+    "Name": "unms3d",
+    "Description": "uf coeff for sr3 (md) - non motorized"
+  },
+  {
+    "Name": "ures3o",
+    "Description": "uf coeff for sr3 (mo) - retail far"
+  },
+  {
+    "Name": "ures3d",
+    "Description": "uf coeff for sr3 (md) - retail far"
+  },
+  {
+    "Name": "uidtwo",
+    "Description": "uf coeff for tw (mo) - int density"
+  },
+  {
+    "Name": "uidtwd",
+    "Description": "uf coeff for tw (md) - int density"
+  },
+  {
+    "Name": "umxtwo",
+    "Description": "uf coeff for tw (mo) - lu mix"
+  },
+  {
+    "Name": "umxtwd",
+    "Description": "uf coeff for tw (md) - lu mix"
+  },
+  {
+    "Name": "unmtwo",
+    "Description": "uf coeff for tw (mo) - non motorized"
+  },
+  {
+    "Name": "unmtwd",
+    "Description": "uf coeff for tw (md) - non motorized"
+  },
+  {
+    "Name": "uretwo",
+    "Description": "uf coeff for tw (mo) - retail far"
+  },
+  {
+    "Name": "uretwd",
+    "Description": "uf coeff for tw (md) - retail far"
+  },
+  {
+    "Name": "uidbko",
+    "Description": "uf coeff for bike (mo) - int density"
+  },
+  {
+    "Name": "uidbkd",
+    "Description": "uf coeff for bike (md) - int density"
+  },
+  {
+    "Name": "umxbko",
+    "Description": "uf coeff for bike (mo) - lu mix"
+  },
+  {
+    "Name": "umxbkd",
+    "Description": "uf coeff for bike (md) - lu mix"
+  },
+  {
+    "Name": "unmbko",
+    "Description": "uf coeff for bike (mo) - non motorized"
+  },
+  {
+    "Name": "unmbkd",
+    "Description": "uf coeff for bike (md) - non motorized"
+  },
+  {
+    "Name": "urebko",
+    "Description": "uf coeff for bike (mo) - retail far"
+  },
+  {
+    "Name": "urebkd",
+    "Description": "uf coeff for bike (md) - retail far"
+  },
+  {
+    "Name": "uidwko",
+    "Description": "uf coeff for walk (mo) - int density"
+  },
+  {
+    "Name": "uidwkd",
+    "Description": "uf coeff for walk (md) - int density"
+  },
+  {
+    "Name": "umxwko",
+    "Description": "uf coeff for walk (mo) - lu mix"
+  },
+  {
+    "Name": "umxwkd",
+    "Description": "uf coeff for walk (md) - lu mix"
+  },
+  {
+    "Name": "unmwko",
+    "Description": "uf coeff for walk (mo) - non motorized"
+  },
+  {
+    "Name": "unmwkd",
+    "Description": "uf coeff for walk (md) - non motorized"
+  },
+  {
+    "Name": "urewko",
+    "Description": "uf coeff for walk (mo) - retail far"
+  },
+  {
+    "Name": "urewkd",
+    "Description": "uf coeff for walk (md) - retail far"
+  },
+  {
+    "Name": "ascctd",
+    "Description": "hb work-inc1 asc for transit walk"
+  },
+  {
+    "Name": "trdivt",
+    "Description": "hb work-inc1 td iv-time coeff"
+  },
+  {
+    "Name": "trdovt",
+    "Description": "hb work-inc1 td ov-time coeff"
+  },
+  {
+    "Name": "trdcos",
+    "Description": "hb work-inc1 td cost coeff"
+  },
+  {
+    "Name": "trdaux",
+    "Description": "hb work-inc1 td aux-time coeff"
+  },
+  {
+    "Name": "trdiwt",
+    "Description": "hb work-inc1 td iw-time coeff"
+  },
+  {
+    "Name": "trdxfr",
+    "Description": "hb work-inc1 td xfr-time coeff"
+  },
+  {
+    "Name": "trdbrd",
+    "Description": "hb work-inc1 td brd-time coeff"
+  },
+  {
+    "Name": "trdnbd",
+    "Description": "hb work-inc1 td nbd coeff"
+  },
+  {
+    "Name": "bdtrdo",
+    "Description": "hb work-inc1 cbd coeff for td (mo)"
+  },
+  {
+    "Name": "bdtrdd",
+    "Description": "hb work-inc1 cbd coeff for td (md)"
+  },
+  {
+    "Name": "uidtdo",
+    "Description": "uf coeff for td (mo) - int density"
+  },
+  {
+    "Name": "uidtdd",
+    "Description": "uf coeff for td (md) - int density"
+  },
+  {
+    "Name": "umxtdo",
+    "Description": "uf coeff for td (mo) - lu mix"
+  },
+  {
+    "Name": "umxtdd",
+    "Description": "uf coeff for td (md) - lu mix"
+  },
+  {
+    "Name": "unmtdo",
+    "Description": "uf coeff for td (mo) - non motorized"
+  },
+  {
+    "Name": "unmtdd",
+    "Description": "uf coeff for td (md) - non motorized"
+  },
+  {
+    "Name": "uretdo",
+    "Description": "uf coeff for td (mo) - retail far"
+  },
+  {
+    "Name": "uretdd",
+    "Description": "uf coeff for td (md) - retail far"
+  },
+  {
+    "Name": "avotda",
+    "Description": "am sov non-work vot"
+  },
+  {
+    "Name": "avots2",
+    "Description": "am hov 2 vot"
+  },
+  {
+    "Name": "avots3",
+    "Description": "am hov 3+ vot"
+  },
+  {
+    "Name": "avotvp",
+    "Description": "am vanpool vot"
+  },
+  {
+    "Name": "avot1v",
+    "Description": "am sov hbw income 1 vot"
+  },
+  {
+    "Name": "avot2v",
+    "Description": "am sov hbw income 2 vot"
+  },
+  {
+    "Name": "avot3v",
+    "Description": "am sov hbw income 3 vot"
+  },
+  {
+    "Name": "avot4v",
+    "Description": "am sov hbw income 4 vot"
+  },
+  {
+    "Name": "avotlt",
+    "Description": "am light truck vot"
+  },
+  {
+    "Name": "avotmd",
+    "Description": "am medium truck vot"
+  },
+  {
+    "Name": "avothv",
+    "Description": "am heavy truck vot"
+  },
+  {
+    "Name": "mvotda",
+    "Description": "md sov non-work vot"
+  },
+  {
+    "Name": "mvots2",
+    "Description": "md hov 2 vot"
+  },
+  {
+    "Name": "mvots3",
+    "Description": "md hov 3+ vot"
+  },
+  {
+    "Name": "mvotvp",
+    "Description": "md vanpool vot"
+  },
+  {
+    "Name": "mvot1v",
+    "Description": "md sov hbw income 1 vot"
+  },
+  {
+    "Name": "mvot2v",
+    "Description": "md sov hbw income 2 vot"
+  },
+  {
+    "Name": "mvot3v",
+    "Description": "md sov hbw income 3 vot"
+  },
+  {
+    "Name": "mvot4v",
+    "Description": "md sov hbw income 4 vot"
+  },
+  {
+    "Name": "mvotlt",
+    "Description": "md light truck vot"
+  },
+  {
+    "Name": "mvotmd",
+    "Description": "md medium truck vot"
+  },
+  {
+    "Name": "mvothv",
+    "Description": "md heavy truck vot"
+  }
+],
+  "Origin_Matrices": [
+  {
+    "Name": "intden",
+    "Description": "uf variable: intersections/sq km"
+  },
+  {
+    "Name": "usemix",
+    "Description": "uf variable: land use mix"
+  },
+  {
+    "Name": "nonmot",
+    "Description": "uf variable: non-motorized ratio"
+  },
+  {
+    "Name": "retfar",
+    "Description": "uf variable: retail far"
+  },
+  {
+    "Name": "bdflag",
+    "Description": "cbd zone flag"
+  },
+  {
+    "Name": "colleg",
+    "Description": "parking cost - hb college trips"
+  },
+  {
+    "Name": "hourly",
+    "Description": "hourly parking cost - non-work trips"
+  },
+  {
+    "Name": "daily",
+    "Description": "daily parking cost - hb work trips"
+  }
+],
+  "Destination_Matrices": [
+  {
+    "Name": "intden",
+    "Description": "uf variable: intersections/sq km"
+  },
+  {
+    "Name": "usemix",
+    "Description": "uf variable: land use mix"
+  },
+  {
+    "Name": "nonmot",
+    "Description": "uf variable: non-motorized ratio"
+  },
+  {
+    "Name": "retfar",
+    "Description": "uf variable: retail far"
+  },
+  {
+    "Name": "bdflag",
+    "Description": "cbd zone flag"
+  },
+  {
+    "Name": "colleg",
+    "Description": "parking cost - hb college trips"
+  },
+  {
+    "Name": "hourly",
+    "Description": "hourly parking cost - non-work trips"
+  },
+  {
+    "Name": "daily",
+    "Description": "daily parking cost - hb work trips"
+  },
+  {
+    "Name": "pnrchg",
+    "Description": "pnr lot parking cost"
+  }
+],
+  "Full_Matrices": [
+  {
+    "Name": "euda",
+    "Description": "da exponentiated utility"
+  },
+  {
+    "Name": "eus2",
+    "Description": "sr 2 exponentiated utility"
+  },
+  {
+    "Name": "eus3",
+    "Description": "sr 3+ exponentiated utility"
+  },
+  {
+    "Name": "eutw",
+    "Description": "tw exponentiated utility"
+  },
+  {
+    "Name": "eutd",
+    "Description": "tw exponentiated utility"
+  },
+  {
+    "Name": "eubk",
+    "Description": "bike exponentiated utility"
+  },
+  {
+    "Name": "euwk",
+    "Description": "walk exponentiated utility"
+  },
+  {
+    "Name": "eusm",
+    "Description": "exponentiated utility sum"
+  },
+  {
+    "Name": "dabcs",
+    "Description": 0
+  },
+  {
+    "Name": "dabtm",
+    "Description": 0
+  },
+  {
+    "Name": "dabct",
+    "Description": "sov bi-directional cost"
+  },
+  {
+    "Name": "dabds",
+    "Description": 0
+  },
+  {
+    "Name": "s2bcs",
+    "Description": 0
+  },
+  {
+    "Name": "s2btm",
+    "Description": 0
+  },
+  {
+    "Name": "s2bct",
+    "Description": "sr 2 bi-directional cost"
+  },
+  {
+    "Name": "s2bds",
+    "Description": 0
+  },
+  {
+    "Name": "s3bcs",
+    "Description": 0
+  },
+  {
+    "Name": "s3btm",
+    "Description": 0
+  },
+  {
+    "Name": "s3bct",
+    "Description": "sr 3+ bi-directional cost"
+  },
+  {
+    "Name": "s3bds",
+    "Description": 0
+  },
+  {
+    "Name": "termtm",
+    "Description": 0
+  },
+  {
+    "Name": "ivtwa",
+    "Description": 0
+  },
+  {
+    "Name": "auxwa",
+    "Description": 0
+  },
+  {
+    "Name": "iwtwa",
+    "Description": 0
+  },
+  {
+    "Name": "brdwa",
+    "Description": 0
+  },
+  {
+    "Name": "nbdwa",
+    "Description": 0
+  },
+  {
+    "Name": "xfrwa",
+    "Description": 0
+  },
+  {
+    "Name": "farbx",
+    "Description": 0
+  },
+  {
+    "Name": "farwa",
+    "Description": 0
+  },
+  {
+    "Name": "auxda",
+    "Description": 0
+  },
+  {
+    "Name": "pnrds",
+    "Description": 0
+  },
+  {
+    "Name": "pnrtm",
+    "Description": 0
+  },
+  {
+    "Name": "pnrch",
+    "Description": 0
+  },
+  {
+    "Name": "dapct",
+    "Description": "total cost to pnr lot"
+  },
+  {
+    "Name": "ivtda",
+    "Description": 0
+  },
+  {
+    "Name": "wlkda",
+    "Description": "total walk time - drive access"
+  },
+  {
+    "Name": "iwtda",
+    "Description": 0
+  },
+  {
+    "Name": "brdda",
+    "Description": 0
+  },
+  {
+    "Name": "nbdda",
+    "Description": 0
+  },
+  {
+    "Name": "xfrda",
+    "Description": 0
+  },
+  {
+    "Name": "farda",
+    "Description": 0
+  },
+  {
+    "Name": "bketm",
+    "Description": 0
+  },
+  {
+    "Name": "wlktm",
+    "Description": 0
+  },
+  {
+    "Name": "coshda",
+    "Description": "hb college sov mode share"
+  },
+  {
+    "Name": "coshs2",
+    "Description": "hb college shared ride 2 mode share"
+  },
+  {
+    "Name": "coshs3",
+    "Description": "hb college shared ride 3+ mode share"
+  },
+  {
+    "Name": "coshtw",
+    "Description": "hb college tw mode share"
+  },
+  {
+    "Name": "coshbk",
+    "Description": "hb college bike mode share"
+  },
+  {
+    "Name": "coshwk",
+    "Description": "hb college walk mode share"
+  },
+  {
+    "Name": "nwshda",
+    "Description": "hb non-work sov mode share"
+  },
+  {
+    "Name": "nwshs2",
+    "Description": "hb non-work shared ride 2 mode share"
+  },
+  {
+    "Name": "nwshs3",
+    "Description": "hb non-work shared ride 3+ mode share"
+  },
+  {
+    "Name": "nwshtw",
+    "Description": "hb non-work tw mode share"
+  },
+  {
+    "Name": "nwshbk",
+    "Description": "hb non-work bike mode share"
+  },
+  {
+    "Name": "nwshwk",
+    "Description": "hb non-work walk mode share"
+  },
+  {
+    "Name": "scshda",
+    "Description": "hb school sov mode share"
+  },
+  {
+    "Name": "scshs2",
+    "Description": "hb school shared ride 2 mode share"
+  },
+  {
+    "Name": "scshs3",
+    "Description": "hb school shared ride 3+ mode share"
+  },
+  {
+    "Name": "scshtw",
+    "Description": "hb school tw mode share"
+  },
+  {
+    "Name": "scshbk",
+    "Description": "hb school bike mode share"
+  },
+  {
+    "Name": "scshwk",
+    "Description": "hb school walk mode share"
+  },
+  {
+    "Name": "nhshda",
+    "Description": "nhb sov mode share"
+  },
+  {
+    "Name": "nhshs2",
+    "Description": "nhb shared ride 2 mode share"
+  },
+  {
+    "Name": "nhshs3",
+    "Description": "nhb shared ride 3+ mode share"
+  },
+  {
+    "Name": "nhshtw",
+    "Description": "nhb transit-walk mode share"
+  },
+  {
+    "Name": "nhshbk",
+    "Description": "nhb bike mode share"
+  },
+  {
+    "Name": "nhshwk",
+    "Description": "nhb walk mode share"
+  },
+  {
+    "Name": "w1shda",
+    "Description": "hb work-inc1 sov mode share"
+  },
+  {
+    "Name": "w1shs2",
+    "Description": "hb work-inc1 shared ride 2 mode share"
+  },
+  {
+    "Name": "w1shs3",
+    "Description": "hb work-inc1 shared ride 3+ mode share"
+  },
+  {
+    "Name": "w1shtw",
+    "Description": "hb work-inc1 tw mode share"
+  },
+  {
+    "Name": "w1shtd",
+    "Description": "hb work-inc1 td mode share"
+  },
+  {
+    "Name": "w1shbk",
+    "Description": "hb work-inc1 bike mode share"
+  },
+  {
+    "Name": "w1shwk",
+    "Description": "hb work-inc1 walk mode share"
+  },
+  {
+    "Name": "w2shda",
+    "Description": "hb work-inc2 sov mode share"
+  },
+  {
+    "Name": "w2shs2",
+    "Description": "hb work-inc2 shared ride 2 mode share"
+  },
+  {
+    "Name": "w2shs3",
+    "Description": "hb work-inc2 shared ride 3+ mode share"
+  },
+  {
+    "Name": "w2shtw",
+    "Description": "hb work-inc2 tw mode share"
+  },
+  {
+    "Name": "w2shtd",
+    "Description": "hb work-inc2 td mode share"
+  },
+  {
+    "Name": "w2shbk",
+    "Description": "hb work-inc2 bike mode share"
+  },
+  {
+    "Name": "w2shwk",
+    "Description": "hb work-inc2 walk mode share"
+  },
+  {
+    "Name": "w3shda",
+    "Description": "hb work-inc3 sov mode share"
+  },
+  {
+    "Name": "w3shs2",
+    "Description": "hb work-inc3 shared ride 2 mode share"
+  },
+  {
+    "Name": "w3shs3",
+    "Description": "hb work-inc3 shared ride 3+ mode share"
+  },
+  {
+    "Name": "w3shtw",
+    "Description": "hb work-inc3 tw mode share"
+  },
+  {
+    "Name": "w3shtd",
+    "Description": "hb work-inc3 td mode share"
+  },
+  {
+    "Name": "w3shbk",
+    "Description": "hb work-inc3 bike mode share"
+  },
+  {
+    "Name": "w3shwk",
+    "Description": "hb work-inc3 walk mode share"
+  },
+  {
+    "Name": "w4shda",
+    "Description": "hb work-inc4 sov mode share"
+  },
+  {
+    "Name": "w4shs2",
+    "Description": "hb work-inc4 shared ride 2 mode share"
+  },
+  {
+    "Name": "w4shs3",
+    "Description": "hb work-inc4 shared ride 3+ mode share"
+  },
+  {
+    "Name": "w4shtw",
+    "Description": "hb work-inc4 tw mode share"
+  },
+  {
+    "Name": "w4shtd",
+    "Description": "hb work-inc4 td mode share"
+  },
+  {
+    "Name": "w4shbk",
+    "Description": "hb work-inc4 bike mode share"
+  },
+  {
+    "Name": "w4shwk",
+    "Description": "hb work-inc4 walk mode share"
+  },
+  {
+    "Name": "lsum1",
+    "Description": "hb work income 1 mode choice log sum"
+  },
+  {
+    "Name": "lsum2",
+    "Description": "hb work income 2 mode choice log sum"
+  },
+  {
+    "Name": "lsum3",
+    "Description": "hb work income 3 mode choice log sum"
+  },
+  {
+    "Name": "lsum4",
+    "Description": "hb work income 4 mode choice log sum"
+  }
+],
+  "Terminal_Matrices": [
+  {
+    "Name": "prodtt",
+    "Description": "terminal times (in minutes) for origin/production trip end"
+  },
+  {
+    "Name": "attrtt",
+    "Description": "terminal times (in minutes) for destination/attraction trip end"
+  },
+  {
+    "Name": "termti",
+    "Description": "terminal times"
+  }
+],
+  "Skim_Matrices": [
+  {
+    "Name": "svtl2g",
+    "Description": "cost skim"
+  },
+  {
+    "Name": "svtl2t",
+    "Description": "time skim"
+  },
+  {
+    "Name": "svtl1d",
+    "Description": "distance skim"
+  },
+  {
+    "Name": "biket",
+    "Description": "bike time"
+  },
+  {
+    "Name": "walkt",
+    "Description": "walk time"
+  }
+]
+}
diff --git a/inputs/transit_counts.csv b/inputs/transit_counts.csv
deleted file mode 100644
index 7e82f679..00000000
--- a/inputs/transit_counts.csv
+++ /dev/null
@@ -1,443 +0,0 @@
-Description,Code,AM Observed,MD Observed,
-1 KINNEAR-SEA CBD,MK,540.8,540.8,
-2 W Q ANN-MADRONA,MK,1400.3,1400.3,
-3 N Q ANNE-MDRONA,MK,1299.7,1299.7,
-4 E Q AN-JUDKN PK,MK,1217.1,1217.1,
-5 SH CC-GRWD-SCBD,MK,1388.2,1388.2,
-7 RNR BCH-SCBD EX,MK,1981.7,1981.7,
-8 SEACTR-CAP-RANR,MK,1485.6,1485.6,
-9 RAINR BCH-BRDWY,MK,578,578,
-10 CAP HILL-SCBD ,MK,855,855,
-11 MADISN PK-SCBD,MK,640,640,
-12 CAP HILL-SCBD ,MK,1384.5,1384.5,
-13 SPU-SEA CBD SB,MK,607.5,607.5,
-14 SUMMIT-MT BAKR,MK,1046.2,1046.2,
-15 CROWN HLL-SCBD,MK,1488.5,1488.5,
-16 NGT-WLFRD-SCBD,MK,819.3,819.3,
-17 LYLH-BLRD-SCBD,MK,833.9,833.9,
-18 LOYAL HTS-SCBD,MK,1066.3,1066.3,
-19 W MAGNOLIA-CBD,MK,122,122,
-21 ARBOR HTS-SCBD,MK,870.2,870.2,
-22 WHITE CTR-SCBD,MK,425.7,425.7,
-23 WHITE CTR-SCBD,MK,338.4,338.4,
-24 MAGNOLIA-SCBD ,MK,376.9,376.9,
-25 LRLHST-UW-SCBD,MK,293.8,293.8,
-26 E GRNLK-SEACBD,MK,840.2,840.2,
-27 COLMAN PK-SCBD,MK,337.4,337.4,
-28 WHTIR HTS-SCBD,MK,1129.6,1129.6,
-30 Sand Point to Udist,MK,449.1,449.1,
-31 UW-FRMNT-MAGNL,MK,320.7,320.7,
-33 MAGNLIA-SEACBD,MK,525.5,525.5,
-34 RNR BC-SCBD EX,MK,104.3,104.3,
-35 HARBOR IS-SCBD,MK,22.3,22.3,
-36 RAINR BCH-SCBD,MK,1678.6,1678.6,
-37 AL JCT-SCBD EX,MK,191.1,191.1,
-38 RNR-BC HL-SODO,MK,6.1,6.1,
-39 RAINR BCH-SCBD,MK,391.5,391.5,
-41 LKCTY-NGT-SCBD,MK,2096.9,2096.9,
-42 RAINER VW-SCBD,MK,19.5,19.5,
-43 UW-CAP HL-SCBD,MK,950.5,950.5,
-44 BLLRD-UW-MNTLK,MK,938.9,938.9,
-45 Q AN-WLNFRD-UW,MK,79,79,
-46 SHLSHL-BLRD-UW,MK,100.8,100.8,
-48 RAIN BC-LYL HT,MK,2033.9,2033.9,
-49 UW-BRDWAY-SCBD,MK,952.4,952.4,
-51 ADM-JCT CIRCLE,MK,44,44,
-53 W SEA CIRCL CW,MK,7.4,7.4,
-54 WHTCR-JCT-SCBD,MK,873.6,873.6,
-55 ADMRL-JCT-SCBD,MK,542.2,542.2,
-56 ALKI-SEACBD EX,MK,395.7,395.7,
-57 AKJCT-ADM-SCBD,MK,161.3,161.3,
-60 WTCR-GTWN-BDWY,MK,788.6,788.6,
-64 LKCT-WDGW-SCBD,MK,389,389,
-65 LKCTY-WDGWD-UW,MK,519,519,
-66 NGATE-UW-SCBD ,MK,617.9,617.9,
-67 NORTHGTE TC-UW,MK,306.8,306.8,
-68 NORTHGATE-UW S,MK,336,336,
-70 UW-SEATTLE CBD,MK,847.3,847.3,
-71 WDG-I5-SCBD EX,MK,614.1,614.1,
-72 LKC-I5-SCBD EX,MK,751.3,751.3,
-73 JCPK-I5-SEA EX,MK,721.5,721.5,
-74 NOAA-UW-LWR QA,MK,362.4,362.4,
-75 BALLRD-NGAT-UW,MK,1048.7,1048.7,
-76 WEDGWOOD-SCBD ,MK,406.7,406.7,
-77 JCK PK-SCBD EX,MK,343.3,343.3,
-79 LKCITY-SCBD EX,MK,168.1,168.1,
-99 WATRFRNT STCAR,MK,0,0,
-101 RENTN TC-SCBD,MK,830.2,830.2,
-102 Fairwood to S,MK,168.9,168.9,
-105 RNT-RNT HGLND,MK,212,212,
-106 RNTN-SKY-SCBD,MK,924.5,924.5,
-107 RNTN-RANR BCH,MK,265.3,265.3,
-110 N RNTN-SW RNT,MK,67.6,67.6,
-111 LK KTHLN-SCBD,MK,324.3,324.3,
-113 SHORWOOD-SCBD,MK,183.9,183.9,
-114 RN HL-NWC-SEA,MK,133.9,133.9,
-116 FAUNTLRY-SCBD,MK,194,194,
-118 TAHQ-VASH-SEA,MK,11,11,
-119 DKTN-VASH-SEA,MK,26.2,26.2,
-120 BUR-DELR-SCBD,MK,1326.8,1326.8,
-121 HILNE CC-SCBD,MK,340.9,340.9,
-122 HILNE CC-SCBD,MK,260.8,260.8,
-123 GREG HTS-SCBD,MK,117.5,117.5,
-124 Tukwila Intl ,MK,601.4,601.4,
-125 SHWD-WTCT-SEA,MK,601.4,601.4,
-128 W SEA-SOCENTR,MK,506.2,506.2,
-129 TUKWILA-INTL ,MK,14.7,14.7,
-131 BURIEN-SEACBD,MK,288.6,288.6,
-132 BURIEN-SEACBD,MK,513.7,513.7,
-133 BURIEN-UW NB*,MK,112.2,112.2,
-134 BURIEN-SEACBD,MK,120.8,120.8,
-139 GRG HT-BURIEN,MK,42.9,42.9,
-140 BUR-SEATC-RNT,MK,364.8,364.8,
-143 BL DI-RNT-SEA,MK,251.8,251.8,
-148 FAIRWD-RENTON,MK,119.3,119.3,
-149 BLK DIA-RENTN,MK,10.5,10.5,
-150 AUB-KENT-SCBD,MK,1124.2,1124.2,
-152 ENUM-AUB-SCBD,MK,107.5,107.5,
-153 KENT-RENTON N,MK,170.7,170.7,
-154 AUBURN-BOEING,MK,29.2,29.2,
-155 FAIRWD-SOCNTR,MK,80.8,80.8,
-156 Scnter-S Tac-,MK,55.1,55.1,
-157 Lake Meridian,MK,85.3,85.3,
-158 LK MRIDN-SCBD,MK,172,172,
-159 TIMBERLN-SCBD,MK,215.4,215.4,
-161 Lk Merid-Kent,MK,158.7,158.7,
-162 KENT TC-SCBD ,MK,137.1,137.1,
-164 GR RV CC-KENT,MK,208.5,208.5,
-166 DES MOIN-KENT,MK,247.7,247.7,
-167 AU-KNT-405-UW,MK,160.1,160.1,
-168 TIMBERLN-KENT,MK,161.5,161.5,
-169 KNT TC-RNT TC,MK,377.2,377.2,
-173 FEDWAY-BOEING,MK,37.3,37.3,
-174 FDWY-STAC-SEA,MK,748,748,
-175 W FED WY-SCBD,MK,93.5,93.5,
-177 FEDWY TC-SCBD,MK,313.8,313.8,
-179 TWIN LKS-SCBD,MK,195.4,195.4,
-180 Se Auburn-Ken,MK,719.1,719.1,
-181 FEDWAY-AUBURN,MK,305.6,305.6,
-182 NE TAC-FED WY,MK,59.8,59.8,
-183 FEDRL WY-KENT,MK,168.2,168.2,
-187 TWIN LK-FEDWY,MK,98.8,98.8,
-190 STAR LK-SCBD ,MK,153.9,153.9,
-192 STAR LK-SCBD ,MK,94.4,94.4,
-196 S FDWY PR-SEA,MK,143.7,143.7,
-197 FEDERAL WY-UW,MK,272.7,272.7,
-200 ISSAQ-N ISSAQ,MK,61.3,61.3,
-201 S MI-MERCR IS,MK,4.8,4.8,
-202 MERCR IS-SCBD,MK,132.1,132.1,
-203 N MERCER ISLN,MK,24.1,24.1,
-204 S MI-MERCR IS,MK,6.5,6.5,
-205 M I-1ST HL-UW,MK,87.5,87.5,
-206 NWPRT HS-NEWC,MK,36.5,36.5,
-207 NWPRT HS-EGTE,MK,38.4,38.4,
-208 NWPRT HS-EGTE,MK,36.7,36.7,
-209 NO BND-ISSAQH,MK,53,53,
-210 ISSQ-EGT-SCBD,MK,85.1,85.1,
-211 IssaqHighland,MK,113,113,
-212 EGTE P&R-SCBD,MK,659.5,659.5,
-213 Mercer I PnR Coven,MK,0,0,
-214 NBND-ISS-SCBD,MK,318,318,
-215 N Bend to Iss,MK,135.5,135.5,
-216 SAMMSH-SEACBD,MK,287.9,287.9,
-217 N IS-EGT-SCBD,MK,142.6,142.6,
-218 ISS HLND-SCBD,MK,701.7,701.7,
-219 FACT-NEWCS CW,MK,57.3,57.3,
-221 Ed Hill-Xroad,MK,234.1,234.1,
-222 OVRL-EGT-BELV,MK,124.5,124.5,
-225 OVLK-EGT-SCBD,MK,89.4,89.4,
-229 OVLK-EGT-SCBD,MK,160.3,160.3,
-230 KNGSG-BTC-RED,MK,428.6,428.6,
-232 DUVLL-RED-BEL,MK,135.2,135.2,
-233 AVNDL-OVLK-BL,MK,164.5,164.5,
-234 KENMR-KRK-BEL,MK,279.4,279.4,
-236 WOOD-KNGT-KRK,MK,98.7,98.7,
-237 Woodvil - BellTC,MK,43.7,43.7,
-238 BTHL-KNGT-KRK,MK,172.9,172.9,
-240 BEL-NEWC-RENT,MK,450.1,450.1,
-242 NRTHCTY-OVRLK,MK,229.5,229.5,
-243 JC PK-LKC-BEL,MK,114.6,114.6,
-244 Kenmore P&R t,MK,120.3,120.3,
-245 KRK-OVRLK-FCT,MK,478.2,478.2,
-247 OVRLK-RNT-KNT,MK,27.7,27.7,
-248 Avondl-Redmnd,MK,137.8,137.8,
-249 BEL-OVRLK-RDM,MK,121.3,121.3,
-250 RED-OVLK-SCBD,MK,114.5,114.5,
-251 UW BTH-RD-KRK,MK,73,73,
-252 KINGSGTE-SCBD,MK,274.5,274.5,
-253 RED-OVRLK-BEL,MK,424.1,424.1,
-255 JNTA-KRK-SCBD,MK,786.1,786.1,
-256 OVERLK-SEACBD,MK,85.6,85.6,
-257 BRICKYRD-SCBD,MK,182.2,182.2,
-260 JUANTA-SEACBD,MK,89.8,89.8,
-261 OVLK-BTC-SCBD,MK,145.6,145.6,
-265 RED-HGHT-SCBD,MK,182.3,182.3,
-266 REDMND-SEACBD,MK,141.1,141.1,
-268 REDMN-SEACBD ,MK,114.3,114.3,
-269 OVLK-SAHL-ISS,MK,144.2,144.2,
-271 ISSQH-BELV-UW,MK,900.7,900.7,
-272 EGT-CRSRDS-UW,MK,186,186,
-277 JUANITA-UW WB,MK,129.4,129.4,
-301 RICH BCH-SCBD,MK,749.3,749.3,
-303 AUR VL-1ST HL,MK,543.7,543.7,
-304 RICH BCH-SCBD,MK,196.7,196.7,
-306 KENMOR-SEACBD,MK,189.7,189.7,
-308 LKFOR PK-SCBD,MK,115.6,115.6,
-311 DUV-WDNV-SCBD,MK,275.3,275.3,
-312 UW BTH-SEACBD,MK,680.9,680.9,
-316 Meridian PK SCBD,MK,364.9,364.9,
-330 SHRL CC-LKCTY,MK,101.3,101.3,
-331 KENMOR-SHR CC,MK,214.8,214.8,
-342 ShorlnPnR RentonTC,MK,128.7,128.7,
-345 SHRL CC-NGATE,MK,219.7,219.7,
-346 AUR VIL-NGATE,MK,232.8,232.8,
-347 MTLK TER-NGTE,MK,197.7,197.7,
-348 RICH BCH-NGTE,MK,204,204,
-355 ShrlnCC-SCBD,MK,398.9,398.9,
-358 AUR VL-SEACBD,MK,1849.2,1849.2,
-372 WDNVL-BTHL-UW,MK,894.6,894.6,
-373 AURVL-15TH-UW,MK,285.2,285.2,
-600 SCBD-GR HLT R,MK,11.4,11.4,
-661 ROYAL BROUGH-,MK,3.6,3.6,
-915 AUBRN-ENUMCLW,MK,73.3,73.3,
-921 BELV-SOMERSET,MK,80.9,80.9,
-941 STR LK-1ST HL,MK,290.4,290.4,
-952 Auburn-KnyDale-Ev,MK,37.5,37.5,
-1 6TH AV-PACIF AV,PT,1152.909091,1152.909091,
-2 19TH-BRIDGEPORT,PT,646.5,646.5,
-3 LAKEWOOD-TACOMA,PT,461.3181818,461.3181818,
-10 PEARL ST,PT,155,155,
-11 POINT DEFIANCE,PT,166.5909091,166.5909091,
-13 N 30TH ST,PT,75.95454545,75.95454545,
-16 UPS-TCC,PT,223.5,223.5,
-26 M L KING JR WY,PT,17,17,
-28 S 12TH ST,PT,196.3636364,196.3636364,
-41 PORTLAND AVE,PT,182.0454545,182.0454545,
-42 MCKINLEY AVE,PT,150.1818182,150.1818182,
-45 YAKIMA,PT,195.9090909,195.9090909,
-48 SHERIDAN-M ST,PT,280.4545455,280.4545455,
-51 TCC-TCOMA MALL,PT,69.22727273,69.22727273,
-52 TCC-TCOMA MALL,PT,226.0909091,226.0909091,
-53 UNIVERSITY PLC,PT,307.9545455,307.9545455,
-54 38TH ST,PT,139.3636364,139.3636364,
-55 Tacoma Mall-Pa,PT,209.7272727,209.7272727,
-56 56TH ST,PT,110.0909091,110.0909091,
-57 Tacoma Mall,PT,164.8636364,164.8636364,
-59 MANITOU,PT,35.54545455,35.54545455,
-60  Port of Tacom,PT,12,12,
-61 Northeast Taco,PT,54.72727273,54.72727273,
-100  Gig Harbor,PT,110.1818182,110.1818182,
-102 GIG HR-TAC EX,PT,89.59090909,89.59090909,
-113 Key Peninsula,PT,5.454545455,5.454545455,
-202 72ND STREET,PT,302.6363636,302.6363636,
-204 Lakewood-Park,PT,305.7272727,305.7272727,
-206 Pacific Hwy-T,PT,164.8181818,164.8181818,
-207 Ft Lewis,PT,29.04545455,29.04545455,
-212 STEILACOOM,PT,138.0909091,138.0909091,
-214 WASHINGTON,PT,226.2272727,226.2272727,
-220 ORCHARD,PT,82.09090909,82.09090909,
-300 SO TACOMA WAY,PT,151.6818182,151.6818182,
-402 MERIDIAN,PT,275.5909091,275.5909091,
-406 BUCKLEY,PT,10.13636364,10.13636364,
-407 Bonney Lake-P,PT,4.636363636,4.636363636,
-408 Sumner to Bon,PT,21.22727273,21.22727273,
-409 Puyallup-Sumn,PT,110.2272727,110.2272727,
-410 112TH STREET,PT,226.6818182,226.6818182,
-413 Wildwood,PT,41.40909091,41.40909091,
-444  Parkland-Spa,PT,13.27272727,13.27272727,
-446 Canyon Rd Shill  ,PT,5.681818182,5.681818182,
-490 South Hill-Ta,PT,75.68181818,75.68181818,
-495 SO HILL-PUYLP,PT,59.54545455,59.54545455,
-496 Sumner Sounde,PT,84.72727273,84.72727273,
-497 Lakeland Hill,PT,54.31818182,54.31818182,
-500 Federal Way,PT,194.4090909,194.4090909,
-501 Milton-Federa,PT,158.8636364,158.8636364,
-601 OL-512-TCC-GH,PT,37.31818182,37.31818182,
-603 OLY-SR512-TAC,PT,62.22727273,62.22727273,
-101 MAR PR-AUR VL,CT,326.7839449,326.7839449,
-105 Bothell-Marin,CT,245.1833695,245.1833695,
-106 Bothell-Marin,CT,19.25737118,19.25737118,
-110 LYNWD TC-EDMD,CT,107.8278099,107.8278099,
-112 EDM CC-MKLTEO,CT,248.2038287,248.2038287,
-113 LynwdTC-Mukil,CT,176.1706612,176.1706612,
-115 EDMDS-MILL CK,CT,318.6798,318.6798,
-116 EDM-ECC-SL FR,CT,250.3488438,250.3488438,
-118 AUR VL-ASH WY,CT,258.6154939,258.6154939,
-119 LYN TC-MEDWDL,CT,63.85509363,63.85509363,
-120 Bothell-Lynwo,CT,8.38499762,8.38499762,
-121 LYN TC-UW BTH,CT,147.3119743,147.3119743,
-130 LYN TC-AUR VL,CT,130.4110119,130.4110119,
-131 EDM CC-AUR VL,CT,114.8319347,114.8319347,
-190 MUKLTO-EDM CC,CT,62.73911986,62.73911986,
-200 ARLNGTN-EV ST,CT,134.5190464,134.5190464,
-201 SMK PT-LYN TC,CT,269.7019141,269.7019141,
-202 ARLNGTN-EV ST,CT,135.4147917,135.4147917,
-207 Smokey Rt Mysvil-Boe,CT,,,
-221 QL CDA-LK STV,CT,49.91916487,49.91916487,
-222 TulalipBay-Ma,CT,41.21648399,41.21648399,
-227 Arlington-Mville,CT,,,
-230 DRNGTN-SMK PT,CT,20.24652395,20.24652395,
-240 STNWD-ARLNGTN,CT,54.3475789,54.3475789,
-247 Stanwd-Marysville,CT,,,
-270 EVRT-GOLD BAR,CT,128.4104437,128.4104437,
-271 Everett-Gold Bar,CT,,,
-275 MONR-EVT STN ,CT,74.82153559,74.82153559,
-277 Gold Bar-Everett,CT,,,
-280 GRAN FLLS-EVT,CT,86.79957259,86.79957259,
-401 Lynwood-Seatt,CT,252.8636192,252.8636192,
-402 Lynwood-Seatt,CT,559.6818431,559.6818431,
-404 Edmonds-Seatt,CT,98.54546389,98.54546389,
-405 EdmondsPnR SCBD,CT,25.02767376,25.02767376,
-406 SEAVIEW-SCBD ,CT,130.0201362,130.0201362,
-408 MTLK TER-SCBD,CT,145.2660944,145.2660944,
-410 MARNR PR-SCBD,CT,121.7737222,121.7737222,
-411 S EVERTT-SCBD,CT,188.1756556,188.1756556,
-412 SLVR FRS-SCBD,CT,283.5089998,283.5089998,
-413 Ashway PnR SCBD,CT,115.3089631,115.3089631,
-414 McCollumP&R-S,CT,,,
- 415 LincolnWay-S,CT,249.9314855,249.9314855,
-416 Edmonds-Seatt,CT,115.2148275,115.2148275,
-417 MKLTEO-SEACBD,CT,144.7422682,144.7422682,
-421 Marysville-Se,CT,129.5412353,129.5412353,
-422 Stanwood-SCBD,CT,19.53005233,19.53005233,
-424 SNOH-MNR-SCBD,CT,39.734462,39.734462,
-425 LkStevens-Sea,CT,47.13870306,47.13870306,
-435 MaysPond-Seat,CT,144.2638433,144.2638433,
-441 EdmDsP&R-Ovrl,CT,35.31598367,35.31598367,
-477 Brier-Seattle,CT,103.1818,103.1818,
-701 Swift-Aurora-Everett#N/A,CT,534.0309947,534.0309947,
-810 McCollum PnR Udist,CT,,,
-812 McCollum PnR Udist,CT,82.19668185,82.19668185,
-821 Marysville-UD,CT,57.2921068,57.2921068,
-851 EdmdsP&R-Udis,CT,133.1786764,133.1786764,
-855 LYNWOOD TC-UW,CT,280.7892829,280.7892829,
-860 MARINER PR-UW,CT,163.950299,163.950299,
-870 EDMONDS-UW SB,CT,122.4073096,122.4073096,
-871 EDM-MTLKTR-UW,CT,19.96018116,19.96018116,
-880 MUKILTEO-UW S,CT,206.3096533,206.3096533,
-885 Swamp Ck Udist,CT,,,
-5 CEDAR HEIGHTS,KT,41,41,
-8 TREMONT NB,KT,13,13,
-19 SouthPark,KT,23,23,
-11 Crosstown LTD,KT,203,203,
-12 SILVERDLE WEST,KT,46,46,
-13 PARKWOOD E LTD,KT,95,95,
-15 MCWLIAMS-SHTTL,KT,58,58,
-17 KITSP MALL LTD,KT,49,49,
-19 CRSSROADS SHTL,KT,56,56,
-20 NAVY YARD CITY,KT,72,72,
-21 PERRY AVENUE,KT,46,46,
-22 GATEWAY EXPRSS,KT,57,57,
-23 KARIOTS/TRCYTN,KT,5,5,
-24 OLYMPIC COLLEG,KT,66,66,
-25 EAST PARK,KT,60,60,
-26 WEST PARK,KT,38,38,
-29 TRENTON AVENUE,KT,37,37,
-32Pouls-Silverdal,KT,28,28,
-33 SILVRDL-BNBRDG,KT,35,35,
-34 BANGOR SHUTTLE,KT,24,24,
-35 OldTownShuttle,KT,9,9,
-36 RIDGETOP SHTTL,KT,11,11,
-37 FAIRGRNDS SHTL,KT,17,17,
-41 LINCOLN DRIVE,KT,8,8,
-43 Viking Ave,KT,0,0,
-81 ANNAPOLIS CMTR,KT,60,60,
-85 MULLENIX EXPRS,KT,6,6,
-86 SOUTHWRTH SHTL,KT,54,54,
-90 POULS TC-BAINB,KT,189,189,
-91 INDIANOLA-BAIN,KT,106,106,
-92 POUL-SUQ-KNGST,KT,4,4,
-93 MANZANITA,KT,41,41,
-94 AGATE POINT,KT,26,26,
-95 BATTLE POINT,KT,50,50,
-96 SUNRISE,KT,30,30,
-97 CRYSTAL SPRNGS,KT,42,42,
-98 FORT WARD,KT,27,27,
-99 BILL POINT,KT,30,30,
-106 ARROW POINT,KT,22,22,
-WD200 Bethel Burley,KT,20.27272727,20.27272727,
-WD01 BURLEY BUS,KT,28.40909091,28.40909091,
-WD02 GLENWOOD,KT,30.27272727,30.27272727,
-WD03 HORSESHOE LK,KT,21.86363636,21.86363636,
-WD04 LONG LAKE DY,KT,32.77272727,32.77272727,
-WD05 MANCHSTR BCH,KT,33.63636364,33.63636364,
-WD06 MILE HILL EX,KT,31.18181818,31.18181818,
-WD07 SALMONBERRY,KT,27.40909091,27.40909091,
-WD08 PARKWOOD,KT,26.40909091,26.40909091,
-WD09 PHLIP/BIELMR,KT,33.5,33.5,
-WD10 PONDEROSA,KT,25.68181818,25.68181818,
-WD11 SIDNEY WOODS,KT,23.54545455,23.54545455,
-WD12 SOUTHWORTH,KT,25.63636364,25.63636364,
-WD13 SUNNYSLOPE,KT,28,28,
-WD14 CAMP UNION,KT,35.22727273,35.22727273,
-WD15 CENT VL LOOP,KT,32.04545455,32.04545455,
-WD16 FRGRND OVRLD,KT,25.59090909,25.59090909,
-WD17 FOSTERWOOD,KT,27.68181818,27.68181818,
-WD18 ILLAHEE,KT,30,30,
-WD19 PRKWOOD EAST,KT,28.40909091,28.40909091,
-WD20 RIDGE RUNNER,KT,35.86363636,35.86363636,
-WD21 SEABECK,KT,34.86363636,34.86363636,
-WD22 WOODMERE,KT,30.45454545,30.45454545,
-WD23 KINGSTON,KT,30.13636364,30.13636364,
-WD24 PORT GAMBLE,KT,31.59090909,31.59090909,
-WD25 SUQUAMISH,KT,32.27272727,32.27272727,
-WD27 Port Orch-Bangor,KT,16.72727273,16.72727273,
-WD 28 Olympic Fjord,KT,15.95454545,15.95454545,
-510 Everett-Seatt,MK,770.4673873,770.4673873,
-511 ASH WY-SEACBD,MK,825.7995128,825.7995128,
-513 Everett-SCBD,MK,,,
-522 WOODNV-SEACBD,MK,811.5,811.5,
-532 BELVUE-EVERTT,MK,356.5875831,356.5875831,
-535 BELV-LYNNWOOD,MK,419.7850604,419.7850604,
-540 REDMOND-UW WB,MK,334.8,334.8,
-545 RED-SCBD-HLGT,MK,1801.2,1801.2,
-550 BELLVU-SEACBD,MK,1999.2,1999.2,
-554 ISSAQ-SEA CBD,MK,388.2,388.2,
-555 ISSAQ-BTC-NGT,MK,205.4,205.4,
-556 ISSAQ-BTC-NGT,MK,260.3,260.3,
-560 BEL-STAC-WSEA,MK,375.2,375.2,
-566 ISSAQ-NGATE E,MK,666.2,666.2,
-574 Lakewood-Seat,PT,667.9431512,667.9431512,
-577 FED WY-SEACBD,MK,505.1,505.1,
-578 Seattle-Puyal,PT,250.8298135,250.8298135,
-586 Tacoma-UDistr,ST,128.4923817,128.4923817,
-590 Tacoma-Seattl,ST,476.9649193,476.9649193,
-592 Tacoma-Seattl,ST,228.9191907,228.9191907,
-593 Tacoma-SCBD,ST,,,
-594 Lakewood-Seat,ST,483.007507,483.007507,
-595 GigHarbor-Sea,ST,134,134,
-599 LAKEWD-TAC EX,ST,20.8868786,20.8868786,
-2 BOEING-WALNUT S,ET,54.59090909,54.59090909,
-3 W Casino Rd-Evr,ET,73.31818182,73.31818182,
-4 N Everett Circu,ET,30.90909091,30.90909091,
-5 Everett Circ SB,ET,24.63636364,24.63636364,
-7 EVERGRN-N COLBY,ET,267.7272727,267.7272727,
-8 MERRIL CK-EV ST,ET,94.36363636,94.36363636,
-9 AIRPORT RD-EVCC,ET,387.4545455,387.4545455,
-12 Mall Circl,ET,22.54545455,22.54545455,
-14 Mall Circl,ET,18.18181818,18.18181818,
-17 Mall Statn NB,ET,50,50,
-18 Mukilteo-Evert,ET,78.40909091,78.40909091,
-25 Everett Circl,ET,23.59090909,23.59090909,
-27 Murphys Conr-M,ET,14,14,
-29 Mall Sta - Col,ET,189.6363636,189.6363636,
-70 Mukilteo to Bo,ET,57.45454545,57.45454545,
-79 Everett-Marysville,ET,19.04545455,19.04545455,
-701 Mall Station,ET,1.136363636,1.136363636,
-702 Everett Station,ET,2.227272727,2.227272727,
-CR Tacoma-Seattle S,ST,4127.568182,4127.568182,
-CR Everett-Seattle N,ST,548.7272727,548.7272727,
-LR Seattle-SeaTac,ST,2840,2840,
-LR Tacoma,ST,552.41,552.41,
-SC Monorail,SC,1596,1596,
-Vashon FF,WF,260,260,
-Ft Fer Brem-Pt Or,KT,393.99,393.99,
-Ft Fer Brem-Annap,KT,164.91,164.91,
-WSF Edmnds-Kngstn,WF,43,43,
-WSF Fauntlr-Vashn,WF,,,
-WSF Fauntlr-Swrth,WF,,,
-WSF Pt Defi-Tahlq,WF,16,16,
-WSF Sea-Bainb Is,WF,594,594,
-WSF Sea-Bremerton ,WF,195,195,
-Vashon FF,WF,,,
-WSF Vashn-Sthwrth,WF,,,
diff --git a/inputs/vdfs/vdfs10to14.txt b/inputs/vdfs/vdfs10to14.txt
index 74a3f4a3..7cb992ef 100644
--- a/inputs/vdfs/vdfs10to14.txt
+++ b/inputs/vdfs/vdfs10to14.txt
@@ -27,6 +27,7 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs14to15.txt b/inputs/vdfs/vdfs14to15.txt
index 409312f7..e28caee3 100644
--- a/inputs/vdfs/vdfs14to15.txt
+++ b/inputs/vdfs/vdfs14to15.txt
@@ -27,6 +27,7 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs15to16.txt b/inputs/vdfs/vdfs15to16.txt
index 1cc17931..3677fade 100644
--- a/inputs/vdfs/vdfs15to16.txt
+++ b/inputs/vdfs/vdfs15to16.txt
@@ -55,6 +55,7 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs16to17.txt b/inputs/vdfs/vdfs16to17.txt
index 6cf49d7f..56d23e12 100644
--- a/inputs/vdfs/vdfs16to17.txt
+++ b/inputs/vdfs/vdfs16to17.txt
@@ -55,6 +55,7 @@ a ft13 =1.265774 * timau .min. (length * 12)
 a ft14 =1.00584 * timau
 a ft15 =1.037034 * timau .min. (length * 12)
 a ft16 =1.0928 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs17to18.txt b/inputs/vdfs/vdfs17to18.txt
index 19b9145e..7e371d10 100644
--- a/inputs/vdfs/vdfs17to18.txt
+++ b/inputs/vdfs/vdfs17to18.txt
@@ -55,6 +55,7 @@ a ft13 =1.265774 * timau .min. (length * 12)
 a ft14 =1.00584 * timau
 a ft15 =1.037034 * timau .min. (length * 12)
 a ft16 =1.0928 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs18to20.txt b/inputs/vdfs/vdfs18to20.txt
index 10fbc44d..f51e1cfa 100644
--- a/inputs/vdfs/vdfs18to20.txt
+++ b/inputs/vdfs/vdfs18to20.txt
@@ -55,6 +55,7 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs20to5.txt b/inputs/vdfs/vdfs20to5.txt
index 2bb78856..8e83cd8d 100644
--- a/inputs/vdfs/vdfs20to5.txt
+++ b/inputs/vdfs/vdfs20to5.txt
@@ -55,6 +55,7 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs5to6.txt b/inputs/vdfs/vdfs5to6.txt
index 66fa8cc0..6d8503e7 100644
--- a/inputs/vdfs/vdfs5to6.txt
+++ b/inputs/vdfs/vdfs5to6.txt
@@ -55,6 +55,7 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs6to7.txt b/inputs/vdfs/vdfs6to7.txt
index 92c03893..518634d6 100644
--- a/inputs/vdfs/vdfs6to7.txt
+++ b/inputs/vdfs/vdfs6to7.txt
@@ -27,6 +27,7 @@ a ft13 =1.265774 * timau .min. (length * 12)
 a ft14 =1.00584 * timau
 a ft15 =1.037034 * timau .min. (length * 12)
 a ft16 =1.0928 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs7to8.txt b/inputs/vdfs/vdfs7to8.txt
index 9f8cc7f2..4822cd43 100644
--- a/inputs/vdfs/vdfs7to8.txt
+++ b/inputs/vdfs/vdfs7to8.txt
@@ -27,5 +27,6 @@ a ft13 =1.265774 * timau .min. (length * 12)
 a ft14 =1.00584 * timau
 a ft15 =1.037034 * timau .min. (length * 12)
 a ft16 =1.0928 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
diff --git a/inputs/vdfs/vdfs8to9.txt b/inputs/vdfs/vdfs8to9.txt
index 92c03893..518634d6 100644
--- a/inputs/vdfs/vdfs8to9.txt
+++ b/inputs/vdfs/vdfs8to9.txt
@@ -27,6 +27,7 @@ a ft13 =1.265774 * timau .min. (length * 12)
 a ft14 =1.00584 * timau
 a ft15 =1.037034 * timau .min. (length * 12)
 a ft16 =1.0928 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
 
diff --git a/inputs/vdfs/vdfs9to10.txt b/inputs/vdfs/vdfs9to10.txt
index 5f9bf07c..66880e3a 100644
--- a/inputs/vdfs/vdfs9to10.txt
+++ b/inputs/vdfs/vdfs9to10.txt
@@ -27,5 +27,6 @@ a ft13 =1.230715 * timau .min. (length * 12)
 a ft14 =1.013045 * timau
 a ft15 =1.109141 * timau .min. (length * 12)
 a ft16 =1.10514 * timau .min. (length * 12)
+a ft17 =0.95 * timau .min. (length * 12)
 a fp1  =up1 / 100
 a fp2  =0
diff --git a/install.bat b/install.bat
deleted file mode 100644
index 0f78a98e..00000000
--- a/install.bat
+++ /dev/null
@@ -1,72 +0,0 @@
-@echo off
-
-:: SoundCast installer batch file
-:: =======================================
-:: Full install instructions are always at
-:: http://soundcast.readthedocs.io
-
-set ZIPINSTALLER='http://www.7-zip.org/a/7z1600-x64.msi'
-set POWERSHELL=%windir%\System32\WindowsPowerShell\v1.0\powershell.exe
-
-:: ------------------------------
-:: Test for python in system path
-
-echo.
-echo Checking for python...
-where python.exe || goto :python-error
-
-echo.
-echo Checking for pip...
-where pip.exe || goto :python-error
-
-echo.
-echo Installing python libraries...
-pip install -r requirements.txt || goto :python-error
-
-:: ----------------
-:: Test for 7-zip
-
-:test-zip
-echo.
-echo Checking for 7-Zip...
-where 7z.exe || goto :zip-missing
-goto :zip-ok
-
-:zip-missing
-echo 7-Zip not found. Installing...
-"%POWERSHELL%" -Command "(New-Object Net.WebClient).DownloadFile(%ZIPINSTALLER%, '7z-setup.msi')" || :zip-error
-
-7z-setup.msi /passive
-setx path "%PATH%;C:\Program Files\7-Zip"
-set PATH=%PATH%;C:\Program Files\7-Zip
-goto :test-zip
-
-:zip-ok
-
-goto :success
-
-:: =======================================
-:python-error
-echo.
-echo Failed with error code #%errorlevel%
-echo You need to install or set up Python. Anaconda Python is easiest:
-echo https://www.continuum.io/downloads
-echo.
-echo Ensure python and pip are in your PATH and try again.
-exit /b %errorlevel
-
-:zip-error
-echo.
-echo Failed with error code #%errorlevel%
-echo You need to install 7-zip and add it to your PATH
-echo http://www.7-zip.org
-echo.
-echo Install it and try again.
-exit /b %errorlevel
-
-
-:: =======================================
-:success
-echo.
-echo Done!
-
diff --git a/output_templates/Benefit_Cost_Template.xlsx b/output_templates/Benefit_Cost_Template.xlsx
deleted file mode 100644
index 8fedafe6..00000000
Binary files a/output_templates/Benefit_Cost_Template.xlsx and /dev/null differ
diff --git a/output_templates/benefit_cost_template.xlsx b/output_templates/benefit_cost_template.xlsx
new file mode 100644
index 00000000..eed0ba18
Binary files /dev/null and b/output_templates/benefit_cost_template.xlsx differ
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 38f35b25..00000000
--- a/requirements.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-# requirements.txt: list of required python libraries
-# (used by pip in install.bat)
-
-pandas
-pandas_highcharts
-pysal
diff --git a/run_soundcast.py b/run_soundcast.py
index 52abca29..88bb2d4a 100644
--- a/run_soundcast.py
+++ b/run_soundcast.py
@@ -1,4 +1,4 @@
-#Copyright [2014] [Puget Sound Regional Council]
+#Copyright [2014] [Puget Sound Regional Council]
 
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -44,23 +44,23 @@
 def accessibility_calcs():
     copy_accessibility_files()
     print 'adding military jobs to regular jobs'
-    returncode=subprocess.call([sys.executable, 'scripts/supplemental/military_parcel_loading.py'])
+    print 'adding JBLM workers to external workers'
+    print 'adjusting non-work externals'
+    print 'creating ixxi file for Daysim'
+    returncode=subprocess.call([sys.executable, 'scripts/supplemental/create_ixxi_work_trips.py'])
     if returncode != 0:
         print 'Military Job loading failed'
         sys.exit(1)
     print 'military jobs loaded'
 
-    if run_update_parking:
-        if base_year == scenario_name:
-            print("----- This is a base-year analysis. Parking parcels are NOT being updated! Input for 'run_update_parking' is over-ridden. -----")
-        else:
-            print 'Starting to update UrbanSim parcel data with 4k parking data file'
-            returncode = subprocess.call([sys.executable,
-                                      'scripts/utils/update_parking.py', base_inputs])
-            if returncode != 0:
-                print 'Update Parking failed'
-                sys.exit(1)
-            print 'Finished updating parking data on parcel file'
+    if base_year != model_year:
+        print 'Starting to update UrbanSim parcel data with 4k parking data file'
+        returncode = subprocess.call([sys.executable,
+                                  'scripts/utils/update_parking.py', scenario_inputs])
+        if returncode != 0:
+            print 'Update Parking failed'
+            sys.exit(1)
+        print 'Finished updating parking data on parcel file'
 
     print 'Beginning Accessibility Calculations'
     returncode = subprocess.call([sys.executable, 'scripts/accessibility/accessibility.py'])
@@ -75,7 +75,7 @@ def build_seed_skims(max_iterations):
     time_copy = datetime.datetime.now()
     returncode = subprocess.call([sys.executable,
         'scripts/skimming/SkimsAndPaths.py',
-        str(max_iterations),
+        str(max_iterations), model_year, 
         '-use_daysim_output_seed_trips'])
     if returncode != 0:
         sys.exit(1)
@@ -88,7 +88,7 @@ def build_free_flow_skims(max_iterations):
     time_copy = datetime.datetime.now()
     returncode = subprocess.call([sys.executable,
         'scripts/skimming/SkimsAndPaths.py',
-        str(max_iterations),
+        str(max_iterations), model_year, 
         '-build_free_flow_skims'])
     if returncode != 0:
         sys.exit(1)
@@ -163,9 +163,24 @@ def run_truck_supplemental(iteration):
             returncode = subprocess.call([sys.executable,'scripts/supplemental/generation.py'])
             if returncode != 0:
                 sys.exit(1)
-        returncode = subprocess.call([sys.executable,'scripts/supplemental/distribution.py'])
+
+        returncode = subprocess.call([sys.executable,'scripts/supplemental/distribute_non_work_ixxi.py'])
         if returncode != 0:
            sys.exit(1)
+        
+        returncode = subprocess.call([sys.executable,'scripts/supplemental/mode_choice_supplemental.py'])
+        if returncode != 0:
+           sys.exit(1)
+
+        #returncode = subprocess.call([sys.executable,'scripts/supplemental/create_ixxi_work_trips.py'])
+        #if returncode != 0:
+        #   sys.exit(1)
+
+        returncode = subprocess.call([sys.executable,'scripts/supplemental/create_airport_trips_combine_all.py'])
+        if returncode != 0:
+           sys.exit(1)
+
+
 @timed
 def daysim_assignment(iteration):
      
@@ -186,7 +201,7 @@ def daysim_assignment(iteration):
      if run_skims_and_paths:
          logger.info("Start of %s iteration of Skims and Paths", str(iteration))
          num_iterations = str(max_iterations_list[iteration])
-         returncode = subprocess.call([sys.executable, 'scripts/skimming/SkimsAndPaths.py', num_iterations])
+         returncode = subprocess.call([sys.executable, 'scripts/skimming/SkimsAndPaths.py', num_iterations, model_year])
          logger.info("End of %s iteration of Skims and Paths", str(iteration))
          print 'return code from skims and paths is ' + str(returncode)
          if returncode != 0:
@@ -210,8 +225,8 @@ def check_convergence(iteration, recipr_sample):
 def run_all_summaries():
 
    if run_network_summary:
+      subprocess.call([sys.executable, 'scripts/summarize/standard/daily_bank.py'])
       subprocess.call([sys.executable, 'scripts/summarize/standard/network_summary.py'])
-      # this summary is producing erronous results, we don't want people to think they are correct.
       subprocess.call([sys.executable, 'scripts/summarize/standard/net_summary_simplify.py'])
       if scenario_name == '2014':
          subprocess.call([sys.executable, 'scripts/summarize/standard/roadway_base_year_validation.py'])
@@ -219,31 +234,23 @@ def run_all_summaries():
 
    if run_soundcast_summary:
       subprocess.call([sys.executable, 'scripts/summarize/calibration/SCsummary.py'])
-
-   #Create a daily network with volumes. Will add counts and summary emme project. 
-   if run_create_daily_bank:
-      subprocess.call([sys.executable, 'scripts/summarize/standard/daily_bank.py'])
-
-   if run_ben_cost:
-      subprocess.call([sys.executable, 'scripts/summarize/benefit_cost/benefit_cost.py'])
-
+      
    if run_landuse_summary:
       subprocess.call([sys.executable, 'scripts/summarize/standard/summarize_land_use_inputs.py'])
-   if run_truck_summary:
-       subprocess.call([sys.executable, 'scripts/summarize/standard/truck_vols.py'])
+   
+#   if run_truck_summary:
+#       subprocess.call([sys.executable, 'scripts/summarize/standard/truck_vols.py'])
+
+   if run_grouped_summary:
+       subprocess.call([sys.executable, 'scripts/summarize/standard/group.py'])
 ##################################################################################################### ###################################################################################################### 
 # Main Script:
 def main():
 ## SET UP INPUTS ##########################################################
 
-    if run_accessibility_calcs:
-        accessibility_calcs()
-
-    if run_accessibility_summary:
-        subprocess.call([sys.executable, 'scripts/summarize/standard/parcel_summary.py'])
+    
 
-    if not os.path.exists('outputs'):
-        os.makedirs('outputs')
+    build_output_dirs()
 
     if run_copy_daysim_code:
         copy_daysim_code()
@@ -257,8 +264,14 @@ def main():
     if run_copy_large_inputs:
         copy_large_inputs()
 
-    if  run_convert_hhinc_2000_2010:
-        subprocess.call([sys.executable, 'scripts/utils/convert_hhinc_2000_2010.py'])
+    if run_accessibility_calcs:
+        accessibility_calcs()
+
+    if run_accessibility_summary:
+        subprocess.call([sys.executable, 'scripts/summarize/standard/parcel_summary.py'])
+
+    #if  run_convert_hhinc_2000_2010:
+    #    subprocess.call([sys.executable, 'scripts/utils/convert_hhinc_2000_2010.py'])
 
 ### IMPORT NETWORKS
 ### ###############################################################
@@ -266,7 +279,7 @@ def main():
         time_copy = datetime.datetime.now()
         logger.info("Start of network importer")
         returncode = subprocess.call([sys.executable,
-        'scripts/network/network_importer.py', base_inputs])
+        'scripts/network/network_importer.py', scenario_inputs])
         logger.info("End of network importer")
         time_network = datetime.datetime.now()
         if returncode != 0:
@@ -309,10 +322,10 @@ def main():
                                                         
                             if not os.path.exists('working'):
                                 os.makedirs('working')
-                            shcopy(base_inputs+'/shadow_pricing/shadow_prices.txt','working/shadow_prices.txt')
+                            #shcopy(scenario_inputs+'/shadow_pricing/shadow_prices.txt','working/shadow_prices.txt')
                             print "copying shadow prices" 
                     except:
-                            print ' error copying shadow pricing file from shadow_pricing at ' + base_inputs+'/shadow_pricing/shadow_prices.txt'
+                            print ' error copying shadow pricing file from shadow_pricing at ' + scenario_inputs+'/shadow_pricing/shadow_prices.txt'
                             sys.exit(1)
                 # Set up your Daysim Configration
                 modify_config([("$SHADOW_PRICE" ,"true"),("$SAMPLE",pop_sample[iteration]),("$RUN_ALL", "true")])
@@ -340,6 +353,12 @@ def main():
            #This function needs an iteration parameter. Value of 1 is fine. 
            daysim_assignment(1)
 
+
+    if should_run_reliability_skims:
+        returncode = subprocess.call([sys.executable,'scripts/skimming/reliability_skims.py'])
+        if returncode != 0:
+            sys.exit(1)
+
 ### SUMMARIZE
 ### ##################################################################
     run_all_summaries()
@@ -361,4 +380,4 @@ def main():
     end_time = datetime.datetime.now()
     elapsed_total = end_time - start_time
     logger.info('------------------------RUN ENDING_----------------------------------------------')
-    logger.info('TOTAL RUN TIME %s'  % str(elapsed_total))
\ No newline at end of file
+    logger.info('TOTAL RUN TIME %s'  % str(elapsed_total))
diff --git a/scripts/EmmeProject.py b/scripts/EmmeProject.py
index 43316adc..332ddf9b 100644
--- a/scripts/EmmeProject.py
+++ b/scripts/EmmeProject.py
@@ -25,6 +25,7 @@
 import json
 from multiprocessing import Pool, pool
 sys.path.append(os.path.join(os.getcwd(),"inputs"))
+sys.path.append(os.getcwd())
 from input_configuration import *
 from EmmeProject import *
 
diff --git a/scripts/accessibility/accessibility.py b/scripts/accessibility/accessibility.py
index 04de7f12..d3465cdf 100644
--- a/scripts/accessibility/accessibility.py
+++ b/scripts/accessibility/accessibility.py
@@ -1,12 +1,13 @@
 import pandana as pdna
-from accessibility_configuration import *
-from emme_configuration import *
 import pandas as pd
 import numpy as np
 import os
 import re
+import sys
 from pyproj import Proj, transform
-
+sys.path.append(os.getcwd())
+from accessibility_configuration import *
+from emme_configuration import *
 
 def assign_nodes_to_dataset(dataset, network, column_name, x_name, y_name):
     """Adds an attribute node_ids to the given dataset."""
@@ -90,11 +91,12 @@ def process_parcels(parcels, transit_df):
   
     # calc the distance from each parcel to nearest transit stop by type
     for new_name, attr in transit_modes.iteritems():
-        print new_name
         # get the records/locations that have this type of transit:
         transit_type_df = transit_df.loc[(transit_df[attr] == 1)]
         parcels=process_dist_attribute(parcels, net, new_name, transit_type_df["x"], transit_type_df["y"])
-
+        #Some parcels share the same network node and therefore have 0 distance. Recode this to .01.
+        field_name = "dist_%s" % new_name
+        parcels.ix[parcels[field_name]==0, field_name] = .01
     # distance to park
     parcel_idx_park = np.where(parcels.NPARKS > 0)[0]
     parcels=process_dist_attribute(parcels, net, "park", parcels.XCOORD_P[parcel_idx_park], parcels.YCOORD_P[parcel_idx_park])
@@ -136,10 +138,31 @@ def clean_up(parcels):
     parcels_final[u'xcoord_p'] = parcels_final[u'xcoord_p'].astype(int)
     return parcels_final
 
-
-
 # read in data
 parcels = pd.DataFrame.from_csv(parcels_file_name, sep = " ", index_col = None )
+
+# Move SeaTac Parcel so that it is on the terminal. 
+parcels.ix[parcels.PARCELID==902588, 'XCOORD_P'] = 1277335
+parcels.ix[parcels.PARCELID==902588, 'YCOORD_P'] = 165468
+
+# Update UW Emp parcel with parking costs
+parcels.ix[parcels.PARCELID==751794, 'PARKDY_P'] = 1144
+parcels.ix[parcels.PARCELID==751794, 'PARKHR_P'] = 1144
+parcels.ix[parcels.PARCELID==751794, 'PPRICDYP'] = 1500
+parcels.ix[parcels.PARCELID==751794, 'PPRICHRP'] = 300
+
+# This UW parcel is in the wrong zone. 
+parcels.ix[parcels.PARCELID==751794, 'TAZ_P'] = 303
+
+
+#check for missing data!
+for col_name in parcels.columns:
+    # daysim does not use EMPRSC_P
+	if col_name <> 'EMPRSC_P':
+		if parcels[col_name].sum() == 0:
+			print col_name + ' column sum is zero! Exiting program.'
+			sys.exit(1)
+
 # nodes must be indexed by node_id column, which is the first column
 nodes = pd.DataFrame.from_csv(nodes_file_name)
 links = pd.DataFrame.from_csv(links_file_name, index_col = None )
@@ -184,5 +207,14 @@ def clean_up(parcels):
 # run all accibility measures
 parcels = process_parcels(parcels, transit_df)
 
+# reduce percieved walk distance for light rail and ferry. This is used to calibrate to 2014 boardings & transfer rates. 
+parcels.ix[parcels.dist_lrt<=1, 'dist_lrt'] = parcels.ix[parcels.dist_lrt<=2.00, 'dist_lrt'] * .5
+parcels.ix[parcels.dist_lrt<=2, 'dist_fry'] = parcels.ix[parcels.dist_lrt<=2.00, 'dist_fry'] * .5
 parcels_done = clean_up(parcels)
+
+if int(model_year) > 2014:
+    #assert percieved distance for UW employment and student parcels- this is based on results/calibration from 2016 network
+    parcels_done.ix[parcels_done.parcelid==797163, 'dist_lrt'] = 0.25
+    parcels_done.ix[parcels_done.parcelid==751794, 'dist_lrt'] = 0.25
+
 parcels_done.to_csv(output_parcels, index = False, sep = ' ')
\ No newline at end of file
diff --git a/scripts/accessibility/accessibility_configuration.py b/scripts/accessibility/accessibility_configuration.py
index 790b8585..8bbcdf64 100644
--- a/scripts/accessibility/accessibility_configuration.py
+++ b/scripts/accessibility/accessibility_configuration.py
@@ -1,6 +1,10 @@
+import os, sys
+sys.path.append(os.getcwd())
+from input_configuration import *
+
 parcels_file_name = 'inputs/accessibility/parcels_urbansim.txt'
 output_parcels = 'inputs/buffered_parcels.txt'
-transit_stops_name = 'inputs/accessibility/transit_stops_2014.csv'
+transit_stops_name = 'inputs/accessibility/transit_stops_' + scenario_name + '.csv'
 nodes_file_name = 'inputs/accessibility/all_streets_nodes_2014.csv'
 links_file_name = 'inputs/accessibility/all_streets_links_2014.csv'
 
@@ -21,7 +25,7 @@
               "sum": ["HH_P", "STUGRD_P", "STUHGH_P", "STUUNI_P", 
                       "EMPMED_P", "EMPOFC_P", "EMPEDU_P", "EMPFOO_P", "EMPGOV_P", "EMPIND_P", 
                       "EMPSVC_P", "EMPOTH_P", "EMPTOT_P", "EMPRET_P",
-                      "PARKDY_P", "PARKHR_P", "NPARKS", "aparks", "daily_weighted_spaces", "hourly_weighted_spaces"],
+                      "PARKDY_P", "PARKHR_P", "NPARKS", "APARKS", "daily_weighted_spaces", "hourly_weighted_spaces"],
               "ave": [ "PPRICDYP", "PPRICHRP"],
               }
 
diff --git a/scripts/accessibility/transit_access.py b/scripts/accessibility/transit_access.py
new file mode 100644
index 00000000..8dfd1b5b
--- /dev/null
+++ b/scripts/accessibility/transit_access.py
@@ -0,0 +1,93 @@
+import pandana as pdna
+import pandas as pd
+import numpy as np
+import os, sys
+os.chdir(r"D:\stefan\sc_calibration\soundcast")
+#from accessibility_configuration import *
+from emme_configuration import *
+import re
+import sys
+from pyproj import Proj, transform
+import h5py 
+
+parcels_file_name = 'inputs/accessibility/parcels_urbansim.txt'
+nodes_file_name = 'inputs/accessibility/all_streets_nodes_2014.csv'
+links_file_name = 'inputs/accessibility/all_streets_links_2014.csv'
+max_dist = 24140.2
+
+def assign_nodes_to_dataset(dataset, network, column_name, x_name, y_name):
+    """Adds an attribute node_ids to the given dataset."""
+    dataset[column_name] = network.get_node_ids(dataset[x_name].values, dataset[y_name].values)
+   
+def h5_to_df(h5_set, columns = None):
+    col_dict = {}
+    if columns == None:
+        columns = h5_set.keys()
+    for col in columns:
+        my_array = np.asarray(h5_set[col])
+        print col, len(my_array)
+        col_dict[col] = my_array
+    df = pd.DataFrame(col_dict)
+    return df
+# read in data
+transit_df = pd.DataFrame.from_csv('inputs/accessibility/freq_transit_stops_2040.csv')
+parcels = pd.DataFrame.from_csv(parcels_file_name, sep = " ", index_col = None )
+# need to get the number of people per parcel
+hh_and_persons = h5py.File('inputs/hh_and_persons.h5', "r")
+hh_df = h5_to_df(hh_and_persons["Household"], ['hhparcel', 'hhsize'])
+hh_df = pd.DataFrame(hh_df.groupby('hhparcel').hhsize.sum(), index = None)
+hh_df.reset_index(inplace = True)
+# join to parcels
+parcels = parcels.merge(hh_df, how = 'left', left_on = 'PARCELID', right_on = 'hhparcel')
+# nodes must be indexed by node_id column, which is the first column
+nodes = pd.DataFrame.from_csv(nodes_file_name)
+links = pd.DataFrame.from_csv(links_file_name, index_col = None )
+
+# get rid of circular links
+links = links.loc[(links.from_node_id <> links.to_node_id)]
+
+# assign impedance
+imp = pd.DataFrame(links.Shape_Length)
+imp = imp.rename(columns = {'Shape_Length':'distance'})
+
+# create pandana network
+net = pdna.network.Network(nodes.x, nodes.y, links.from_node_id, links.to_node_id, imp)
+
+# get transit stops
+#transit_df['tstops'] = 1
+
+# assign network nodes to parcels, for buffer variables
+assign_nodes_to_dataset(parcels, net, 'node_ids', 'XCOORD_P', 'YCOORD_P')
+
+# assign network nodes to transit stops, for buffer variable
+#assign_nodes_to_dataset(transit_df, net, 'node_ids', 'x', 'y')
+net.init_pois(1, max_dist, 1)
+net.set_pois('tstops', transit_df.x, transit_df.y)
+res = net.nearest_pois(max_dist, 'tstops', num_pois=1, max_distance=999999)
+res[res <> 999999] = (res[res <> 999999]/5280.).astype(res.dtypes) # convert to miles
+res_name = 'dist_tstops'
+parcels[res_name] = res.loc[parcels.node_ids].values
+   
+        #Some parcels share the same network node and therefore have 0 distance. Recode this to .01.
+field_name = "dist_tstops"
+parcels.ix[parcels[field_name]==0, field_name] = .01
+test = parcels[(parcels.dist_tstops<=.5)]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/scripts/archive/archive.py b/scripts/archive/archive.py
deleted file mode 100644
index 09e09128..00000000
--- a/scripts/archive/archive.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import sys
-from scripts.skimming.SkimsAndPaths import delete_matrices
-from multiprocessing import Pool
-from scripts.EmmeProject import *
-from emme_configuration import *
-import argparse
-
-def mummify(project_name):
-	'''Remove all matrix file types from Emme project.'''
-	
-	my_project = EmmeProject(project_name)
-	
-	delete_matrices(my_project, "FULL")
-	delete_matrices(my_project, "ORIGIN")
-	delete_matrices(my_project, "DESTINATION")
-
-def start_pool(project_list):
-	'''Run parallel instances of Emme to process multiple times of day.'''
-	pool = Pool(processes=parallel_instances)
-	pool.map(mummify,project_list[0:parallel_instances])
-
-	pool.close()
-
-def main():
-	'''Shrink Soundcast output files for all times of day.'''
-
-	for i in range (0, 12, parallel_instances):
-	    l = project_list[i:i+parallel_instances]
-	    start_pool(l)
-
-if __name__ == '__main__':
-	main()
\ No newline at end of file
diff --git a/scripts/archive/revive.py b/scripts/archive/revive.py
deleted file mode 100644
index 83b5e47a..00000000
--- a/scripts/archive/revive.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from scripts.skimming.SkimsAndPaths import *
-from scripts.EmmeProject import *
-
-def revive(project_name):
-    '''Populate emme trip tables and skims with h5 data'''
-    
-    my_project = EmmeProject(project_name)
-
-    # Create empty matrices
-    define_matrices(my_project)
-    
-    # Load trip tables
-    hdf5_trips_to_Emme(my_project, hdf5_file_path)
-    
-    # Populate intrazonals
-    populate_intrazonals(my_project)
-    
-    # Load skims from hdf5
-    # For a single TOD
-    skim_h5 = h5py.File(r'D:/archive/soundcast/inputs/' + my_project.tod + '.h5', "r+")
-
-    zonesDim = len(my_project.current_scenario.zone_numbers)
-    zones = my_project.current_scenario.zone_numbers
-
-    #Create a dictionary lookup where key is the taz id and value is it's numpy index.
-    dictZoneLookup = dict((value,index) for index,value in enumerate(zones))
-
-    #create an index of trips for this TOD. This prevents iterating over the entire array (all trips).
-    tod_index = create_trip_tod_indices(my_project.tod)
-
-    matrix_dict = text_to_dictionary('demand_matrix_dictionary')
-    uniqueMatrices = set(matrix_dict.values())
-
-    for matrix in my_project.bank.matrices():
-        # Add the matrix if its available from the skims h5 database
-        if matrix.name in skim_h5['Skims'].keys():
-
-            print 'processing matrix: ' + str(matrix.name)
-
-            emme_matrix = ematrix.MatrixData(indices=[zones,zones],type='f')
-            matrix_id = my_project.bank.matrix(str(matrix.name)).id
-
-            emme_matrix.from_numpy(skim_h5['Skims'][matrix.name][:])
-            my_project.bank.matrix(matrix.id).set_data(emme_matrix, my_project.current_scenario)
-
-            print 'added matrix: ' + str(matrix.name)
-        else:
-            continue
-
-def start_pool(project_list):
-    pool = Pool(processes=parallel_instances)
-    pool.map(revive,project_list[0:parallel_instances])
-    
-    pool.close()
-
-def main():
-
-    for i in range (0, 12, parallel_instances):
-        l = project_list[i:i+parallel_instances]
-        start_pool(l)
-
-if __name__ == '__main__':
-	main()
\ No newline at end of file
diff --git a/scripts/bikes/bike_configuration.py b/scripts/bikes/bike_configuration.py
index 507d8034..749122a8 100644
--- a/scripts/bikes/bike_configuration.py
+++ b/scripts/bikes/bike_configuration.py
@@ -1,6 +1,7 @@
 
 #################################### BIKE MODEL ####################################
-bike_assignment_tod = ['7to8','8to9']
+bike_assignment_tod = ['5to6','7to8','8to9','9to10','10to14','14to15','15to16','16to17',
+                        '17to18']
 
 # Distance perception penalties for link AADT from Broach et al., 2012
 # 1 is AADT 10k-20k, 2 is 20k-30k, 3 is 30k+
@@ -37,10 +38,5 @@
 
 avg_bike_speed = 10 # miles per hour
 
-# Outputs directory
-bike_link_vol = 'outputs/bike_volumes.csv'
-bike_count_data = 'inputs/bikes/bike_count_links.csv'
-edges_file = 'inputs/bikes/edges_0.txt'
-
 # Multiplier for storing skim results
 bike_skim_mult = 100    # divide by 100 to store as int
\ No newline at end of file
diff --git a/scripts/bikes/bike_model.py b/scripts/bikes/bike_model.py
index 62990895..6a3fb384 100644
--- a/scripts/bikes/bike_model.py
+++ b/scripts/bikes/bike_model.py
@@ -3,6 +3,7 @@
 import os, sys
 import h5py
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 from EmmeProject import *
 from input_configuration import *
 from bike_configuration import *
@@ -27,7 +28,7 @@ def get_link_attribute(attr, network):
     for i in network.links():
         link_dict[i.id] = i[attr]
     df = pd.DataFrame({'link_id': link_dict.keys(), attr: link_dict.values()})
-    print df.head(4)
+
     return df
 
 def bike_facility_weight(my_project, link_df):
@@ -59,8 +60,11 @@ def volume_weight(my_project, df):
     df['volume_wt'] = pd.cut(df['@tveh'], bins=aadt_bins, labels=aadt_labels, right=False)
     df['volume_wt'] = df['volume_wt'].astype('int')
 
-    # Replace bin label with weight value
-    df = df.replace(to_replace=aadt_dict)
+    # Replace bin label with weight value, only for links with no bike facilities
+    over_df = df[df['facility_wt'] < 0].replace(to_replace=aadt_dict)
+    over_df['volume_wt'] = 0
+    under_df = df[df['facility_wt'] >= 0]
+    df = over_df.append(under_df)
 
     return df
 
@@ -71,6 +75,12 @@ def process_attributes(my_project):
 	for attr in ['@bkfac', '@upslp']:
 		if attr not in my_project.current_scenario.attributes('LINK'):
 			my_project.current_scenario.create_extra_attribute('LINK',attr)
+		else:
+			try:
+				my_project.current_scenario.delete_extra_attribute(attr)
+				my_project.current_scenario.create_extra_attribute('LINK',attr)
+			except:
+				print 'unable to recreate bike link attributes'
 
 	import_attributes = my_project.m.tool("inro.emme.data.network.import_attribute_values")
 	filename = r'inputs/bikes/emme_attr.in'
@@ -135,7 +145,7 @@ def calc_bike_weight(my_project, link_df):
 	df['total_wt'] = 1 - np.float(facility_dict['facility_wt']['premium']) + df['facility_wt'] + df['slope_wt'] + df['volume_wt']
 
 	# Write link data for analysis
-	df.to_csv(r'outputs/bike_attr.csv')
+	df.to_csv(r'outputs/bike/bike_attr.csv')
 
 	# export total link weight as an Emme attribute file ('@bkwt.in')
 	write_generalized_time(df=df)
@@ -261,82 +271,10 @@ def get_aadt(my_project):
     
     return df
     
-        
-   
-def write_link_counts(my_project, tod):
-	'''Write bike link volumes to file for comparisons to counts '''
-
-	my_project.change_active_database(tod)
-
-	network = my_project.current_scenario.get_network()
-
-	# Load bike count data from file
-	bike_counts = pd.read_csv(bike_count_data)
-
-	# Load edges file to join proper node IDs
-	edges_df = pd.read_csv(edges_file)
-
-	df = bike_counts.merge(edges_df, 
-		on=['INode','JNode'])
-	
-	# if the link is twoway, also get the other directoin IJ and JI and append to original df
-	twoway_links_df = df[df['Oneway'] == 2]
-
-	# Replace I with J node for twoway links, for Emme IJ and geodatabase IJ pairs
-	twoway_links_df.loc[:,'tempINode'] = twoway_links_df.loc[:,'JNode']
-	twoway_links_df.loc[:,'tempJNode'] = twoway_links_df.loc[:,'INode']
-	twoway_links_df.loc[:,'tempNewINode'] = twoway_links_df.loc[:,'NewJNode']
-	twoway_links_df.loc[:,'tempNewJNode'] = twoway_links_df.loc[:,'NewINode']
-	# remove old IJ values and replace with the new swapped values
-	twoway_links_df.drop(['INode','JNode','NewINode','NewJNode'],axis=1,inplace=True)
-	twoway_links_df.loc[:,'INode'] = twoway_links_df.loc[:,'tempINode']
-	twoway_links_df.loc[:,'JNode'] = twoway_links_df.loc[:,'tempJNode']
-	twoway_links_df.loc[:,'NewINode'] = twoway_links_df.loc[:,'tempNewINode']
-	twoway_links_df.loc[:,'NewJNode'] = twoway_links_df.loc[:,'tempNewJNode']
-	twoway_links_df.drop(['tempINode','tempJNode','tempNewINode','tempNewJNode'],axis=1,inplace=True)
-
-	df = pd.concat([df,twoway_links_df])
-	df = df.reset_index()
-	list_model_vols = []
-
-	for row in df.index:
-		i = df.iloc[row]['NewINode']
-		j = df.loc[row]['NewJNode']
-		link = network.link(i, j)
-		x = {}
-		x['EmmeINode'] = i
-		x['EmmeJNode'] = j
-		x['gdbINode'] = df.iloc[row]['INode']
-		x['gdbJNode'] = df.iloc[row]['JNode']
-		x['LocationID'] = df.iloc[row]['LocationID']
-		if link != None:
-			x['bvol' + tod] = link['@bvol']
-		else:
-			x['bvol' + tod] = None
-		list_model_vols.append(x)
-	print len(list_model_vols)
-
-	df_count =  pd.DataFrame(list_model_vols)
-
-	if os.path.exists(bike_link_vol):
-		'''append column to existing TOD results'''
-		df = pd.read_csv(bike_link_vol)
-		df['bvol'+tod] = df_count['bvol'+tod]
-		df.to_csv(bike_link_vol,index=False) 
-	else:
-		df_count.to_csv(bike_link_vol,index=False) 
-
 def main():
 	
 	print 'running bike model'
 
-	# Remove any existing results
-	if os.path.exists(bike_link_vol):
-		try:
-		    os.remove(bike_link_vol)
-		except OSError:
-		    pass
-
 	filepath = r'projects/' + master_project + r'/' + master_project + '.emp'
 	my_project = EmmeProject(filepath)
 
@@ -351,8 +289,5 @@ def main():
 		print 'assigning bike trips for: ' + str(tod)
 		bike_assignment(my_project, tod)
 
-		# Write link volumes
-		write_link_counts(my_project, tod)
-
 if __name__ == "__main__":
 	main()
\ No newline at end of file
diff --git a/scripts/data_wrangling.py b/scripts/data_wrangling.py
index 6824e55e..60695412 100644
--- a/scripts/data_wrangling.py
+++ b/scripts/data_wrangling.py
@@ -23,6 +23,7 @@
 import random
 import shutil
 sys.path.append(os.path.join(os.getcwd(),"inputs"))
+sys.path.append(os.getcwd())
 from input_configuration import *
 from logcontroller import *
 from input_configuration import *
@@ -31,6 +32,7 @@
 import input_configuration # Import as a module to access inputs as a dictionary
 from emme_configuration import *
 import emme_configuration
+import input_configuration
 import glob
 
 
@@ -60,59 +62,51 @@ def copy_accessibility_files():
     
     print 'Copying UrbanSim parcel file'
     try:
-        if os.path.isfile(base_inputs+'/landuse/parcels_urbansim.txt'):
-            shcopy(base_inputs+'/landuse/parcels_urbansim.txt','inputs/accessibility')
-        # the file may need to be reformatted- like this coming right out of urbansim
-        elif os.path.isfile(base_inputs+'/landuse/parcels.dat'):
-            print 'the file is ' + base_inputs +'/landuse/parcels.dat'
-            print "Parcels file is being reformatted to Daysim format"
-            parcels = pd.DataFrame.from_csv(base_inputs+'/landuse/parcels.dat',sep=" " )
-            print 'Read in unformatted parcels file'
-            for col in parcels.columns:
-                print col
-                new_col = [x.upper() for x in col]
-                new_col = ''.join(new_col)
-                parcels=parcels.rename(columns = {col:new_col})
-                print new_col
-            parcels.to_csv(base_inputs+'/landuse/parcels_urbansim.txt', sep = " ")
-            shcopy(base_inputs+'/landuse/parcels_urbansim.txt','inputs/accesibility')
-
-    except Exception as ex:
-        template = "An exception of type {0} occured. Arguments:\n{1!r}"
-        message = template.format(type(ex).__name__, ex.args)
-        print message
+        shcopy(scenario_inputs+'/landuse/parcels_urbansim.txt','inputs/accessibility')
+    except:
+        print 'error copying urbansim parcel file at ' + scenario_inputs + '/landuse/parcels_urbansim.txt'
+        sys.exit(1)
+      
+    
+    print 'Copying Transit stop file'
+    try:      
+        shcopy(scenario_inputs+'/landuse/transit_stops_' + scenario_name + '.csv','inputs/accessibility')
+    except:
+        print 'error copying transit stops file at ' + scenario_inputs + '/landuse/transit_stops_' + scenario_name + '.csv'
         sys.exit(1)
 
-
+    
     print 'Copying Military parcel file'
     try:
-        shcopy(base_inputs+'/landuse/parcels_military.csv','inputs/accessibility')
+        shcopy(scenario_inputs+'/landuse/parcels_military.csv','inputs/accessibility')
     except:
-        print 'error copying military parcel file at ' + base_inputs+'/landuse/parcels_military.csv'
+        print 'error copying military parcel file at ' + scenario_inputs+'/landuse/parcels_military.csv'
         sys.exit(1)
 
+    
+    print 'Copying JBLM file'
     try:
-        shcopy(base_inputs+'/landuse/distribute_jblm_jobs.csv','Inputs/accessibility')
+        shcopy(scenario_inputs+'/landuse/distribute_jblm_jobs.csv','Inputs/accessibility')
     except:
-        print 'error copying military parcel file at ' + base_inputs+'/landuse/parcels_military.csv'
+        print 'error copying military parcel file at ' + scenario_inputs+'/landuse/distribute_jblm_jobs.csv'
         sys.exit(1)
 
-
+    
     print 'Copying Hourly and Daily Parking Files'
-    if run_update_parking: 
+    if base_year != model_year: 
         try:
-            shcopy(base_inputs+'/landuse/hourly_parking_costs.csv','Inputs/accessibility')
-            shcopy(base_inputs+'/landuse/daily_parking_costs.csv','Inputs/accessibility')
+            shcopy(scenario_inputs+'/landuse/hourly_parking_costs.csv','Inputs/accessibility')
+            shcopy(scenario_inputs+'/landuse/daily_parking_costs.csv','Inputs/accessibility')
         except:
-            print 'error copying parking file at' + base_inputs+'/landuse/' + ' either hourly or daily parking costs'
+            print 'error copying parking file at' + scenario_inputs+'/landuse/' + ' either hourly or daily parking costs'
             sys.exit(1)
 
 @timed
 def copy_seed_skims():
     print 'You have decided to start your run by copying seed skims that Daysim will use on the first iteration. Interesting choice! This will probably take around 15 minutes because the files are big. Starting now...'
-    if not(os.path.isdir(base_inputs+'/seed_skims')):
-           print 'It looks like you do not hava directory called' + base_inputs+'/seed_skims, where the code is expecting the files to be. Please make sure to put your seed_skims there.'
-    for filename in glob.glob(os.path.join(base_inputs+'/seed_skims', '*.*')):
+    if not(os.path.isdir(scenario_inputs+'/seed_skims')):
+           print 'It looks like you do not hava directory called' + scenario_inputs+'/seed_skims, where the code is expecting the files to be. Please make sure to put your seed_skims there.'
+    for filename in glob.glob(os.path.join(scenario_inputs+'/seed_skims', '*.*')):
         shutil.copy(filename, 'inputs')
     print 'Done copying seed skims.'
 
@@ -170,6 +164,7 @@ def setup_emme_bank_folders():
 
 @timed
 def setup_emme_project_folders():
+    emme_toolbox_path = os.path.join(os.environ['EMMEPATH'], 'toolboxes')
     #tod_dict = json.load(open(os.path.join('inputs', 'skim_params', 'time_of_day.json')))
 
     tod_dict = text_to_dictionary('time_of_day')
@@ -189,11 +184,13 @@ def setup_emme_project_folders():
     database.open()
     desktop.project.save()
     desktop.close()
+    shcopy(emme_toolbox_path + '/standard.mtbx', os.path.join('projects', master_project))
 
     # Create time of day projects, associate with emmebank
     tod_list.append('TruckModel') 
     tod_list.append('Supplementals')
-    emme_toolbox_path = os.path.join(os.environ['EMMEPATH'], 'toolboxes')
+
+    
     for tod in tod_list:
         project = app.create_project('projects', tod)
         desktop = app.start_dedicated(False, "cth", project)
@@ -208,23 +205,19 @@ def setup_emme_project_folders():
 @timed    
 def copy_large_inputs():
     print 'Copying large inputs...' 
-    shcopy(base_inputs+'/etc/daysim_outputs_seed_trips.h5','Inputs')
-    dir_util.copy_tree(base_inputs+'/networks','Inputs/networks')
-    dir_util.copy_tree(base_inputs+'/trucks','Inputs/trucks')
-    dir_util.copy_tree(base_inputs+'/tolls','Inputs/tolls')
-    dir_util.copy_tree(base_inputs+'/Fares','Inputs/Fares')
-    dir_util.copy_tree(base_inputs+'/bikes','Inputs/bikes')
-    dir_util.copy_tree(base_inputs+'/supplemental/distribution','inputs/supplemental/distribution')
-    dir_util.copy_tree(base_inputs+'/supplemental/generation','inputs/supplemental/generation')
-    dir_util.copy_tree(base_inputs+'supplemental/parameters','inputs/supplemental/parameters')
-    dir_util.copy_tree(base_inputs+'supplemental/input','inputs/supplemental/input')
-    dir_util.copy_tree(base_inputs+'/supplemental/trips','outputs/supplemental')
-    dir_util.copy_tree(base_inputs+'/corridors','Inputs/corridors')
-    shcopy(base_inputs+'/landuse/hh_and_persons.h5','Inputs')
-    shcopy(base_inputs+'/etc/survey.h5','scripts/summarize')
+    if run_skims_and_paths_seed_trips:
+        shcopy(scenario_inputs+'/etc/daysim_outputs_seed_trips.h5','Inputs')
+    dir_util.copy_tree(scenario_inputs+'/networks','Inputs/networks')
+    dir_util.copy_tree(scenario_inputs+'/trucks','Inputs/trucks')
+    dir_util.copy_tree(scenario_inputs+'/tolls','Inputs/tolls')
+    dir_util.copy_tree(scenario_inputs+'/Fares','Inputs/Fares')
+    dir_util.copy_tree(scenario_inputs+'/bikes','Inputs/bikes')
+    dir_util.copy_tree(base_inputs+'/observed','Inputs/observed')
+    dir_util.copy_tree(base_inputs+'/corridors','inputs/corridors')
+    dir_util.copy_tree(scenario_inputs+'/parking','inputs/parking')
+    dir_util.copy_tree(scenario_inputs+'/supplemental','inputs/supplemental')
+    shcopy(scenario_inputs+'/landuse/hh_and_persons.h5','Inputs')
     shcopy(base_inputs+'/etc/survey.h5','scripts/summarize/inputs/calibration')
-    shcopy(base_inputs+'/4k/auto.h5','Inputs/4k')
-    shcopy(base_inputs+'/4k/transit.h5','Inputs/4k')
     # node to node short distance files:
     shcopy(base_inputs+'/short_distance_files/node_index_2014.txt', 'Inputs')
     shcopy(base_inputs+'/short_distance_files/node_to_node_distance_2014.h5', 'Inputs')
@@ -287,4 +280,9 @@ def check_inputs():
         print 'Warning: the following files are missing and may be needed to complete the model run:'
         for file in missing_list:
             logger.info('- ' + file)
-            print file
\ No newline at end of file
+            print file
+
+def build_output_dirs():
+    for path in ['outputs',r'outputs/daysim','outputs/bike','outputs/network','outputs/transit']:
+        if not os.path.exists(path):
+            os.makedirs(path)
\ No newline at end of file
diff --git a/scripts/logcontroller.py b/scripts/logcontroller.py
index c2ca5401..5df5c077 100644
--- a/scripts/logcontroller.py
+++ b/scripts/logcontroller.py
@@ -17,7 +17,8 @@
 from functools import wraps
 from time import time
 import datetime
-import os
+import os, sys
+sys.path.append(os.getcwd())
 
 def setup_custom_logger(name):
     logging.basicConfig(filename=main_log_file,format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')
diff --git a/scripts/network/daysim_zone_inputs.py b/scripts/network/daysim_zone_inputs.py
index 38b9eae1..04610a15 100644
--- a/scripts/network/daysim_zone_inputs.py
+++ b/scripts/network/daysim_zone_inputs.py
@@ -1,6 +1,8 @@
+import os, sys
 import pysal as ps
 import numpy as np
 import pandas as pd
+sys.path.append(os.getcwd())
 
 # open nodes file & convert to pandas dataframe
 nodes = ps.open('inputs/networks/junctions.dbf')
diff --git a/scripts/network/network_importer.py b/scripts/network/network_importer.py
index 1c6b234e..a97dc643 100644
--- a/scripts/network/network_importer.py
+++ b/scripts/network/network_importer.py
@@ -11,109 +11,11 @@
 import json
 from multiprocessing import Pool, pool
 sys.path.append(os.path.join(os.getcwd(),"inputs"))
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 from emme_configuration import *
 from input_configuration import *
-
-class EmmeProject:
-    def __init__(self, filepath):
-        self.desktop = app.start_dedicated(True, "cth", filepath)
-        self.m = _m.Modeller(self.desktop)
-        pathlist = filepath.split("/")
-        self.fullpath = filepath
-        self.filename = pathlist.pop()
-        self.dir = "/".join(pathlist) + "/"
-        self.bank = self.m.emmebank
-        self.tod = self.bank.title
-        self.current_scenario = list(self.bank.scenarios())[0]
-        self.data_explorer = self.desktop.data_explorer()
-    def network_counts_by_element(self, element):
-        network = self.current_scenario.get_network()
-        d = network.element_totals
-        count = d[element]
-        return count
-    def change_active_database(self, database_name):
-        for database in self.data_explorer.databases():
-            #print database.title()
-            if database.title() == database_name:
-                
-                database.open()
-                print 'changed'
-                self.bank = self.m.emmebank
-                self.tod = self.bank.title
-                print self.tod
-                self.current_scenario = list(self.bank.scenarios())[0]
-    def process_modes(self, mode_file):
-        NAMESPACE = "inro.emme.data.network.mode.mode_transaction"
-        process_modes = self.m.tool(NAMESPACE)
-        process_modes(transaction_file = mode_file,
-              revert_on_error = True,
-              scenario = self.current_scenario)
-                
-    def create_scenario(self, scenario_number, scenario_title = 'test'):
-        NAMESPACE = "inro.emme.data.scenario.create_scenario"
-        create_scenario = self.m.tool(NAMESPACE)
-        create_scenario(scenario_id=scenario_number,
-                        scenario_title= scenario_title)
-    def network_calculator(self, type, **kwargs):
-        spec = json_to_dictionary(type)
-        for name, value in kwargs.items():
-            if name == 'selections_by_link':
-                spec['selections']['link'] = value
-            else:
-                spec[name] = value
-        NAMESPACE = "inro.emme.network_calculation.network_calculator"
-        network_calc = self.m.tool(NAMESPACE)
-        self.network_calc_result = network_calc(spec)
-
-   
-    def delete_links(self):
-        if self.network_counts_by_element('links') > 0:
-            NAMESPACE = "inro.emme.data.network.base.delete_links"
-            delete_links = self.m.tool(NAMESPACE)
-            #delete_links(selection="@dist=9", condition="cascade")
-            delete_links(condition="cascade")
-
-    def delete_nodes(self):
-        if self.network_counts_by_element('regular_nodes') > 0:
-            NAMESPACE = "inro.emme.data.network.base.delete_nodes"
-            delete_nodes = self.m.tool(NAMESPACE)
-            delete_nodes(condition="cascade")
-    def process_vehicles(self,vehicle_file):
-          NAMESPACE = "inro.emme.data.network.transit.vehicle_transaction"
-          process = self.m.tool(NAMESPACE)
-          process(transaction_file = vehicle_file,
-            revert_on_error = True,
-            scenario = self.current_scenario)
-
-    def process_base_network(self, basenet_file):
-        NAMESPACE = "inro.emme.data.network.base.base_network_transaction"
-        process = self.m.tool(NAMESPACE)
-        process(transaction_file = basenet_file,
-              revert_on_error = True,
-              scenario = self.current_scenario)
-    def process_turn(self, turn_file):
-        NAMESPACE = "inro.emme.data.network.turn.turn_transaction"
-        process = self.m.tool(NAMESPACE)
-        process(transaction_file = turn_file,
-            revert_on_error = False,
-            scenario = self.current_scenario)
-
-    def process_transit(self, transit_file):
-        NAMESPACE = "inro.emme.data.network.transit.transit_line_transaction"
-        process = self.m.tool(NAMESPACE)
-        process(transaction_file = transit_file,
-            revert_on_error = True,
-            scenario = self.current_scenario)
-    def process_shape(self, linkshape_file):
-        NAMESPACE = "inro.emme.data.network.base.link_shape_transaction"
-        process = self.m.tool(NAMESPACE)
-        process(transaction_file = linkshape_file,
-            revert_on_error = True,
-            scenario = self.current_scenario)
-    def change_scenario(self):
-
-        self.current_scenario = list(self.bank.scenarios())[0]
-
+from EmmeProject import *
 
     
 def json_to_dictionary(dict_name):
@@ -143,7 +45,7 @@ def import_tolls(emmeProject):
 
     tod_4k = sound_cast_net_dict[emmeProject.tod]
 
-    attr_file= ['inputs/tolls/' + tod_4k + '_roadway_tolls.in', 'inputs/tolls/ferry_vehicle_fares.in', 'inputs/networks/rdly/' + tod_4k + '_rdly.txt']
+    attr_file= ['inputs/tolls/' + tod_4k + '_roadway_tolls.in', 'inputs/tolls/ferry_vehicle_fares.in']
 
     # set tolls
     #for file in attr_file:
@@ -169,13 +71,6 @@ def import_tolls(emmeProject):
                              7: "@trkc3"},
               revert_on_error=True)
 
-    
-    #@rdly:
-    import_attributes(attr_file[2], scenario = emmeProject.current_scenario,
-             revert_on_error=True)
-    
-    #We are using the same rdly has 4k. No need to factor. 
-    #emmeProject.network_calculator("link_calculation", result = "@rdly", expression = "@rdly * .50")
 
 
 def multiwordReplace(text, replace_dict):
@@ -195,6 +90,164 @@ def update_headways(emmeProject, headways_df):
             network.delete_transit_line(transit_line.id)
     emmeProject.current_scenario.publish_network(network)
 
+def distance_pricing(distance_rate, hot_rate, emmeProject):
+   toll_atts = ["@toll1", "@toll2", "@toll3", "@trkc1", "@trkc2", "@trkc3"]
+   network = emmeProject.current_scenario.get_network()
+   for link in network.links():
+        if link.data3 > 0:
+            if add_distance_pricing:
+                for att in toll_atts:
+                    link[att] = link[att] + (link.length * distance_rate)
+            if add_hot_lane_tolls:
+                # is the link a managed lane:
+                if int(link.i_node.id) > min_hov_node and int(link.j_node.id) > min_hov_node:
+                    # get the modes allowed
+                    test = [i[1].id for i in enumerate(link.modes)]
+                    # if sov modes are allowed, they should be tolled
+                    if 's' in test or 'e' in test:
+                        print hot_rate
+                        link['@toll1'] = link['@toll1'] + (link.length * hot_rate)
+                        link['@toll2'] = link['@toll2'] + (link.length * hot_rate)
+                       
+               
+
+            
+    
+   emmeProject.current_scenario.publish_network(network)
+
+def arterial_delay(emmeProject, factor):
+    network = emmeProject.current_scenario.get_network()
+    #if '@rdly' in network.attributes('LINK'):
+    #    emmeProject.delete_extra_attribute('@rdly')
+    emmeProject.create_extra_attribute('LINK', '@rdly', 'arterial delay', True)
+    network = emmeProject.current_scenario.get_network()
+    attribute_dict = {'arterial_flag' : 'LINK', 
+                      'lane_3to5' : 'LINK', 
+                      'lanecap_3to5' : 'LINK', 
+                      'oneway_arterial_flag' : 'LINK', 
+                      'red' : 'LINK', 
+                      'rdly' : 'LINK',  
+                      'number_arts_entering_int' : 'NODE', 
+                      'sum_arts_lane_3to5_entering_int' : 'NODE',
+                      'sum_arts_lanecap_3to5_entering_int' : 'NODE', 
+                      'number_oneway_arts_entering_int' : 'NODE', 
+                      'cycle' : 'NODE'}
+
+    for name, type in attribute_dict.iteritems():
+        network.create_attribute(type, name)
+    
+    for link in network.links():
+        ftype = link.data3
+        lanes = link.num_lanes
+        lanecap = link.data1
+
+
+    # 1) Set temporary link attributes
+
+        # Temp Link Attribute [arterial_flag] = 1 for arterials (ftype=3,4 or 6)
+        if ftype == 3 or ftype == 4 or ftype == 6: 
+            link.arterial_flag = 1
+        else: 
+            link.arterial_flag = 0
+
+        # Temp Link Attribute [lane_3to5] = (minimum of 5 or lanes+2 (range 3-5) wherever arterial_flag=1)
+        if link.arterial_flag == 1: 
+            link.lane_3to5 = min(5, (lanes+2))
+        else:
+            link.lane_3to5 = 0
+
+        # Temp Link Attribute [lanecap_3to5] = (minimum of 5*lanecap or (lanes+2)*lanecap wherever arterial_flag=1)
+        if link.arterial_flag == 1:
+            link.lanecap_3to5 = min((5*lanecap),((lanes+2)*lanecap))
+        else:
+            link.lanecap_3to5 = 0 
+
+        # Temp Link Attribute [oneway_arterial_flag] = 1 for oneway arterials (ftype=4)
+        if ftype == 4: 
+            link.oneway_arterial_flag = 1
+        else:
+            link.oneway_arterial_flag = 0
+
+
+    # 2) Aggregate link attributes to temporary node attributes
+
+    for node in network.nodes():
+
+        node.number_arts_entering_int = 0    
+        node.sum_arts_lane_3to5_entering_int = 0
+        node.sum_arts_lanecap_3to5_entering_int = 0
+        node.number_oneway_arts_entering_int = 0
+            
+        for link in node.incoming_links():
+
+            # Temp Node Attribute [number_arts_entering_int] = number of arterial links (arterial_flag = 1) entering the intersection (node --> j-node of link)
+            node.number_arts_entering_int = node.number_arts_entering_int + link.arterial_flag
+                
+            # Temp Node Attribute [sum_arts_lane_3to5_entering_int] = sum of lane_3to5 for arterials (arterial_flag = 1) entering the intersection (node --> j-node of link) 
+            node.sum_arts_lane_3to5_entering_int = node.sum_arts_lane_3to5_entering_int + link.lane_3to5
+        
+            # Temp Node Attribute [sum_arts_lanecap_3to5_entering_int] = sum of lanecap_3to5 for arterials (arterial_flag = 1) entering the intersection (node --> j-node of link)
+            node.sum_arts_lanecap_3to5_entering_int = node.sum_arts_lanecap_3to5_entering_int + link.lanecap_3to5
+        
+            # Temp Node Attribute [number_oneway_arts_entering_int] = number of oneway arterial links (ftype=4) entering the intersection (node --> j-node of link)
+            node.number_oneway_arts_entering_int = node.number_oneway_arts_entering_int + link.oneway_arterial_flag
+        
+ 
+    # 3) Calculate intersection cycle time (permanent node attribute - [cycle] in minutes at all nodes)
+
+    for node in network.nodes():
+
+        node.cycle = 0.0
+
+        for link in node.incoming_links():
+
+            # Permanent Node Attribute [cycle] = signal cycle duration in minutes where number_arts_entering_int > 2 or number_oneway_arts_entering_int > 1
+            if (node.number_arts_entering_int > 2) or (node.number_oneway_arts_entering_int > 1):
+                node.cycle = 1.0 + (node.sum_arts_lane_3to5_entering_int / 8.0) * (node.number_arts_entering_int / 4.0)
+            else:
+                node.cycle = 0.0
+              
+
+    # 4) Calculate red time (permanent link attribute - [red] in minutes at j-node of every arterial link
+
+    for node in network.nodes():
+
+        for link in node.incoming_links():
+
+            link.red = 0.0
+
+            if node.sum_arts_lanecap_3to5_entering_int > 0.0 and link.data3 <> 0 and link.data3 <> 5:
+                link.red = 1.2 * node.cycle * (1 - (node.number_arts_entering_int * link.lanecap_3to5) / (2 * node.sum_arts_lanecap_3to5_entering_int))
+            else:
+                link.red = 0.0
+
+            # Use this to match 4K macro results.  Revise in the future to consider freeway and expressway links that end at signalized intersections.
+            if link.data3 == 1 or link.data3 == 2:
+                link.red = 0.0
+        
+
+    # 5) Calculate intersection delay factor (permanent link attribute - [rdly] in minutes for every arterial link
+            # restrict to arterials for now - not ul3=0 or 5
+
+    for node in network.nodes():
+
+        for link in node.incoming_links():
+
+            link.rdly = 0.0
+
+            if node.cycle:
+                link.rdly = min(1,max(0.2,(link.red * link.red) / (2 * node.cycle)))
+
+    # 6) Factor rdly by 0.5
+    
+            link.rdly = link.rdly * factor
+            link['@rdly'] = link.rdly
+            
+            #print link.i_node, link.j_node, link.rdly, link.data3
+            
+    for name, type in attribute_dict.iteritems():
+            network.delete_attribute(type, name)
+    emmeProject.current_scenario.publish_network(network)
 
 def run_importer(project_name):
     my_project = EmmeProject(project_name)
@@ -212,6 +265,8 @@ def run_importer(project_name):
         my_project.process_modes('inputs/networks/' + mode_file)
         
         my_project.process_base_network('inputs/networks/' + value + base_net_name)
+        if import_shape:
+            my_project.process_shape('inputs/networks/' + value + shape_name)
         my_project.process_turn('inputs/networks/' + value + turns_name)
         if my_project.tod in load_transit_tod:
            my_project.process_vehicles('inputs/networks/' + transit_vehicle_file)
@@ -219,18 +274,21 @@ def run_importer(project_name):
            update_headways(my_project, headway_df)
         #import tolls
         import_tolls(my_project)
+        arterial_delay(my_project, rdly_factor)
+        if add_distance_pricing or add_hot_lane_tolls:
+            distance_pricing(distance_rate_dict[value], hot_rate_dict[value], my_project)
         
-        
-
+       
 def main():
-    print network_summary_project
+
     run_importer(network_summary_project)
     
-    returncode = subprocess.call([sys.executable,'scripts/network/daysim_zone_inputs.py'])
-    if returncode != 0:
-        sys.exit(1)
+    if run_daysim_zone_inputs:
+        returncode = subprocess.call([sys.executable,'scripts/network/daysim_zone_inputs.py'])
+        if returncode != 0:
+            sys.exit(1)
     
-    print 'done'
+    print 'networks imported'
 
 if __name__ == "__main__":
     main()
diff --git a/scripts/shadow_pricing_check.py b/scripts/shadow_pricing_check.py
index 4fd2207b..dbfb7a02 100644
--- a/scripts/shadow_pricing_check.py
+++ b/scripts/shadow_pricing_check.py
@@ -19,6 +19,7 @@
 from input_configuration import *
 import sys
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 
 def get_percent_rmse(urbansim_file, daysim_file, guide_file):
     urbansim_data = pd.io.parsers.read_table(urbansim_file, sep = ' ') #Read in UrbanSim data
diff --git a/scripts/skimming/SkimsAndPaths.py b/scripts/skimming/SkimsAndPaths.py
index 56d1992e..43977fbc 100644
--- a/scripts/skimming/SkimsAndPaths.py
+++ b/scripts/skimming/SkimsAndPaths.py
@@ -19,6 +19,7 @@
 import argparse
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
 sys.path.append(os.path.join(os.getcwd(),"inputs"))
+sys.path.append(os.getcwd())
 from emme_configuration import *
 from EmmeProject import *
 
@@ -59,18 +60,21 @@
 	hdf5_file_path = 'inputs/daysim_outputs_seed_trips.h5'
 else:
 	print 'Using DAYSIM OUTPUTS'
-	hdf5_file_path = 'outputs/daysim_outputs.h5'
+	hdf5_file_path = 'outputs/daysim/daysim_outputs.h5'
 
 def parse_args():
     """Parse command line arguments for max number of assignment iterations"""
     return sys.argv[1]
 
+def get_model_year():
+    return sys.argv[2]
+
 def create_hdf5_skim_container2(hdf5_name):
     #create containers for TOD skims
     start_time = time.time()
 
     hdf5_filename = os.path.join('inputs/', hdf5_name +'.h5').replace("\\","/")
-    print hdf5_filename
+
     my_user_classes = json_to_dictionary('user_classes')
 
     # IOError will occur if file already exists with "w-", so in this case
@@ -665,8 +669,8 @@ def average_skims_to_hdf5_concurrent(my_project, average_skims):
             matrix_name= 'ivtwa' + item
             matrix_value = emmeMatrix_to_numpyMatrix(matrix_name, my_project.bank, 'uint16', 100)
             #open old skim and average
-            if average_skims:
-                matrix_value = average_matrices(np_old_matrices[matrix_name], matrix_value)
+            #if average_skims:
+            #    matrix_value = average_matrices(np_old_matrices[matrix_name], matrix_value)
             my_store["Skims"].create_dataset(matrix_name, data=matrix_value.astype('uint16'),compression='gzip')
             print matrix_name+' was transferred to the HDF5 container.'
 
@@ -675,8 +679,8 @@ def average_skims_to_hdf5_concurrent(my_project, average_skims):
             matrix_value = emmeMatrix_to_numpyMatrix(matrix_name, my_project.bank, 'uint16', 100)
             #open old skim and average
             print matrix_name
-            if average_skims:
-                matrix_value = average_matrices(np_old_matrices[matrix_name], matrix_value)
+            #if average_skims:
+            #    matrix_value = average_matrices(np_old_matrices[matrix_name], matrix_value)
             my_store["Skims"].create_dataset(matrix_name, data=matrix_value.astype('uint16'),compression='gzip')
             print matrix_name+' was transferred to the HDF5 container.'
         #Transit, All Modes:
@@ -686,8 +690,8 @@ def average_skims_to_hdf5_concurrent(my_project, average_skims):
             matrix_name= key
             matrix_value = emmeMatrix_to_numpyMatrix(matrix_name, my_project.bank, 'uint16', 100)
             #open old skim and average
-            if average_skims:
-                matrix_value = average_matrices(np_old_matrices[matrix_name], matrix_value)
+            #if average_skims:
+            #    matrix_value = average_matrices(np_old_matrices[matrix_name], matrix_value)
             my_store["Skims"].create_dataset(matrix_name, data=matrix_value.astype('uint16'),compression='gzip')
             print matrix_name+' was transferred to the HDF5 container.'
 
@@ -801,10 +805,10 @@ def hdf5_trips_to_Emme(my_project, hdf_filename):
     for matrix_name in ['lttrk','metrk','hvtrk']:
         demand_matrix = load_trucks_external(my_project, matrix_name, zonesDim)
         demand_matrices.update({matrix_name : demand_matrix})
-
+        
     # Load in supplemental trips
     # We're assuming all trips are only for income 2, toll classes
-    for matrix_name in ['svtl2', 'trnst', 'bike', 'h2tl2', 'h3tl2', 'walk']:
+    for matrix_name in ['svtl2', 'h2tl2', 'h3tl2', 'litrat', 'bike', 'walk']:
         demand_matrix = load_supplemental_trips(my_project, matrix_name, zonesDim)
         demand_matrices.update({matrix_name : demand_matrix})
 
@@ -837,8 +841,8 @@ def hdf5_trips_to_Emme(my_project, hdf_filename):
         
         #Regular Daysim Output:            
         else:
-            if vot[x] < 15: vot[x]=1
-            elif vot[x] < 25: vot[x]=2
+            if vot[x] < 13.07: vot[x]=1
+            elif vot[x] < 26.14: vot[x]=2
             else: vot[x]=3
 
         #get the matrix name from matrix_dict. Throw out school bus (8) for now.
@@ -929,7 +933,7 @@ def load_supplemental_trips(my_project, matrix_name, zonesDim):
     demand_matrix = np.zeros((zonesDim,zonesDim), np.float16)
     hdf_file = h5py.File(supplemental_loc + tod + '.h5', "r")
     # Call correct mode name by removing income class value when needed
-    if matrix_name not in ['bike', 'trnst', 'walk']:
+    if matrix_name not in ['bike', 'litrat', 'walk']:
         mode_name = matrix_name[:-1]
     else:
         mode_name = matrix_name
@@ -1263,22 +1267,21 @@ def create_node_attributes(node_attribute_dict, my_project):
                        extra_attribute_description=key,
                        extra_attribute_default_value = value['init_value'],
                        overwrite=True)
-
         
         network_calc = my_project.tool("inro.emme.network_calculation.network_calculator")  
         node_calculator_spec = json_to_dictionary("node_calculation")
-        transit_tod = transit_network_tod_dict[tod]
-        
-        if transit_tod in transit_node_constants.keys():
-            for line_id, attribute_dict in transit_node_constants[transit_tod].iteritems():
+        model_year = get_model_year()
+      
+        for line_id, attribute_dict in transit_node_constants[model_year].iteritems():
+
+            for attribute_name, value in attribute_dict.iteritems():
+            #Load in the necessary Dictionarie
+                mod_calc = node_calculator_spec
+                mod_calc["result"] = attribute_name
+                mod_calc["expression"] = value
+                mod_calc["selections"]["node"] = "Line = " + line_id
+                network_calc(mod_calc)
 
-                for attribute_name, value in attribute_dict.iteritems():
-                #Load in the necessary Dictionarie
-                    mod_calc = node_calculator_spec
-                    mod_calc["result"] = attribute_name
-                    mod_calc["expression"] = value
-                    mod_calc["selections"]["node"] = "Line = " + line_id
-                    network_calc(mod_calc)
 
 
 def delete_matrices_parallel(project_name):
@@ -1344,6 +1347,8 @@ def run_assignments_parallel(project_name):
     attribute_based_toll_cost_skims(my_project, "@toll1")
     attribute_based_toll_cost_skims(my_project, "@toll2")
     attribute_based_toll_cost_skims(my_project, "@toll3")
+    attribute_based_toll_cost_skims(my_project, "@trkc2")
+    attribute_based_toll_cost_skims(my_project, "@trkc3")
     class_specific_volumes(my_project)
 
     ##dispose emmebank
@@ -1370,7 +1375,7 @@ def main():
 
         
         #want pooled processes finished before executing more code in main:
-        #run_assignments_parallel('projects/5to6/5to6.emp')
+        # run_assignments_parallel('projects/6to7/6to7.emp')
         
         start_transit_pool(project_list)
         #run_transit('projects/20to5/20to5.emp')
diff --git a/scripts/skimming/reliability_skims.py b/scripts/skimming/reliability_skims.py
new file mode 100644
index 00000000..dc40b0eb
--- /dev/null
+++ b/scripts/skimming/reliability_skims.py
@@ -0,0 +1,105 @@
+import array as _array
+import inro.emme.desktop.app as app
+import inro.modeller as _m
+import inro.emme.matrix as ematrix
+import inro.emme.database.matrix
+import inro.emme.database.emmebank as _eb
+import json
+import numpy as np
+import time
+import os,sys
+#os.chdir(r"D:\stefan\sc_calibration\soundcast")
+import h5py
+import Tkinter, tkFileDialog
+import multiprocessing as mp
+import subprocess
+from multiprocessing import Pool
+import logging
+import datetime
+import argparse
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.path.join(os.getcwd(),"inputs"))
+sys.path.append(os.getcwd())
+from emme_configuration import *
+from EmmeProject import *
+
+# Time of day periods
+#tods = ['5to6', '6to7', '7to8', '8to9', '9to10', '10to14', '14to15', '15to16', '16to17', '17to18', '18to20', '20to5' ]
+project_list = ['Projects/' + tod + '/' + tod + '.emp' for tod in tods]
+
+def json_to_dictionary(dict_name):
+
+    #Determine the Path to the input files and load them
+
+    input_filename = os.path.join('inputs/skim_params/',dict_name+'.json').replace("\\","/")
+    
+    my_dictionary = json.load(open(input_filename))
+    
+    return(my_dictionary)
+
+
+def start_pool(project_list):
+    #An Emme databank can only be used by one process at a time. Emme Modeler API only allows one instance of Modeler and
+    #it cannot be destroyed/recreated in same script. In order to run things con-currently in the same script, must have
+    #seperate projects/banks for each time period and have a pool for each project/bank.
+    #Fewer pools than projects/banks will cause script to crash.
+
+    pool = Pool(processes=parallel_instances)
+    
+    pool.map(run_skim,project_list[0:parallel_instances])
+    
+    pool.close()
+
+
+def run_skim(project_name):
+    #Function to calculate reliability skims
+
+     start_time_skim = time.time()
+
+     my_project = EmmeProject(project_name)
+     attribute_name = '@reliab'
+
+     skim_desig = 'r'
+
+     skim_traffic = my_project.m.tool("inro.emme.traffic_assignment.path_based_traffic_analysis")
+
+     skim_specification = json_to_dictionary("general_attribute_based_skim")
+
+     my_user_classes = json_to_dictionary("user_classes")
+
+     my_project.create_extra_attribute("LINK", attribute_name, 'reliability index', True)
+
+     exp = '0.max.(timau-((length * 60 / ul2) * (1 + .72 * (1 * (volau + @bveh) / (ul1* lanes)) ^ 7.2)))'
+
+     my_project.network_calculator("link_calculation", result = attribute_name, expression = exp, selections_by_link = "ul3=1,2")
+
+     mod_skim = skim_specification
+     for x in range (0, len(mod_skim["classes"])):
+
+        matrix_name= my_user_classes["Highway"][x]["Name"]+skim_desig
+
+        if my_project.bank.matrix(matrix_name):
+
+            my_project.delete_matrix(matrix_name)
+
+        my_project.create_matrix(matrix_name, 'reliability skim', 'FULL')
+
+        mod_skim["classes"][x]["analysis"]["results"]["od_values"] = matrix_name
+
+        mod_skim["path_analysis"]["link_component"] = attribute_name
+
+     skim_traffic(mod_skim)
+
+
+def main():
+
+    for i in range (0, 12, parallel_instances):
+
+        l = project_list[i:i+parallel_instances]
+
+        start_pool(l)
+
+
+if __name__ == "__main__":
+
+    main()
\ No newline at end of file
diff --git a/scripts/summarize/benefit_cost/aq_crash_calcs.py b/scripts/summarize/benefit_cost/aq_crash_calcs.py
new file mode 100644
index 00000000..7a14444a
--- /dev/null
+++ b/scripts/summarize/benefit_cost/aq_crash_calcs.py
@@ -0,0 +1,177 @@
+import os
+import sys
+import argparse
+abspath = os.path.abspath(__file__)
+dname = os.path.dirname(abspath)
+sys.path.append(dname)
+sys.path.append(os.path.join(os.getcwd(),"scripts\summarize"))
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+import pandas as pd
+import time
+import h5py
+import math
+import itertools
+import collections
+import xlsxwriter
+import numpy as np
+import inro.emme.desktop.app as app
+import inro.modeller as _m
+from EmmeProject import *
+import datetime
+from aq_crash_configuration import *
+from input_configuration import *
+from emme_configuration import *
+from h5toDF import *
+
+
+def group_vmt_speed(my_project):
+    speed_bins = [-10, 0, 10, 20, 30, 40, 50, 60, 70]
+    speed_dict = {}
+    max_speed  = 60
+
+    for item in speed_bins:
+        speed_dict[item] = {'Car' : 0, 'Light Truck' : 0,  'Medium Truck' : 0, 'Heavy Truck': 0}
+
+    for key, value in sound_cast_net_dict.iteritems():
+
+        my_project.change_active_database(key)
+        network = my_project.current_scenario.get_network()
+
+        for link in network.links():
+            if link['auto_time']:
+                speed = link['length']* MINS_HR/link['auto_time']
+                #speed = int(round(speed, -1))
+                speed = int(speed - speed%10)
+                if speed == 70:
+                    speed = max_speed
+            
+             
+            if speed > 0 and speed <100:
+
+               speed_dict[speed]['Car'] = speed_dict[speed]['Car'] + (link['@svtl1']+ link['@svtl2'] + link['@svtl3'] + + link['@h2tl1'] + link['@h2tl2'] +
+               link['@h2tl3'] + link['@h3tl1'] +link['@h3tl2'] + link['@h3tl3'] )*link['length']
+
+               speed_dict[speed]['Light Truck'] = speed_dict[speed]['Light Truck'] + link['@lttrk'] * link['length']
+               speed_dict[speed]['Medium Truck'] = speed_dict[speed]['Medium Truck'] + link['@mveh'] * link['length']
+               speed_dict[speed]['Heavy Truck'] = speed_dict[speed]['Heavy Truck'] + link['@hveh'] * link['length']
+
+                    
+    return speed_dict
+
+def group_vmt_class(my_project):
+    network = my_project.current_scenario.get_network()
+
+    # store vmt by functional class 1= Freeway, 3= Expressway, etc
+    vmt_func_class= {1 : 0, 3 : 0,  5 : 0, 7 : 0}
+    
+    for key, value in sound_cast_net_dict.iteritems():
+        my_project.change_active_database(key)
+        network = my_project.current_scenario.get_network()
+
+        for link in network.links():
+            # Only bigger facility types are included
+            if link['volume_delay_func'] in vmt_func_class.keys():
+                vmt_func_class[link['volume_delay_func']]+=link['auto_volume']*link['length']
+
+    vmt_fc_df = pd.DataFrame(vmt_func_class.items())
+    vmt_fc_df.columns = ['Functional Class', 'VMT']
+    return vmt_fc_df
+
+
+def emissions_calc(vmt_speed_dict, model_year):
+    if model_year == 2040:
+       pollutant_rates = pd.DataFrame.from_csv(pollutant_file_2040)
+    else:
+        pollutant_rates = pd.DataFrame.from_csv(pollutant_file)
+
+    vehicle_type_tons = {'Car': {'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0},
+                         'Light Truck':{'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0},
+                         'Medium Truck': {'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0},
+                         'Heavy Truck': {'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0}}
+
+    for index, row in pollutant_rates.iterrows():
+        vehicle_type_tons['Car'][row.name] += vmt_speed_dict[row['Speed Class']]['Car'] * row['Car']
+        vehicle_type_tons['Light Truck'][row.name] += vmt_speed_dict[row['Speed Class']]['Light Truck'] * row['Light Truck']
+        vehicle_type_tons['Medium Truck'][row.name] += vmt_speed_dict[row['Speed Class']]['Medium Truck'] * row['Medium Truck']
+        vehicle_type_tons['Heavy Truck'][row.name]  += vmt_speed_dict[row['Speed Class']]['Heavy Truck'] * row['Heavy Truck']
+
+    df_veh_type_tons = pd.DataFrame(vehicle_type_tons)
+    df_veh_type_tons = df_veh_type_tons/1000000
+    df_veh_type_dollars = pd.DataFrame([df_veh_type_tons.loc['Carbon Dioxide']*CO2_COST, df_veh_type_tons.loc['Carbon Monoxide']*CO_COST,
+                           df_veh_type_tons.loc['Nitrogen Oxide']*NO_COST,df_veh_type_tons.loc['Volatile Organic Compound']*VOC_COST,
+                           df_veh_type_tons.loc['Particulate Matter']*PM_COST])
+    df_emissions = pd.concat([df_veh_type_tons, df_veh_type_dollars])
+    return df_emissions
+
+
+def noise_calc(vmt_speed_dict):
+    noise_vmt = {'Car VMT': 0, 'Truck VMT' : 0}
+    for speed, vmt in vmt_speed_dict.iteritems():
+        noise_vmt['Car VMT'] += vmt['Car']
+        noise_vmt['Truck VMT'] += vmt['Light Truck']+ vmt['Medium Truck']+ vmt['Heavy Truck']
+    
+    return pd.DataFrame(noise_vmt, index = [0])
+
+def injury_calc(injury_file, my_project):
+    # For collisions, we assume a rate of collisions per VMT by facility type.
+    # To do: We should also include PMT by walking and biking
+    injury_rates = pd.DataFrame.from_csv(injury_file)
+    vmt_func_class = group_vmt_class(my_project)
+
+    injury_rates_vmt = pd.merge(injury_rates, vmt_func_class, on = 'Functional Class')
+    injury_rates_vmt['Property Damage Total'] = injury_rates_vmt['Property Damage Rate']*injury_rates_vmt['VMT']/SAFETY_FACTOR
+    injury_rates_vmt['Injury Total'] = injury_rates_vmt['Injury Rate']*injury_rates_vmt['VMT']/SAFETY_FACTOR
+    injury_rates_vmt['Fatality Total'] = injury_rates_vmt['Fatality Rate']*injury_rates_vmt['VMT']/SAFETY_FACTOR
+    injury_rates_vmt['Property Damage Cost'] = injury_rates_vmt['Property Damage Total']* PROPERTYD_COST
+    injury_rates_vmt['Injury Cost'] = injury_rates_vmt['Injury Rate']*injury_rates_vmt['VMT']*INJURY_COST
+    injury_rates_vmt['Fatality Cost'] = injury_rates_vmt['Fatality Rate']*injury_rates_vmt['VMT']*FATALITY_COST
+    return injury_rates_vmt
+
+def auto_own_cost_calc(daysim_outputs):
+    daysim_outputs['hhvehs'].loc[daysim_outputs['hhvehs']==4] = FOUR_PLUS_CAR_AVG
+    hh_auto_zone = daysim_outputs.groupby(['hhtaz']).sum()['hhvehs']*ANNUAL_OWNERSHIP_COST/ANNUALIZATION
+    return hh_auto_zone
+
+def write_results(bc_writer, start_row, REPORT_ROW_GAP, output_dfs):
+    for df in output_dfs:
+        df.to_excel(excel_writer = bc_writer, sheet_name =  'Raw Costs', na_rep = 0, startrow = start_row)
+        start_row = start_row + REPORT_ROW_GAP
+
+
+def main():
+
+    with open('scripts/summarize/benefit_cost/benefit_configuration.json') as config_file:
+      config = json.load(config_file)
+      parser = argparse.ArgumentParser()
+
+    parser.add_argument("inputpath")
+    parser.add_argument("outputfile")
+
+    args = parser.parse_args()
+
+
+    #### Get EMME project set up##############
+    filepath = args.inputpath +'/'+ project
+    emme_project = EmmeProject(filepath)
+    ## Calculate link level benefits
+    vmt_speed_dict = group_vmt_speed(emme_project)
+    df_emissions = emissions_calc(vmt_speed_dict, model_year)
+    noise_vmt = noise_calc(vmt_speed_dict)
+    injury_rates_vmt = injury_calc(injury_file, emme_project)
+    print 'injury calcs'
+    ### Get auto ownership data###
+    daysim_outputs = pd.read_csv(args.inputpath +'/outputs/daysim/_household.tsv', sep = '\t')
+    print daysim_outputs.columns
+    auto_own_cost = pd.DataFrame(auto_own_cost_calc(daysim_outputs))
+    print 'auto own'
+    # Write it out
+    output_dfs = [df_emissions, noise_vmt, injury_rates_vmt, auto_own_cost]
+
+
+    bc_writer =  pd.ExcelWriter(args.outputfile, engine = 'xlsxwriter')
+    start_row = 1
+    write_results(bc_writer, start_row, REPORT_ROW_GAP, output_dfs)
+    bc_writer.close()
+
+if __name__ == "__main__":
+        main()
\ No newline at end of file
diff --git a/scripts/summarize/benefit_cost/benefit_cost_configuration.py b/scripts/summarize/benefit_cost/aq_crash_configuration.py
similarity index 94%
rename from scripts/summarize/benefit_cost/benefit_cost_configuration.py
rename to scripts/summarize/benefit_cost/aq_crash_configuration.py
index 490b7d5f..d2b9fd04 100644
--- a/scripts/summarize/benefit_cost/benefit_cost_configuration.py
+++ b/scripts/summarize/benefit_cost/aq_crash_configuration.py
@@ -2,6 +2,7 @@
 output_name = 'TransFu2010'
 
 pollutant_file = 'scripts/summarize/inputs/benefit_cost/pollutant_rates.csv'
+pollutant_file_2040 = 'scripts/summarize/inputs/benefit_cost/pollutant_rates_2040.csv'
 injury_file ='scripts/summarize/inputs/benefit_cost/injury_rates.csv'
 
 #truck_file =somewhere
@@ -52,4 +53,3 @@
 guidefile = 'scripts/summarize/inputs/calibration/CatVarDict.xlsx'
 districtfile = 'scripts/summarize/inputs/calibration/TAZ_TAD_County.csv'
 report_output_location = 'outputs'
-bc_outputs_file = 'outputs/BenefitCost.xlsx'
\ No newline at end of file
diff --git a/scripts/summarize/benefit_cost/bc2.py b/scripts/summarize/benefit_cost/bc2.py
new file mode 100644
index 00000000..1bad4b46
--- /dev/null
+++ b/scripts/summarize/benefit_cost/bc2.py
@@ -0,0 +1,221 @@
+import argparse
+import tables
+import re
+import json
+import itertools
+import numpy as np
+import pandas as pd
+import collections
+
+
+
+def read_zz_components(config):
+    """Returns a generator that returns a dictionary of each zone-to-zone
+    benefit component.
+
+    """
+
+    # Grumble, grumble, Python 2.6 and stupid nested context managers
+    # Too lazy to wrap this with 'with' until Py27
+    base = tables.open_file(config["baseline"]["filepath"])
+    alt = tables.open_file(config["alternative"]["filepath"])
+
+    # Benefits by Period:
+    for per in config["timeperiods"]["periods"]:
+        period = per["period"]
+        code = per["code"]
+        trperiod = per["trperiod"]
+        trcode = per["trcode"]
+        assignper = per["assignper"]
+        fareper = per["fareper"]
+        distper = per["distper"]
+        walkper = per["walkper"]
+        bikeper = per["bikeper"]
+
+
+        for zzben in config["benefits-by-period"]:
+            cp = zzben["costpath"]
+            vp = zzben["volumepath"]
+
+            # Substitute various time period placeholders
+            cp = cp.replace("${PER}",period)
+            cp = cp.replace("${CODE}",code)
+            cp = cp.replace("${TRPER}",trperiod)
+            cp = cp.replace("${TRCODE}",trcode)
+            cp = cp.replace("${ASSIGNPER}",assignper)
+            cp = cp.replace("${FAREPER}",fareper)
+            cp = cp.replace("${DISTPER}",distper)
+            cp = cp.replace("${WALKPER}",walkper)
+            cp = cp.replace("${BIKEPER}",bikeper)
+
+            vp = vp.replace("${PER}",period)
+            vp = vp.replace("${CODE}",code)
+            vp = vp.replace("${TRPER}",trperiod)
+            vp = vp.replace("${TRCODE}",trcode)
+            vp = vp.replace("${ASSIGNPER}",assignper)
+            vp = vp.replace("${FAREPER}",fareper)
+            vp = vp.replace("${DISTPER}",distper)
+            vp = vp.replace("${WALKPER}",walkper)
+            vp = vp.replace("${BIKEPER}",bikeper)
+
+            zzben["basecost"] = base.get_node(cp).read()
+            zzben["basevol"] = base.get_node(vp).read()
+            zzben["altcost"] = alt.get_node(cp).read()
+            zzben["altvol"] = alt.get_node(vp).read()
+            zzben["timeperiod"] = period
+
+            if "description" not in zzben.keys():
+                zzben["description"] = "%s %s" % (zzben["timeperiod"],
+                                                  zzben["userclass"])
+            else:
+                pass
+
+            yield zzben
+
+    base.close()
+    alt.close()
+
+
+def calc_zz_benefit(zzben, config):
+    """Calculate consumer surplus benefits based on costs and volumes of
+    two scenarios. Takes a benefit component dictionary containing
+    cost and volume arrays of both scenarios, as returned by
+    read_zz_components(). Returns the calculated benefit as an array.
+
+    """
+    max_zone = config["max_zone_id"]
+    
+    bc = zzben["basecost"]
+    bc = bc[0:max_zone, 0:max_zone]
+
+    bv = zzben["basevol"]
+    bv = bv[0:max_zone, 0:max_zone]
+    
+    ac = zzben["altcost"]
+    ac = ac[0:max_zone, 0:max_zone]
+
+    av = zzben["altvol"]
+    av = av[0:max_zone, 0:max_zone]
+
+    # this can happen if there is no transit service between an od pair
+    if (bc>1000000).any() or (ac>1000000).any():
+        print "There is no transit service between an o-d pair, you may want to check your network."
+
+    bc[bc>1000000] = 0
+    ac[ac>1000000] = 0
+    rawben = (bc - ac) * ((bv + av) / 2)
+
+
+    return rawben
+
+
+
+def calc_zz_benefits(zzbens, config):
+    """Returns a generator that returns a dictionary containing the
+    calculated raw benefit for each benefit component.
+
+    """
+    for zzben in zzbens:
+        rawben = calc_zz_benefit(zzben, config)
+        zzben["rawben"] = rawben
+        yield zzben
+
+def calc_zz_dollar_benefits(zzbens, config):
+    """Returns a generator that returns a dictionary containing calculated
+    benefit (in dollars) for each benefit component. Takes a list of
+    zone-to-zone benefit dictionaries and a dictionary containing the
+    appropriate unit conversions.
+
+    """
+
+    for zzben in zzbens:
+        dollarben = to_dollars(zzben, config)
+        zzben["dollarben"] = dollarben
+        yield zzben
+
+def to_dollars(ben, config):
+    """Converts raw benefits of a given unit type to dollars. Takes a
+    benefit dictionary containing the raw benefit, and returns a
+    dictionary with the dollar benefits array calculated. Should work
+    for all benefit types.
+
+    """
+
+    userclass = ben["userclass"]
+    timeperiod = ben["timeperiod"]
+    benarray = ben["rawben"]
+
+    try:
+        units = ben["costunits"]
+    except:
+        units = "minutes"
+    #print 'raw benefits '
+    #print benarray[1:5, 1:5]
+    if units == "minutes":
+        conv = config["vot"][userclass][timeperiod] / 60.0
+    elif units == "cents":
+        conv = 0.01
+    elif units == "dollars":
+        conv = 1.0 # could simply return, but this is more an example
+    elif units == "distance":
+         conv = config['operating cost'][userclass]
+    else:
+        raise ValueError(units + " is not a valid unit type")
+
+    converted_ben_array = benarray*conv
+
+    return converted_ben_array
+
+
+def all_dollar_benefits(config):
+    zzdollars = calc_zz_dollar_benefits(calc_zz_benefits(read_zz_components(config), config), config)
+    allbens = itertools.chain(zzdollars)
+
+    return(allbens)
+
+
+def write_benefits(outputpath, benefits, benefittype="dollarben"):
+    benefit_dict = {}
+    first_ben = True
+
+    for benefit in benefits:
+                print 'calculating benefit for'
+                print benefit['userclass']
+                print benefit['timeperiod']
+                bgroup = str(benefit['reportgroup'])
+                barray = benefit[benefittype]
+                base_trips = benefit['basevol']
+                scen_trips = benefit['altvol']
+                #hard codes
+                base_trips =base_trips[0:3700, 0:3700]
+                scen_trips =scen_trips[0:3700, 0:3700]
+                trips = (base_trips+scen_trips)/2
+
+                orig_ben_array = np.sum(barray, axis =1)
+                dest_ben_array = np.sum(barray, axis =0)
+                avg_ben = (orig_ben_array + dest_ben_array)/2
+
+                orig_trip_array = np.sum(trips, axis =1)
+                dest_trip_array = np.sum(trips, axis =0)
+                avg_trip = (orig_trip_array + dest_trip_array)/2
+
+                if bgroup in benefit_dict.keys():
+                   benefit_dict[bgroup] += avg_ben
+                   benefit_dict[bgroup+'_trip']+= avg_trip
+
+                else:
+                   benefit_dict[bgroup] = avg_ben
+                   benefit_dict[bgroup+'_trip'] = avg_trip
+
+    ben_df = pd.DataFrame(benefit_dict)
+    ben_df.to_csv(outputpath)
+
+
+if __name__ == '__main__':
+
+    with open('scripts/summarize/benefit_cost/benefit_configuration.json') as config_file:
+      config = json.load(config_file)
+
+    bens = all_dollar_benefits(config)
+
+    write_benefits(config["outputpath"], bens)
\ No newline at end of file
diff --git a/scripts/summarize/benefit_cost/benefit_configuration.json b/scripts/summarize/benefit_cost/benefit_configuration.json
new file mode 100644
index 00000000..045afbd6
--- /dev/null
+++ b/scripts/summarize/benefit_cost/benefit_configuration.json
@@ -0,0 +1,886 @@
+{
+  "description": "example soundcast use",
+  "year": 2040,
+  "outputpath": "d:/bca_outputs/soundcast_bc_test.csv",
+  "constantdollars": 2014,
+  "max_zone_id": 3700,
+
+  "baseline": {
+    "description": "2040_no_build",
+    "filepath": "d:/bca_outputs/2040_no_build.h5",
+    "inputpath": "D:/soundcast_2040_no_build2",
+    "aqoutputpath": "d:/bca_outputs/aq_crash_base.xlsx"
+  },
+
+  "alternative": {
+    "description": "2040 constrained plan",
+    "filepath": "d:/bca_outputs/2040_constrained_plan.h5",
+    "inputpath": "D:/soundcast_2040_constrained",
+    "aqoutputpath": "d:/bca_outputs/aq_crash_constrained_plan.xlsx"
+  },
+
+
+  "timeperiods": {
+    "description": "Conversion of time periods to skim/triptable code",
+    "periods": [
+      {
+        "period": "5to6",
+        "trperiod": "5to6",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "5to6",
+        "fareper": "9to10",
+        "distper": "7to8",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "6to7",
+        "trperiod": "6to7",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "6to7",
+        "fareper": "9to10",
+        "distper": "7to8",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "7to8",
+        "trperiod": "7to8",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "7to8",
+        "fareper": "9to10",
+        "distper": "7to8",
+        "bikeper": "7to8",
+        "walkper": "5to6"
+      },
+      {
+        "period": "8to9",
+        "trperiod": "8to9",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "8to9",
+        "fareper": "9to10",
+        "distper": "7to8",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "9to10",
+        "trperiod": "9to10",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "9to10",
+        "fareper": "9to10",
+        "distper": "7to8",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "10to14",
+        "trperiod": "10to14",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "10to14",
+        "fareper": "9to10",
+        "distper": "7to8",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "14to15",
+        "trperiod": "14to15",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "14to15",
+        "fareper": "9to10",
+        "distper": "17to18",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "15to16",
+        "trperiod": "15to16",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "15to16",
+        "fareper": "9to10",
+        "distper": "17to18",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "16to17",
+        "trperiod": "16to17",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "16to17",
+        "fareper": "9to10",
+        "distper": "17to18",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "17to18",
+        "trperiod": "17to18",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "17to18",
+        "fareper": "9to10",
+        "distper": "17to18",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "18to20",
+        "trperiod": "18to20",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "18to20",
+        "fareper": "9to10",
+        "distper": "17to18",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      },
+      {
+        "period": "20to5",
+        "trperiod": "10to14",
+        "code": "a",
+        "trcode": "a",
+        "assignper": "20to5",
+        "fareper": "9to10",
+        "distper": "17to18",
+        "walkper": "5to6",
+        "bikeper": "7to8"
+      }
+    ]
+  },
+
+  "benefits-by-period": [
+    {
+      "userclass": "SOV Toll Income Level 1 Time",
+      "costpath": "/${PER}/svtl1t",
+      "volumepath": "/${PER}/svtl1",
+       "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "SOV Toll Income Level 2 Time",
+      "costpath": "/${PER}/svtl2t",
+      "volumepath": "/${PER}/svtl2",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "SOV Toll Income Level 3 Time",
+      "costpath": "/${PER}/svtl3t",
+      "volumepath": "/${PER}/svtl3",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 1 Time",
+      "costpath": "/${PER}/h2tl1t",
+      "volumepath": "/${PER}/h2tl1",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 2 Time",
+      "costpath": "/${PER}/h2tl2t",
+      "volumepath": "/${PER}/h2tl2",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 3 Time",
+      "costpath": "/${PER}/h2tl3t",
+      "volumepath": "/${PER}/h2tl3",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 1 Time",
+      "costpath": "/${PER}/h3tl1t",
+      "volumepath": "/${PER}/h3tl1",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 2 Time",
+      "costpath": "/${PER}/h3tl2t",
+      "volumepath": "/${PER}/h3tl2",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 3 Time",
+      "costpath": "/${PER}/h3tl3t",
+      "volumepath": "/${PER}/h3tl3",
+      "reportgroup": "Auto Time"
+    },
+    {
+      "userclass": "Transit In-Vehicle Time",
+      "costpath": "/${TRPER}/ivtwa",
+      "volumepath": "/${PER}/trnst",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Light Rail Transit In-Vehicle Time",
+      "costpath": "/${TRPER}/ivtwr",
+      "volumepath": "/${PER}/litrat",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Medium Trucks Time",
+      "costpath": "/${PER}/metrkt",
+      "volumepath": "/${PER}/metrk",
+      "reportgroup": "Truck Time"
+    },
+    {
+      "userclass": "Heavy Trucks Time",
+      "costpath": "/${PER}/hvtrkt",
+      "volumepath": "/${PER}/hvtrk",
+      "reportgroup": "Truck Time"
+    },
+	{
+      "userclass": "SOV Toll Income Level 1 Reliability",
+      "costpath": "/${PER}/svtl1r",
+      "volumepath": "/${PER}/svtl1",
+       "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "SOV Toll Income Level 2 Reliability",
+      "costpath": "/${PER}/svtl2r",
+      "volumepath": "/${PER}/svtl2",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "SOV Toll Income Level 3 Reliability",
+      "costpath": "/${PER}/svtl3r",
+      "volumepath": "/${PER}/svtl3",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 1 Reliability",
+      "costpath": "/${PER}/h2tl1r",
+      "volumepath": "/${PER}/h2tl1",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 2 Reliability",
+      "costpath": "/${PER}/h2tl2r",
+      "volumepath": "/${PER}/h2tl2",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 3 Reliability",
+      "costpath": "/${PER}/h2tl3r",
+      "volumepath": "/${PER}/h2tl3",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 1 Reliability",
+      "costpath": "/${PER}/h3tl1r",
+      "volumepath": "/${PER}/h3tl1",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 2 Reliability",
+      "costpath": "/${PER}/h3tl2r",
+      "volumepath": "/${PER}/h3tl2",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 3 Reliability",
+      "costpath": "/${PER}/h3tl3r",
+      "volumepath": "/${PER}/h3tl3",
+      "reportgroup": "Auto Reliability"
+    },
+    {
+      "userclass": "Medium Trucks Reliability",
+      "costpath": "/${PER}/metrkr",
+      "volumepath": "/${PER}/metrk",
+      "reportgroup": "Truck Reliability"
+    },
+    {
+      "userclass": "Heavy Trucks Reliability",
+      "costpath": "/${PER}/hvtrkr",
+      "volumepath": "/${PER}/hvtrk",
+      "reportgroup": "Truck Reliability"
+    },
+    {
+      "userclass": "Transit Initial Wait Time",
+      "costpath": "/${TRPER}/iwtwa",
+      "volumepath": "/${PER}/trnst",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Transit Transfer Wait Time",
+      "costpath": "/${TRPER}/xfrwa",
+      "volumepath": "/${PER}/trnst",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Transit Walk Time",
+      "costpath": "/${TRPER}/auxwa",
+      "volumepath": "/${PER}/trnst",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Light Rail Transit Initial Wait Time",
+      "costpath": "/${TRPER}/iwtwr",
+      "volumepath": "/${PER}/litrat",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Light Rail Transit Transfer Wait Time",
+      "costpath": "/${TRPER}/xfrwr",
+      "volumepath": "/${PER}/litrat",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "Light Rail Transit Walk Time",
+      "costpath": "/${TRPER}/auxwr",
+      "volumepath": "/${PER}/litrat",
+      "reportgroup": "Transit Time"
+    },
+    {
+      "userclass": "SOV Toll Income Level 1 Operating Cost",
+      "costpath": "/${DISTPER}/svtl1d",
+      "volumepath": "/${PER}/svtl1",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "SOV Toll Income Level 2 Operating Cost",
+      "costpath": "/${DISTPER}/svtl2d",
+      "volumepath": "/${PER}/svtl2",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "SOV Toll Income Level 3 Operating Cost",
+      "costpath": "/${DISTPER}/svtl3d",
+      "volumepath": "/${PER}/svtl3",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 1 Operating Cost",
+      "costpath": "/${DISTPER}/h2tl1d",
+      "volumepath": "/${PER}/h2tl1",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 2 Operating Cost",
+      "costpath": "/${DISTPER}/h2tl2d",
+      "volumepath": "/${PER}/h2tl2",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "HOV2 Toll Income Level 3 Operating Cost",
+      "costpath": "/${DISTPER}/h2tl3d",
+      "volumepath": "/${PER}/h2tl3",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 1 Operating Cost",
+      "costpath": "/${DISTPER}/h3tl1d",
+      "volumepath": "/${PER}/h3tl1",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 2 Operating Cost",
+      "costpath": "/${DISTPER}/h3tl2d",
+      "volumepath": "/${PER}/h3tl2",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "HOV3 Toll Income Level 3 Operating Cost",
+      "costpath": "/${DISTPER}/h3tl3d",
+      "volumepath": "/${PER}/h3tl3",
+      "costunits": "distance",
+      "reportgroup": "Auto Operating Cost"
+    },
+    {
+      "userclass": "Medium Trucks Operating Cost",
+      "costpath": "/${DISTPER}/metrkd",
+      "volumepath": "/${PER}/metrk",
+      "costunits": "distance",
+      "reportgroup": "Trucks Operating Cost"
+    },
+    {
+      "userclass": "Heavy Trucks Operating Cost",
+      "costpath": "/${DISTPER}/hvtrkd",
+      "volumepath": "/${PER}/hvtrk",
+      "costunits": "distance",
+      "reportgroup": "Trucks Operating Cost"
+    }
+  ],
+
+  "vot": {
+    "description": "Value of time in Dollars per hour",
+    "SOV Toll Income Level 1 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "SOV Toll Income Level 2 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "SOV Toll Income Level 3 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV2 Toll Income Level 1 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV2 Toll Income Level 2 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV2 Toll Income Level 3 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV3 Toll Income Level 1 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV3 Toll Income Level 2 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV3 Toll Income Level 3 Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Transit In-Vehicle Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Light Rail Transit In-Vehicle Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Medium Trucks Time": {
+      "5to6": 49.000,
+      "6to7": 49.000,
+      "7to8": 49.000,
+      "8to9": 49.000,
+      "9to10": 49.000,
+      "10to14": 49.000,
+      "14to15": 49.000,
+      "15to16": 49.000,
+      "16to17": 49.000,
+      "17to18": 49.000,
+      "18to20": 49.000,
+      "20to5": 49.000
+    },
+    "Heavy Trucks Time": {
+      "5to6": 54.450,
+      "6to7": 54.450,
+      "7to8": 54.450,
+      "8to9": 54.450,
+      "9to10": 54.450,
+      "10to14": 54.450,
+      "14to15": 54.450,
+      "15to16": 54.450,
+      "16to17": 54.450,
+      "17to18": 54.450,
+      "18to20": 54.450,
+      "20to5": 54.450
+    },
+    "Transit Initial Wait Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Transit Walk Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Transit Transfer Wait Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Light Rail Transit Initial Wait Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Light Rail Transit Transfer Wait Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Light Rail Transit Walk Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Walk Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Bike Time": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "SOV Toll Income Level 1 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "SOV Toll Income Level 2 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "SOV Toll Income Level 3 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV2 Toll Income Level 1 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV2 Toll Income Level 2 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV2 Toll Income Level 3 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV3 Toll Income Level 1 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV3 Toll Income Level 2 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "HOV3 Toll Income Level 3 Reliability": {
+      "5to6": 25.000,
+      "6to7": 25.000,
+      "7to8": 25.000,
+      "8to9": 25.000,
+      "9to10": 25.000,
+      "10to14": 25.000,
+      "14to15": 25.000,
+      "15to16": 25.000,
+      "16to17": 25.000,
+      "17to18": 25.000,
+      "18to20": 25.000,
+      "20to5": 25.000
+    },
+    "Medium Trucks Reliability": {
+      "5to6": 49.000,
+      "6to7": 49.000,
+      "7to8": 49.000,
+      "8to9": 49.000,
+      "9to10": 49.000,
+      "10to14": 49.000,
+      "14to15": 49.000,
+      "15to16": 49.000,
+      "16to17": 49.000,
+      "17to18": 49.000,
+      "18to20": 49.000,
+      "20to5": 49.000
+    },
+    "Heavy Trucks Reliability": {
+      "5to6": 54.450,
+      "6to7": 54.450,
+      "7to8": 54.450,
+      "8to9": 54.450,
+      "9to10": 54.450,
+      "10to14": 54.450,
+      "14to15": 54.450,
+      "15to16": 54.450,
+      "16to17": 54.450,
+      "17to18": 54.450,
+      "18to20": 54.450,
+      "20to5": 54.450
+    }
+  },
+  "operating cost": {
+    "description": "Operating Cost per mile",
+    "SOV Toll Income Level 1 Operating Cost": 0.15,
+    "SOV Toll Income Level 2 Operating Cost": 0.15,
+    "SOV Toll Income Level 3 Operating Cost": 0.15,
+    "HOV2 Toll Income Level 1 Operating Cost": 0.15,
+    "HOV2 Toll Income Level 2 Operating Cost": 0.15,
+    "HOV2 Toll Income Level 3 Operating Cost": 0.15,
+    "HOV3 Toll Income Level 1 Operating Cost": 0.15,
+    "HOV3 Toll Income Level 2 Operating Cost": 0.15,
+    "HOV3 Toll Income Level 3 Operating Cost": 0.15,
+    "Medium Trucks Operating Cost": 0.99,
+    "Heavy Trucks Operating Cost": 0.99
+    }
+}
diff --git a/scripts/summarize/benefit_cost/benefit_controller.py b/scripts/summarize/benefit_cost/benefit_controller.py
new file mode 100644
index 00000000..eafda902
--- /dev/null
+++ b/scripts/summarize/benefit_cost/benefit_controller.py
@@ -0,0 +1,44 @@
+import os
+import sys
+import json
+import numpy as np
+import subprocess
+sys.path.append(os.getcwd())
+
+
+
+if __name__ == '__main__':
+    with open('scripts/summarize/benefit_cost/benefit_configuration.json') as config_file:
+      config = json.load(config_file)
+
+    print 'writing out the skims for both the baseline and the alternative runs.'
+    # write out skims to h5 for baseline
+    returncode = subprocess.call([sys.executable,'scripts/summarize/benefit_cost/emme2h5.py', config['baseline']['filepath'],'scripts/summarize/benefit_cost/benefit_configuration.json', config['baseline']['inputpath']])
+    if returncode != 0:
+      print 'emme2h5.py failed for the baseline'
+      sys.exit(1)
+
+   # write out skims to h5 for alternative
+    returncode = subprocess.call([sys.executable,'scripts/summarize/benefit_cost/emme2h5.py', config['alternative']['filepath'],'scripts/summarize/benefit_cost/benefit_configuration.json', config['alternative']['inputpath']])
+    if returncode != 0:
+      print 'emme2h5.py failed for the alternative'
+      sys.exit(1)
+
+    print 'doing consumer surplus calculations on the skims.'
+    # do consumer surplus calculations on skims and trips
+    returncode = subprocess.call([sys.executable,'scripts/summarize/benefit_cost/bc2.py'])
+    if returncode != 0:
+      print 'bc2.py failed'
+      sys.exit(1)
+
+    print 'doing air quality and crash calculations with the emme networks.'
+    # do air quality and crash calculations
+    returncode = subprocess.call([sys.executable, config['baseline']['inputpath']+'/scripts/summarize/benefit_cost/aq_crash_calcs.py', config['baseline']['inputpath'], config['baseline']['aqoutputpath']])
+    if returncode != 0:
+      print 'aq_crash_calcs.py failed for the baseline'
+      sys.exit(1)
+    
+    returncode = subprocess.call([sys.executable,config['alternative']['inputpath']+'/scripts/summarize/benefit_cost/aq_crash_calcs.py', config['alternative']['inputpath'], config['alternative']['aqoutputpath']])
+    if returncode != 0:
+      print 'aq_crash_calcs.py failed for the alternative'
+      sys.exit(1)
diff --git a/scripts/summarize/benefit_cost/emme2h5.py b/scripts/summarize/benefit_cost/emme2h5.py
new file mode 100644
index 00000000..da7d3fab
--- /dev/null
+++ b/scripts/summarize/benefit_cost/emme2h5.py
@@ -0,0 +1,231 @@
+import argparse, os, sys, fnmatch
+import tables
+import numpy as np
+import inro.emme.matrix as ematrix
+import inro.emme.database.emmebank as ebank
+import re
+import json
+from datetime import datetime, timedelta
+sys.path.append(os.getcwd())
+
+# Being bad and setting a global compression variable for HDF5
+# stores
+filters = tables.Filters(complevel=1, complib='zlib')
+
+converted = 0
+num_to_convert = 0
+start = 0
+
+def emmemat2np(emmefullmatrix, dtype=np.float32):
+    """
+    Converts an emmematrix object into a numpy matrix
+    """
+    mat = emmefullmatrix.get_data()
+    npmat = mat.to_numpy()
+   
+    return(npmat)
+
+def convert_fullmats(srcbankpath, h5filepath, h5grouppath=None, mats=None, dryrun=False):
+    """
+    Converts the full matrices in an Emmebank and stores them in an
+    HDF5 file. Optionally specify the destination group and a list of
+    matrices to convert. By default, creates a destination group with
+    the name of the emmebank and converts all matrices.
+    """
+
+    global num_to_convert, converted, start
+
+    # Context managers work with Emme 4.0.8 or newer, otherwise the
+    # 'with' keyword fails on Emmebank()
+    # Sadly, opening multiple files in a single un-nested with block
+    # was added in Python 2.7, and so this is a little nesty.
+    # See: http://goo.gl/ucEsR for details.
+    with ebank.Emmebank(srcbankpath) as srcbank:
+        h5grouppath = h5grouppath or "/" + re.sub('[^0-9a-zA-Z]+',
+                                                  '', srcbank.title)
+
+        # Hacky work-around for Emmebanks that were created with an
+        # error-producing scenario 9999. According to Chris J.,
+        # sometimes scenario 9999 is bogus and should be removed, but
+        # sometimes is not. This is a crude heuristic, and it would be
+        # better to do some clean-up in the model.
+        sn = [x.number for x in srcbank.scenarios()]
+        if len(sn) > 1 and 9999 in sn:
+            try:
+                srcbank.delete_scenario(9999)
+            except:
+                pass
+         
+        with tables.File(h5filepath, mode="a", filters=filters) as h5file:
+            for mat in srcbank.matrices():
+                full_mat_name = h5grouppath+'/'+mat.name
+                if full_mat_name not in mats:
+                    pass
+                else:
+                    if dryrun:
+                        print "%s/%s" % (h5grouppath,mat.name),
+                    else:
+                        print("Converting: %s/%s\n         -> %s/%s"
+                               % (srcbankpath, mat.name, h5grouppath, mat.name))
+
+                    group = get_or_create_group(h5file, h5grouppath)
+                    
+
+                    if not dryrun:
+                        a = h5file.create_array(h5grouppath,
+                                               mat.name,
+                                               emmemat2np(mat))
+                        a.attrs.description = mat.description
+
+                    # Housekeeping
+                    converted += 1
+                    mats.remove(full_mat_name)
+
+                    now = datetime.now()
+                    fraction = 1.0 * converted / num_to_convert
+                    time_so_far = now - start
+
+                    seconds_so_far = time_so_far.seconds
+                    total_estimate = 1.0 * seconds_so_far / fraction
+                    seconds_left = total_estimate - seconds_so_far
+                    time_left = timedelta(seconds=seconds_left)
+
+                    if not dryrun:
+                        print "            Finished %d of %d. Estimated time remaining: %s\n" % (
+                                           converted, num_to_convert, time_left)
+
+
+def get_or_create_group(h5file, h5grouppath):
+    try:
+        group = h5file.getNode(h5grouppath)
+        return group
+    except:
+        pass
+
+    #If we're here, the group doesn't exist.  Create it.
+
+    paths = filter(lambda x: x != "", h5grouppath.split("/"))
+    base = "/"
+    
+
+    for x in paths:
+        try:
+            h5file.create_group(base, x)
+        except:
+            pass
+        base = base + "/" + x
+
+
+
+def get_matrix_defs_from_json(json_file):
+    with file(json_file) as jfile:
+        config = json.load(jfile)
+
+    matrices = set()
+
+    # Benefits by Period:
+    for per in config["timeperiods"]["periods"]:
+        period = per["period"]
+        code = per["code"]
+        trperiod = per["trperiod"]
+        trcode = per["trcode"]
+        assignper = per["assignper"]
+        fareper = per["fareper"]
+        distper = per["distper"]
+        walkper = per["walkper"]
+        bikeper = per["bikeper"]
+
+
+        for zzben in config["benefits-by-period"]:
+            cp = zzben["costpath"]
+            vp = zzben["volumepath"]
+
+            # Substitute various time period placeholders
+            cp = cp.replace("${PER}",period)
+            cp = cp.replace("${CODE}",code)
+            cp = cp.replace("${TRPER}",trperiod)
+            cp = cp.replace("${TRCODE}",trcode)
+            cp = cp.replace("${ASSIGNPER}",assignper)
+            cp = cp.replace("${FAREPER}",fareper)
+            cp = cp.replace("${DISTPER}",distper)
+            cp = cp.replace("${WALKPER}",walkper)
+            cp = cp.replace("${BIKEPER}",bikeper)
+
+            vp = vp.replace("${PER}",period)
+            vp = vp.replace("${CODE}",code)
+            vp = vp.replace("${TRPER}",trperiod)
+            vp = vp.replace("${TRCODE}",trcode)
+            vp = vp.replace("${ASSIGNPER}",assignper)
+            vp = vp.replace("${FAREPER}",fareper)
+            vp = vp.replace("${DISTPER}",distper)
+            vp = vp.replace("${WALKPER}",walkper)
+            vp = vp.replace("${BIKEPER}",bikeper)
+
+            matrices.add(cp)
+            matrices.add(vp)
+
+    return matrices
+
+
+def find_all_emmebanks(root):
+    emmebanks = []
+
+    for path,names,files in os.walk(root):
+        for filename in fnmatch.filter(files,'emmebank'):
+            emmebanks.append(os.path.join(path,filename))
+
+    return emmebanks
+
+
+
+if __name__ == '__main__':
+    # Set up our command line options
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-a", "--append",
+                        help="Append output to existing HDF5 file",
+                        action="store_true")
+
+    parser.add_argument("outputfile",
+                        help='''HDF5 file for output,
+                        will not overwrite existing''')
+
+    parser.add_argument("jsonfile",
+                        help='''JSON config file for BC2''')
+
+    parser.add_argument("inputfolder",
+                        help="Specify root folder containing emmebanks for conversion")
+
+    args = parser.parse_args()
+
+    if args.append==False and os.path.isfile(args.outputfile):
+        raise IOError("Output file exists. Not overwriting.")
+
+    # Fetch the matrix definitions we'll need to import
+    matrices = get_matrix_defs_from_json(args.jsonfile)
+    num_to_convert = len(matrices)
+
+    # Recursively find all the emmebanks in the root folder
+    emmebanks = find_all_emmebanks(args.inputfolder)
+
+    # Check to see if all specified matrices exist in an emmebank somewhere
+    print "Finding matrices in emmebanks:"
+    start = datetime.now()
+    for bank in emmebanks:
+        convert_fullmats(bank, args.outputfile, mats=matrices, dryrun=True)
+
+    if len(matrices)>0:
+        print "\n\n*** %d MATRICES MISSING ***" % len(matrices)
+        for m in sorted(matrices):
+            print "   "+m
+        #sys.exit(2)
+
+    # Convert all matrices we need, from all matrices available
+    start = datetime.now()
+    converted = 0
+    matrices = get_matrix_defs_from_json(args.jsonfile)
+
+    for bank in emmebanks:
+        convert_fullmats(bank, args.outputfile, mats=matrices)
+
+    print "Completed in",datetime.now()-start
+
diff --git a/scripts/summarize/benefit_cost/benefit_cost.py b/scripts/summarize/benefit_cost/miscellaneous_old_summary.py
similarity index 81%
rename from scripts/summarize/benefit_cost/benefit_cost.py
rename to scripts/summarize/benefit_cost/miscellaneous_old_summary.py
index 2c691368..df4a9b30 100644
--- a/scripts/summarize/benefit_cost/benefit_cost.py
+++ b/scripts/summarize/benefit_cost/miscellaneous_old_summary.py
@@ -60,10 +60,13 @@ def auto_own_cost(output_df, max_income):
 
 def nonmotorized_benefits(trips, mode, max_income):
     nonmotorized_trips_dist=  trips.loc[(trips['mode']== mode) & (trips['hhincome']<max_income)]
+
     if mode != 'Transit':
         trips_people = nonmotorized_trips_dist.groupby(['hhno', 'pno_x']).agg({'travtime' :[np.sum]})
     else:
+        nonmotorized_trips_dist['dorp']=pd.to_numeric(nonmotorized_trips_dist['dorp'], errors = coerce)
         trips_people = nonmotorized_trips_dist.groupby(['hhno', 'pno_x']).agg({'dorp' :[np.sum]})
+
     people_times = {'Time': trips_people.mean(), 'People': trips_people.count()}
     return  people_times
 
@@ -86,8 +89,6 @@ def group_vmt_speed(my_project):
 
     for key, value in sound_cast_net_dict.iteritems():
 
-        print 'Getting VMT by Speed bin for time period ' + key
-        
         my_project.change_active_database(key)
         network = my_project.current_scenario.get_network()
 
@@ -102,13 +103,12 @@ def group_vmt_speed(my_project):
              
             if speed > 0 and speed <100:
 
-               speed_dict[speed]['Car'] = speed_dict[speed]['Car'] + (link['@svtl1']+ link['@svtl2'] + link['@svtl3'] + link['@svnt1'] +  link['@svnt2'] + link['@svnt3'] + link['@h2tl1'] + link['@h2tl2'] +
-               link['@h2tl3'] + link['@h2nt1'] + link['@h2nt2'] + link['@h2nt3'] + link['@h3tl1'] +
-               link['@h3tl2'] + link['@h3tl3'] + link['@h3nt1'] + link['@h3nt2'] + link['@h3nt3'])*link['length']
+               speed_dict[speed]['Car'] = speed_dict[speed]['Car'] + (link['@svtl1']+ link['@svtl2'] + link['@svtl3'] + + link['@h2tl1'] + link['@h2tl2'] +
+               link['@h2tl3'] + link['@h3tl1'] +link['@h3tl2'] + link['@h3tl3'] )*link['length']
 
                speed_dict[speed]['Light Truck'] = speed_dict[speed]['Light Truck'] + link['@lttrk'] * link['length']
-               speed_dict[speed]['Medium Truck'] = speed_dict[speed]['Medium Truck'] + link['@mveh'] * link['length']/MED_TRUCK_FACTOR
-               speed_dict[speed]['Heavy Truck'] = speed_dict[speed]['Heavy Truck'] + link['@hveh'] * link['length']/HV_TRUCK_FACTOR
+               speed_dict[speed]['Medium Truck'] = speed_dict[speed]['Medium Truck'] + link['@mveh'] * link['length']
+               speed_dict[speed]['Heavy Truck'] = speed_dict[speed]['Heavy Truck'] + link['@hveh'] * link['length']
 
                     
     return speed_dict
@@ -120,8 +120,6 @@ def group_vmt_class(my_project):
     vmt_func_class= {1 : 0, 3 : 0,  5 : 0, 7 : 0}
     
     for key, value in sound_cast_net_dict.iteritems():
-
-        print 'Getting VMT by Facility Type for Time Period ' + key
         my_project.change_active_database(key)
         network = my_project.current_scenario.get_network()
 
@@ -135,8 +133,12 @@ def group_vmt_class(my_project):
     return vmt_fc_df
 
 
-def emissions_calc(vmt_speed_dict):
-    pollutant_rates = pd.DataFrame.from_csv(pollutant_file)
+def emissions_calc(vmt_speed_dict, model_year):
+    if model_year == 2040:
+       pollutant_rates = pd.DataFrame.from_csv(pollutant_file_2040)
+    else:
+        pollutant_rates = pd.DataFrame.from_csv(pollutant_file)
+
     vehicle_type_tons = {'Car': {'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0},
                          'Light Truck':{'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0},
                          'Medium Truck': {'Carbon Dioxide': 0, 'Carbon Monoxide': 0, 'Nitrogen Oxide':0 , 'Volatile Organic Compound': 0, 'Particulate Matter': 0},
@@ -180,6 +182,37 @@ def injury_calc(injury_file, my_project):
     injury_rates_vmt['Fatality Cost'] = injury_rates_vmt['Fatality Rate']*injury_rates_vmt['VMT']*FATALITY_COST
     return injury_rates_vmt
 
+def truck_costs(my_project):
+   
+    truck_dict = {'Truck Medium VHT': 0, 'Truck Heavy VHT': 0, 'Truck Medium VMT': 0, 'Truck Heavy VMT': 0, 'Truck Medium Tolls': 0, 'Truck Heavy Tolls': 0}
+
+    for key, value in sound_cast_net_dict.iteritems():
+        my_project.change_active_database(key)
+        #these are already in network_summary, but just repeating to make this a standalone script
+        my_project.network_calculator("link_calculation", result = None, expression = '@mveh*timau/60')
+        truck_dict['Truck Medium VHT']+= my_project.network_calc_result['sum']
+        my_project.network_calculator("link_calculation", result = None, expression = '@hveh*timau/60')
+        truck_dict['Truck Heavy VHT']+= my_project.network_calc_result['sum']
+
+        #get truck vmt to calculate truck operating costs
+        my_project.network_calculator("link_calculation", result = None, expression = '@mveh*length')
+        truck_dict['Truck Medium VMT']+= my_project.network_calc_result['sum']
+        my_project.network_calculator("link_calculation", result = None, expression = '@hveh*length')
+        truck_dict['Truck Heavy VMT']+= my_project.network_calc_result['sum']
+
+        #truck toll costs
+        my_project.network_calculator("link_calculation", result = None, expression = '@mveh*@trkc2/100')
+        truck_dict['Truck Medium Tolls']+= my_project.network_calc_result['sum']
+        my_project.network_calculator("link_calculation", result = None, expression = '@hveh*@trkc3/100')
+        truck_dict['Truck Heavy Tolls']+= my_project.network_calc_result['sum']
+
+    return pd.DataFrame(truck_dict, index = [0])
+
+def write_results(bc_writer, start_row, REPORT_ROW_GAP, output_dfs):
+    for df in output_dfs:
+        df.to_excel(excel_writer = bc_writer, sheet_name =  'Raw Costs', na_rep = 0, startrow = start_row)
+        start_row = start_row + REPORT_ROW_GAP
+
 
 def main():
 
@@ -223,12 +256,6 @@ def main():
 
     bc_outputs_by_mode['Total Household Time Impedances'] = impedance_inc_mode(trips, MAX_INC,"travtime") / MINS_HR
     bc_outputs_by_mode[' Household Low-Income Time'] = impedance_inc_mode(trips, LOW_INC_MAX, "travtime") / MINS_HR
-
-
-
-   # Also break this down for Work only
-
-   # per Trips
  
     # Calculate Reliability Impacts
     # Reliability is already included in travel times in our vdfs - may want to
@@ -265,7 +292,6 @@ def main():
     # this. I think the problem is capturing the short walk trips and we should be able
     # to get this with the 2014 dataset.
 
-    print bc_costs
     walk_times = nonmotorized_benefits(trips, 'Walk', MAX_INC)
     bike_times = nonmotorized_benefits(trips, 'Bike', MAX_INC)
     transit_walk_times = nonmotorized_benefits(trips, 'Transit', MAX_INC)
@@ -289,54 +315,20 @@ def main():
     emme_project = EmmeProject(project)
     # Calculate link level benefits
     vmt_speed_dict = group_vmt_speed(emme_project)
-    df_emissions = emissions_calc(vmt_speed_dict)
+    df_emissions = emissions_calc(vmt_speed_dict, model_year)
     noise_vmt = noise_calc(vmt_speed_dict)
     injury_rates_vmt = injury_calc(injury_file, emme_project)
-   
-    # measures to add annual vehicle hours of delay
-
-    # Average Distance and Travel Time to Work from Home Location
-
-    # Aggregate Logsum based Time measure
-
-    # Optionally calculate Project Costs, operating and maintenance cost
-
-    # Outputs and Visualization
+    truck_outputs = truck_costs(emme_project)
 
-  
+    # Write it out
+    output_dfs = [pd.DataFrame(bc_people, index = [0]), pd.DataFrame(bc_outputs_by_mode), mode_users(trips, MAX_INC),mode_users(trips, LOW_INC_MAX),
+                 pd.DataFrame(bc_costs.items(), columns= ['Measure', 'Value']).sort_index(by=['Measure']),
+                 pd.DataFrame(bc_health_outputs.items(), columns= ['Measure', 'Value']),
+                 df_emissions, noise_vmt, injury_rates_vmt, truck_outputs]
 
     bc_writer = pd.ExcelWriter(bc_outputs_file, engine = 'xlsxwriter')
-    START_ROW = 1
-
-    pd.DataFrame(bc_people, index = [0]).to_excel(excel_writer = bc_writer, sheet_name =  'Raw Costs', na_rep = 0, startrow = START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-
-    pd.DataFrame(bc_outputs_by_mode).to_excel(excel_writer = bc_writer, sheet_name =  'Raw Costs', na_rep = 0, startrow = START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-
-    mode_users(trips, MAX_INC).to_excel(excel_writer = bc_writer, sheet_name =  'Raw Costs', na_rep = 0, startrow = START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-    mode_users(trips, LOW_INC_MAX).to_excel(excel_writer = bc_writer, sheet_name =  'Raw Costs', na_rep = 0, startrow = START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-    pd.DataFrame(bc_costs.items(), columns= ['Measure', 'Value']).sort_index(by=['Measure']).to_excel(excel_writer = bc_writer, sheet_name ='Raw Costs', na_rep = 0, startrow = START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-    pd.DataFrame(bc_health_outputs.items(), columns= ['Measure', 'Value']).sort_index(by=['Measure']).to_excel(excel_writer = bc_writer, sheet_name ='Raw Costs', na_rep = 0, startrow = START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-    df_emissions.to_excel(excel_writer = bc_writer, sheet_name = 'Raw Costs', na_rep = 0, startrow =START_ROW)
-    START_ROW = START_ROW + REPORT_ROW_GAP
-
-    noise_vmt.to_excel(excel_writer = bc_writer, sheet_name = 'Raw Costs', na_rep = 0, startrow =START_ROW)
-
-    START_ROW = START_ROW + REPORT_ROW_GAP
-    injury_rates_vmt.to_excel(excel_writer = bc_writer, sheet_name = 'Raw Costs', na_rep = 0, startrow =START_ROW)
-
-
+    start_row = 1
+    write_results(bc_writer, start_row, REPORT_ROW_GAP, output_dfs)
     bc_writer.close()
 
 if __name__ == "__main__":
diff --git a/scripts/summarize/benefit_cost/special_needs_zones.csv b/scripts/summarize/benefit_cost/special_needs_zones.csv
new file mode 100644
index 00000000..c4fe55e8
--- /dev/null
+++ b/scripts/summarize/benefit_cost/special_needs_zones.csv
@@ -0,0 +1,3701 @@
+TAZ,Low Income,Minority
+1,0,0
+2,1,1
+3,1,1
+4,1,1
+5,1,0
+6,1,0
+7,1,1
+8,1,1
+9,1,0
+10,1,0
+11,0,1
+12,0,1
+13,0,1
+14,0,1
+15,0,1
+16,0,1
+17,1,1
+18,1,1
+19,1,1
+20,1,1
+21,0,0
+22,0,0
+23,1,1
+24,1,1
+25,1,1
+26,1,1
+27,1,1
+28,1,1
+29,1,1
+30,1,1
+31,1,1
+32,0,0
+33,0,0
+34,0,0
+35,0,0
+36,0,0
+37,0,0
+38,0,0
+39,0,0
+40,0,0
+41,0,0
+42,0,0
+43,0,0
+44,1,1
+45,1,1
+46,1,1
+47,1,1
+48,1,1
+49,1,1
+50,0,0
+51,0,0
+52,1,1
+53,1,1
+54,0,0
+55,0,0
+56,0,0
+57,0,0
+58,0,0
+59,1,1
+60,1,1
+61,0,0
+62,0,0
+63,0,0
+64,0,0
+65,1,1
+66,0,0
+67,1,1
+68,1,0
+69,1,0.5
+70,1,0
+71,1,1
+72,1,0
+73,1,1
+74,1,1
+75,1,1
+76,1,1
+77,1,1
+78,1,1
+79,0,0
+80,0,0
+81,0,0
+82,0,0
+83,0,0
+84,0,0
+85,0,0
+86,0,0
+87,0,0
+88,0,0
+89,0,0
+90,0,0
+91,0,0
+92,1,0
+93,1,0
+94,1,0
+95,1,0
+96,1,0
+97,0,0
+98,0,0
+99,0,0
+100,0,0
+101,0,0
+102,0,0
+103,0,0
+104,0,0
+105,0,0
+106,0,0
+107,0,0
+108,0,0
+109,0,0
+110,0,0
+111,0,0
+112,0,0
+113,0,0
+114,0,0
+115,0,0
+116,0,0
+117,0,0
+118,0,0
+119,0,0
+120,0,0
+121,0,0
+122,0,0
+123,0,0
+124,0,0
+125,0,0
+126,0,0
+127,0,0
+128,0,0
+129,0,0
+130,0,0
+131,0,0
+132,0,0
+133,0,0
+134,0,0
+135,0,0
+136,0,0
+137,0,0
+138,0,0
+139,0,0
+140,0,0
+141,0,0
+142,0,0
+143,0,0
+144,0,0
+145,0,0
+146,0,0
+147,0,0
+148,0,0
+149,0,0
+150,0,0
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diff --git a/scripts/summarize/calibration/SCsummary.py b/scripts/summarize/calibration/SCsummary.py
index c5fba931..49806f03 100644
--- a/scripts/summarize/calibration/SCsummary.py
+++ b/scripts/summarize/calibration/SCsummary.py
@@ -12,8 +12,8 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import os
-import sys
+import os, sys
+sys.path.append(os.getcwd())
 sys.path.append(os.path.join(os.getcwd(),"scripts\summarize"))
 import numpy as np
 import pandas as pd
@@ -25,7 +25,7 @@
 from input_configuration import *
 from summary_functions import *
 from calibration_summary_configuration import *
-
+pd.options.mode.chained_assignment = None  # mute chained assignment warnings
 
 
 def DistrictSummary(data1, data2, name1, name2, location, districtfile):
@@ -1390,13 +1390,16 @@ def LongTerm(data1, data2, name1, name2, location, districtfile):
 
     #Auto Ownership
     ao1 = data1['Household'][['hhvehs', 'hhexpfac']].groupby('hhvehs').sum()['hhexpfac'] / data1['Household']['hhexpfac'].sum() * 100
-    for i in range(5, len(ao1)): #This loop groups households that own 4 or more cars into "4+"
-        ao1[4] = ao1[4] + ao1[i]
-        ao1 = ao1.drop([i])
+    # Group households of 4+ vehicles together
+    for i in ao1.index.values:
+        if i > 4:
+            ao1[4] = ao1[4] + ao1[i]
+            ao1 = ao1.drop([i])
     ao2 = data2['Household'][['hhvehs', 'hhexpfac']].groupby('hhvehs').sum()['hhexpfac'] / data2['Household']['hhexpfac'].sum() * 100
-    for i in range(5, len(ao2)):
-        ao2[4] = ao2[4] + ao2[i]
-        ao2 = ao2.drop([i])
+    for i in ao2.index.values:
+        if i > 4: 
+            ao2[4] = ao2[4] + ao2[i]
+            ao2 = ao2.drop([i])
     ao = pd.DataFrame()
     ao['% of Households (' + name1 + ')'] = ao1
     ao['% of Households (' + name2 + ')'] = ao2
diff --git a/scripts/summarize/calibration/calibration_summary_configuration.py b/scripts/summarize/calibration/calibration_summary_configuration.py
index 37cc5f53..14f5fd9d 100644
--- a/scripts/summarize/calibration/calibration_summary_configuration.py
+++ b/scripts/summarize/calibration/calibration_summary_configuration.py
@@ -1,5 +1,5 @@
 # Calibration Summary Configuration
-h5_results_file = 'outputs/daysim_outputs.h5'
+h5_results_file = 'outputs/daysim/daysim_outputs.h5'
 h5_results_name = 'DaysimOutputs'
 h5_comparison_file = 'scripts/summarize/inputs/calibration/survey.h5'
 h5_comparison_name = 'Survey'
@@ -9,5 +9,5 @@
 LEHD_work_flows = 'scripts/summarize/inputs/calibration/HFAZ_WFAZ_LEHD2014.xlsx'
 
 acs_data = 'scripts/summarize/inputs/calibration/ACS_2014.xlsx'
-report_output_location = 'outputs'
+report_output_location = 'outputs/daysim'
 bc_outputs_file = 'outputs/BenefitCost.xlsx'
\ No newline at end of file
diff --git a/scripts/summarize/calibration/get_skims.py b/scripts/summarize/calibration/get_skims.py
index 6c192135..7cc19050 100644
--- a/scripts/summarize/calibration/get_skims.py
+++ b/scripts/summarize/calibration/get_skims.py
@@ -16,6 +16,7 @@
 import pandas as pd
 import numpy as np
 import sys, os
+sys.path.append(os.getcwd())
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
 
 def from_dict(skim, file_location, tod, taz_map): #Skim matrix for specified locations from a dictionary mapping TAZ's to place names
diff --git a/scripts/summarize/calibration/summary_functions.py b/scripts/summarize/calibration/summary_functions.py
index 926314e5..2adbbff7 100644
--- a/scripts/summarize/calibration/summary_functions.py
+++ b/scripts/summarize/calibration/summary_functions.py
@@ -15,8 +15,8 @@
 import pandas as pd
 import numpy as np
 import math
-import sys
-import os
+import sys, os
+sys.path.append(os.getcwd())
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
 
 #Computation functions
diff --git a/scripts/summarize/exploratory/low_income_summary.ipynb b/scripts/summarize/exploratory/low_income_summary.ipynb
new file mode 100644
index 00000000..62dd0c88
--- /dev/null
+++ b/scripts/summarize/exploratory/low_income_summary.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xlsxwriter\n",
+    "import time\n",
+    "import h5toDF\n",
+    "import xlautofit\n",
+    "import math\n",
+    "from summary_functions import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#################################### WHERE ARE YOU RUNNING? ####################################\n",
+    "model_dir = 'Z:/Stefan/Soundcast_feb_twg/'\n",
+    "\n",
+    "\n",
+    "### OTHER PATHS. FOR A TYPICAL RUN, YOU DON'T HAVE TO CHANGE THESE ######################################\n",
+    "h5_results_file = 'outputs/daysim_outputs.h5'\n",
+    "h5_results_name = 'DaysimOutputs'\n",
+    "h5_comparison_file = 'scripts/summarize/survey.h5'\n",
+    "h5_comparison_name = 'Survey'\n",
+    "guidefile = 'scripts/summarize/inputs/calibration/CatVarDict.xlsx'\n",
+    "districtfile = 'scripts/summarize/inputs/calibration/TAZ_TAD_County.csv'\n",
+    "report_output_location = 'outputs'\n",
+    "\n",
+    "parcel_decay_file = 'inputs/buffered_parcels.dat'\n",
+    "\n",
+    "\n",
+    "\n",
+    "h5_results_file = model_dir + h5_results_file\n",
+    "h5_comparison_file =  model_dir + h5_comparison_file\n",
+    "guidefile = model_dir + guidefile\n",
+    "districtfile = model_dir + districtfile\n",
+    "report_output_location = 'outputs'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---Begin DaysimOutputs conversion---\n",
+      "Guide import complete\n",
+      "Guide converted to dictionary in 0.0 seconds\n",
+      "Household File import/recode complete in 1.9 seconds\n",
+      "HouseholdDay File import/recode complete in 0.7 seconds\n",
+      "Person File import/recode complete in 9.9 seconds\n",
+      "PersonDay File import/recode complete in 4.2 seconds\n",
+      "Tour File import/recode complete in 9.6 seconds\n",
+      "Trip File import/recode complete in 22.2 seconds\n",
+      "---DaysimOutputs import/recode complete in 48.5 seconds---\n"
+     ]
+    }
+   ],
+   "source": [
+    "#READ IN YOUR DATA\n",
+    "data1 = h5toDF.convert(h5_results_file,guidefile,h5_results_name)\n",
+    "zone_district = pd.DataFrame.from_csv(districtfile, index_col = None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trip_variables = ['otaz', 'dtaz', 'travtime', 'travcost', 'travdist', 'pno', 'mode', 'tour_id', 'opcl', 'dpcl', 'dorp']\n",
+    "hh_variables = ['hhno', 'hhincome', 'hhvehs', 'hhtaz']\n",
+    "person_variables = ['hhno', 'pno', 'pagey', 'pgend', 'id']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_variables_trips_model(output_df,trip_variables, hh_variables, person_variables):\n",
+    "    trip_data = output_df['Trip'][trip_variables]\n",
+    "    hh_data = output_df['Household'][hh_variables]\n",
+    "    person_data = output_df['Person'][person_variables]\n",
+    "    tour_data = output_df['Tour'][['hhno', 'pno', 'id']]\n",
+    "    tour_data.rename(columns = {'id': 'tour_id'}, inplace = True)\n",
+    "\n",
+    "    merge_hh_person = pd.merge(hh_data, person_data, 'inner', on = 'hhno')\n",
+    "    merge_hh_person.reset_index()\n",
+    "    tour_data.reset_index()\n",
+    "    merge_hh_tour = pd.merge(merge_hh_person, tour_data, 'inner', on =('hhno', 'pno'))\n",
+    "    merge_trip_hh = pd.merge(merge_hh_tour, trip_data, 'outer', on= 'tour_id')\n",
+    "    return merge_trip_hh  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trips_model = get_variables_trips_model(data1, trip_variables, hh_variables, person_variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_trips = trips_model.loc[(trips_model['hhincome']<25000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_length = low_inc_trips['travdist'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.524602349104249"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "low_inc_length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_driver_trips = low_inc_trips.loc[(low_inc_trips['dorp']=='Driver')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_vmt = low_inc_driver_trips['travdist'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4625490.466874632"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "low_inc_vmt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def get_variables_persons(output_df, hh_variables, person_variables):\n",
+    "    \n",
+    "    person_variables = ['hhno', 'pno', 'pagey', 'pgend', 'pwaudist', 'pwtyp']\n",
+    "    hh_variables = ['hhno', 'hhincome', 'hhvehs', 'hhtaz', 'hhexpfac']\n",
+    "    hh_data = output_df['Household'][hh_variables]\n",
+    "    person_data = output_df['Person'][person_variables]\n",
+    "\n",
+    "    merge_hh_person = pd.merge(hh_data, person_data, 'inner', on = 'hhno')\n",
+    "    merge_hh_person.reset_index()\n",
+    "\n",
+    "    return merge_hh_person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "people_model = get_variables_persons(data1, hh_variables, person_variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_people = people_model.loc[(people_model['hhincome']<25000)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_ct = low_inc_people.count()['hhno']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.506198492434368"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "low_inc_vmt/low_inc_ct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_wrkr = low_inc_people.loc[(low_inc_people['pwtyp']!= 'Not a Paid Worker')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.787918742353263"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "low_inc_wrkr['pwaudist'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trips_by_mode =low_inc_trips.groupby('mode').count()['hhno']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "l_inc_modes = trips_by_mode/low_inc_trips.count()['hhno']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "l_inc_modes.to_clipboard()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/scripts/summarize/exploratory/mode_choice_centers.ipynb b/scripts/summarize/exploratory/mode_choice_centers.ipynb
new file mode 100644
index 00000000..589a553a
--- /dev/null
+++ b/scripts/summarize/exploratory/mode_choice_centers.ipynb
@@ -0,0 +1,2068 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xlsxwriter\n",
+    "import time\n",
+    "import h5toDF\n",
+    "import xlautofit\n",
+    "import math\n",
+    "from summary_functions import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#################################### WHERE ARE YOU RUNNING? ####################################\n",
+    "model_dir = 'Z:/Stefan/Soundcast_feb_twg/'\n",
+    "\n",
+    "\n",
+    "### OTHER PATHS. FOR A TYPICAL RUN, YOU DON'T HAVE TO CHANGE THESE ######################################\n",
+    "h5_results_file = 'outputs/daysim_outputs.h5'\n",
+    "h5_results_name = 'DaysimOutputs'\n",
+    "h5_comparison_file = 'scripts/summarize/survey.h5'\n",
+    "h5_comparison_name = 'Survey'\n",
+    "guidefile = 'scripts/summarize/inputs/calibration/CatVarDict.xlsx'\n",
+    "districtfile = 'scripts/summarize/inputs/calibration/TAZ_TAD_County.csv'\n",
+    "report_output_location = 'outputs'\n",
+    "\n",
+    "parcel_decay_file = 'inputs/buffered_parcels.dat'\n",
+    "\n",
+    "\n",
+    "\n",
+    "h5_results_file = model_dir + h5_results_file\n",
+    "h5_comparison_file =  model_dir + h5_comparison_file\n",
+    "guidefile = model_dir + guidefile\n",
+    "districtfile = model_dir + districtfile\n",
+    "report_output_location = 'outputs'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---Begin DaysimOutputs conversion---\n",
+      "Guide import complete\n",
+      "Guide converted to dictionary in 0.0 seconds\n",
+      "Household File import/recode complete in 1.4 seconds\n",
+      "HouseholdDay File import/recode complete in 0.5 seconds\n",
+      "Person File import/recode complete in 5.1 seconds\n",
+      "PersonDay File import/recode complete in 2.1 seconds\n",
+      "Tour File import/recode complete in 7.0 seconds\n",
+      "Trip File import/recode complete in 20.1 seconds\n",
+      "---DaysimOutputs import/recode complete in 36.1 seconds---\n",
+      "---Begin Survey conversion---\n",
+      "Guide import complete\n",
+      "Guide converted to dictionary in 0.0 seconds\n",
+      "Household File import/recode complete in 0.0 seconds\n",
+      "HouseholdDay File import/recode complete in 0.0 seconds\n",
+      "Person File import/recode complete in 0.1 seconds\n",
+      "PersonDay File import/recode complete in 0.0 seconds\n",
+      "WARNING: Negative Travel Time Present!\n",
+      "Tour File import/recode complete in 0.1 seconds\n",
+      "WARNING: Negative Travel Distance Present!\n",
+      "WARNING: Negative Travel Time Present!\n",
+      "Trip File import/recode complete in 0.1 seconds\n",
+      "---Survey import/recode complete in 0.4 seconds---\n"
+     ]
+    }
+   ],
+   "source": [
+    "#READ IN YOUR DATA\n",
+    "data1 = h5toDF.convert(h5_results_file,guidefile,h5_results_name)\n",
+    "data2 = h5toDF.convert(h5_comparison_file,guidefile, h5_comparison_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trip_variables = ['otaz', 'dtaz', 'travtime', 'travcost', 'travdist', 'pno', 'mode', 'tour_id', 'opcl', 'dpcl', 'dorp', 'dpurp', 'opurp', 'trexpfac']\n",
+    "hh_variables = ['hhno', 'hhincome', 'hhvehs', 'hhtaz', 'hhexpfac']\n",
+    "person_variables = ['hhno', 'pno', 'pagey', 'pgend', 'id']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_variables_trips_model(output_df,trip_variables, hh_variables, person_variables):\n",
+    "    trip_data = output_df['Trip'][trip_variables]\n",
+    "    hh_data = output_df['Household'][hh_variables]\n",
+    "    person_data = output_df['Person'][person_variables]\n",
+    "    tour_data = output_df['Tour'][['hhno', 'pno', 'id']]\n",
+    "    tour_data.rename(columns = {'id': 'tour_id'}, inplace = True)\n",
+    "\n",
+    "    merge_hh_person = pd.merge(hh_data, person_data, 'inner', on = 'hhno')\n",
+    "    merge_hh_person.reset_index()\n",
+    "    tour_data.reset_index()\n",
+    "    merge_hh_tour = pd.merge(merge_hh_person, tour_data, 'inner', on =('hhno', 'pno'))\n",
+    "    merge_trip_hh = pd.merge(merge_hh_tour, trip_data, 'outer', on= 'tour_id')\n",
+    "    return merge_trip_hh  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_variables_trips_survey(output_df,trip_variables, hh_variables):\n",
+    "    trip_data = output_df['Trip'][trip_variables]\n",
+    "    hh_data = output_df['Household'][hh_variables]\n",
+    "\n",
+    "    merge_trip_hh = pd.merge(trip_data, hh_data, on = 'hhno')\n",
+    "    return merge_trip_hh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trips_model = get_variables_trips_model(data1, trip_variables, hh_variables, person_variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trips_survey = get_variables_trips_model(data2, trip_variables, hh_variables, person_variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rgc_file = pd.read_csv(model_dir+'scripts/summarize/inputs/rgc_taz.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>taz</th>\n",
+       "      <th>center</th>\n",
+       "      <th>lat_1</th>\n",
+       "      <th>lon_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3317</td>\n",
+       "      <td>Puyallup South Hill</td>\n",
+       "      <td>47.156235</td>\n",
+       "      <td>-122.286753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3051</td>\n",
+       "      <td>Lakewood</td>\n",
+       "      <td>47.161986</td>\n",
+       "      <td>-122.510059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3314</td>\n",
+       "      <td>Puyallup South Hill</td>\n",
+       "      <td>47.156235</td>\n",
+       "      <td>-122.286753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3315</td>\n",
+       "      <td>Puyallup South Hill</td>\n",
+       "      <td>47.156235</td>\n",
+       "      <td>-122.286753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3046</td>\n",
+       "      <td>Lakewood</td>\n",
+       "      <td>47.161986</td>\n",
+       "      <td>-122.510059</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    taz               center      lat_1       lon_1\n",
+       "0  3317  Puyallup South Hill  47.156235 -122.286753\n",
+       "1  3051             Lakewood  47.161986 -122.510059\n",
+       "2  3314  Puyallup South Hill  47.156235 -122.286753\n",
+       "3  3315  Puyallup South Hill  47.156235 -122.286753\n",
+       "4  3046             Lakewood  47.161986 -122.510059"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rgc_file.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trips_rgc = pd.merge(trips_model, rgc_file, left_on = 'dtaz', right_on='taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trips_rgc_mode =trips_rgc.groupby(['mode', 'center']).count().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mode</th>\n",
+       "      <th>center</th>\n",
+       "      <th>hhno</th>\n",
+       "      <th>hhincome</th>\n",
+       "      <th>hhvehs</th>\n",
+       "      <th>hhtaz</th>\n",
+       "      <th>hhexpfac</th>\n",
+       "      <th>pno_x</th>\n",
+       "      <th>pagey</th>\n",
+       "      <th>pgend</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pno_y</th>\n",
+       "      <th>opcl</th>\n",
+       "      <th>dpcl</th>\n",
+       "      <th>dorp</th>\n",
+       "      <th>dpurp</th>\n",
+       "      <th>opurp</th>\n",
+       "      <th>trexpfac</th>\n",
+       "      <th>taz</th>\n",
+       "      <th>lat_1</th>\n",
+       "      <th>lon_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Auburn</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>...</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "      <td>184</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "      <td>1797</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Bothell Canyon Park</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>...</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "      <td>405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Bremerton</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>...</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "      <td>308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Burien</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>...</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "      <td>361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>...</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "      <td>647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Federal Way</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>...</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Issaquah</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>...</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Kent</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>...</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "      <td>521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Kirkland Totem Lake</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>...</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "      <td>868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Lakewood</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>...</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "      <td>435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Lynnwood</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>...</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "      <td>508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Puyallup Downtown</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>...</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "      <td>203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Puyallup South Hill</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "      <td>500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Redmond Downtown</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>...</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "      <td>816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Redmond-Overlake</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>...</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "      <td>580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Renton</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>...</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "      <td>768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>SeaTac</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>...</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "      <td>453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Seattle Downtown</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "      <td>10712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Seattle First Hill/Capitol Hill</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "      <td>7140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Seattle Northgate</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>...</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "      <td>861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Seattle South Lake Union</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>2827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Seattle University Community</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "      <td>3271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Seattle Uptown</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>1406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Silverdale</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>...</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "      <td>595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Tacoma Downtown</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "      <td>3097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Tacoma Mall</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>...</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "      <td>780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>Tukwila</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>...</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "      <td>583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Bike</td>\n",
+       "      <td>University Place</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>...</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "      <td>314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>HOV2</td>\n",
+       "      <td>Auburn</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "      <td>2934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>Transit</td>\n",
+       "      <td>University Place</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>...</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "      <td>201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Auburn</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>1944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>...</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "      <td>41021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Bothell Canyon Park</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "      <td>3018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Bremerton</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "      <td>3833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Burien</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "      <td>3407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "      <td>10444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Federal Way</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>1169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Issaquah</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "      <td>2549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Kent</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "      <td>4463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Kirkland Totem Lake</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "      <td>8750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Lakewood</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "      <td>3745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Lynnwood</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "      <td>4615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Puyallup Downtown</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "      <td>2118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Puyallup South Hill</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "      <td>3841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Redmond Downtown</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "      <td>10195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Redmond-Overlake</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>8096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Renton</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "      <td>7943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>SeaTac</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "      <td>3609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Seattle Downtown</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>...</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "      <td>165474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Seattle First Hill/Capitol Hill</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>...</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "      <td>94317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Seattle Northgate</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "      <td>7516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Seattle South Lake Union</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>...</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "      <td>35288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Seattle University Community</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "      <td>40369</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Seattle Uptown</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "      <td>14937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Silverdale</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "      <td>7905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Tacoma Downtown</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "      <td>30864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Tacoma Mall</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "      <td>6752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>Tukwila</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "      <td>6289</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>Walk</td>\n",
+       "      <td>University Place</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "      <td>2663</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>203 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        mode                           center    hhno  hhincome  hhvehs  \\\n",
+       "0       Bike                           Auburn     184       184     184   \n",
+       "1       Bike                         Bellevue    1797      1797    1797   \n",
+       "2       Bike              Bothell Canyon Park     405       405     405   \n",
+       "3       Bike                        Bremerton     308       308     308   \n",
+       "4       Bike                           Burien     361       361     361   \n",
+       "5       Bike                          Everett     647       647     647   \n",
+       "6       Bike                      Federal Way      70        70      70   \n",
+       "7       Bike                         Issaquah     168       168     168   \n",
+       "8       Bike                             Kent     521       521     521   \n",
+       "9       Bike              Kirkland Totem Lake     868       868     868   \n",
+       "10      Bike                         Lakewood     435       435     435   \n",
+       "11      Bike                         Lynnwood     508       508     508   \n",
+       "12      Bike                Puyallup Downtown     203       203     203   \n",
+       "13      Bike              Puyallup South Hill     500       500     500   \n",
+       "14      Bike                 Redmond Downtown     816       816     816   \n",
+       "15      Bike                 Redmond-Overlake     580       580     580   \n",
+       "16      Bike                           Renton     768       768     768   \n",
+       "17      Bike                           SeaTac     453       453     453   \n",
+       "18      Bike                 Seattle Downtown   10712     10712   10712   \n",
+       "19      Bike  Seattle First Hill/Capitol Hill    7140      7140    7140   \n",
+       "20      Bike                Seattle Northgate     861       861     861   \n",
+       "21      Bike         Seattle South Lake Union    2827      2827    2827   \n",
+       "22      Bike     Seattle University Community    3271      3271    3271   \n",
+       "23      Bike                   Seattle Uptown    1406      1406    1406   \n",
+       "24      Bike                       Silverdale     595       595     595   \n",
+       "25      Bike                  Tacoma Downtown    3097      3097    3097   \n",
+       "26      Bike                      Tacoma Mall     780       780     780   \n",
+       "27      Bike                          Tukwila     583       583     583   \n",
+       "28      Bike                 University Place     314       314     314   \n",
+       "29      HOV2                           Auburn    2934      2934    2934   \n",
+       "..       ...                              ...     ...       ...     ...   \n",
+       "173  Transit                 University Place     201       201     201   \n",
+       "174     Walk                           Auburn    1944      1944    1944   \n",
+       "175     Walk                         Bellevue   41021     41021   41021   \n",
+       "176     Walk              Bothell Canyon Park    3018      3018    3018   \n",
+       "177     Walk                        Bremerton    3833      3833    3833   \n",
+       "178     Walk                           Burien    3407      3407    3407   \n",
+       "179     Walk                          Everett   10444     10444   10444   \n",
+       "180     Walk                      Federal Way    1169      1169    1169   \n",
+       "181     Walk                         Issaquah    2549      2549    2549   \n",
+       "182     Walk                             Kent    4463      4463    4463   \n",
+       "183     Walk              Kirkland Totem Lake    8750      8750    8750   \n",
+       "184     Walk                         Lakewood    3745      3745    3745   \n",
+       "185     Walk                         Lynnwood    4615      4615    4615   \n",
+       "186     Walk                Puyallup Downtown    2118      2118    2118   \n",
+       "187     Walk              Puyallup South Hill    3841      3841    3841   \n",
+       "188     Walk                 Redmond Downtown   10195     10195   10195   \n",
+       "189     Walk                 Redmond-Overlake    8096      8096    8096   \n",
+       "190     Walk                           Renton    7943      7943    7943   \n",
+       "191     Walk                           SeaTac    3609      3609    3609   \n",
+       "192     Walk                 Seattle Downtown  165474    165474  165474   \n",
+       "193     Walk  Seattle First Hill/Capitol Hill   94317     94317   94317   \n",
+       "194     Walk                Seattle Northgate    7516      7516    7516   \n",
+       "195     Walk         Seattle South Lake Union   35288     35288   35288   \n",
+       "196     Walk     Seattle University Community   40369     40369   40369   \n",
+       "197     Walk                   Seattle Uptown   14937     14937   14937   \n",
+       "198     Walk                       Silverdale    7905      7905    7905   \n",
+       "199     Walk                  Tacoma Downtown   30864     30864   30864   \n",
+       "200     Walk                      Tacoma Mall    6752      6752    6752   \n",
+       "201     Walk                          Tukwila    6289      6289    6289   \n",
+       "202     Walk                 University Place    2663      2663    2663   \n",
+       "\n",
+       "      hhtaz  hhexpfac   pno_x   pagey   pgend   ...     pno_y    opcl    dpcl  \\\n",
+       "0       184       184     184     184     184   ...       184     184     184   \n",
+       "1      1797      1797    1797    1797    1797   ...      1797    1797    1797   \n",
+       "2       405       405     405     405     405   ...       405     405     405   \n",
+       "3       308       308     308     308     308   ...       308     308     308   \n",
+       "4       361       361     361     361     361   ...       361     361     361   \n",
+       "5       647       647     647     647     647   ...       647     647     647   \n",
+       "6        70        70      70      70      70   ...        70      70      70   \n",
+       "7       168       168     168     168     168   ...       168     168     168   \n",
+       "8       521       521     521     521     521   ...       521     521     521   \n",
+       "9       868       868     868     868     868   ...       868     868     868   \n",
+       "10      435       435     435     435     435   ...       435     435     435   \n",
+       "11      508       508     508     508     508   ...       508     508     508   \n",
+       "12      203       203     203     203     203   ...       203     203     203   \n",
+       "13      500       500     500     500     500   ...       500     500     500   \n",
+       "14      816       816     816     816     816   ...       816     816     816   \n",
+       "15      580       580     580     580     580   ...       580     580     580   \n",
+       "16      768       768     768     768     768   ...       768     768     768   \n",
+       "17      453       453     453     453     453   ...       453     453     453   \n",
+       "18    10712     10712   10712   10712   10712   ...     10712   10712   10712   \n",
+       "19     7140      7140    7140    7140    7140   ...      7140    7140    7140   \n",
+       "20      861       861     861     861     861   ...       861     861     861   \n",
+       "21     2827      2827    2827    2827    2827   ...      2827    2827    2827   \n",
+       "22     3271      3271    3271    3271    3271   ...      3271    3271    3271   \n",
+       "23     1406      1406    1406    1406    1406   ...      1406    1406    1406   \n",
+       "24      595       595     595     595     595   ...       595     595     595   \n",
+       "25     3097      3097    3097    3097    3097   ...      3097    3097    3097   \n",
+       "26      780       780     780     780     780   ...       780     780     780   \n",
+       "27      583       583     583     583     583   ...       583     583     583   \n",
+       "28      314       314     314     314     314   ...       314     314     314   \n",
+       "29     2934      2934    2934    2934    2934   ...      2934    2934    2934   \n",
+       "..      ...       ...     ...     ...     ...   ...       ...     ...     ...   \n",
+       "173     201       201     201     201     201   ...       201     201     201   \n",
+       "174    1944      1944    1944    1944    1944   ...      1944    1944    1944   \n",
+       "175   41021     41021   41021   41021   41021   ...     41021   41021   41021   \n",
+       "176    3018      3018    3018    3018    3018   ...      3018    3018    3018   \n",
+       "177    3833      3833    3833    3833    3833   ...      3833    3833    3833   \n",
+       "178    3407      3407    3407    3407    3407   ...      3407    3407    3407   \n",
+       "179   10444     10444   10444   10444   10444   ...     10444   10444   10444   \n",
+       "180    1169      1169    1169    1169    1169   ...      1169    1169    1169   \n",
+       "181    2549      2549    2549    2549    2549   ...      2549    2549    2549   \n",
+       "182    4463      4463    4463    4463    4463   ...      4463    4463    4463   \n",
+       "183    8750      8750    8750    8750    8750   ...      8750    8750    8750   \n",
+       "184    3745      3745    3745    3745    3745   ...      3745    3745    3745   \n",
+       "185    4615      4615    4615    4615    4615   ...      4615    4615    4615   \n",
+       "186    2118      2118    2118    2118    2118   ...      2118    2118    2118   \n",
+       "187    3841      3841    3841    3841    3841   ...      3841    3841    3841   \n",
+       "188   10195     10195   10195   10195   10195   ...     10195   10195   10195   \n",
+       "189    8096      8096    8096    8096    8096   ...      8096    8096    8096   \n",
+       "190    7943      7943    7943    7943    7943   ...      7943    7943    7943   \n",
+       "191    3609      3609    3609    3609    3609   ...      3609    3609    3609   \n",
+       "192  165474    165474  165474  165474  165474   ...    165474  165474  165474   \n",
+       "193   94317     94317   94317   94317   94317   ...     94317   94317   94317   \n",
+       "194    7516      7516    7516    7516    7516   ...      7516    7516    7516   \n",
+       "195   35288     35288   35288   35288   35288   ...     35288   35288   35288   \n",
+       "196   40369     40369   40369   40369   40369   ...     40369   40369   40369   \n",
+       "197   14937     14937   14937   14937   14937   ...     14937   14937   14937   \n",
+       "198    7905      7905    7905    7905    7905   ...      7905    7905    7905   \n",
+       "199   30864     30864   30864   30864   30864   ...     30864   30864   30864   \n",
+       "200    6752      6752    6752    6752    6752   ...      6752    6752    6752   \n",
+       "201    6289      6289    6289    6289    6289   ...      6289    6289    6289   \n",
+       "202    2663      2663    2663    2663    2663   ...      2663    2663    2663   \n",
+       "\n",
+       "       dorp   dpurp   opurp  trexpfac     taz   lat_1   lon_1  \n",
+       "0       184     184     184       184     184     184     184  \n",
+       "1      1797    1797    1797      1797    1797    1797    1797  \n",
+       "2       405     405     405       405     405     405     405  \n",
+       "3       308     308     308       308     308     308     308  \n",
+       "4       361     361     361       361     361     361     361  \n",
+       "5       647     647     647       647     647     647     647  \n",
+       "6        70      70      70        70      70      70      70  \n",
+       "7       168     168     168       168     168     168     168  \n",
+       "8       521     521     521       521     521     521     521  \n",
+       "9       868     868     868       868     868     868     868  \n",
+       "10      435     435     435       435     435     435     435  \n",
+       "11      508     508     508       508     508     508     508  \n",
+       "12      203     203     203       203     203     203     203  \n",
+       "13      500     500     500       500     500     500     500  \n",
+       "14      816     816     816       816     816     816     816  \n",
+       "15      580     580     580       580     580     580     580  \n",
+       "16      768     768     768       768     768     768     768  \n",
+       "17      453     453     453       453     453     453     453  \n",
+       "18    10712   10712   10712     10712   10712   10712   10712  \n",
+       "19     7140    7140    7140      7140    7140    7140    7140  \n",
+       "20      861     861     861       861     861     861     861  \n",
+       "21     2827    2827    2827      2827    2827    2827    2827  \n",
+       "22     3271    3271    3271      3271    3271    3271    3271  \n",
+       "23     1406    1406    1406      1406    1406    1406    1406  \n",
+       "24      595     595     595       595     595     595     595  \n",
+       "25     3097    3097    3097      3097    3097    3097    3097  \n",
+       "26      780     780     780       780     780     780     780  \n",
+       "27      583     583     583       583     583     583     583  \n",
+       "28      314     314     314       314     314     314     314  \n",
+       "29     2934    2934    2934      2934    2934    2934    2934  \n",
+       "..      ...     ...     ...       ...     ...     ...     ...  \n",
+       "173     201     201     201       201     201     201     201  \n",
+       "174    1944    1944    1944      1944    1944    1944    1944  \n",
+       "175   41021   41021   41021     41021   41021   41021   41021  \n",
+       "176    3018    3018    3018      3018    3018    3018    3018  \n",
+       "177    3833    3833    3833      3833    3833    3833    3833  \n",
+       "178    3407    3407    3407      3407    3407    3407    3407  \n",
+       "179   10444   10444   10444     10444   10444   10444   10444  \n",
+       "180    1169    1169    1169      1169    1169    1169    1169  \n",
+       "181    2549    2549    2549      2549    2549    2549    2549  \n",
+       "182    4463    4463    4463      4463    4463    4463    4463  \n",
+       "183    8750    8750    8750      8750    8750    8750    8750  \n",
+       "184    3745    3745    3745      3745    3745    3745    3745  \n",
+       "185    4615    4615    4615      4615    4615    4615    4615  \n",
+       "186    2118    2118    2118      2118    2118    2118    2118  \n",
+       "187    3841    3841    3841      3841    3841    3841    3841  \n",
+       "188   10195   10195   10195     10195   10195   10195   10195  \n",
+       "189    8096    8096    8096      8096    8096    8096    8096  \n",
+       "190    7943    7943    7943      7943    7943    7943    7943  \n",
+       "191    3609    3609    3609      3609    3609    3609    3609  \n",
+       "192  165474  165474  165474    165474  165474  165474  165474  \n",
+       "193   94317   94317   94317     94317   94317   94317   94317  \n",
+       "194    7516    7516    7516      7516    7516    7516    7516  \n",
+       "195   35288   35288   35288     35288   35288   35288   35288  \n",
+       "196   40369   40369   40369     40369   40369   40369   40369  \n",
+       "197   14937   14937   14937     14937   14937   14937   14937  \n",
+       "198    7905    7905    7905      7905    7905    7905    7905  \n",
+       "199   30864   30864   30864     30864   30864   30864   30864  \n",
+       "200    6752    6752    6752      6752    6752    6752    6752  \n",
+       "201    6289    6289    6289      6289    6289    6289    6289  \n",
+       "202    2663    2663    2663      2663    2663    2663    2663  \n",
+       "\n",
+       "[203 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trips_rgc_mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trips_survey_rgc =pd.merge(trips_survey, rgc_file, left_on = 'dtaz', right_on='taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trips_survey_rgc_mode =trips_survey_rgc.groupby(['mode', 'center']).sum().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trips_survey_model = pd.merge(trips_rgc_mode, trips_survey_rgc_mode, on = ['mode','center'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trips_survey_model.to_clipboard()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/scripts/summarize/inputs/benefit_cost/pollutant_rates _2040.csv b/scripts/summarize/inputs/benefit_cost/pollutant_rates _2040.csv
new file mode 100644
index 00000000..6a7a858d
--- /dev/null
+++ b/scripts/summarize/inputs/benefit_cost/pollutant_rates _2040.csv	
@@ -0,0 +1,36 @@
+Pollutant,Speed Class,Car,Light Truck,Medium Truck,Heavy Truck,,,,,,,,
+Carbon Dioxide,0,650.924,806.907,3177.06,4205.69,,,,,,,,
+Carbon Dioxide,10,316.021,395.25,1517.57,2409.68,,,,,,,,
+Carbon Dioxide,20,243.863,307.267,1128.46,1974,,,,,,,,
+Carbon Dioxide,30,206.549,259.829,918.232,1611.34,,,,,,,,
+Carbon Dioxide,40,194.51,245.164,800.84,1521.25,,,,,,,,
+Carbon Dioxide,50,189.118,238.502,708.247,1360.17,,,,,,,,
+Carbon Dioxide,60,191.395,242.919,660.89,1479.02,,,,,,,,
+Carbon Monoxide,0,1.497263478,1.581181789,3.74331357,1.39961,,,,,,,,
+Carbon Monoxide,10,1.077655028,1.156100861,2.022171947,0.598088,,,,,,,,
+Carbon Monoxide,20,0.765248763,0.844683007,1.730279147,0.460626,,,,,,,,
+Carbon Monoxide,30,0.717382429,0.793438373,1.405766075,0.376886,,,,,,,,
+Carbon Monoxide,40,0.648879242,0.723212875,1.197959422,0.32982,,,,,,,,
+Carbon Monoxide,50,0.680867531,0.745653539,1.038947303,0.299854,,,,,,,,
+Carbon Monoxide,60,0.805231452,0.895753549,0.879629562,0.264178,,,,,,,,
+Nitrogen Oxide,0,0.035095262,0.075723329,2.618881252,4.70472,,,,,,,,
+Nitrogen Oxide,10,0.035705235,0.059574483,1.234011461,2.37791,,,,,,,,
+Nitrogen Oxide,20,0.033658256,0.053775251,0.88359289,1.81036,,,,,,,,
+Nitrogen Oxide,30,0.037797304,0.055934137,0.750240627,1.4613,,,,,,,,
+Nitrogen Oxide,40,0.042577968,0.060439717,0.682703973,1.31868,,,,,,,,
+Nitrogen Oxide,50,0.049157417,0.066692568,0.637295342,1.17735,,,,,,,,
+Nitrogen Oxide,60,0.062679854,0.084102664,0.588438671,1.25419,,,,,,,,
+Volatile Organic Compound,0,0.017478348,0.014916184,0.39773883,0.293332,,,,,,,,
+Volatile Organic Compound,10,0.010840867,0.010406722,0.16367625,0.10367,,,,,,,,
+Volatile Organic Compound,20,0.00872414,0.008664635,0.100651172,0.0655094,,,,,,,,
+Volatile Organic Compound,30,0.00782058,0.007946732,0.07229175,0.049837,,,,,,,,
+Volatile Organic Compound,40,0.007185131,0.007192521,0.052071131,0.0399706,,,,,,,,
+Volatile Organic Compound,50,0.007371384,0.007361562,0.038195886,0.0337545,,,,,,,,
+Volatile Organic Compound,60,0.008489339,0.00876955,0.030868813,0.0283159,,,,,,,,
+Particulate Matter,0,0.028961518,0.032189625,0.17614939,0.33100641,,,,,,,,
+Particulate Matter,10,0.014550376,0.016458871,0.06850447,0.1255506,,,,,,,,
+Particulate Matter,20,0.011048392,0.012510321,0.03959713,0.08057083,,,,,,,,
+Particulate Matter,30,0.007438433,0.008518489,0.02954346,0.05281127,,,,,,,,
+Particulate Matter,40,0.005269299,0.00608817,0.02149736,0.03717227,,,,,,,,
+Particulate Matter,50,0.003887093,0.004630936,0.01771826,0.02255332,,,,,,,,
+Particulate Matter,60,0.003359975,0.00411675,0.01554132,0.01879773,,,,,,,,
diff --git a/scripts/summarize/inputs/calibration/CatVarDict.xlsx b/scripts/summarize/inputs/calibration/CatVarDict.xlsx
index c0c924fe..85bf4147 100644
Binary files a/scripts/summarize/inputs/calibration/CatVarDict.xlsx and b/scripts/summarize/inputs/calibration/CatVarDict.xlsx differ
diff --git a/scripts/summarize/inputs/calibration/district_lookup.csv b/scripts/summarize/inputs/calibration/district_lookup.csv
new file mode 100644
index 00000000..9efae9f4
--- /dev/null
+++ b/scripts/summarize/inputs/calibration/district_lookup.csv
@@ -0,0 +1,3701 @@
+taz,tad,county,district,district_name,lat_taz,lon_taz,TAZ,lat_district,lon_district
+1.0,1.0,King,3,North Seattle-Shoreline,47.728095099423854,-122.36802206128222,1,47.717471,-122.338958
+2.0,1.0,King,3,North Seattle-Shoreline,47.72868255036953,-122.358250073533,2,47.717471,-122.338958
+3.0,1.0,King,3,North Seattle-Shoreline,47.73123810226349,-122.35027022580269,3,47.717471,-122.338958
+4.0,1.0,King,3,North Seattle-Shoreline,47.7258170395125,-122.35033144532623,4,47.717471,-122.338958
+5.0,1.0,King,3,North Seattle-Shoreline,47.73038522674738,-122.34145984827984,5,47.717471,-122.338958
+6.0,1.0,King,3,North Seattle-Shoreline,47.732299908664636,-122.33805921276631,6,47.717471,-122.338958
+7.0,1.0,King,3,North Seattle-Shoreline,47.725945603713186,-122.34230357915148,7,47.717471,-122.338958
+8.0,1.0,King,3,North Seattle-Shoreline,47.7254799048141,-122.33366187072927,8,47.717471,-122.338958
+9.0,1.0,King,3,North Seattle-Shoreline,47.73071521625671,-122.3313925474974,9,47.717471,-122.338958
+10.0,1.0,King,3,North Seattle-Shoreline,47.72940929378152,-122.32643594391413,10,47.717471,-122.338958
+11.0,2.0,King,3,North Seattle-Shoreline,47.72866690627156,-122.31909248253376,11,47.717471,-122.338958
+12.0,2.0,King,3,North Seattle-Shoreline,47.72305574516861,-122.31537158560423,12,47.717471,-122.338958
+13.0,2.0,King,3,North Seattle-Shoreline,47.7300729096713,-122.30890378470266,13,47.717471,-122.338958
+14.0,2.0,King,3,North Seattle-Shoreline,47.722766595724856,-122.30819118480808,14,47.717471,-122.338958
+15.0,2.0,King,3,North Seattle-Shoreline,47.73033515121397,-122.3008513180035,15,47.717471,-122.338958
+16.0,2.0,King,3,North Seattle-Shoreline,47.72316554901291,-122.30021864013894,16,47.717471,-122.338958
+17.0,2.0,King,3,North Seattle-Shoreline,47.73012110980764,-122.29440489878336,17,47.717471,-122.338958
+18.0,2.0,King,3,North Seattle-Shoreline,47.72353392079781,-122.29473814570856,18,47.717471,-122.338958
+19.0,2.0,King,3,North Seattle-Shoreline,47.72991291380258,-122.28685928277378,19,47.717471,-122.338958
+20.0,2.0,King,3,North Seattle-Shoreline,47.72275189269517,-122.28676552866304,20,47.717471,-122.338958
+21.0,2.0,King,3,North Seattle-Shoreline,47.712269134717424,-122.27891015603622,21,47.717471,-122.338958
+22.0,2.0,King,3,North Seattle-Shoreline,47.71538630328351,-122.28715006915316,22,47.717471,-122.338958
+23.0,2.0,King,3,North Seattle-Shoreline,47.71517453459773,-122.29439744589277,23,47.717471,-122.338958
+24.0,2.0,King,3,North Seattle-Shoreline,47.716059947485505,-122.30082528802065,24,47.717471,-122.338958
+25.0,2.0,King,3,North Seattle-Shoreline,47.71575828788443,-122.30852901264193,25,47.717471,-122.338958
+26.0,1.0,King,3,North Seattle-Shoreline,47.716047194418366,-122.31544207729952,26,47.717471,-122.338958
+27.0,1.0,King,3,North Seattle-Shoreline,47.71650407961226,-122.32196995476014,27,47.717471,-122.338958
+28.0,1.0,King,3,North Seattle-Shoreline,47.71874564426842,-122.32681023796879,28,47.717471,-122.338958
+29.0,1.0,King,3,North Seattle-Shoreline,47.71835934247762,-122.33322049057863,29,47.717471,-122.338958
+30.0,1.0,King,3,North Seattle-Shoreline,47.71411884520533,-122.33671248611219,30,47.717471,-122.338958
+31.0,1.0,King,3,North Seattle-Shoreline,47.71794926472258,-122.34178535559346,31,47.717471,-122.338958
+32.0,1.0,King,3,North Seattle-Shoreline,47.721401789700096,-122.34764358223929,32,47.717471,-122.338958
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+38.0,1.0,King,3,North Seattle-Shoreline,47.716621012261974,-122.36813280883275,38,47.717471,-122.338958
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+57.0,5.0,King,3,North Seattle-Shoreline,47.70144343349514,-122.28088493603752,57,47.717471,-122.338958
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+64.0,5.0,King,3,North Seattle-Shoreline,47.69871588848832,-122.30830057016635,64,47.717471,-122.338958
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+68.0,4.0,King,3,North Seattle-Shoreline,47.69857221991478,-122.3203589275468,68,47.717471,-122.338958
+69.0,4.0,King,3,North Seattle-Shoreline,47.70501459682519,-122.32635988483469,69,47.717471,-122.338958
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+71.0,4.0,King,3,North Seattle-Shoreline,47.70487477521017,-122.33181802426279,71,47.717471,-122.338958
+72.0,4.0,King,3,North Seattle-Shoreline,47.698652041903436,-122.33206399783785,72,47.717471,-122.338958
+73.0,4.0,King,3,North Seattle-Shoreline,47.70425885233039,-122.3363091629942,73,47.717471,-122.338958
+74.0,4.0,King,3,North Seattle-Shoreline,47.69868261980466,-122.33736139052108,74,47.717471,-122.338958
+75.0,4.0,King,3,North Seattle-Shoreline,47.70323328676601,-122.34198074914028,75,47.717471,-122.338958
+76.0,4.0,King,3,North Seattle-Shoreline,47.699394921154536,-122.34228505348592,76,47.717471,-122.338958
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+78.0,4.0,King,3,North Seattle-Shoreline,47.69962535545722,-122.34732553745356,78,47.717471,-122.338958
+79.0,3.0,King,3,North Seattle-Shoreline,47.7014517319416,-122.3527271600197,79,47.717471,-122.338958
+80.0,3.0,King,3,North Seattle-Shoreline,47.69992415051158,-122.35790678035944,80,47.717471,-122.338958
+81.0,3.0,King,3,North Seattle-Shoreline,47.698609698866356,-122.36313724270828,81,47.717471,-122.338958
+82.0,3.0,King,3,North Seattle-Shoreline,47.69637494409995,-122.36842165639929,82,47.717471,-122.338958
+83.0,3.0,King,3,North Seattle-Shoreline,47.69888261145041,-122.37257483708478,83,47.717471,-122.338958
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+86.0,3.0,King,3,North Seattle-Shoreline,47.69478266160378,-122.38218385750608,86,47.717471,-122.338958
+87.0,3.0,King,3,North Seattle-Shoreline,47.69314363250425,-122.37377257609806,87,47.717471,-122.338958
+88.0,3.0,King,3,North Seattle-Shoreline,47.69244918868818,-122.36872580196258,88,47.717471,-122.338958
+89.0,3.0,King,3,North Seattle-Shoreline,47.69334463312026,-122.3633392407081,89,47.717471,-122.338958
+90.0,3.0,King,3,North Seattle-Shoreline,47.69332062387728,-122.35800007084111,90,47.717471,-122.338958
+91.0,3.0,King,3,North Seattle-Shoreline,47.69420598779704,-122.35264548477863,91,47.717471,-122.338958
+92.0,4.0,King,3,North Seattle-Shoreline,47.69419192582427,-122.34725828356292,92,47.717471,-122.338958
+93.0,4.0,King,3,North Seattle-Shoreline,47.6937289293785,-122.34069128401579,93,47.717471,-122.338958
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+189.0,5.0,King,3,North Seattle-Shoreline,47.68122944455418,-122.29313407948057,189,47.717471,-122.338958
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+347.0,8.0,King,4,Seattle CBD,47.639958438205745,-122.41039849803008,347,47.603653,-122.32836
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+349.0,8.0,King,4,Seattle CBD,47.64353610492395,-122.39675606476195,349,47.603653,-122.32836
+350.0,8.0,King,4,Seattle CBD,47.64499705145219,-122.38767302611805,350,47.603653,-122.32836
+351.0,8.0,King,4,Seattle CBD,47.64166237706687,-122.37873772261123,351,47.603653,-122.32836
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+353.0,9.0,King,4,Seattle CBD,47.6427551743659,-122.36841739202421,353,47.603653,-122.32836
+354.0,9.0,King,4,Seattle CBD,47.64337643691879,-122.36396849194406,354,47.603653,-122.32836
+355.0,9.0,King,4,Seattle CBD,47.64354389156959,-122.35794889164775,355,47.603653,-122.32836
+356.0,9.0,King,4,Seattle CBD,47.64450466451996,-122.35147615976335,356,47.603653,-122.32836
+357.0,9.0,King,4,Seattle CBD,47.6412165756223,-122.35115385503002,357,47.603653,-122.32836
+358.0,9.0,King,4,Seattle CBD,47.64086802613635,-122.34442146597219,358,47.603653,-122.32836
+359.0,9.0,King,4,Seattle CBD,47.64312914163997,-122.34471461117668,359,47.603653,-122.32836
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+512.0,11.0,King,4,Seattle CBD,47.6089867084738,-122.33633168844969,512,47.603653,-122.32836
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+514.0,11.0,King,4,Seattle CBD,47.609915327212455,-122.33411072358095,514,47.603653,-122.32836
+515.0,11.0,King,4,Seattle CBD,47.61037510608664,-122.33300077950857,515,47.603653,-122.32836
+516.0,11.0,King,4,Seattle CBD,47.61091694979073,-122.33202273113446,516,47.603653,-122.32836
+517.0,11.0,King,4,Seattle CBD,47.611930101099006,-122.33114639549244,517,47.603653,-122.32836
+518.0,11.0,King,4,Seattle CBD,47.6054920061901,-122.33906579347513,518,47.603653,-122.32836
+519.0,11.0,King,4,Seattle CBD,47.6058916083003,-122.33810594455288,519,47.603653,-122.32836
+520.0,11.0,King,4,Seattle CBD,47.606351832615786,-122.336991080276,520,47.603653,-122.32836
+521.0,11.0,King,4,Seattle CBD,47.60681615736805,-122.33588104025424,521,47.603653,-122.32836
+522.0,11.0,King,4,Seattle CBD,47.60728046765386,-122.3347706193686,522,47.603653,-122.32836
+523.0,11.0,King,4,Seattle CBD,47.60774513151922,-122.33366053777678,523,47.603653,-122.32836
+524.0,11.0,King,4,Seattle CBD,47.608211044383886,-122.33255118155165,524,47.603653,-122.32836
+525.0,11.0,King,4,Seattle CBD,47.60874914865817,-122.33154015614413,525,47.603653,-122.32836
+526.0,11.0,King,4,Seattle CBD,47.604053327176295,-122.33776033864058,526,47.603653,-122.32836
+527.0,11.0,King,4,Seattle CBD,47.60446115931289,-122.33679562286999,527,47.603653,-122.32836
+528.0,11.0,King,4,Seattle CBD,47.604925575755466,-122.33568581932744,528,47.603653,-122.32836
+529.0,11.0,King,4,Seattle CBD,47.60539015767998,-122.33457607918842,529,47.603653,-122.32836
+530.0,11.0,King,4,Seattle CBD,47.60585371353121,-122.33346573782184,530,47.603653,-122.32836
+531.0,11.0,King,4,Seattle CBD,47.606318482179134,-122.33235568527776,531,47.603653,-122.32836
+532.0,11.0,King,4,Seattle CBD,47.6069538469999,-122.33111671910869,532,47.603653,-122.32836
+533.0,11.0,King,4,Seattle CBD,47.602577394823705,-122.33641557450103,533,47.603653,-122.32836
+534.0,11.0,King,4,Seattle CBD,47.602769574886636,-122.33529449945976,534,47.603653,-122.32836
+535.0,11.0,King,4,Seattle CBD,47.60349919862851,-122.3343813585982,535,47.603653,-122.32836
+536.0,11.0,King,4,Seattle CBD,47.60396363954074,-122.33327178643164,536,47.603653,-122.32836
+537.0,11.0,King,4,Seattle CBD,47.6044270861671,-122.33216069540718,537,47.603653,-122.32836
+538.0,11.0,King,4,Seattle CBD,47.60489188633144,-122.33105074149832,538,47.603653,-122.32836
+539.0,11.0,King,4,Seattle CBD,47.60566576445486,-122.32972603778528,539,47.603653,-122.32836
+540.0,11.0,King,4,Seattle CBD,47.60222260901453,-122.33328947581164,540,47.603653,-122.32836
+541.0,11.0,King,4,Seattle CBD,47.60248416892785,-122.33187458798919,541,47.603653,-122.32836
+542.0,11.0,King,4,Seattle CBD,47.602726763969784,-122.33057245582931,542,47.603653,-122.32836
+543.0,11.0,King,4,Seattle CBD,47.603471381601324,-122.32974797466946,543,47.603653,-122.32836
+544.0,11.0,King,4,Seattle CBD,47.604061954153735,-122.32854293618657,544,47.603653,-122.32836
+545.0,11.0,King,4,Seattle CBD,47.60225622055302,-122.328606079972,545,47.603653,-122.32836
+546.0,11.0,King,4,Seattle CBD,47.60264236608933,-122.32696319179584,546,47.603653,-122.32836
+547.0,12.0,King,4,Seattle CBD,47.61763354483882,-122.32744980172184,547,47.603653,-122.32836
+548.0,12.0,King,4,Seattle CBD,47.61583558753368,-122.32793033324612,548,47.603653,-122.32836
+549.0,12.0,King,4,Seattle CBD,47.61854876883117,-122.32449509084658,549,47.603653,-122.32836
+550.0,12.0,King,4,Seattle CBD,47.61645604542138,-122.32508455464412,550,47.603653,-122.32836
+551.0,12.0,King,4,Seattle CBD,47.6187222680591,-122.3221520346806,551,47.603653,-122.32836
+552.0,12.0,King,4,Seattle CBD,47.6163939192811,-122.32212632433796,552,47.603653,-122.32836
+553.0,12.0,King,4,Seattle CBD,47.61758412579529,-122.31886527612572,553,47.603653,-122.32836
+554.0,12.0,King,4,Seattle CBD,47.61762403229603,-122.31482126701435,554,47.603653,-122.32836
+555.0,13.0,King,4,Seattle CBD,47.61905221392958,-122.31005435269549,555,47.603653,-122.32836
+556.0,13.0,King,4,Seattle CBD,47.6165082486534,-122.31058964421936,556,47.603653,-122.32836
+557.0,13.0,King,4,Seattle CBD,47.61897946795159,-122.3053948317848,557,47.603653,-122.32836
+558.0,13.0,King,4,Seattle CBD,47.61827783587483,-122.29925971066929,558,47.603653,-122.32836
+559.0,13.0,King,4,Seattle CBD,47.61814249823696,-122.29305777884848,559,47.603653,-122.32836
+560.0,13.0,King,4,Seattle CBD,47.61579021557346,-122.2864042341531,560,47.603653,-122.32836
+561.0,13.0,King,4,Seattle CBD,47.61469117041962,-122.2926991374012,561,47.603653,-122.32836
+562.0,13.0,King,4,Seattle CBD,47.614700564503316,-122.2994266156964,562,47.603653,-122.32836
+563.0,13.0,King,4,Seattle CBD,47.615411070952966,-122.304858525387,563,47.603653,-122.32836
+564.0,13.0,King,4,Seattle CBD,47.6143368073382,-122.30975265283493,564,47.603653,-122.32836
+565.0,12.0,King,4,Seattle CBD,47.61439756513965,-122.31514521679856,565,47.603653,-122.32836
+566.0,12.0,King,4,Seattle CBD,47.613583811399394,-122.31896507426201,566,47.603653,-122.32836
+567.0,12.0,King,4,Seattle CBD,47.61410527646877,-122.3220915885068,567,47.603653,-122.32836
+568.0,12.0,King,4,Seattle CBD,47.614090256152245,-122.32511374114357,568,47.603653,-122.32836
+569.0,12.0,King,4,Seattle CBD,47.61400490171376,-122.3279591343812,569,47.603653,-122.32836
+570.0,12.0,King,4,Seattle CBD,47.6127213386871,-122.32943897500698,570,47.603653,-122.32836
+571.0,12.0,King,4,Seattle CBD,47.61075050094321,-122.32915110232592,571,47.603653,-122.32836
+572.0,12.0,King,4,Seattle CBD,47.60856466519506,-122.32929848328935,572,47.603653,-122.32836
+573.0,12.0,King,4,Seattle CBD,47.60958546989847,-122.32690396137195,573,47.603653,-122.32836
+574.0,12.0,King,4,Seattle CBD,47.61219156021711,-122.32530417013494,574,47.603653,-122.32836
+575.0,12.0,King,4,Seattle CBD,47.6105146263505,-122.32468270399308,575,47.603653,-122.32836
+576.0,12.0,King,4,Seattle CBD,47.61166385871688,-122.3221864197812,576,47.603653,-122.32836
+577.0,12.0,King,4,Seattle CBD,47.60721364914999,-122.32799805509994,577,47.603653,-122.32836
+578.0,12.0,King,4,Seattle CBD,47.60573922789909,-122.32699671539845,578,47.603653,-122.32836
+579.0,12.0,King,4,Seattle CBD,47.60815816715517,-122.32559935343174,579,47.603653,-122.32836
+580.0,12.0,King,4,Seattle CBD,47.60673106850961,-122.32429399180376,580,47.603653,-122.32836
+581.0,12.0,King,4,Seattle CBD,47.60983825132797,-122.3227924302081,581,47.603653,-122.32836
+582.0,12.0,King,4,Seattle CBD,47.60778471043198,-122.32295563073393,582,47.603653,-122.32836
+583.0,12.0,King,4,Seattle CBD,47.60861156691306,-122.32167595083195,583,47.603653,-122.32836
+584.0,12.0,King,4,Seattle CBD,47.60994122634697,-122.3187042837988,584,47.603653,-122.32836
+585.0,12.0,King,4,Seattle CBD,47.610886077178144,-122.31469872187814,585,47.603653,-122.32836
+586.0,13.0,King,4,Seattle CBD,47.61047160873766,-122.31019866372988,586,47.603653,-122.32836
+587.0,13.0,King,4,Seattle CBD,47.610458123896656,-122.30515963075293,587,47.603653,-122.32836
+588.0,13.0,King,4,Seattle CBD,47.610468242719634,-122.29949208354694,588,47.603653,-122.32836
+589.0,13.0,King,4,Seattle CBD,47.61045381993365,-122.29280314653872,589,47.603653,-122.32836
+590.0,13.0,King,4,Seattle CBD,47.61098301125111,-122.28696244952991,590,47.603653,-122.32836
+591.0,13.0,King,4,Seattle CBD,47.60977180903971,-122.28335303912912,591,47.603653,-122.32836
+592.0,13.0,King,4,Seattle CBD,47.606490935314476,-122.28680866307084,592,47.603653,-122.32836
+593.0,13.0,King,4,Seattle CBD,47.603422077991674,-122.28773289280564,593,47.603653,-122.32836
+594.0,13.0,King,4,Seattle CBD,47.60615162450637,-122.29344100999563,594,47.603653,-122.32836
+595.0,13.0,King,4,Seattle CBD,47.602985013072356,-122.29445265702236,595,47.603653,-122.32836
+596.0,13.0,King,4,Seattle CBD,47.60619353545893,-122.2996742142896,596,47.603653,-122.32836
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+598.0,13.0,King,4,Seattle CBD,47.606149569591075,-122.30517352673408,598,47.603653,-122.32836
+599.0,13.0,King,4,Seattle CBD,47.60299658159358,-122.30504200264635,599,47.603653,-122.32836
+600.0,13.0,King,4,Seattle CBD,47.60618123597093,-122.31024839508518,600,47.603653,-122.32836
+601.0,13.0,King,4,Seattle CBD,47.6030052431942,-122.31026899438436,601,47.603653,-122.32836
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+621.0,13.0,King,4,Seattle CBD,47.59884248540565,-122.28907603436558,621,47.603653,-122.32836
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+623.0,13.0,King,4,Seattle CBD,47.59741800223422,-122.2998519034512,623,47.603653,-122.32836
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+629.0,11.0,King,4,Seattle CBD,47.59678157822047,-122.32389919283956,629,47.603653,-122.32836
+630.0,14.0,King,5,West-South Seattle,47.59544653232332,-122.32823068534141,630,47.551159999999996,-122.332714
+631.0,14.0,King,5,West-South Seattle,47.59580900183034,-122.33161579255285,631,47.551159999999996,-122.332714
+632.0,14.0,King,5,West-South Seattle,47.595735359639704,-122.33487923869808,632,47.551159999999996,-122.332714
+633.0,14.0,King,5,West-South Seattle,47.59481580343751,-122.3381216685742,633,47.551159999999996,-122.332714
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+638.0,13.0,King,4,Seattle CBD,47.59375814140577,-122.30008456446433,638,47.603653,-122.32836
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+641.0,14.0,King,5,West-South Seattle,47.588472048847066,-122.28908346689815,641,47.551159999999996,-122.332714
+642.0,14.0,King,5,West-South Seattle,47.5901170563825,-122.29497225522735,642,47.551159999999996,-122.332714
+643.0,14.0,King,5,West-South Seattle,47.58591146597088,-122.29558728500704,643,47.551159999999996,-122.332714
+644.0,14.0,King,5,West-South Seattle,47.59019610964914,-122.29995177502438,644,47.551159999999996,-122.332714
+645.0,14.0,King,5,West-South Seattle,47.58599248816932,-122.30043819381052,645,47.551159999999996,-122.332714
+646.0,14.0,King,5,West-South Seattle,47.588620671039116,-122.30422865706345,646,47.551159999999996,-122.332714
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+648.0,14.0,King,5,West-South Seattle,47.5893567755389,-122.312585190323,648,47.551159999999996,-122.332714
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+674.0,16.0,King,5,West-South Seattle,47.57964386999799,-122.3514401247336,674,47.551159999999996,-122.332714
+675.0,16.0,King,5,West-South Seattle,47.57770113903546,-122.36599564971571,675,47.551159999999996,-122.332714
+676.0,15.0,King,5,West-South Seattle,47.590203422559995,-122.38738374112856,676,47.551159999999996,-122.332714
+677.0,15.0,King,5,West-South Seattle,47.588659761367424,-122.38917073933108,677,47.551159999999996,-122.332714
+678.0,15.0,King,5,West-South Seattle,47.589174567469605,-122.38453019484152,678,47.551159999999996,-122.332714
+679.0,15.0,King,5,West-South Seattle,47.583929122288005,-122.38175056487343,679,47.551159999999996,-122.332714
+680.0,15.0,King,5,West-South Seattle,47.5830937513074,-122.3924705053932,680,47.551159999999996,-122.332714
+681.0,15.0,King,5,West-South Seattle,47.58152893839269,-122.40024505275957,681,47.551159999999996,-122.332714
+682.0,15.0,King,5,West-South Seattle,47.57833656507228,-122.39024447334023,682,47.551159999999996,-122.332714
+683.0,15.0,King,5,West-South Seattle,47.57838618493876,-122.38397997727148,683,47.551159999999996,-122.332714
+684.0,15.0,King,5,West-South Seattle,47.57823595840427,-122.37879616378628,684,47.551159999999996,-122.332714
+685.0,16.0,King,5,West-South Seattle,47.57890860341154,-122.37423378796407,685,47.551159999999996,-122.332714
+686.0,16.0,King,5,West-South Seattle,47.570795098959586,-122.37450539436855,686,47.551159999999996,-122.332714
+687.0,15.0,King,5,West-South Seattle,47.57275247053789,-122.3787675308998,687,47.551159999999996,-122.332714
+688.0,15.0,King,5,West-South Seattle,47.572901512030676,-122.38408493004707,688,47.551159999999996,-122.332714
+689.0,15.0,King,5,West-South Seattle,47.57294852564722,-122.3907474149062,689,47.551159999999996,-122.332714
+690.0,15.0,King,5,West-South Seattle,47.574770590605965,-122.39859495518084,690,47.551159999999996,-122.332714
+691.0,15.0,King,5,West-South Seattle,47.5783997871091,-122.40775038260998,691,47.551159999999996,-122.332714
+692.0,15.0,King,5,West-South Seattle,47.57377686700653,-122.40766911018835,692,47.551159999999996,-122.332714
+693.0,15.0,King,5,West-South Seattle,47.57561763906918,-122.41613712978815,693,47.551159999999996,-122.332714
+694.0,15.0,King,5,West-South Seattle,47.5658667922702,-122.406333643143,694,47.551159999999996,-122.332714
+695.0,15.0,King,5,West-South Seattle,47.5679771011349,-122.40082385098442,695,47.551159999999996,-122.332714
+696.0,15.0,King,5,West-South Seattle,47.56740421938353,-122.3910489712562,696,47.551159999999996,-122.332714
+697.0,15.0,King,5,West-South Seattle,47.567402292509186,-122.3834710027922,697,47.551159999999996,-122.332714
+698.0,15.0,King,5,West-South Seattle,47.56679978365157,-122.3782499513817,698,47.551159999999996,-122.332714
+699.0,16.0,King,5,West-South Seattle,47.567186934076936,-122.37274920578848,699,47.551159999999996,-122.332714
+700.0,16.0,King,5,West-South Seattle,47.56812650804528,-122.36694943758421,700,47.551159999999996,-122.332714
+701.0,16.0,King,5,West-South Seattle,47.567880228334225,-122.36042035222324,701,47.551159999999996,-122.332714
+702.0,16.0,King,5,West-South Seattle,47.563762824098255,-122.35614428691628,702,47.551159999999996,-122.332714
+703.0,16.0,King,5,West-South Seattle,47.565535953740365,-122.35158461319841,703,47.551159999999996,-122.332714
+704.0,14.0,King,5,West-South Seattle,47.56433942788697,-122.34193103697909,704,47.551159999999996,-122.332714
+705.0,14.0,King,5,West-South Seattle,47.56943698197389,-122.3367663223014,705,47.551159999999996,-122.332714
+706.0,14.0,King,5,West-South Seattle,47.56945608375824,-122.33164319430128,706,47.551159999999996,-122.332714
+707.0,14.0,King,5,West-South Seattle,47.56908146926977,-122.3244897490016,707,47.551159999999996,-122.332714
+708.0,14.0,King,5,West-South Seattle,47.56527884770409,-122.31764458689243,708,47.551159999999996,-122.332714
+709.0,14.0,King,5,West-South Seattle,47.56615869529422,-122.30968315680391,709,47.551159999999996,-122.332714
+710.0,14.0,King,5,West-South Seattle,47.56723692476908,-122.30423022631176,710,47.551159999999996,-122.332714
+711.0,14.0,King,5,West-South Seattle,47.5659024132281,-122.29919896528894,711,47.551159999999996,-122.332714
+712.0,14.0,King,5,West-South Seattle,47.569424432220856,-122.29375628798864,712,47.551159999999996,-122.332714
+713.0,14.0,King,5,West-South Seattle,47.563505583832296,-122.2916641359158,713,47.551159999999996,-122.332714
+714.0,14.0,King,5,West-South Seattle,47.56815307105814,-122.287850441032,714,47.551159999999996,-122.332714
+715.0,14.0,King,5,West-South Seattle,47.56621255423321,-122.28058281296906,715,47.551159999999996,-122.332714
+716.0,14.0,King,5,West-South Seattle,47.564775037328346,-122.2734455892241,716,47.551159999999996,-122.332714
+717.0,14.0,King,5,West-South Seattle,47.56420101602875,-122.26921042603294,717,47.551159999999996,-122.332714
+718.0,17.0,King,5,West-South Seattle,47.55585823748952,-122.26539128892851,718,47.551159999999996,-122.332714
+719.0,17.0,King,5,West-South Seattle,47.55531368679871,-122.2723230546742,719,47.551159999999996,-122.332714
+720.0,17.0,King,5,West-South Seattle,47.55694829729255,-122.27976675193798,720,47.551159999999996,-122.332714
+721.0,17.0,King,5,West-South Seattle,47.555483417972276,-122.28624402273887,721,47.551159999999996,-122.332714
+722.0,17.0,King,5,West-South Seattle,47.55571359730335,-122.29678753953192,722,47.551159999999996,-122.332714
+723.0,17.0,King,5,West-South Seattle,47.55707327121881,-122.30888338435877,723,47.551159999999996,-122.332714
+724.0,17.0,King,5,West-South Seattle,47.557278338137635,-122.31580359053021,724,47.551159999999996,-122.332714
+725.0,14.0,King,5,West-South Seattle,47.560104575899274,-122.32506434917394,725,47.551159999999996,-122.332714
+726.0,14.0,King,5,West-South Seattle,47.562751128854856,-122.33151106226185,726,47.551159999999996,-122.332714
+727.0,14.0,King,5,West-South Seattle,47.55788340999954,-122.33273274518518,727,47.551159999999996,-122.332714
+728.0,14.0,King,5,West-South Seattle,47.561620037573,-122.33673090987317,728,47.551159999999996,-122.332714
+729.0,16.0,King,5,West-South Seattle,47.557347565552426,-122.36158874030208,729,47.551159999999996,-122.332714
+730.0,16.0,King,5,West-South Seattle,47.55748721391668,-122.36446535077887,730,47.551159999999996,-122.332714
+731.0,16.0,King,5,West-South Seattle,47.55914051750693,-122.37103906989944,731,47.551159999999996,-122.332714
+732.0,15.0,King,5,West-South Seattle,47.56244181438821,-122.3780167348308,732,47.551159999999996,-122.332714
+733.0,15.0,King,5,West-South Seattle,47.55926156215094,-122.37884781864702,733,47.551159999999996,-122.332714
+734.0,15.0,King,5,West-South Seattle,47.55470785783768,-122.37891287032822,734,47.551159999999996,-122.332714
+735.0,15.0,King,5,West-South Seattle,47.56293220758517,-122.38354328188231,735,47.551159999999996,-122.332714
+736.0,15.0,King,5,West-South Seattle,47.559269289286306,-122.38414592870241,736,47.551159999999996,-122.332714
+737.0,15.0,King,5,West-South Seattle,47.55477773309864,-122.38425789211293,737,47.551159999999996,-122.332714
+738.0,15.0,King,5,West-South Seattle,47.56111760027747,-122.38948830497505,738,47.551159999999996,-122.332714
+739.0,15.0,King,5,West-South Seattle,47.554395993714245,-122.3897865676218,739,47.551159999999996,-122.332714
+740.0,15.0,King,5,West-South Seattle,47.561539770863284,-122.39649286362436,740,47.551159999999996,-122.332714
+741.0,15.0,King,5,West-South Seattle,47.55555381535984,-122.39480908502442,741,47.551159999999996,-122.332714
+742.0,15.0,King,5,West-South Seattle,47.556321727116746,-122.39972879353144,742,47.551159999999996,-122.332714
+743.0,15.0,King,5,West-South Seattle,47.545879954558245,-122.39696508944289,743,47.551159999999996,-122.332714
+744.0,15.0,King,5,West-South Seattle,47.54954812586626,-122.3913732717342,744,47.551159999999996,-122.332714
+745.0,15.0,King,5,West-South Seattle,47.54350875242983,-122.3923019255378,745,47.551159999999996,-122.332714
+746.0,15.0,King,5,West-South Seattle,47.54215430309044,-122.38867312966543,746,47.551159999999996,-122.332714
+747.0,15.0,King,5,West-South Seattle,47.54889301234028,-122.38495558520893,747,47.551159999999996,-122.332714
+748.0,15.0,King,5,West-South Seattle,47.54258531056989,-122.38449644971277,748,47.551159999999996,-122.332714
+749.0,15.0,King,5,West-South Seattle,47.54810954279748,-122.37978000042573,749,47.551159999999996,-122.332714
+750.0,15.0,King,5,West-South Seattle,47.54213514702609,-122.37915833196313,750,47.551159999999996,-122.332714
+751.0,16.0,King,5,West-South Seattle,47.54802262429407,-122.37046033370191,751,47.551159999999996,-122.332714
+752.0,16.0,King,5,West-South Seattle,47.54152685124261,-122.37279094561177,752,47.551159999999996,-122.332714
+753.0,16.0,King,5,West-South Seattle,47.543233642345285,-122.36356043926929,753,47.551159999999996,-122.332714
+754.0,16.0,King,5,West-South Seattle,47.55122782592182,-122.35760148739536,754,47.551159999999996,-122.332714
+755.0,16.0,King,5,West-South Seattle,47.54230509165331,-122.35834003894772,755,47.551159999999996,-122.332714
+756.0,16.0,King,5,West-South Seattle,47.54851209558353,-122.35093220408471,756,47.551159999999996,-122.332714
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+759.0,17.0,King,5,West-South Seattle,47.5532409207225,-122.33553348695592,759,47.551159999999996,-122.332714
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+772.0,17.0,King,5,West-South Seattle,47.54078133479801,-122.27498798221413,772,47.551159999999996,-122.332714
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+816.0,17.0,King,5,West-South Seattle,47.5291505248705,-122.26556342274141,816,47.551159999999996,-122.332714
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+819.0,17.0,King,5,West-South Seattle,47.528973662721704,-122.27740846254862,819,47.551159999999996,-122.332714
+820.0,17.0,King,5,West-South Seattle,47.52471546622278,-122.274439917275,820,47.551159999999996,-122.332714
+821.0,17.0,King,5,West-South Seattle,47.52873730233016,-122.28416666580549,821,47.551159999999996,-122.332714
+822.0,17.0,King,5,West-South Seattle,47.5224728685349,-122.28242528255629,822,47.551159999999996,-122.332714
+823.0,17.0,King,5,West-South Seattle,47.525006285728026,-122.28887087148777,823,47.551159999999996,-122.332714
+824.0,17.0,King,5,West-South Seattle,47.524676178253934,-122.31227830850432,824,47.551159999999996,-122.332714
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+826.0,17.0,King,5,West-South Seattle,47.52053255015889,-122.32083354948706,826,47.551159999999996,-122.332714
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+828.0,16.0,King,5,West-South Seattle,47.51936350190549,-122.33408791193821,828,47.551159999999996,-122.332714
+829.0,16.0,King,5,West-South Seattle,47.51977740504067,-122.3490628891971,829,47.551159999999996,-122.332714
+830.0,16.0,King,5,West-South Seattle,47.521030118566635,-122.35679236288172,830,47.551159999999996,-122.332714
+831.0,16.0,King,5,West-South Seattle,47.51949097499608,-122.36126182249308,831,47.551159999999996,-122.332714
+832.0,16.0,King,5,West-South Seattle,47.51923126639996,-122.37133542797821,832,47.551159999999996,-122.332714
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+834.0,15.0,King,5,West-South Seattle,47.517550328184974,-122.38637581284546,834,47.551159999999996,-122.332714
+835.0,15.0,King,5,West-South Seattle,47.514307408378976,-122.39278416985191,835,47.551159999999996,-122.332714
+836.0,15.0,King,5,West-South Seattle,47.51134254350135,-122.3863759611018,836,47.551159999999996,-122.332714
+837.0,15.0,King,5,West-South Seattle,47.510964873588236,-122.37954381206563,837,47.551159999999996,-122.332714
+838.0,15.0,King,5,West-South Seattle,47.5129026938424,-122.37368543625479,838,47.551159999999996,-122.332714
+839.0,15.0,King,5,West-South Seattle,47.50683672958965,-122.38080135210916,839,47.551159999999996,-122.332714
+840.0,15.0,King,5,West-South Seattle,47.50279000698167,-122.38167861181722,840,47.551159999999996,-122.332714
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+844.0,17.0,King,5,West-South Seattle,47.51297852632929,-122.2761652286797,844,47.551159999999996,-122.332714
+845.0,17.0,King,5,West-South Seattle,47.51975526521589,-122.27521866472756,845,47.551159999999996,-122.332714
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+848.0,17.0,King,5,West-South Seattle,47.52151724039154,-122.26598509283487,848,47.551159999999996,-122.332714
+849.0,17.0,King,5,West-South Seattle,47.51707171133173,-122.26565635669435,849,47.551159999999996,-122.332714
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+852.0,17.0,King,5,West-South Seattle,47.50867990217089,-122.2611281118688,852,47.551159999999996,-122.332714
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+855.0,17.0,King,5,West-South Seattle,47.50366635948078,-122.24749466807523,855,47.551159999999996,-122.332714
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+865.0,18.0,King,5,West-South Seattle,47.514645502779906,-122.34381131429821,865,47.551159999999996,-122.332714
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+870.0,19.0,King,7,Renton-FedWay-Kent,47.51738541507957,-122.32711719801307,870,47.401118,-122.226377
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+1276.0,37.0,King,7,Renton-FedWay-Kent,47.394116470175035,-122.12815989546486,1276,47.401118,-122.226377
+1277.0,37.0,King,7,Renton-FedWay-Kent,47.39414639275383,-122.11755002263165,1277,47.401118,-122.226377
+1278.0,37.0,King,7,Renton-FedWay-Kent,47.39369817473981,-122.09784859353313,1278,47.401118,-122.226377
+1279.0,34.0,King,7,Renton-FedWay-Kent,47.390800554931424,-122.06697787166107,1279,47.401118,-122.226377
+1280.0,34.0,King,7,Renton-FedWay-Kent,47.401450446988214,-122.06881143570436,1280,47.401118,-122.226377
+1281.0,34.0,King,7,Renton-FedWay-Kent,47.41065498494886,-122.06466263273941,1281,47.401118,-122.226377
+1282.0,37.0,King,7,Renton-FedWay-Kent,47.41024804335567,-122.0882153058842,1282,47.401118,-122.226377
+1283.0,37.0,King,7,Renton-FedWay-Kent,47.406450074279185,-122.10503922236929,1283,47.401118,-122.226377
+1284.0,37.0,King,7,Renton-FedWay-Kent,47.419272927993745,-122.12530037330812,1284,47.401118,-122.226377
+1285.0,37.0,King,7,Renton-FedWay-Kent,47.409342435309064,-122.14908830585286,1285,47.401118,-122.226377
+1286.0,37.0,King,7,Renton-FedWay-Kent,47.408033297211894,-122.1594104557748,1286,47.401118,-122.226377
+1287.0,36.0,King,7,Renton-FedWay-Kent,47.41902592569298,-122.15270796774814,1287,47.401118,-122.226377
+1288.0,36.0,King,7,Renton-FedWay-Kent,47.42678615782298,-122.14928709603592,1288,47.401118,-122.226377
+1289.0,36.0,King,7,Renton-FedWay-Kent,47.42585496327129,-122.16095097884104,1289,47.401118,-122.226377
+1290.0,35.0,King,7,Renton-FedWay-Kent,47.40531422394832,-122.17048672882166,1290,47.401118,-122.226377
+1291.0,35.0,King,7,Renton-FedWay-Kent,47.412513387337114,-122.1711303382757,1291,47.401118,-122.226377
+1292.0,36.0,King,7,Renton-FedWay-Kent,47.42225515920328,-122.17059270315347,1292,47.401118,-122.226377
+1293.0,35.0,King,7,Renton-FedWay-Kent,47.40497119844578,-122.18141968778677,1293,47.401118,-122.226377
+1294.0,35.0,King,7,Renton-FedWay-Kent,47.41219052235997,-122.18162446819431,1294,47.401118,-122.226377
+1295.0,36.0,King,7,Renton-FedWay-Kent,47.41946315020338,-122.181315206551,1295,47.401118,-122.226377
+1296.0,36.0,King,7,Renton-FedWay-Kent,47.4272610699252,-122.18468080694313,1296,47.401118,-122.226377
+1297.0,35.0,King,7,Renton-FedWay-Kent,47.40533465588771,-122.19171817835407,1297,47.401118,-122.226377
+1298.0,35.0,King,7,Renton-FedWay-Kent,47.412000256808525,-122.19204552670756,1298,47.401118,-122.226377
+1299.0,36.0,King,7,Renton-FedWay-Kent,47.42128244385437,-122.19184589116999,1299,47.401118,-122.226377
+1300.0,35.0,King,7,Renton-FedWay-Kent,47.403660639882155,-122.20292954400686,1300,47.401118,-122.226377
+1301.0,35.0,King,7,Renton-FedWay-Kent,47.41224667447946,-122.20225046840864,1301,47.401118,-122.226377
+1302.0,36.0,King,7,Renton-FedWay-Kent,47.41950083352898,-122.20226738465328,1302,47.401118,-122.226377
+1303.0,25.0,King,7,Renton-FedWay-Kent,47.40756989204264,-122.21372572605101,1303,47.401118,-122.226377
+1304.0,36.0,King,7,Renton-FedWay-Kent,47.41950814094455,-122.21549485172469,1304,47.401118,-122.226377
+1305.0,36.0,King,7,Renton-FedWay-Kent,47.42665713926381,-122.20766331167135,1305,47.401118,-122.226377
+1306.0,36.0,King,7,Renton-FedWay-Kent,47.43642111090409,-122.21146537094549,1306,47.401118,-122.226377
+1307.0,36.0,King,7,Renton-FedWay-Kent,47.436338182214406,-122.20110277799158,1307,47.401118,-122.226377
+1308.0,36.0,King,7,Renton-FedWay-Kent,47.43495418100128,-122.19167169043628,1308,47.401118,-122.226377
+1309.0,36.0,King,7,Renton-FedWay-Kent,47.44243785134061,-122.19162359756369,1309,47.401118,-122.226377
+1310.0,36.0,King,7,Renton-FedWay-Kent,47.43400913403925,-122.1806767683256,1310,47.401118,-122.226377
+1311.0,36.0,King,7,Renton-FedWay-Kent,47.44185564211926,-122.17840774791901,1311,47.401118,-122.226377
+1312.0,36.0,King,7,Renton-FedWay-Kent,47.435970519581225,-122.16448279142334,1312,47.401118,-122.226377
+1313.0,36.0,King,7,Renton-FedWay-Kent,47.44402667793464,-122.16130272592466,1313,47.401118,-122.226377
+1314.0,37.0,King,7,Renton-FedWay-Kent,47.437282513735596,-122.14928961047438,1314,47.401118,-122.226377
+1315.0,37.0,King,7,Renton-FedWay-Kent,47.439019886865175,-122.13288653298204,1315,47.401118,-122.226377
+1316.0,37.0,King,7,Renton-FedWay-Kent,47.436493276504976,-122.10683174402607,1316,47.401118,-122.226377
+1317.0,37.0,King,7,Renton-FedWay-Kent,47.43666977067523,-122.09061991170206,1317,47.401118,-122.226377
+1318.0,39.0,King,6,East Side,47.43469117307952,-122.0731868197673,1318,47.611096999999994,-122.14481299999998
+1319.0,39.0,King,6,East Side,47.417140410091505,-122.05532459491377,1319,47.611096999999994,-122.14481299999998
+1320.0,39.0,King,6,East Side,47.4299056834291,-122.05195671381948,1320,47.611096999999994,-122.14481299999998
+1321.0,39.0,King,6,East Side,47.42284517516474,-122.0335512636961,1321,47.611096999999994,-122.14481299999998
+1322.0,39.0,King,6,East Side,47.43565846110841,-122.02636712125872,1322,47.611096999999994,-122.14481299999998
+1323.0,39.0,King,6,East Side,47.43545235560048,-122.00030881344229,1323,47.611096999999994,-122.14481299999998
+1324.0,39.0,King,6,East Side,47.450236300654325,-122.02238930386936,1324,47.611096999999994,-122.14481299999998
+1325.0,40.0,King,6,East Side,47.448139708453894,-122.06039829481612,1325,47.611096999999994,-122.14481299999998
+1326.0,37.0,King,7,Renton-FedWay-Kent,47.45699307382201,-122.09548370671136,1326,47.401118,-122.226377
+1327.0,37.0,King,7,Renton-FedWay-Kent,47.45034457631206,-122.11320957915753,1327,47.401118,-122.226377
+1328.0,37.0,King,7,Renton-FedWay-Kent,47.44723414412582,-122.12670807057472,1328,47.401118,-122.226377
+1329.0,37.0,King,7,Renton-FedWay-Kent,47.442861012939424,-122.1371842383775,1329,47.401118,-122.226377
+1330.0,37.0,King,7,Renton-FedWay-Kent,47.45236633254676,-122.14205298292248,1330,47.401118,-122.226377
+1331.0,37.0,King,7,Renton-FedWay-Kent,47.46199332798742,-122.1364193498119,1331,47.401118,-122.226377
+1332.0,37.0,King,7,Renton-FedWay-Kent,47.45103525425145,-122.16225369552427,1332,47.401118,-122.226377
+1333.0,20.0,King,7,Renton-FedWay-Kent,47.45911520601081,-122.16134926170038,1333,47.401118,-122.226377
+1334.0,20.0,King,7,Renton-FedWay-Kent,47.46254986279306,-122.15383576081659,1334,47.401118,-122.226377
+1335.0,20.0,King,7,Renton-FedWay-Kent,47.468187726731216,-122.16513174832492,1335,47.401118,-122.226377
+1336.0,37.0,King,7,Renton-FedWay-Kent,47.44785602888962,-122.17979170956586,1336,47.401118,-122.226377
+1337.0,37.0,King,7,Renton-FedWay-Kent,47.452815940688374,-122.176988721598,1337,47.401118,-122.226377
+1338.0,37.0,King,7,Renton-FedWay-Kent,47.459439915403394,-122.17759184529758,1338,47.401118,-122.226377
+1339.0,20.0,King,7,Renton-FedWay-Kent,47.467075203840245,-122.17552215161808,1339,47.401118,-122.226377
+1340.0,20.0,King,7,Renton-FedWay-Kent,47.448789698105905,-122.19145501663898,1340,47.401118,-122.226377
+1341.0,20.0,King,7,Renton-FedWay-Kent,47.45617292273175,-122.19516456468023,1341,47.401118,-122.226377
+1342.0,20.0,King,7,Renton-FedWay-Kent,47.460405639893246,-122.1904675826264,1342,47.401118,-122.226377
+1343.0,20.0,King,7,Renton-FedWay-Kent,47.46608985584975,-122.1976778411249,1343,47.401118,-122.226377
+1344.0,20.0,King,7,Renton-FedWay-Kent,47.45642953638479,-122.20271743487976,1344,47.401118,-122.226377
+1345.0,20.0,King,7,Renton-FedWay-Kent,47.447221013674096,-122.20571693675105,1345,47.401118,-122.226377
+1346.0,20.0,King,7,Renton-FedWay-Kent,47.448948738480176,-122.21379148105042,1346,47.401118,-122.226377
+1347.0,20.0,King,7,Renton-FedWay-Kent,47.45595005601206,-122.2089841815495,1347,47.401118,-122.226377
+1348.0,20.0,King,7,Renton-FedWay-Kent,47.46406963226315,-122.21282301291521,1348,47.401118,-122.226377
+1349.0,20.0,King,7,Renton-FedWay-Kent,47.471346202696154,-122.2098329688318,1349,47.401118,-122.226377
+1350.0,20.0,King,7,Renton-FedWay-Kent,47.47379471192624,-122.21307096000456,1350,47.401118,-122.226377
+1351.0,20.0,King,7,Renton-FedWay-Kent,47.476789255291536,-122.20621888019785,1351,47.401118,-122.226377
+1352.0,20.0,King,7,Renton-FedWay-Kent,47.479047063808146,-122.21300430552401,1352,47.401118,-122.226377
+1353.0,20.0,King,7,Renton-FedWay-Kent,47.48316258393103,-122.21321579275127,1353,47.401118,-122.226377
+1354.0,20.0,King,7,Renton-FedWay-Kent,47.48143848432582,-122.20635092298521,1354,47.401118,-122.226377
+1355.0,20.0,King,7,Renton-FedWay-Kent,47.485925108449145,-122.2050194332874,1355,47.401118,-122.226377
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+1420.0,41.0,King,6,East Side,47.54728900427175,-122.18811686952715,1420,47.611096999999994,-122.14481299999998
+1421.0,41.0,King,6,East Side,47.520836769309035,-122.1742138866634,1421,47.611096999999994,-122.14481299999998
+1422.0,41.0,King,6,East Side,47.528674069955386,-122.17993416304779,1422,47.611096999999994,-122.14481299999998
+1423.0,41.0,King,6,East Side,47.53697691854771,-122.18114149792156,1423,47.611096999999994,-122.14481299999998
+1424.0,41.0,King,6,East Side,47.54455683497793,-122.17293495922323,1424,47.611096999999994,-122.14481299999998
+1425.0,41.0,King,6,East Side,47.52653232210474,-122.17001521140169,1425,47.611096999999994,-122.14481299999998
+1426.0,41.0,King,6,East Side,47.53422949538902,-122.16916698059714,1426,47.611096999999994,-122.14481299999998
+1427.0,42.0,King,6,East Side,47.52365313788806,-122.15133672714144,1427,47.611096999999994,-122.14481299999998
+1428.0,42.0,King,6,East Side,47.54000211337032,-122.15475064846628,1428,47.611096999999994,-122.14481299999998
+1429.0,42.0,King,6,East Side,47.53029772841168,-122.13972308212853,1429,47.611096999999994,-122.14481299999998
+1430.0,44.0,King,6,East Side,47.54148636783906,-122.13740375293736,1430,47.611096999999994,-122.14481299999998
+1431.0,42.0,King,6,East Side,47.53587323825728,-122.11839788506286,1431,47.611096999999994,-122.14481299999998
+1432.0,42.0,King,6,East Side,47.528256204013566,-122.09905556015995,1432,47.611096999999994,-122.14481299999998
+1433.0,42.0,King,6,East Side,47.528794172420795,-122.07134952434177,1433,47.611096999999994,-122.14481299999998
+1434.0,42.0,King,6,East Side,47.52861991182114,-122.06157011452456,1434,47.611096999999994,-122.14481299999998
+1435.0,42.0,King,6,East Side,47.5206981645393,-122.04310389638334,1435,47.611096999999994,-122.14481299999998
+1436.0,42.0,King,6,East Side,47.53496901116245,-122.05164154736192,1436,47.611096999999994,-122.14481299999998
+1437.0,42.0,King,6,East Side,47.52564750600069,-122.03100580081829,1437,47.611096999999994,-122.14481299999998
+1438.0,42.0,King,6,East Side,47.53130918941693,-122.03934346489895,1438,47.611096999999994,-122.14481299999998
+1439.0,42.0,King,6,East Side,47.53331606618761,-122.03215452272076,1439,47.611096999999994,-122.14481299999998
+1440.0,42.0,King,6,East Side,47.53668169985488,-122.04184607594892,1440,47.611096999999994,-122.14481299999998
+1441.0,42.0,King,6,East Side,47.54113495653594,-122.04693094970312,1441,47.611096999999994,-122.14481299999998
+1442.0,42.0,King,6,East Side,47.54267377621687,-122.04398728378159,1442,47.611096999999994,-122.14481299999998
+1443.0,42.0,King,6,East Side,47.5437119042261,-122.05463852568228,1443,47.611096999999994,-122.14481299999998
+1444.0,45.0,King,6,East Side,47.54878789432607,-122.05282222712944,1444,47.611096999999994,-122.14481299999998
+1445.0,45.0,King,6,East Side,47.55544058492012,-122.05025996346295,1445,47.611096999999994,-122.14481299999998
+1446.0,45.0,King,6,East Side,47.55461442906893,-122.06204233434494,1446,47.611096999999994,-122.14481299999998
+1447.0,42.0,King,6,East Side,47.54441283992903,-122.06019021285105,1447,47.611096999999994,-122.14481299999998
+1448.0,42.0,King,6,East Side,47.54831071160204,-122.06828402095572,1448,47.611096999999994,-122.14481299999998
+1449.0,42.0,King,6,East Side,47.5495696283022,-122.0862991681512,1449,47.611096999999994,-122.14481299999998
+1450.0,44.0,King,6,East Side,47.56273232167197,-122.08546729314672,1450,47.611096999999994,-122.14481299999998
+1451.0,42.0,King,6,East Side,47.55209559677748,-122.10657967608152,1451,47.611096999999994,-122.14481299999998
+1452.0,42.0,King,6,East Side,47.5628657031069,-122.11194700550905,1452,47.611096999999994,-122.14481299999998
+1453.0,42.0,King,6,East Side,47.54977336015659,-122.11981905188816,1453,47.611096999999994,-122.14481299999998
+1454.0,44.0,King,6,East Side,47.55469380563851,-122.13077826149006,1454,47.611096999999994,-122.14481299999998
+1455.0,44.0,King,6,East Side,47.548051130471535,-122.14120087685316,1455,47.611096999999994,-122.14481299999998
+1456.0,44.0,King,6,East Side,47.55457276168826,-122.14755496843048,1456,47.611096999999994,-122.14481299999998
+1457.0,44.0,King,6,East Side,47.55872464108737,-122.15674403413946,1457,47.611096999999994,-122.14481299999998
+1458.0,44.0,King,6,East Side,47.549291122458,-122.16062187824751,1458,47.611096999999994,-122.14481299999998
+1459.0,41.0,King,6,East Side,47.55746042672834,-122.17390996243782,1459,47.611096999999994,-122.14481299999998
+1460.0,41.0,King,6,East Side,47.55656085596922,-122.18437570329209,1460,47.611096999999994,-122.14481299999998
+1461.0,41.0,King,6,East Side,47.554542443535965,-122.19305385687515,1461,47.611096999999994,-122.14481299999998
+1462.0,47.0,King,6,East Side,47.571211223537254,-122.18590145569144,1462,47.611096999999994,-122.14481299999998
+1463.0,41.0,King,6,East Side,47.57510879892561,-122.17795786869301,1463,47.611096999999994,-122.14481299999998
+1464.0,41.0,King,6,East Side,47.570177726851405,-122.17615556936256,1464,47.611096999999994,-122.14481299999998
+1465.0,44.0,King,6,East Side,47.568859779027726,-122.17201950838242,1465,47.611096999999994,-122.14481299999998
+1466.0,44.0,King,6,East Side,47.57607764770642,-122.17180350704054,1466,47.611096999999994,-122.14481299999998
+1467.0,44.0,King,6,East Side,47.56386368630184,-122.16470382495892,1467,47.611096999999994,-122.14481299999998
+1468.0,44.0,King,6,East Side,47.5744167943576,-122.16521967045429,1468,47.611096999999994,-122.14481299999998
+1469.0,44.0,King,6,East Side,47.56658869375446,-122.15079676018746,1469,47.611096999999994,-122.14481299999998
+1470.0,44.0,King,6,East Side,47.57265418320384,-122.1518781075516,1470,47.611096999999994,-122.14481299999998
+1471.0,44.0,King,6,East Side,47.576949467511604,-122.1541907235992,1471,47.611096999999994,-122.14481299999998
+1472.0,44.0,King,6,East Side,47.57523811930458,-122.14364961228372,1472,47.611096999999994,-122.14481299999998
+1473.0,44.0,King,6,East Side,47.56135851143754,-122.13950817482365,1473,47.611096999999994,-122.14481299999998
+1474.0,44.0,King,6,East Side,47.56674521053264,-122.13688750412865,1474,47.611096999999994,-122.14481299999998
+1475.0,44.0,King,6,East Side,47.57343399210517,-122.1330483243482,1475,47.611096999999994,-122.14481299999998
+1476.0,44.0,King,6,East Side,47.57653527905928,-122.13328954980685,1476,47.611096999999994,-122.14481299999998
+1477.0,44.0,King,6,East Side,47.563532910088966,-122.12630535208471,1477,47.611096999999994,-122.14481299999998
+1478.0,44.0,King,6,East Side,47.570173993165206,-122.12720321824474,1478,47.611096999999994,-122.14481299999998
+1479.0,42.0,King,6,East Side,47.569937107087185,-122.11477232024141,1479,47.611096999999994,-122.14481299999998
+1480.0,43.0,King,6,East Side,47.533502274721116,-122.23418108239392,1480,47.611096999999994,-122.14481299999998
+1481.0,43.0,King,6,East Side,47.534620287442294,-122.22123052653193,1481,47.611096999999994,-122.14481299999998
+1482.0,43.0,King,6,East Side,47.54390657130652,-122.2285010059683,1482,47.611096999999994,-122.14481299999998
+1483.0,43.0,King,6,East Side,47.54904231254567,-122.23347894658436,1483,47.611096999999994,-122.14481299999998
+1484.0,43.0,King,6,East Side,47.55399348748953,-122.22491336540251,1484,47.611096999999994,-122.14481299999998
+1485.0,43.0,King,6,East Side,47.54830202822134,-122.21696827476143,1485,47.611096999999994,-122.14481299999998
+1486.0,43.0,King,6,East Side,47.54742457992688,-122.21105711251292,1486,47.611096999999994,-122.14481299999998
+1487.0,43.0,King,6,East Side,47.56142463298113,-122.21412305231512,1487,47.611096999999994,-122.14481299999998
+1488.0,43.0,King,6,East Side,47.56367052239265,-122.22120427331043,1488,47.611096999999994,-122.14481299999998
+1489.0,43.0,King,6,East Side,47.56793962463761,-122.23033241264535,1489,47.611096999999994,-122.14481299999998
+1490.0,43.0,King,6,East Side,47.570942718939236,-122.22155872936196,1490,47.611096999999994,-122.14481299999998
+1491.0,43.0,King,6,East Side,47.57317784897812,-122.21027755481529,1491,47.611096999999994,-122.14481299999998
+1492.0,43.0,King,6,East Side,47.57968254450734,-122.2238381446368,1492,47.611096999999994,-122.14481299999998
+1493.0,43.0,King,6,East Side,47.577064703359525,-122.23461247559187,1493,47.611096999999994,-122.14481299999998
+1494.0,43.0,King,6,East Side,47.58479969922714,-122.23742309128056,1494,47.611096999999994,-122.14481299999998
+1495.0,43.0,King,6,East Side,47.5839707523088,-122.24696073353823,1495,47.611096999999994,-122.14481299999998
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+1500.0,47.0,King,6,East Side,47.5931478233085,-122.20569983953816,1500,47.611096999999994,-122.14481299999998
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+1502.0,47.0,King,6,East Side,47.58213384878758,-122.19249802477243,1502,47.611096999999994,-122.14481299999998
+1503.0,47.0,King,6,East Side,47.58955500425103,-122.19453997855615,1503,47.611096999999994,-122.14481299999998
+1504.0,47.0,King,6,East Side,47.5921543037047,-122.18963073186787,1504,47.611096999999994,-122.14481299999998
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+1512.0,44.0,King,6,East Side,47.58549286903978,-122.14939743367054,1512,47.611096999999994,-122.14481299999998
+1513.0,44.0,King,6,East Side,47.592179527476,-122.14756142014453,1513,47.611096999999994,-122.14481299999998
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+1515.0,44.0,King,6,East Side,47.588019283803966,-122.13770295574858,1515,47.611096999999994,-122.14481299999998
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+1564.0,47.0,King,6,East Side,47.61349011925375,-122.14825038971328,1564,47.611096999999994,-122.14481299999998
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+1567.0,48.0,King,6,East Side,47.6213092865931,-122.13924168608351,1567,47.611096999999994,-122.14481299999998
+1568.0,48.0,King,6,East Side,47.623782382282045,-122.1350320804587,1568,47.611096999999994,-122.14481299999998
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+1570.0,48.0,King,6,East Side,47.621630389830415,-122.12737605825029,1570,47.611096999999994,-122.14481299999998
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+1572.0,48.0,King,6,East Side,47.61969446147553,-122.11618243415971,1572,47.611096999999994,-122.14481299999998
+1573.0,48.0,King,6,East Side,47.61623085749743,-122.10524096419952,1573,47.611096999999994,-122.14481299999998
+1574.0,48.0,King,6,East Side,47.62496400575059,-122.09418356387305,1574,47.611096999999994,-122.14481299999998
+1575.0,48.0,King,6,East Side,47.626222722535545,-122.1038354475908,1575,47.611096999999994,-122.14481299999998
+1576.0,48.0,King,6,East Side,47.62413211378072,-122.11363115405642,1576,47.611096999999994,-122.14481299999998
+1577.0,48.0,King,6,East Side,47.6288157175409,-122.11515524040684,1577,47.611096999999994,-122.14481299999998
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+1579.0,51.0,King,6,East Side,47.629501502171536,-122.139227360759,1579,47.611096999999994,-122.14481299999998
+1580.0,51.0,King,6,East Side,47.62659473114184,-122.1491913620587,1580,47.611096999999994,-122.14481299999998
+1581.0,51.0,King,6,East Side,47.63002428343706,-122.14711067919214,1581,47.611096999999994,-122.14481299999998
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+1585.0,51.0,King,6,East Side,47.62531069599683,-122.17120154753229,1585,47.611096999999994,-122.14481299999998
+1586.0,51.0,King,6,East Side,47.62629387904519,-122.17914994659313,1586,47.611096999999994,-122.14481299999998
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+1588.0,50.0,King,6,East Side,47.62587134678525,-122.18979827287492,1588,47.611096999999994,-122.14481299999998
+1589.0,50.0,King,6,East Side,47.62621747262058,-122.19360656958908,1589,47.611096999999994,-122.14481299999998
+1590.0,50.0,King,6,East Side,47.62651175933814,-122.19897375871365,1590,47.611096999999994,-122.14481299999998
+1591.0,50.0,King,6,East Side,47.62652055456007,-122.20434479477959,1591,47.611096999999994,-122.14481299999998
+1592.0,50.0,King,6,East Side,47.62107546603505,-122.21245001110492,1592,47.611096999999994,-122.14481299999998
+1593.0,50.0,King,6,East Side,47.62833925947703,-122.21243521521579,1593,47.611096999999994,-122.14481299999998
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+1600.0,50.0,King,6,East Side,47.64576098448217,-122.21756172828646,1600,47.611096999999994,-122.14481299999998
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+1604.0,50.0,King,6,East Side,47.63537027774725,-122.19637333702343,1604,47.611096999999994,-122.14481299999998
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+1606.0,51.0,King,6,East Side,47.63982574742112,-122.19196508236328,1606,47.611096999999994,-122.14481299999998
+1607.0,51.0,King,6,East Side,47.64252892927245,-122.18972756655113,1607,47.611096999999994,-122.14481299999998
+1608.0,51.0,King,6,East Side,47.63997302808918,-122.18045255648424,1608,47.611096999999994,-122.14481299999998
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+1610.0,51.0,King,6,East Side,47.63888846230299,-122.15787991756822,1610,47.611096999999994,-122.14481299999998
+1611.0,51.0,King,6,East Side,47.63903255482934,-122.14848805147928,1611,47.611096999999994,-122.14481299999998
+1612.0,51.0,King,6,East Side,47.63618787476409,-122.13624743788931,1612,47.611096999999994,-122.14481299999998
+1613.0,51.0,King,6,East Side,47.64140085091771,-122.13910398565993,1613,47.611096999999994,-122.14481299999998
+1614.0,51.0,King,6,East Side,47.63368570346992,-122.1260504708044,1614,47.611096999999994,-122.14481299999998
+1615.0,51.0,King,6,East Side,47.63877502444109,-122.1289159108922,1615,47.611096999999994,-122.14481299999998
+1616.0,51.0,King,6,East Side,47.644272161939966,-122.12768824784678,1616,47.611096999999994,-122.14481299999998
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+1708.0,53.0,King,6,East Side,47.701760365010394,-122.11724033102549,1708,47.611096999999994,-122.14481299999998
+1709.0,53.0,King,6,East Side,47.69668999158736,-122.09914665356104,1709,47.611096999999994,-122.14481299999998
+1710.0,53.0,King,6,East Side,47.70166909553318,-122.10194209410092,1710,47.611096999999994,-122.14481299999998
+1711.0,61.0,King,6,East Side,47.70970362099446,-122.09488097468865,1711,47.611096999999994,-122.14481299999998
+1712.0,61.0,King,6,East Side,47.70740861923722,-122.1052825733362,1712,47.611096999999994,-122.14481299999998
+1713.0,61.0,King,6,East Side,47.71184808894744,-122.11110656144136,1713,47.611096999999994,-122.14481299999998
+1714.0,61.0,King,6,East Side,47.70705959240654,-122.12207926196423,1714,47.611096999999994,-122.14481299999998
+1715.0,52.0,King,6,East Side,47.70479704430225,-122.16895167639143,1715,47.611096999999994,-122.14481299999998
+1716.0,53.0,King,6,East Side,47.7088366652544,-122.16592035169859,1716,47.611096999999994,-122.14481299999998
+1717.0,56.0,King,6,East Side,47.707304340424436,-122.17615369804196,1717,47.611096999999994,-122.14481299999998
+1718.0,56.0,King,6,East Side,47.70768003655633,-122.1903129806683,1718,47.611096999999994,-122.14481299999998
+1719.0,56.0,King,6,East Side,47.70698667138156,-122.20419781134164,1719,47.611096999999994,-122.14481299999998
+1720.0,55.0,King,6,East Side,47.707592652816885,-122.21445038551249,1720,47.611096999999994,-122.14481299999998
+1721.0,55.0,King,6,East Side,47.70547660248428,-122.22847183668655,1721,47.611096999999994,-122.14481299999998
+1722.0,55.0,King,6,East Side,47.705862498707894,-122.24092626938372,1722,47.611096999999994,-122.14481299999998
+1723.0,55.0,King,6,East Side,47.718328560064364,-122.24806757354703,1723,47.611096999999994,-122.14481299999998
+1724.0,55.0,King,6,East Side,47.714982417521604,-122.22833443720268,1724,47.611096999999994,-122.14481299999998
+1725.0,55.0,King,6,East Side,47.72393871976036,-122.23525714514936,1725,47.611096999999994,-122.14481299999998
+1726.0,55.0,King,6,East Side,47.72587464923074,-122.22565359742292,1726,47.611096999999994,-122.14481299999998
+1727.0,55.0,King,6,East Side,47.71464222038714,-122.21288490021746,1727,47.611096999999994,-122.14481299999998
+1728.0,55.0,King,6,East Side,47.7218141108541,-122.2147659020172,1728,47.611096999999994,-122.14481299999998
+1729.0,56.0,King,6,East Side,47.71484024326135,-122.20241809170021,1729,47.611096999999994,-122.14481299999998
+1730.0,55.0,King,6,East Side,47.72167623025642,-122.20151932048613,1730,47.611096999999994,-122.14481299999998
+1731.0,56.0,King,6,East Side,47.714544464726046,-122.19094083053554,1731,47.611096999999994,-122.14481299999998
+1732.0,55.0,King,6,East Side,47.72197641033106,-122.19278116476715,1732,47.611096999999994,-122.14481299999998
+1733.0,56.0,King,6,East Side,47.71442470950689,-122.17520030232343,1733,47.611096999999994,-122.14481299999998
+1734.0,56.0,King,6,East Side,47.72222611293726,-122.18202799515068,1734,47.611096999999994,-122.14481299999998
+1735.0,61.0,King,6,East Side,47.72221805546829,-122.16987950846735,1735,47.611096999999994,-122.14481299999998
+1736.0,61.0,King,6,East Side,47.71494337347225,-122.15938393726921,1736,47.611096999999994,-122.14481299999998
+1737.0,61.0,King,6,East Side,47.72473039298325,-122.15837005640518,1737,47.611096999999994,-122.14481299999998
+1738.0,61.0,King,6,East Side,47.721632747283316,-122.14808046414906,1738,47.611096999999994,-122.14481299999998
+1739.0,61.0,King,6,East Side,47.72008449877532,-122.13869904582806,1739,47.611096999999994,-122.14481299999998
+1740.0,61.0,King,6,East Side,47.71510696005646,-122.1226348721764,1740,47.611096999999994,-122.14481299999998
+1741.0,61.0,King,6,East Side,47.71840247510429,-122.09860644232357,1741,47.611096999999994,-122.14481299999998
+1742.0,61.0,King,6,East Side,47.727762717394235,-122.09189669548296,1742,47.611096999999994,-122.14481299999998
+1743.0,61.0,King,6,East Side,47.7264014681761,-122.1081732303438,1743,47.611096999999994,-122.14481299999998
+1744.0,61.0,King,6,East Side,47.73447803523421,-122.09816753104471,1744,47.611096999999994,-122.14481299999998
+1745.0,61.0,King,6,East Side,47.727544945665635,-122.12147830196231,1745,47.611096999999994,-122.14481299999998
+1746.0,61.0,King,6,East Side,47.728289741242655,-122.13243133837996,1746,47.611096999999994,-122.14481299999998
+1747.0,60.0,King,6,East Side,47.73339327011762,-122.15926820375992,1747,47.611096999999994,-122.14481299999998
+1748.0,61.0,King,6,East Side,47.7283547546581,-122.17004581747852,1748,47.611096999999994,-122.14481299999998
+1749.0,60.0,King,6,East Side,47.733879950174895,-122.16953735589294,1749,47.611096999999994,-122.14481299999998
+1750.0,56.0,King,6,East Side,47.72945263649096,-122.18216607838677,1750,47.611096999999994,-122.14481299999998
+1751.0,55.0,King,6,East Side,47.72904608992506,-122.19379290303404,1751,47.611096999999994,-122.14481299999998
+1752.0,55.0,King,6,East Side,47.728698569214366,-122.2035309589812,1752,47.611096999999994,-122.14481299999998
+1753.0,55.0,King,6,East Side,47.72938385712308,-122.21576773747914,1753,47.611096999999994,-122.14481299999998
+1754.0,59.0,King,6,East Side,47.734036020979225,-122.23332809721741,1754,47.611096999999994,-122.14481299999998
+1755.0,59.0,King,6,East Side,47.73499781835517,-122.24231403971581,1755,47.611096999999994,-122.14481299999998
+1756.0,55.0,King,6,East Side,47.72953730930174,-122.25517670604552,1756,47.611096999999994,-122.14481299999998
+1757.0,59.0,King,6,East Side,47.74305185324648,-122.25678868940993,1757,47.611096999999994,-122.14481299999998
+1758.0,59.0,King,6,East Side,47.756765568456075,-122.25524839731108,1758,47.611096999999994,-122.14481299999998
+1759.0,59.0,King,6,East Side,47.74661174705739,-122.24469384140822,1759,47.611096999999994,-122.14481299999998
+1760.0,59.0,King,6,East Side,47.755840421020935,-122.24373701649185,1760,47.611096999999994,-122.14481299999998
+1761.0,59.0,King,6,East Side,47.74527780295537,-122.234469863307,1761,47.611096999999994,-122.14481299999998
+1762.0,59.0,King,6,East Side,47.75254523978465,-122.23199254587765,1762,47.611096999999994,-122.14481299999998
+1763.0,59.0,King,6,East Side,47.73715650221853,-122.2251056250095,1763,47.611096999999994,-122.14481299999998
+1764.0,59.0,King,6,East Side,47.745258114871035,-122.22476650178403,1764,47.611096999999994,-122.14481299999998
+1765.0,59.0,King,6,East Side,47.73632120047734,-122.21355259498192,1765,47.611096999999994,-122.14481299999998
+1766.0,59.0,King,6,East Side,47.746312648828784,-122.21677284739893,1766,47.611096999999994,-122.14481299999998
+1767.0,60.0,King,6,East Side,47.742045916690884,-122.20616993415312,1767,47.611096999999994,-122.14481299999998
+1768.0,60.0,King,6,East Side,47.75461557591749,-122.20999642042601,1768,47.611096999999994,-122.14481299999998
+1769.0,60.0,King,6,East Side,47.752759627324856,-122.2033155756851,1769,47.611096999999994,-122.14481299999998
+1770.0,60.0,King,6,East Side,47.73903008961675,-122.19659788826144,1770,47.611096999999994,-122.14481299999998
+1771.0,60.0,King,6,East Side,47.73654349350493,-122.18992656441498,1771,47.611096999999994,-122.14481299999998
+1772.0,60.0,King,6,East Side,47.749006336381065,-122.19291330966487,1772,47.611096999999994,-122.14481299999998
+1773.0,60.0,King,6,East Side,47.75652242020065,-122.19305659666551,1773,47.611096999999994,-122.14481299999998
+1774.0,60.0,King,6,East Side,47.73837019041435,-122.181288621627,1774,47.611096999999994,-122.14481299999998
+1775.0,60.0,King,6,East Side,47.75097982863089,-122.18363302605972,1775,47.611096999999994,-122.14481299999998
+1776.0,60.0,King,6,East Side,47.74912349481354,-122.17822435167034,1776,47.611096999999994,-122.14481299999998
+1777.0,60.0,King,6,East Side,47.75638960449008,-122.17362006923187,1777,47.611096999999994,-122.14481299999998
+1778.0,60.0,King,6,East Side,47.74327735675213,-122.16755645326396,1778,47.611096999999994,-122.14481299999998
+1779.0,60.0,King,6,East Side,47.7409954090639,-122.15612483635643,1779,47.611096999999994,-122.14481299999998
+1780.0,61.0,King,6,East Side,47.742690387122046,-122.15053752473328,1780,47.611096999999994,-122.14481299999998
+1781.0,61.0,King,6,East Side,47.752030225207825,-122.16028823203176,1781,47.611096999999994,-122.14481299999998
+1782.0,61.0,King,6,East Side,47.74471477049934,-122.13848276378627,1782,47.611096999999994,-122.14481299999998
+1783.0,61.0,King,6,East Side,47.757139685853865,-122.14115855775276,1783,47.611096999999994,-122.14481299999998
+1784.0,61.0,King,6,East Side,47.746040722194046,-122.1219248709126,1784,47.611096999999994,-122.14481299999998
+1785.0,61.0,King,6,East Side,47.75702875223056,-122.11984990427759,1785,47.611096999999994,-122.14481299999998
+1786.0,61.0,King,6,East Side,47.741794869370715,-122.10756761330136,1786,47.611096999999994,-122.14481299999998
+1787.0,61.0,King,6,East Side,47.754628095767906,-122.1009848730793,1787,47.611096999999994,-122.14481299999998
+1788.0,61.0,King,6,East Side,47.741689000611686,-122.09064154365791,1788,47.611096999999994,-122.14481299999998
+1789.0,61.0,King,6,East Side,47.75248416734849,-122.08749299494116,1789,47.611096999999994,-122.14481299999998
+1790.0,61.0,King,6,East Side,47.76700885665564,-122.09381702622515,1790,47.611096999999994,-122.14481299999998
+1791.0,61.0,King,6,East Side,47.768406645879686,-122.1180117815527,1791,47.611096999999994,-122.14481299999998
+1792.0,61.0,King,6,East Side,47.77004207824563,-122.14210929681593,1792,47.611096999999994,-122.14481299999998
+1793.0,61.0,King,6,East Side,47.761987368736804,-122.15021296166316,1793,47.611096999999994,-122.14481299999998
+1794.0,61.0,King,6,East Side,47.75910993316231,-122.15765580964748,1794,47.611096999999994,-122.14481299999998
+1795.0,60.0,King,6,East Side,47.76950420069638,-122.16643798201969,1795,47.611096999999994,-122.14481299999998
+1796.0,60.0,King,6,East Side,47.76303868558937,-122.17730891338614,1796,47.611096999999994,-122.14481299999998
+1797.0,60.0,King,6,East Side,47.77273528997645,-122.18525715239656,1797,47.611096999999994,-122.14481299999998
+1798.0,60.0,King,6,East Side,47.760749372849176,-122.19147149922993,1798,47.611096999999994,-122.14481299999998
+1799.0,60.0,King,6,East Side,47.76435411379241,-122.19679736038117,1799,47.611096999999994,-122.14481299999998
+1800.0,60.0,King,6,East Side,47.77141043774184,-122.19623282207596,1800,47.611096999999994,-122.14481299999998
+1801.0,60.0,King,6,East Side,47.76259428539053,-122.2046264881572,1801,47.611096999999994,-122.14481299999998
+1802.0,60.0,King,6,East Side,47.77165350092416,-122.2080168283595,1802,47.611096999999994,-122.14481299999998
+1803.0,60.0,King,6,East Side,47.75466384381085,-122.21555588950069,1803,47.611096999999994,-122.14481299999998
+1804.0,60.0,King,6,East Side,47.762297917150924,-122.21571763898199,1804,47.611096999999994,-122.14481299999998
+1805.0,60.0,King,6,East Side,47.77060071849318,-122.21844528284365,1805,47.611096999999994,-122.14481299999998
+1806.0,59.0,King,6,East Side,47.75582704698432,-122.22390230529655,1806,47.611096999999994,-122.14481299999998
+1807.0,59.0,King,6,East Side,47.76181092080157,-122.22852377716923,1807,47.611096999999994,-122.14481299999998
+1808.0,59.0,King,6,East Side,47.77174437875394,-122.22845946579838,1808,47.611096999999994,-122.14481299999998
+1809.0,59.0,King,6,East Side,47.761981058588105,-122.23831737990156,1809,47.611096999999994,-122.14481299999998
+1810.0,59.0,King,6,East Side,47.77266542838976,-122.23859455903036,1810,47.611096999999994,-122.14481299999998
+1811.0,59.0,King,6,East Side,47.767235532103975,-122.24657964738212,1811,47.611096999999994,-122.14481299999998
+1812.0,59.0,King,6,East Side,47.76079594786474,-122.25657723293313,1812,47.611096999999994,-122.14481299999998
+1813.0,59.0,King,6,East Side,47.766336981094646,-122.25662700483707,1813,47.611096999999994,-122.14481299999998
+1814.0,59.0,King,6,East Side,47.7728821584956,-122.25600976358908,1814,47.611096999999994,-122.14481299999998
+1815.0,59.0,King,6,East Side,47.77321446121727,-122.26591060464656,1815,47.611096999999994,-122.14481299999998
+1816.0,58.0,King,3,North Seattle-Shoreline,47.769924228398295,-122.27376440273574,1816,47.717471,-122.338958
+1817.0,58.0,King,3,North Seattle-Shoreline,47.772516366635294,-122.28206490002277,1817,47.717471,-122.338958
+1818.0,58.0,King,3,North Seattle-Shoreline,47.7672245388704,-122.28839264645306,1818,47.717471,-122.338958
+1819.0,58.0,King,3,North Seattle-Shoreline,47.77402085233186,-122.29419624102249,1819,47.717471,-122.338958
+1820.0,58.0,King,3,North Seattle-Shoreline,47.76765326534482,-122.29556772564388,1820,47.717471,-122.338958
+1821.0,58.0,King,3,North Seattle-Shoreline,47.77416970960633,-122.30252314950759,1821,47.717471,-122.338958
+1822.0,58.0,King,3,North Seattle-Shoreline,47.766563553082584,-122.30187487824514,1822,47.717471,-122.338958
+1823.0,58.0,King,3,North Seattle-Shoreline,47.77442095672324,-122.31139080873535,1823,47.717471,-122.338958
+1824.0,58.0,King,3,North Seattle-Shoreline,47.76702168455631,-122.31042339744673,1824,47.717471,-122.338958
+1825.0,58.0,King,3,North Seattle-Shoreline,47.77343143792129,-122.31646199899643,1825,47.717471,-122.338958
+1826.0,58.0,King,3,North Seattle-Shoreline,47.76664985422639,-122.31857169721813,1826,47.717471,-122.338958
+1827.0,58.0,King,3,North Seattle-Shoreline,47.774231816119006,-122.32440091177564,1827,47.717471,-122.338958
+1828.0,58.0,King,3,North Seattle-Shoreline,47.76697621777996,-122.32614836174945,1828,47.717471,-122.338958
+1829.0,57.0,King,3,North Seattle-Shoreline,47.77408272597703,-122.33256053773769,1829,47.717471,-122.338958
+1830.0,57.0,King,3,North Seattle-Shoreline,47.766881698403104,-122.33241090557571,1830,47.717471,-122.338958
+1831.0,57.0,King,3,North Seattle-Shoreline,47.774448289807474,-122.34031618008005,1831,47.717471,-122.338958
+1832.0,57.0,King,3,North Seattle-Shoreline,47.76727860293227,-122.3407990743614,1832,47.717471,-122.338958
+1833.0,57.0,King,3,North Seattle-Shoreline,47.77413749467515,-122.35094561358524,1833,47.717471,-122.338958
+1834.0,57.0,King,3,North Seattle-Shoreline,47.76687055009336,-122.34858191652654,1834,47.717471,-122.338958
+1835.0,57.0,King,3,North Seattle-Shoreline,47.77412259896872,-122.36124265457987,1835,47.717471,-122.338958
+1836.0,57.0,King,3,North Seattle-Shoreline,47.76745784789267,-122.35600848162115,1836,47.717471,-122.338958
+1837.0,57.0,King,3,North Seattle-Shoreline,47.76781255920603,-122.3639148948556,1837,47.717471,-122.338958
+1838.0,57.0,King,3,North Seattle-Shoreline,47.772504561008226,-122.37153530435043,1838,47.717471,-122.338958
+1839.0,57.0,King,3,North Seattle-Shoreline,47.77422835207721,-122.38005845574976,1839,47.717471,-122.338958
+1840.0,57.0,King,3,North Seattle-Shoreline,47.774379437340976,-122.38838844784559,1840,47.717471,-122.338958
+1841.0,57.0,King,3,North Seattle-Shoreline,47.76725776995785,-122.38327524724613,1841,47.717471,-122.338958
+1842.0,57.0,King,3,North Seattle-Shoreline,47.76271145338654,-122.37403189164408,1842,47.717471,-122.338958
+1843.0,57.0,King,3,North Seattle-Shoreline,47.75437521644896,-122.37396661071472,1843,47.717471,-122.338958
+1844.0,57.0,King,3,North Seattle-Shoreline,47.7605275885235,-122.36017895119647,1844,47.717471,-122.338958
+1845.0,57.0,King,3,North Seattle-Shoreline,47.75365555878334,-122.3568602289851,1845,47.717471,-122.338958
+1846.0,57.0,King,3,North Seattle-Shoreline,47.76001618396607,-122.35044245942363,1846,47.717471,-122.338958
+1847.0,57.0,King,3,North Seattle-Shoreline,47.7521596432481,-122.34973607706856,1847,47.717471,-122.338958
+1848.0,57.0,King,3,North Seattle-Shoreline,47.75961869065114,-122.34046837482042,1848,47.717471,-122.338958
+1849.0,57.0,King,3,North Seattle-Shoreline,47.75234676623341,-122.34017851310928,1849,47.717471,-122.338958
+1850.0,57.0,King,3,North Seattle-Shoreline,47.76005682258357,-122.33043217657087,1850,47.717471,-122.338958
+1851.0,57.0,King,3,North Seattle-Shoreline,47.75220504519026,-122.33219558580728,1851,47.717471,-122.338958
+1852.0,57.0,King,3,North Seattle-Shoreline,47.75817764605253,-122.32579043942208,1852,47.717471,-122.338958
+1853.0,57.0,King,3,North Seattle-Shoreline,47.75234742049432,-122.3268451024073,1853,47.717471,-122.338958
+1854.0,58.0,King,3,North Seattle-Shoreline,47.759737703760464,-122.31834559899806,1854,47.717471,-122.338958
+1855.0,58.0,King,3,North Seattle-Shoreline,47.75215393991133,-122.31865564643999,1855,47.717471,-122.338958
+1856.0,58.0,King,3,North Seattle-Shoreline,47.76122565752561,-122.3045406036034,1856,47.717471,-122.338958
+1857.0,58.0,King,3,North Seattle-Shoreline,47.757307431728144,-122.30815032528179,1857,47.717471,-122.338958
+1858.0,58.0,King,3,North Seattle-Shoreline,47.75169137232065,-122.30834030295914,1858,47.717471,-122.338958
+1859.0,58.0,King,3,North Seattle-Shoreline,47.7523165019705,-122.29559379273223,1859,47.717471,-122.338958
+1860.0,58.0,King,3,North Seattle-Shoreline,47.75885141120234,-122.28923186410229,1860,47.717471,-122.338958
+1861.0,58.0,King,3,North Seattle-Shoreline,47.7532804441621,-122.28372557240273,1861,47.717471,-122.338958
+1862.0,58.0,King,3,North Seattle-Shoreline,47.76033875736785,-122.27753426937154,1862,47.717471,-122.338958
+1863.0,58.0,King,3,North Seattle-Shoreline,47.76285030101479,-122.26704062141157,1863,47.717471,-122.338958
+1864.0,58.0,King,3,North Seattle-Shoreline,47.75620507366503,-122.26906242110391,1864,47.717471,-122.338958
+1865.0,58.0,King,3,North Seattle-Shoreline,47.749326119308996,-122.28099584934921,1865,47.717471,-122.338958
+1866.0,58.0,King,3,North Seattle-Shoreline,47.738096071079504,-122.2874294052972,1866,47.717471,-122.338958
+1867.0,58.0,King,3,North Seattle-Shoreline,47.744608928329704,-122.29411672543716,1867,47.717471,-122.338958
+1868.0,58.0,King,3,North Seattle-Shoreline,47.73764471712769,-122.29648355872999,1868,47.717471,-122.338958
+1869.0,58.0,King,3,North Seattle-Shoreline,47.74294632666816,-122.3075170510684,1869,47.717471,-122.338958
+1870.0,58.0,King,3,North Seattle-Shoreline,47.73570077672861,-122.30738378289705,1870,47.717471,-122.338958
+1871.0,58.0,King,3,North Seattle-Shoreline,47.74489280326805,-122.31841977891592,1871,47.717471,-122.338958
+1872.0,58.0,King,3,North Seattle-Shoreline,47.737625017346964,-122.31822883548529,1872,47.717471,-122.338958
+1873.0,57.0,King,3,North Seattle-Shoreline,47.7448824196356,-122.32659126054644,1873,47.717471,-122.338958
+1874.0,57.0,King,3,North Seattle-Shoreline,47.73850763995699,-122.32543614374651,1874,47.717471,-122.338958
+1875.0,57.0,King,3,North Seattle-Shoreline,47.74512327953526,-122.33196771680583,1875,47.717471,-122.338958
+1876.0,57.0,King,3,North Seattle-Shoreline,47.73741679678482,-122.33047249619312,1876,47.717471,-122.338958
+1877.0,57.0,King,3,North Seattle-Shoreline,47.74506167253188,-122.33998923406826,1877,47.717471,-122.338958
+1878.0,57.0,King,3,North Seattle-Shoreline,47.73777431562704,-122.33978683858179,1878,47.717471,-122.338958
+1879.0,57.0,King,3,North Seattle-Shoreline,47.74375482404809,-122.351459682724,1879,47.717471,-122.338958
+1880.0,57.0,King,3,North Seattle-Shoreline,47.73738246624588,-122.34886919197969,1880,47.717471,-122.338958
+1881.0,57.0,King,3,North Seattle-Shoreline,47.75093378396902,-122.36301766667788,1881,47.717471,-122.338958
+1882.0,57.0,King,3,North Seattle-Shoreline,47.741816076479466,-122.3679062635046,1882,47.717471,-122.338958
+1883.0,61.0,King,6,East Side,47.76879943137312,-122.07642316433143,1883,47.611096999999994,-122.14481299999998
+1884.0,61.0,King,6,East Side,47.7583197147248,-122.07322163582874,1884,47.611096999999994,-122.14481299999998
+1885.0,62.0,King,6,East Side,47.75062986121006,-122.06897872577196,1885,47.611096999999994,-122.14481299999998
+1886.0,62.0,King,6,East Side,47.74228347017175,-122.07336978982624,1886,47.611096999999994,-122.14481299999998
+1887.0,62.0,King,6,East Side,47.76428095600174,-122.05479659889579,1887,47.611096999999994,-122.14481299999998
+1888.0,62.0,King,6,East Side,47.74143986983527,-122.05684623031095,1888,47.611096999999994,-122.14481299999998
+1889.0,62.0,King,6,East Side,47.74327737710714,-122.03935870309584,1889,47.611096999999994,-122.14481299999998
+1890.0,62.0,King,6,East Side,47.76930244433037,-122.03477010571092,1890,47.611096999999994,-122.14481299999998
+1891.0,62.0,King,6,East Side,47.762036907167406,-122.01387038739628,1891,47.611096999999994,-122.14481299999998
+1892.0,62.0,King,6,East Side,47.74567879577262,-122.02364892015595,1892,47.611096999999994,-122.14481299999998
+1893.0,62.0,King,6,East Side,47.76243096880662,-121.98459111947872,1893,47.611096999999994,-122.14481299999998
+1894.0,62.0,King,6,East Side,47.76085228262763,-121.92425003545796,1894,47.611096999999994,-122.14481299999998
+1895.0,63.0,King,6,East Side,47.739984192583364,-121.78628631634658,1895,47.611096999999994,-122.14481299999998
+1896.0,63.0,King,6,East Side,47.700387053956966,-121.86553218108736,1896,47.611096999999994,-122.14481299999998
+1897.0,62.0,King,6,East Side,47.72845432435714,-121.9392404855127,1897,47.611096999999994,-122.14481299999998
+1898.0,62.0,King,6,East Side,47.72676219617586,-121.99947131675889,1898,47.611096999999994,-122.14481299999998
+1899.0,62.0,King,6,East Side,47.724379435958525,-122.01799501668772,1899,47.611096999999994,-122.14481299999998
+1900.0,62.0,King,6,East Side,47.7097132917581,-122.01741071238676,1900,47.611096999999994,-122.14481299999998
+1901.0,62.0,King,6,East Side,47.72606996359336,-122.0396733561474,1901,47.611096999999994,-122.14481299999998
+1902.0,62.0,King,6,East Side,47.70783112523442,-122.03810647246205,1902,47.611096999999994,-122.14481299999998
+1903.0,62.0,King,6,East Side,47.72516974696701,-122.06178584430893,1903,47.611096999999994,-122.14481299999998
+1904.0,62.0,King,6,East Side,47.709022236569396,-122.05938412932949,1904,47.611096999999994,-122.14481299999998
+1905.0,62.0,King,6,East Side,47.69386777121434,-122.06571155557691,1905,47.611096999999994,-122.14481299999998
+1906.0,62.0,King,6,East Side,47.72754259235285,-122.07771834482227,1906,47.611096999999994,-122.14481299999998
+1907.0,62.0,King,6,East Side,47.70926981001414,-122.07996749935265,1907,47.611096999999994,-122.14481299999998
+1908.0,62.0,King,6,East Side,47.69695101962647,-122.08371085586954,1908,47.611096999999994,-122.14481299999998
+1909.0,54.0,King,6,East Side,47.68252116824481,-122.09519826812195,1909,47.611096999999994,-122.14481299999998
+1910.0,54.0,King,6,East Side,47.67984745929977,-122.08743533705768,1910,47.611096999999994,-122.14481299999998
+1911.0,54.0,King,6,East Side,47.680998051230134,-122.07091616833316,1911,47.611096999999994,-122.14481299999998
+1912.0,54.0,King,6,East Side,47.68125291662351,-122.0542805270656,1912,47.611096999999994,-122.14481299999998
+1913.0,54.0,King,6,East Side,47.68383744582668,-122.03948444084436,1913,47.611096999999994,-122.14481299999998
+1914.0,54.0,King,6,East Side,47.688094328218774,-122.02058817306549,1914,47.611096999999994,-122.14481299999998
+1915.0,54.0,King,6,East Side,47.66832773932722,-122.01517152104708,1915,47.611096999999994,-122.14481299999998
+1916.0,54.0,King,6,East Side,47.67345303296385,-121.98825576453696,1916,47.611096999999994,-122.14481299999998
+1917.0,54.0,King,6,East Side,47.68247734074136,-121.96712748123664,1917,47.611096999999994,-122.14481299999998
+1918.0,62.0,King,6,East Side,47.70055516772394,-121.94954133894056,1918,47.611096999999994,-122.14481299999998
+1919.0,63.0,King,6,East Side,47.66472752100474,-121.89745414691177,1919,47.611096999999994,-122.14481299999998
+1920.0,63.0,King,6,East Side,47.64537878016469,-121.85957466602704,1920,47.611096999999994,-122.14481299999998
+1921.0,54.0,King,6,East Side,47.656400825137304,-121.94435425637265,1921,47.611096999999994,-122.14481299999998
+1922.0,54.0,King,6,East Side,47.634183369141645,-121.96610981710172,1922,47.611096999999994,-122.14481299999998
+1923.0,54.0,King,6,East Side,47.64385936808546,-121.98791674382912,1923,47.611096999999994,-122.14481299999998
+1924.0,54.0,King,6,East Side,47.65262044172925,-122.01402871631944,1924,47.611096999999994,-122.14481299999998
+1925.0,54.0,King,6,East Side,47.65606546309879,-122.02977231131598,1925,47.611096999999994,-122.14481299999998
+1926.0,54.0,King,6,East Side,47.664653368553154,-122.05052256869848,1926,47.611096999999994,-122.14481299999998
+1927.0,54.0,King,6,East Side,47.65537978281714,-122.04826773082115,1927,47.611096999999994,-122.14481299999998
+1928.0,54.0,King,6,East Side,47.6646594465794,-122.07415558806431,1928,47.611096999999994,-122.14481299999998
+1929.0,54.0,King,6,East Side,47.67049867248335,-122.08937001764528,1929,47.611096999999994,-122.14481299999998
+1930.0,54.0,King,6,East Side,47.66227782510269,-122.08861237824928,1930,47.611096999999994,-122.14481299999998
+1931.0,54.0,King,6,East Side,47.667557549305364,-122.09664229882178,1931,47.611096999999994,-122.14481299999998
+1932.0,54.0,King,6,East Side,47.67108663466709,-122.10319808644388,1932,47.611096999999994,-122.14481299999998
+1933.0,49.0,King,6,East Side,47.64974530329178,-122.08497045514088,1933,47.611096999999994,-122.14481299999998
+1934.0,49.0,King,6,East Side,47.64974025808763,-122.0689435709602,1934,47.611096999999994,-122.14481299999998
+1935.0,49.0,King,6,East Side,47.63885392172353,-122.07044552046541,1935,47.611096999999994,-122.14481299999998
+1936.0,49.0,King,6,East Side,47.62343022383335,-122.06223439378269,1936,47.611096999999994,-122.14481299999998
+1937.0,49.0,King,6,East Side,47.63418982788778,-122.05456540946643,1937,47.611096999999994,-122.14481299999998
+1938.0,49.0,King,6,East Side,47.63107070102392,-122.04106749209144,1938,47.611096999999994,-122.14481299999998
+1939.0,49.0,King,6,East Side,47.62111569902711,-122.04369934933766,1939,47.611096999999994,-122.14481299999998
+1940.0,49.0,King,6,East Side,47.64442975715738,-122.04038737763649,1940,47.611096999999994,-122.14481299999998
+1941.0,49.0,King,6,East Side,47.63364391275944,-122.02754715563677,1941,47.611096999999994,-122.14481299999998
+1942.0,49.0,King,6,East Side,47.6219525090556,-122.02453199941084,1942,47.611096999999994,-122.14481299999998
+1943.0,46.0,King,6,East Side,47.6245361696605,-122.0066136281664,1943,47.611096999999994,-122.14481299999998
+1944.0,46.0,King,6,East Side,47.61699462884879,-121.98534062667377,1944,47.611096999999994,-122.14481299999998
+1945.0,46.0,King,6,East Side,47.603264041448796,-121.96423908639866,1945,47.611096999999994,-122.14481299999998
+1946.0,63.0,King,6,East Side,47.6089387772823,-121.94203875857045,1946,47.611096999999994,-122.14481299999998
+1947.0,63.0,King,6,East Side,47.58911839844614,-121.84130381141688,1947,47.611096999999994,-122.14481299999998
+1948.0,63.0,King,6,East Side,47.597125799106635,-121.90957666399773,1948,47.611096999999994,-122.14481299999998
+1949.0,63.0,King,6,East Side,47.58287195634322,-121.91613334846123,1949,47.611096999999994,-122.14481299999998
+1950.0,63.0,King,6,East Side,47.56786889385237,-121.91345780344272,1950,47.611096999999994,-122.14481299999998
+1951.0,46.0,King,6,East Side,47.5732276432181,-121.95970833255768,1951,47.611096999999994,-122.14481299999998
+1952.0,46.0,King,6,East Side,47.593316045340785,-121.9777573922034,1952,47.611096999999994,-122.14481299999998
+1953.0,46.0,King,6,East Side,47.607193568176115,-122.00638797477241,1953,47.611096999999994,-122.14481299999998
+1954.0,46.0,King,6,East Side,47.59446002085034,-121.99764421669596,1954,47.611096999999994,-122.14481299999998
+1955.0,46.0,King,6,East Side,47.583376588553456,-121.99568794597158,1955,47.611096999999994,-122.14481299999998
+1956.0,46.0,King,6,East Side,47.61007064195242,-122.02614310742008,1956,47.611096999999994,-122.14481299999998
+1957.0,46.0,King,6,East Side,47.59420349414812,-122.02334125285023,1957,47.611096999999994,-122.14481299999998
+1958.0,45.0,King,6,East Side,47.58430469908883,-122.02476653544927,1958,47.611096999999994,-122.14481299999998
+1959.0,49.0,King,6,East Side,47.60858703333108,-122.04254241523664,1959,47.611096999999994,-122.14481299999998
+1960.0,49.0,King,6,East Side,47.61023043160406,-122.05673410727027,1960,47.611096999999994,-122.14481299999998
+1961.0,49.0,King,6,East Side,47.59686879986994,-122.04620473798587,1961,47.611096999999994,-122.14481299999998
+1962.0,45.0,King,6,East Side,47.58645559659479,-122.04620216870882,1962,47.611096999999994,-122.14481299999998
+1963.0,49.0,King,6,East Side,47.606308166551074,-122.06780670027874,1963,47.611096999999994,-122.14481299999998
+1964.0,49.0,King,6,East Side,47.59486074466742,-122.07097475668968,1964,47.611096999999994,-122.14481299999998
+1965.0,45.0,King,6,East Side,47.58126290294572,-122.0687776513486,1965,47.611096999999994,-122.14481299999998
+1966.0,45.0,King,6,East Side,47.57433594045053,-122.04757429101377,1966,47.611096999999994,-122.14481299999998
+1967.0,45.0,King,6,East Side,47.56387805210906,-122.0440780578042,1967,47.611096999999994,-122.14481299999998
+1968.0,45.0,King,6,East Side,47.56997474926273,-122.02997253031175,1968,47.611096999999994,-122.14481299999998
+1969.0,45.0,King,6,East Side,47.55515619845586,-122.03359279731771,1969,47.611096999999994,-122.14481299999998
+1970.0,45.0,King,6,East Side,47.54893019162613,-122.03514856040869,1970,47.611096999999994,-122.14481299999998
+1971.0,46.0,King,6,East Side,47.54124349266221,-122.0264548340316,1971,47.611096999999994,-122.14481299999998
+1972.0,45.0,King,6,East Side,47.575600782095925,-122.01740466284964,1972,47.611096999999994,-122.14481299999998
+1973.0,45.0,King,6,East Side,47.5635927123189,-122.0165892786853,1973,47.611096999999994,-122.14481299999998
+1974.0,46.0,King,6,East Side,47.553367739928035,-122.00948137850621,1974,47.611096999999994,-122.14481299999998
+1975.0,46.0,King,6,East Side,47.54054147788106,-122.011859402287,1975,47.611096999999994,-122.14481299999998
+1976.0,45.0,King,6,East Side,47.57227746191843,-122.00140688383492,1976,47.611096999999994,-122.14481299999998
+1977.0,46.0,King,6,East Side,47.5468478852409,-121.99553955693158,1977,47.611096999999994,-122.14481299999998
+1978.0,46.0,King,6,East Side,47.55122861707378,-121.98437857361387,1978,47.611096999999994,-122.14481299999998
+1979.0,46.0,King,6,East Side,47.54641380949467,-121.93628220116604,1979,47.611096999999994,-122.14481299999998
+1980.0,63.0,King,6,East Side,47.54749203968518,-121.88202703791694,1980,47.611096999999994,-122.14481299999998
+1981.0,63.0,King,6,East Side,47.52012474128049,-121.9013577838513,1981,47.611096999999994,-122.14481299999998
+1982.0,63.0,King,6,East Side,47.55532473299258,-121.85788259042242,1982,47.611096999999994,-122.14481299999998
+1983.0,63.0,King,6,East Side,47.539625842630386,-121.86838427484751,1983,47.611096999999994,-122.14481299999998
+1984.0,63.0,King,6,East Side,47.51929989049991,-121.87220540883007,1984,47.611096999999994,-122.14481299999998
+1985.0,63.0,King,6,East Side,47.525960173161344,-121.84973619837272,1985,47.611096999999994,-122.14481299999998
+1986.0,63.0,King,6,East Side,47.5483769639143,-121.7826308244505,1986,47.611096999999994,-122.14481299999998
+1987.0,39.0,King,6,East Side,47.51301347926189,-121.82415446768229,1987,47.611096999999994,-122.14481299999998
+1988.0,39.0,King,6,East Side,47.51795291201127,-121.80216218819815,1988,47.611096999999994,-122.14481299999998
+1989.0,39.0,King,6,East Side,47.49145612109547,-121.79596203002598,1989,47.611096999999994,-122.14481299999998
+1990.0,63.0,King,6,East Side,47.493822313773634,-121.75792432277728,1990,47.611096999999994,-122.14481299999998
+1991.0,39.0,King,6,East Side,47.48106679622613,-121.7709597427734,1991,47.611096999999994,-122.14481299999998
+1992.0,63.0,King,6,East Side,47.54725178206128,-121.73638178945829,1992,47.611096999999994,-122.14481299999998
+1993.0,63.0,King,6,East Side,47.50539456994499,-121.73038333318728,1993,47.611096999999994,-122.14481299999998
+1994.0,63.0,King,6,East Side,47.63289020271658,-121.68054278930408,1994,47.611096999999994,-122.14481299999998
+1995.0,63.0,King,6,East Side,47.65652522923934,-121.35898280921629,1995,47.611096999999994,-122.14481299999998
+1996.0,63.0,King,6,East Side,47.47001852288986,-121.50143815723398,1996,47.611096999999994,-122.14481299999998
+1997.0,63.0,King,6,East Side,47.387053939830636,-121.58522969615049,1997,47.611096999999994,-122.14481299999998
+1998.0,39.0,King,6,East Side,47.45622640619539,-121.75172739952356,1998,47.611096999999994,-122.14481299999998
+1999.0,39.0,King,6,East Side,47.46604903894621,-121.81890214863766,1999,47.611096999999994,-122.14481299999998
+2000.0,39.0,King,6,East Side,47.43024993834025,-121.8968830367654,2000,47.611096999999994,-122.14481299999998
+2001.0,39.0,King,6,East Side,47.41817775791779,-121.98980414227532,2001,47.611096999999994,-122.14481299999998
+2002.0,39.0,King,6,East Side,47.39433339561629,-121.99334868393869,2002,47.611096999999994,-122.14481299999998
+2003.0,39.0,King,6,East Side,47.40240504675963,-122.02520033210837,2003,47.611096999999994,-122.14481299999998
+2004.0,34.0,King,7,Renton-FedWay-Kent,47.389685408668306,-122.03252384658244,2004,47.401118,-122.226377
+2005.0,34.0,King,7,Renton-FedWay-Kent,47.384950600981206,-122.043607985243,2005,47.401118,-122.226377
+2006.0,34.0,King,7,Renton-FedWay-Kent,47.38136547257348,-122.06068162517784,2006,47.401118,-122.226377
+2007.0,33.0,King,7,Renton-FedWay-Kent,47.36176031807801,-122.10926669643585,2007,47.401118,-122.226377
+2008.0,34.0,King,7,Renton-FedWay-Kent,47.36714602421853,-122.08912944072112,2008,47.401118,-122.226377
+2009.0,34.0,King,7,Renton-FedWay-Kent,47.36506449725373,-122.07386340301964,2009,47.401118,-122.226377
+2010.0,34.0,King,7,Renton-FedWay-Kent,47.37184811553778,-122.05437067079235,2010,47.401118,-122.226377
+2011.0,34.0,King,7,Renton-FedWay-Kent,47.36260839207407,-122.05518725517838,2011,47.401118,-122.226377
+2012.0,34.0,King,7,Renton-FedWay-Kent,47.37317726501654,-122.04065146032572,2012,47.401118,-122.226377
+2013.0,34.0,King,7,Renton-FedWay-Kent,47.3633505995106,-122.03325714187277,2013,47.401118,-122.226377
+2014.0,34.0,King,7,Renton-FedWay-Kent,47.373827520190325,-122.02879935583763,2014,47.401118,-122.226377
+2015.0,34.0,King,7,Renton-FedWay-Kent,47.376297477880314,-122.00600187250042,2015,47.401118,-122.226377
+2016.0,32.0,King,7,Renton-FedWay-Kent,47.36725817706116,-121.98977343734842,2016,47.401118,-122.226377
+2017.0,38.0,King,7,Renton-FedWay-Kent,47.37234149216538,-121.90130487699196,2017,47.401118,-122.226377
+2018.0,38.0,King,7,Renton-FedWay-Kent,47.30023156069403,-121.61825432956185,2018,47.401118,-122.226377
+2019.0,38.0,King,7,Renton-FedWay-Kent,47.32783359951684,-121.85297678274044,2019,47.401118,-122.226377
+2020.0,32.0,King,7,Renton-FedWay-Kent,47.33620953903954,-121.96924208051249,2020,47.401118,-122.226377
+2021.0,32.0,King,7,Renton-FedWay-Kent,47.347569349502784,-122.00328458256142,2021,47.401118,-122.226377
+2022.0,32.0,King,7,Renton-FedWay-Kent,47.35356951062498,-122.02827418752649,2022,47.401118,-122.226377
+2023.0,32.0,King,7,Renton-FedWay-Kent,47.33756903631259,-122.02709995731294,2023,47.401118,-122.226377
+2024.0,32.0,King,7,Renton-FedWay-Kent,47.352412976882974,-122.04540602594429,2024,47.401118,-122.226377
+2025.0,32.0,King,7,Renton-FedWay-Kent,47.33528902125888,-122.04264699272308,2025,47.401118,-122.226377
+2026.0,34.0,King,7,Renton-FedWay-Kent,47.354417339985524,-122.0656190508818,2026,47.401118,-122.226377
+2027.0,34.0,King,7,Renton-FedWay-Kent,47.34764124709297,-122.07039553245596,2027,47.401118,-122.226377
+2028.0,32.0,King,7,Renton-FedWay-Kent,47.334886491292714,-122.06106965317144,2028,47.401118,-122.226377
+2029.0,34.0,King,7,Renton-FedWay-Kent,47.35534102975936,-122.10214827324532,2029,47.401118,-122.226377
+2030.0,34.0,King,7,Renton-FedWay-Kent,47.34871816247698,-122.09591238499418,2030,47.401118,-122.226377
+2031.0,32.0,King,7,Renton-FedWay-Kent,47.33631321999116,-122.08549538891295,2031,47.401118,-122.226377
+2032.0,32.0,King,7,Renton-FedWay-Kent,47.334345829010154,-122.11061624073994,2032,47.401118,-122.226377
+2033.0,33.0,King,7,Renton-FedWay-Kent,47.35504021056318,-122.11683899049,2033,47.401118,-122.226377
+2034.0,33.0,King,7,Renton-FedWay-Kent,47.34810771169589,-122.12776115435116,2034,47.401118,-122.226377
+2035.0,32.0,King,7,Renton-FedWay-Kent,47.33629552091838,-122.14156729701573,2035,47.401118,-122.226377
+2036.0,32.0,King,7,Renton-FedWay-Kent,47.323054653820286,-122.14787048756027,2036,47.401118,-122.226377
+2037.0,32.0,King,7,Renton-FedWay-Kent,47.3092613775434,-122.15648025259249,2037,47.401118,-122.226377
+2038.0,32.0,King,7,Renton-FedWay-Kent,47.32325722497318,-122.11823780636914,2038,47.401118,-122.226377
+2039.0,32.0,King,7,Renton-FedWay-Kent,47.30796545446957,-122.11903571071915,2039,47.401118,-122.226377
+2040.0,32.0,King,7,Renton-FedWay-Kent,47.32016122482458,-122.08130695009905,2040,47.401118,-122.226377
+2041.0,32.0,King,7,Renton-FedWay-Kent,47.30924988252156,-122.06509217652712,2041,47.401118,-122.226377
+2042.0,32.0,King,7,Renton-FedWay-Kent,47.31961351146607,-122.04539508980606,2042,47.401118,-122.226377
+2043.0,32.0,King,7,Renton-FedWay-Kent,47.2993714551054,-122.03776839170595,2043,47.401118,-122.226377
+2044.0,32.0,King,7,Renton-FedWay-Kent,47.32002550404066,-122.02039499522458,2044,47.401118,-122.226377
+2045.0,32.0,King,7,Renton-FedWay-Kent,47.3010774068405,-122.01481540823687,2045,47.401118,-122.226377
+2046.0,32.0,King,7,Renton-FedWay-Kent,47.304146642228915,-121.97602465350417,2046,47.401118,-122.226377
+2047.0,38.0,King,7,Renton-FedWay-Kent,47.29163978137277,-121.87700058608357,2047,47.401118,-122.226377
+2048.0,38.0,King,7,Renton-FedWay-Kent,47.25402338080115,-121.92702321077007,2048,47.401118,-122.226377
+2049.0,38.0,King,7,Renton-FedWay-Kent,47.27258108951503,-121.95972995583523,2049,47.401118,-122.226377
+2050.0,32.0,King,7,Renton-FedWay-Kent,47.28441188383343,-122.020004654867,2050,47.401118,-122.226377
+2051.0,38.0,King,7,Renton-FedWay-Kent,47.26031433740086,-122.00781022508616,2051,47.401118,-122.226377
+2052.0,38.0,King,7,Renton-FedWay-Kent,47.26161330349064,-122.04260444262384,2052,47.401118,-122.226377
+2053.0,32.0,King,7,Renton-FedWay-Kent,47.29114839142159,-122.06591084559557,2053,47.401118,-122.226377
+2054.0,38.0,King,7,Renton-FedWay-Kent,47.271179344075136,-122.06968091726742,2054,47.401118,-122.226377
+2055.0,32.0,King,7,Renton-FedWay-Kent,47.29679728816254,-122.12014438383244,2055,47.401118,-122.226377
+2056.0,32.0,King,7,Renton-FedWay-Kent,47.287788808297,-122.11213428044088,2056,47.401118,-122.226377
+2057.0,38.0,King,7,Renton-FedWay-Kent,47.268841132323466,-122.11075133827828,2057,47.401118,-122.226377
+2058.0,32.0,King,7,Renton-FedWay-Kent,47.294203439393996,-122.15554897961277,2058,47.401118,-122.226377
+2059.0,38.0,King,7,Renton-FedWay-Kent,47.279483347080756,-122.15344967255855,2059,47.401118,-122.226377
+2060.0,30.0,King,7,Renton-FedWay-Kent,47.293233930747355,-122.18438585832436,2060,47.401118,-122.226377
+2061.0,38.0,King,7,Renton-FedWay-Kent,47.250288930926466,-122.08885252507432,2061,47.401118,-122.226377
+2062.0,38.0,King,7,Renton-FedWay-Kent,47.23574340589457,-122.08316958710913,2062,47.401118,-122.226377
+2063.0,38.0,King,7,Renton-FedWay-Kent,47.235515858870926,-122.0243369381502,2063,47.401118,-122.226377
+2064.0,38.0,King,7,Renton-FedWay-Kent,47.23119920548106,-121.97454788282371,2064,47.401118,-122.226377
+2065.0,38.0,King,7,Renton-FedWay-Kent,47.21232499833308,-121.93762198800323,2065,47.401118,-122.226377
+2066.0,38.0,King,7,Renton-FedWay-Kent,47.210332489583685,-121.97602176032078,2066,47.401118,-122.226377
+2067.0,38.0,King,7,Renton-FedWay-Kent,47.20297634545938,-121.97787187016534,2067,47.401118,-122.226377
+2068.0,38.0,King,7,Renton-FedWay-Kent,47.22103024360886,-122.0029743705254,2068,47.401118,-122.226377
+2069.0,38.0,King,7,Renton-FedWay-Kent,47.21006377153815,-122.00249995249477,2069,47.401118,-122.226377
+2070.0,38.0,King,7,Renton-FedWay-Kent,47.20296437611397,-122.00427893924879,2070,47.401118,-122.226377
+2071.0,38.0,King,7,Renton-FedWay-Kent,47.2190851448194,-122.03735728261556,2071,47.401118,-122.226377
+2072.0,38.0,King,7,Renton-FedWay-Kent,47.20176869144618,-122.02663654406395,2072,47.401118,-122.226377
+2073.0,38.0,King,7,Renton-FedWay-Kent,47.220312664610084,-122.07619712144093,2073,47.401118,-122.226377
+2074.0,38.0,King,7,Renton-FedWay-Kent,47.20056983991329,-122.05918379987686,2074,47.401118,-122.226377
+2075.0,38.0,King,7,Renton-FedWay-Kent,47.201081325008715,-122.08138940637764,2075,47.401118,-122.226377
+2076.0,38.0,King,7,Renton-FedWay-Kent,47.18518762697226,-122.03030409448685,2076,47.401118,-122.226377
+2077.0,38.0,King,7,Renton-FedWay-Kent,47.19186502425562,-122.00845152490095,2077,47.401118,-122.226377
+2078.0,38.0,King,7,Renton-FedWay-Kent,47.18376848444829,-121.99746964003711,2078,47.401118,-122.226377
+2079.0,38.0,King,7,Renton-FedWay-Kent,47.1783562703145,-121.97564129843788,2079,47.401118,-122.226377
+2080.0,38.0,King,7,Renton-FedWay-Kent,47.168979368649595,-121.93623255659095,2080,47.401118,-122.226377
+2081.0,38.0,King,7,Renton-FedWay-Kent,47.1878536947351,-121.60392253566799,2081,47.401118,-122.226377
+2082.0,21.0,King,5,West-South Seattle,47.47400960921684,-122.47936779584762,2082,47.551159999999996,-122.332714
+2083.0,21.0,King,5,West-South Seattle,47.475434221327454,-122.45418606688357,2083,47.551159999999996,-122.332714
+2084.0,21.0,King,5,West-South Seattle,47.42793515316759,-122.44935822919821,2084,47.551159999999996,-122.332714
+2085.0,21.0,King,5,West-South Seattle,47.43099517000381,-122.48755943918171,2085,47.551159999999996,-122.332714
+2086.0,21.0,King,5,West-South Seattle,47.39432693209378,-122.49227484167648,2086,47.551159999999996,-122.332714
+2087.0,21.0,King,5,West-South Seattle,47.37740971173538,-122.431293372471,2087,47.551159999999996,-122.332714
+2088.0,21.0,King,5,West-South Seattle,47.35567128108653,-122.505510583974,2088,47.551159999999996,-122.332714
+2089.0,64.0,Snohomish,1,Suburban Snohomish,48.26604244807118,-122.37787974299006,2089,,
+2090.0,64.0,Snohomish,1,Suburban Snohomish,48.28227394474961,-122.34232554621248,2090,,
+2091.0,64.0,Snohomish,1,Suburban Snohomish,48.28079256547533,-122.30421335136332,2091,,
+2092.0,64.0,Snohomish,1,Suburban Snohomish,48.282881216844046,-122.2615281321301,2092,,
+2093.0,65.0,Snohomish,1,Suburban Snohomish,48.26851240923337,-122.20060449960772,2093,,
+2094.0,64.0,Snohomish,1,Suburban Snohomish,48.25491928580433,-122.23317104828959,2094,,
+2095.0,64.0,Snohomish,1,Suburban Snohomish,48.250930042586965,-122.26846243650276,2095,,
+2096.0,64.0,Snohomish,1,Suburban Snohomish,48.26085068564091,-122.30516431110256,2096,,
+2097.0,64.0,Snohomish,1,Suburban Snohomish,48.24499308193641,-122.30375759440979,2097,,
+2098.0,64.0,Snohomish,1,Suburban Snohomish,48.262674969473416,-122.34450388023443,2098,,
+2099.0,64.0,Snohomish,1,Suburban Snohomish,48.24555707488842,-122.34007552229376,2099,,
+2100.0,64.0,Snohomish,1,Suburban Snohomish,48.24169130767329,-122.36126803622507,2100,,
+2101.0,64.0,Snohomish,1,Suburban Snohomish,48.208561649977206,-122.3401373578529,2101,,
+2102.0,64.0,Snohomish,1,Suburban Snohomish,48.22249486480241,-122.29741089267613,2102,,
+2103.0,64.0,Snohomish,1,Suburban Snohomish,48.22407601835496,-122.23935463313506,2103,,
+2104.0,64.0,Snohomish,1,Suburban Snohomish,48.20699004025468,-122.24630605236901,2104,,
+2105.0,65.0,Snohomish,1,Suburban Snohomish,48.229398681278255,-122.19453624643549,2105,,
+2106.0,65.0,Snohomish,1,Suburban Snohomish,48.210150865315285,-122.17319163899502,2106,,
+2107.0,65.0,Snohomish,1,Suburban Snohomish,48.26710657261418,-122.10424188035242,2107,,
+2108.0,68.0,Snohomish,1,Suburban Snohomish,48.26272487625649,-121.96383097034986,2108,,
+2109.0,68.0,Snohomish,1,Suburban Snohomish,48.183308146580075,-121.40329053037472,2109,,
+2110.0,68.0,Snohomish,1,Suburban Snohomish,48.16263202903296,-121.94424683196831,2110,,
+2111.0,68.0,Snohomish,1,Suburban Snohomish,48.22344627341421,-122.0597184961666,2111,,
+2112.0,68.0,Snohomish,1,Suburban Snohomish,48.21596987419207,-122.09290048900192,2112,,
+2113.0,68.0,Snohomish,1,Suburban Snohomish,48.19667222813005,-122.04749351541278,2113,,
+2114.0,68.0,Snohomish,1,Suburban Snohomish,48.16222452928047,-122.07741181951592,2114,,
+2115.0,65.0,Snohomish,1,Suburban Snohomish,48.19568888555885,-122.10834089024394,2115,,
+2116.0,65.0,Snohomish,1,Suburban Snohomish,48.18111565173856,-122.10524854564838,2116,,
+2117.0,65.0,Snohomish,1,Suburban Snohomish,48.2009747603101,-122.12635940833401,2117,,
+2118.0,65.0,Snohomish,1,Suburban Snohomish,48.19390880678339,-122.12295338880452,2118,,
+2119.0,65.0,Snohomish,1,Suburban Snohomish,48.17432315613949,-122.11655218252787,2119,,
+2120.0,65.0,Snohomish,1,Suburban Snohomish,48.15521555968272,-122.10498084549525,2120,,
+2121.0,65.0,Snohomish,1,Suburban Snohomish,48.187211508086975,-122.13852716544324,2121,,
+2122.0,65.0,Snohomish,1,Suburban Snohomish,48.171395724754326,-122.13183412852293,2122,,
+2123.0,65.0,Snohomish,1,Suburban Snohomish,48.15786903965317,-122.12803643173751,2123,,
+2124.0,65.0,Snohomish,1,Suburban Snohomish,48.19601032456386,-122.16703334073273,2124,,
+2125.0,65.0,Snohomish,1,Suburban Snohomish,48.1794377921712,-122.17615285454364,2125,,
+2126.0,65.0,Snohomish,1,Suburban Snohomish,48.16333492367761,-122.14571417862759,2126,,
+2127.0,65.0,Snohomish,1,Suburban Snohomish,48.1647326083212,-122.16098728746206,2127,,
+2128.0,65.0,Snohomish,1,Suburban Snohomish,48.1580107115893,-122.17867239600632,2128,,
+2129.0,65.0,Snohomish,1,Suburban Snohomish,48.15990139216047,-122.18663848393795,2129,,
+2130.0,64.0,Snohomish,1,Suburban Snohomish,48.17438300379936,-122.21369131775022,2130,,
+2131.0,64.0,Snohomish,1,Suburban Snohomish,48.17170392962842,-122.25239639554104,2131,,
+2132.0,64.0,Snohomish,1,Suburban Snohomish,48.178134502313654,-122.28608939446345,2132,,
+2133.0,64.0,Snohomish,1,Suburban Snohomish,48.175745066882925,-122.31666255792757,2133,,
+2134.0,64.0,Snohomish,1,Suburban Snohomish,48.15607630846448,-122.32260595815079,2134,,
+2135.0,64.0,Snohomish,1,Suburban Snohomish,48.17385868935821,-122.34509276565441,2135,,
+2136.0,64.0,Snohomish,1,Suburban Snohomish,48.159274814180975,-122.35133515605995,2136,,
+2137.0,64.0,Snohomish,1,Suburban Snohomish,48.136290986922205,-122.36143516062624,2137,,
+2138.0,64.0,Snohomish,1,Suburban Snohomish,48.138383884649926,-122.33224664761428,2138,,
+2139.0,64.0,Snohomish,1,Suburban Snohomish,48.13944396207215,-122.29527404231584,2139,,
+2140.0,64.0,Snohomish,1,Suburban Snohomish,48.13847082679822,-122.27124558085659,2140,,
+2141.0,64.0,Snohomish,1,Suburban Snohomish,48.148146923254785,-122.23765067926928,2141,,
+2142.0,64.0,Snohomish,1,Suburban Snohomish,48.132099076569936,-122.231961201083,2142,,
+2143.0,64.0,Snohomish,1,Suburban Snohomish,48.13681481141413,-122.20289611274029,2143,,
+2144.0,64.0,Snohomish,1,Suburban Snohomish,48.142721525724276,-122.1931537248048,2144,,
+2145.0,67.0,Snohomish,1,Suburban Snohomish,48.14369283424031,-122.17365090920406,2145,,
+2146.0,67.0,Snohomish,1,Suburban Snohomish,48.12757210746578,-122.1749331190443,2146,,
+2147.0,67.0,Snohomish,1,Suburban Snohomish,48.14290248704111,-122.15139687570179,2147,,
+2148.0,67.0,Snohomish,1,Suburban Snohomish,48.123982339531686,-122.15163502036243,2148,,
+2149.0,67.0,Snohomish,1,Suburban Snohomish,48.12179158199398,-122.1267193491747,2149,,
+2150.0,67.0,Snohomish,1,Suburban Snohomish,48.130603905035855,-122.10057600745613,2150,,
+2151.0,68.0,Snohomish,1,Suburban Snohomish,48.11156127061879,-122.05520248521444,2151,,
+2152.0,68.0,Snohomish,1,Suburban Snohomish,48.12048382289422,-122.02045161887293,2152,,
+2153.0,68.0,Snohomish,1,Suburban Snohomish,48.0919354425797,-121.96891612165548,2153,,
+2154.0,68.0,Snohomish,1,Suburban Snohomish,48.08433269503809,-122.02031607841222,2154,,
+2155.0,67.0,Snohomish,1,Suburban Snohomish,48.09245204508934,-122.09011511487837,2155,,
+2156.0,67.0,Snohomish,1,Suburban Snohomish,48.084183770666456,-122.12238267646069,2156,,
+2157.0,67.0,Snohomish,1,Suburban Snohomish,48.078032115103206,-122.13159742493649,2157,,
+2158.0,67.0,Snohomish,1,Suburban Snohomish,48.10316078620389,-122.15152896530421,2158,,
+2159.0,66.0,Snohomish,1,Suburban Snohomish,48.08848960508949,-122.14731286788192,2159,,
+2160.0,66.0,Snohomish,1,Suburban Snohomish,48.08254896403629,-122.14878211292437,2160,,
+2161.0,66.0,Snohomish,1,Suburban Snohomish,48.07736432661185,-122.14767500827585,2161,,
+2162.0,66.0,Snohomish,1,Suburban Snohomish,48.08871862783021,-122.1609597186625,2162,,
+2163.0,66.0,Snohomish,1,Suburban Snohomish,48.08056540524198,-122.16114222296235,2163,,
+2164.0,66.0,Snohomish,1,Suburban Snohomish,48.080184295627745,-122.17010379285831,2164,,
+2165.0,67.0,Snohomish,1,Suburban Snohomish,48.11545602775522,-122.17646585778276,2165,,
+2166.0,67.0,Snohomish,1,Suburban Snohomish,48.10870891131112,-122.16932749482667,2166,,
+2167.0,66.0,Snohomish,1,Suburban Snohomish,48.094736473288926,-122.16832693346687,2167,,
+2168.0,67.0,Snohomish,1,Suburban Snohomish,48.10753578349318,-122.18138913003936,2168,,
+2169.0,66.0,Snohomish,1,Suburban Snohomish,48.0919088901046,-122.17939318148208,2169,,
+2170.0,66.0,Snohomish,1,Suburban Snohomish,48.0805071278566,-122.1789925105499,2170,,
+2171.0,64.0,Snohomish,1,Suburban Snohomish,48.111514505845115,-122.20497351677382,2171,,
+2172.0,64.0,Snohomish,1,Suburban Snohomish,48.08810917852645,-122.18966042141308,2172,,
+2173.0,64.0,Snohomish,1,Suburban Snohomish,48.0866103232694,-122.21166222792299,2173,,
+2174.0,64.0,Snohomish,1,Suburban Snohomish,48.10932856299354,-122.23737319217646,2174,,
+2175.0,64.0,Snohomish,1,Suburban Snohomish,48.07117312638894,-122.2440174351178,2175,,
+2176.0,64.0,Snohomish,1,Suburban Snohomish,48.09900303661311,-122.2709729438927,2176,,
+2177.0,64.0,Snohomish,1,Suburban Snohomish,48.102066027212324,-122.31077088568344,2177,,
+2178.0,64.0,Snohomish,1,Suburban Snohomish,48.09750607115046,-122.32959299943842,2178,,
+2179.0,64.0,Snohomish,1,Suburban Snohomish,48.06815814263903,-122.28964982740663,2179,,
+2180.0,64.0,Snohomish,1,Suburban Snohomish,48.048956211962576,-122.26668837668034,2180,,
+2181.0,64.0,Snohomish,1,Suburban Snohomish,48.047294057907706,-122.24456193081235,2181,,
+2182.0,64.0,Snohomish,1,Suburban Snohomish,48.057603733975924,-122.21889788960031,2182,,
+2183.0,64.0,Snohomish,1,Suburban Snohomish,48.04251449362937,-122.20930027259249,2183,,
+2184.0,64.0,Snohomish,1,Suburban Snohomish,48.06694022082508,-122.20043940561243,2184,,
+2185.0,64.0,Snohomish,1,Suburban Snohomish,48.06478870716004,-122.18952867654578,2185,,
+2186.0,66.0,Snohomish,1,Suburban Snohomish,48.068589507136146,-122.1807087735869,2186,,
+2187.0,66.0,Snohomish,1,Suburban Snohomish,48.05661727736,-122.18244793373276,2187,,
+2188.0,66.0,Snohomish,1,Suburban Snohomish,48.05656392022081,-122.17872170079576,2188,,
+2189.0,66.0,Snohomish,1,Suburban Snohomish,48.04971079060211,-122.18042989049505,2189,,
+2190.0,66.0,Snohomish,1,Suburban Snohomish,48.072060677954376,-122.1717485871908,2190,,
+2191.0,66.0,Snohomish,1,Suburban Snohomish,48.064817352730074,-122.17229462034643,2191,,
+2192.0,66.0,Snohomish,1,Suburban Snohomish,48.05878389992116,-122.17232610617936,2192,,
+2193.0,66.0,Snohomish,1,Suburban Snohomish,48.05406053248653,-122.17235342929173,2193,,
+2194.0,66.0,Snohomish,1,Suburban Snohomish,48.04632024143406,-122.17045302897553,2194,,
+2195.0,66.0,Snohomish,1,Suburban Snohomish,48.068650287814684,-122.16061666403529,2195,,
+2196.0,66.0,Snohomish,1,Suburban Snohomish,48.05826517171653,-122.16528342738556,2196,,
+2197.0,66.0,Snohomish,1,Suburban Snohomish,48.056516762344685,-122.16222724479714,2197,,
+2198.0,66.0,Snohomish,1,Suburban Snohomish,48.0687225850452,-122.14786773150827,2198,,
+2199.0,66.0,Snohomish,1,Suburban Snohomish,48.05841703728464,-122.14965687859535,2199,,
+2200.0,66.0,Snohomish,1,Suburban Snohomish,48.049764630326116,-122.1478271054986,2200,,
+2201.0,66.0,Snohomish,1,Suburban Snohomish,48.04625862447709,-122.15889216099652,2201,,
+2202.0,70.0,Snohomish,1,Suburban Snohomish,48.03836370127519,-122.15034605664579,2202,,
+2203.0,67.0,Snohomish,1,Suburban Snohomish,48.06587389033329,-122.128302011439,2203,,
+2204.0,67.0,Snohomish,1,Suburban Snohomish,48.05802541137615,-122.13266698341944,2204,,
+2205.0,67.0,Snohomish,1,Suburban Snohomish,48.04823474858998,-122.13552907874364,2205,,
+2206.0,67.0,Snohomish,1,Suburban Snohomish,48.03929220010537,-122.13562722086763,2206,,
+2207.0,67.0,Snohomish,1,Suburban Snohomish,48.04470869909621,-122.12484920298063,2207,,
+2208.0,67.0,Snohomish,1,Suburban Snohomish,48.06258138440986,-122.11530574237922,2208,,
+2209.0,67.0,Snohomish,1,Suburban Snohomish,48.04481408045756,-122.11490423610594,2209,,
+2210.0,67.0,Snohomish,1,Suburban Snohomish,48.060601347617684,-122.08832804346228,2210,,
+2211.0,67.0,Snohomish,1,Suburban Snohomish,48.03927878364306,-122.10436192807482,2211,,
+2212.0,70.0,Snohomish,1,Suburban Snohomish,48.039188685324575,-122.07936780490546,2212,,
+2213.0,68.0,Snohomish,1,Suburban Snohomish,48.057524064290746,-122.04567975862837,2213,,
+2214.0,68.0,Snohomish,1,Suburban Snohomish,48.07634077317961,-121.96939120526129,2214,,
+2215.0,68.0,Snohomish,1,Suburban Snohomish,48.03697421516876,-121.73265596413952,2215,,
+2216.0,68.0,Snohomish,1,Suburban Snohomish,48.01048224877746,-121.95293992969499,2216,,
+2217.0,68.0,Snohomish,1,Suburban Snohomish,48.042772927834605,-122.00254637606905,2217,,
+2218.0,72.0,Snohomish,1,Suburban Snohomish,47.97780312591394,-121.99926671445057,2218,,
+2219.0,70.0,Snohomish,1,Suburban Snohomish,48.02940421676604,-122.04237354832537,2219,,
+2220.0,70.0,Snohomish,1,Suburban Snohomish,48.01007583119354,-122.03891326402571,2220,,
+2221.0,70.0,Snohomish,1,Suburban Snohomish,48.02620655414201,-122.05986908006405,2221,,
+2222.0,70.0,Snohomish,1,Suburban Snohomish,48.015434950385504,-122.0567263861304,2222,,
+2223.0,70.0,Snohomish,1,Suburban Snohomish,47.99738269508688,-122.04631558518854,2223,,
+2224.0,70.0,Snohomish,1,Suburban Snohomish,47.99843019140932,-122.059975983508,2224,,
+2225.0,70.0,Snohomish,1,Suburban Snohomish,47.982144236251,-122.05613584913162,2225,,
+2226.0,72.0,Snohomish,1,Suburban Snohomish,47.97224503873164,-122.04138199807751,2226,,
+2227.0,70.0,Snohomish,1,Suburban Snohomish,48.02290788928605,-122.07072433698369,2227,,
+2228.0,70.0,Snohomish,1,Suburban Snohomish,48.01324643145957,-122.0745944161081,2228,,
+2229.0,70.0,Snohomish,1,Suburban Snohomish,47.99315222393077,-122.07162311235304,2229,,
+2230.0,70.0,Snohomish,1,Suburban Snohomish,47.98108220527094,-122.07629910932316,2230,,
+2231.0,70.0,Snohomish,1,Suburban Snohomish,48.022460673002385,-122.08357874215169,2231,,
+2232.0,70.0,Snohomish,1,Suburban Snohomish,48.014973867265816,-122.0867635465912,2232,,
+2233.0,70.0,Snohomish,1,Suburban Snohomish,47.99778958602624,-122.09339665145464,2233,,
+2234.0,70.0,Snohomish,1,Suburban Snohomish,47.986144814589174,-122.09151301472731,2234,,
+2235.0,70.0,Snohomish,1,Suburban Snohomish,48.02211016136529,-122.1008124790986,2235,,
+2236.0,70.0,Snohomish,1,Suburban Snohomish,48.007060284783,-122.10299259133207,2236,,
+2237.0,70.0,Snohomish,1,Suburban Snohomish,47.996256642643004,-122.1017776264188,2237,,
+2238.0,70.0,Snohomish,1,Suburban Snohomish,47.98580777152114,-122.10097882700228,2238,,
+2239.0,70.0,Snohomish,1,Suburban Snohomish,48.02894427228514,-122.11495960607296,2239,,
+2240.0,70.0,Snohomish,1,Suburban Snohomish,48.028416277388295,-122.12747262462595,2240,,
+2241.0,70.0,Snohomish,1,Suburban Snohomish,48.01020007662285,-122.12319906738804,2241,,
+2242.0,70.0,Snohomish,1,Suburban Snohomish,48.00650050271404,-122.11155623026423,2242,,
+2243.0,70.0,Snohomish,1,Suburban Snohomish,47.99814181494912,-122.12107195727013,2243,,
+2244.0,70.0,Snohomish,1,Suburban Snohomish,47.99701624449806,-122.10865048784352,2244,,
+2245.0,70.0,Snohomish,1,Suburban Snohomish,47.99413481472317,-122.11607068478222,2245,,
+2246.0,70.0,Snohomish,1,Suburban Snohomish,47.98478877937313,-122.10854276759157,2246,,
+2247.0,70.0,Snohomish,1,Suburban Snohomish,47.98430187375949,-122.12472584857142,2247,,
+2248.0,70.0,Snohomish,1,Suburban Snohomish,48.02185232323418,-122.14156028691423,2248,,
+2249.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.00956997598395,-122.17582749872518,2249,47.884636,-122.261059
+2250.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.00433698032993,-122.1844877805252,2250,47.884636,-122.261059
+2251.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.00497621772218,-122.1935799194038,2251,47.884636,-122.261059
+2252.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.01185827949874,-122.19971840785568,2252,47.884636,-122.261059
+2253.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.002353517301046,-122.20435578830887,2253,47.884636,-122.261059
+2254.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.00793443208259,-122.20993830743308,2254,47.884636,-122.261059
+2255.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,48.00578571390381,-122.21985748089344,2255,47.884636,-122.261059
+2256.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.99605157912362,-122.21116946672441,2256,47.884636,-122.261059
+2257.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.998351481081976,-122.20460787295751,2257,47.884636,-122.261059
+2258.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.99440635500554,-122.20470936781807,2258,47.884636,-122.261059
+2259.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.998844350700615,-122.1950518630251,2259,47.884636,-122.261059
+2260.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.994296164246045,-122.19519587768197,2260,47.884636,-122.261059
+2261.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.99442733528983,-122.1867388609595,2261,47.884636,-122.261059
+2262.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.984835371679495,-122.17632398298427,2262,47.884636,-122.261059
+2263.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98887302220486,-122.18147793713301,2263,47.884636,-122.261059
+2264.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.9840632674448,-122.18193203685804,2264,47.884636,-122.261059
+2265.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.9898697691368,-122.18576783361387,2265,47.884636,-122.261059
+2266.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98464319365314,-122.18672689453452,2266,47.884636,-122.261059
+2267.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.989689839601276,-122.19532263258014,2267,47.884636,-122.261059
+2268.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98443847538559,-122.19544602500747,2268,47.884636,-122.261059
+2269.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98980205944237,-122.20483145733714,2269,47.884636,-122.261059
+2270.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98454925123554,-122.20495938430156,2270,47.884636,-122.261059
+2271.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.989857267656355,-122.20977537462689,2271,47.884636,-122.261059
+2272.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.9846099096962,-122.20991003113735,2272,47.884636,-122.261059
+2273.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98667604311819,-122.2125595475434,2273,47.884636,-122.261059
+2274.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98633969774295,-122.21875937710199,2274,47.884636,-122.261059
+2275.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97728928378575,-122.22249164376608,2275,47.884636,-122.261059
+2276.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.978690779592284,-122.21610111738754,2276,47.884636,-122.261059
+2277.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98068050395628,-122.21285992200869,2277,47.884636,-122.261059
+2278.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.977889846171294,-122.2128716861783,2278,47.884636,-122.261059
+2279.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98064611976595,-122.21000896167658,2279,47.884636,-122.261059
+2280.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.9778970686833,-122.2100216693505,2280,47.884636,-122.261059
+2281.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98061042443906,-122.20717478722612,2281,47.884636,-122.261059
+2282.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97791809065858,-122.20719534820464,2282,47.884636,-122.261059
+2283.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98057053316485,-122.20364515627509,2283,47.884636,-122.261059
+2284.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97792566809167,-122.2036737375448,2284,47.884636,-122.261059
+2285.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.980510534299576,-122.19831466396252,2285,47.884636,-122.261059
+2286.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.977954654155816,-122.19838741703742,2286,47.884636,-122.261059
+2287.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98052413291545,-122.19167924260866,2287,47.884636,-122.261059
+2288.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97794046195029,-122.19265727090263,2288,47.884636,-122.261059
+2289.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.98050082019624,-122.1848455062912,2289,47.884636,-122.261059
+2290.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97832924145562,-122.18758831743868,2290,47.884636,-122.261059
+2291.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.968887624042175,-122.19291334121479,2291,47.884636,-122.261059
+2292.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.974423832910006,-122.19416340748823,2292,47.884636,-122.261059
+2293.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97308504532766,-122.19879353271955,2293,47.884636,-122.261059
+2294.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.967903576391706,-122.19937094559464,2294,47.884636,-122.261059
+2295.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97264788321827,-122.204834954578,2295,47.884636,-122.261059
+2296.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.966222500113936,-122.20469298453402,2296,47.884636,-122.261059
+2297.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.972550146237324,-122.20978219958354,2297,47.884636,-122.261059
+2298.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.96593927151271,-122.20948985465692,2298,47.884636,-122.261059
+2299.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97376409936,-122.21356655948362,2299,47.884636,-122.261059
+2300.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.96672245541504,-122.21439957690752,2300,47.884636,-122.261059
+2301.0,69.0,Snohomish,2,Everett-Lynwood-Edmonds,47.97351251629336,-122.22035007299488,2301,47.884636,-122.261059
+2302.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.96342219514035,-122.23204633971771,2302,47.884636,-122.261059
+2303.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.95488831860971,-122.2727926078636,2303,47.884636,-122.261059
+2304.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94856225058064,-122.29670451040268,2304,47.884636,-122.261059
+2305.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.932629515813574,-122.30633675237442,2305,47.884636,-122.261059
+2306.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94330985010014,-122.29524266496485,2306,47.884636,-122.261059
+2307.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.93023207146514,-122.29736315570658,2307,47.884636,-122.261059
+2308.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.95016958790494,-122.27826831170887,2308,47.884636,-122.261059
+2309.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.93353518517939,-122.27749407296416,2309,47.884636,-122.261059
+2310.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94658705802618,-122.26588442749778,2310,47.884636,-122.261059
+2311.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.93144971986706,-122.25499856391295,2311,47.884636,-122.261059
+2312.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94993769383705,-122.2521376903944,2312,47.884636,-122.261059
+2313.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.938528804184934,-122.24651297400429,2313,47.884636,-122.261059
+2314.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.93138349298137,-122.23856325029509,2314,47.884636,-122.261059
+2315.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.952324657338856,-122.24238998561705,2315,47.884636,-122.261059
+2316.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94968987159282,-122.23402642951595,2316,47.884636,-122.261059
+2317.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94093120412893,-122.23406996879751,2317,47.884636,-122.261059
+2318.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.95628797370023,-122.218698437751,2318,47.884636,-122.261059
+2319.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.948375680803316,-122.22430508970554,2319,47.884636,-122.261059
+2320.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.9391779693351,-122.22334890987926,2320,47.884636,-122.261059
+2321.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.92986877511166,-122.22800211858231,2321,47.884636,-122.261059
+2322.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.95502916724526,-122.21008800278143,2322,47.884636,-122.261059
+2323.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94272348542793,-122.21226728914532,2323,47.884636,-122.261059
+2324.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.92952137382447,-122.21778527360979,2324,47.884636,-122.261059
+2325.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.9567852984694,-122.20347014952087,2325,47.884636,-122.261059
+2326.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.94251286665738,-122.20308399098192,2326,47.884636,-122.261059
+2327.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.931213106271855,-122.20688866601051,2327,47.884636,-122.261059
+2328.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.92387391973625,-122.21287086028549,2328,47.884636,-122.261059
+2329.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.93038979966386,-122.20100181900459,2329,47.884636,-122.261059
+2330.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.95650534062018,-122.19396382051072,2330,47.884636,-122.261059
+2331.0,71.0,Snohomish,2,Everett-Lynwood-Edmonds,47.93729528462728,-122.18439910807774,2331,47.884636,-122.261059
+2332.0,70.0,Snohomish,1,Suburban Snohomish,47.96024046725485,-122.17889846948448,2332,,
+2333.0,70.0,Snohomish,1,Suburban Snohomish,47.964672809248476,-122.15509194297677,2333,,
+2334.0,70.0,Snohomish,1,Suburban Snohomish,47.95134870100046,-122.13937696406748,2334,,
+2335.0,70.0,Snohomish,1,Suburban Snohomish,47.93297546008878,-122.16047890044169,2335,,
+2336.0,72.0,Snohomish,1,Suburban Snohomish,47.934720074655935,-122.13515748549854,2336,,
+2337.0,70.0,Snohomish,1,Suburban Snohomish,47.97454927199746,-122.1193996475532,2337,,
+2338.0,70.0,Snohomish,1,Suburban Snohomish,47.967784012319285,-122.12099915838031,2338,,
+2339.0,70.0,Snohomish,1,Suburban Snohomish,47.960982794592816,-122.10975120900034,2339,,
+2340.0,72.0,Snohomish,1,Suburban Snohomish,47.950556752203816,-122.11007817628088,2340,,
+2341.0,72.0,Snohomish,1,Suburban Snohomish,47.939664277032826,-122.11365792170082,2341,,
+2342.0,72.0,Snohomish,1,Suburban Snohomish,47.94071255999008,-122.104253905142,2342,,
+2343.0,72.0,Snohomish,1,Suburban Snohomish,47.92310182371061,-122.11527119224422,2343,,
+2344.0,70.0,Snohomish,1,Suburban Snohomish,47.96578536715281,-122.09761063057421,2344,,
+2345.0,70.0,Snohomish,1,Suburban Snohomish,47.97458172641007,-122.07750895544271,2345,,
+2346.0,70.0,Snohomish,1,Suburban Snohomish,47.96221132216181,-122.08636487344296,2346,,
+2347.0,70.0,Snohomish,1,Suburban Snohomish,47.95792986122461,-122.07661405042671,2347,,
+2348.0,72.0,Snohomish,1,Suburban Snohomish,47.94470780304532,-122.08745302815834,2348,,
+2349.0,72.0,Snohomish,1,Suburban Snohomish,47.940580362563544,-122.09458083135338,2349,,
+2350.0,72.0,Snohomish,1,Suburban Snohomish,47.929929696873124,-122.09322173216972,2350,,
+2351.0,72.0,Snohomish,1,Suburban Snohomish,47.931686119114616,-122.08286259085229,2351,,
+2352.0,72.0,Snohomish,1,Suburban Snohomish,47.92319915021701,-122.10330779704806,2352,,
+2353.0,72.0,Snohomish,1,Suburban Snohomish,47.92040426004056,-122.09184944389068,2353,,
+2354.0,72.0,Snohomish,1,Suburban Snohomish,47.913651001798755,-122.10314784403194,2354,,
+2355.0,72.0,Snohomish,1,Suburban Snohomish,47.914404419056225,-122.09365556228904,2355,,
+2356.0,72.0,Snohomish,1,Suburban Snohomish,47.925253456805535,-122.0781083837587,2356,,
+2357.0,72.0,Snohomish,1,Suburban Snohomish,47.90856261993567,-122.08737900984032,2357,,
+2358.0,72.0,Snohomish,1,Suburban Snohomish,47.947801979488965,-122.0583273514077,2358,,
+2359.0,72.0,Snohomish,1,Suburban Snohomish,47.94928223669509,-122.022870562917,2359,,
+2360.0,72.0,Snohomish,1,Suburban Snohomish,47.948514436122075,-121.98919907420358,2360,,
+2361.0,87.0,Snohomish,1,Suburban Snohomish,47.93713218961065,-121.929442414257,2361,,
+2362.0,87.0,Snohomish,1,Suburban Snohomish,47.92767365843646,-121.86160528820524,2362,,
+2363.0,87.0,Snohomish,1,Suburban Snohomish,47.91307340553347,-121.66942223397541,2363,,
+2364.0,87.0,Snohomish,1,Suburban Snohomish,47.90924010514143,-121.34202635136721,2364,,
+2365.0,87.0,Snohomish,1,Suburban Snohomish,47.81411362715863,-121.70700971688969,2365,,
+2366.0,87.0,Snohomish,1,Suburban Snohomish,47.871793007186795,-121.82761257657177,2366,,
+2367.0,87.0,Snohomish,1,Suburban Snohomish,47.88447849809637,-121.89257039000636,2367,,
+2368.0,87.0,Snohomish,1,Suburban Snohomish,47.86488071938035,-121.89328200536438,2368,,
+2369.0,87.0,Snohomish,1,Suburban Snohomish,47.88940492511709,-121.92653834553349,2369,,
+2370.0,87.0,Snohomish,1,Suburban Snohomish,47.91154979682215,-121.96240413918905,2370,,
+2371.0,77.0,Snohomish,1,Suburban Snohomish,47.87061896089126,-121.953878106965,2371,,
+2372.0,77.0,Snohomish,1,Suburban Snohomish,47.8892381654251,-121.97922350199396,2372,,
+2373.0,77.0,Snohomish,1,Suburban Snohomish,47.870102734154386,-121.97723088416069,2373,,
+2374.0,72.0,Snohomish,1,Suburban Snohomish,47.921994841655646,-122.03267251178663,2374,,
+2375.0,72.0,Snohomish,1,Suburban Snohomish,47.9018396493343,-122.0059470347264,2375,,
+2376.0,77.0,Snohomish,1,Suburban Snohomish,47.8809469842797,-122.00506679738014,2376,,
+2377.0,72.0,Snohomish,1,Suburban Snohomish,47.900206774499566,-122.05567421894935,2377,,
+2378.0,77.0,Snohomish,1,Suburban Snohomish,47.89046261473918,-122.07111136308622,2378,,
+2379.0,77.0,Snohomish,1,Suburban Snohomish,47.90384975310776,-122.1011640915559,2379,,
+2380.0,77.0,Snohomish,1,Suburban Snohomish,47.87774739291029,-122.09642489739666,2380,,
+2381.0,77.0,Snohomish,1,Suburban Snohomish,47.89696398988535,-122.1231421115876,2381,,
+2382.0,77.0,Snohomish,1,Suburban Snohomish,47.91241362404735,-122.15483150089213,2382,,
+2383.0,76.0,Snohomish,1,Suburban Snohomish,47.922321363002034,-122.19549767725883,2383,,
+2384.0,76.0,Snohomish,1,Suburban Snohomish,47.91388145011287,-122.18411005214769,2384,,
+2385.0,76.0,Snohomish,1,Suburban Snohomish,47.90408723553567,-122.17912102296154,2385,,
+2386.0,76.0,Snohomish,1,Suburban Snohomish,47.896496408329426,-122.16761330187755,2386,,
+2387.0,76.0,Snohomish,1,Suburban Snohomish,47.9001212762074,-122.19051576492222,2387,,
+2388.0,76.0,Snohomish,1,Suburban Snohomish,47.91194063796272,-122.20045804747112,2388,,
+2389.0,76.0,Snohomish,1,Suburban Snohomish,47.902609064079705,-122.20154606736796,2389,,
+2390.0,79.0,Snohomish,1,Suburban Snohomish,47.90144213250733,-122.21026136849925,2390,,
+2391.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.91678353740838,-122.2172188569608,2391,47.884636,-122.261059
+2392.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.91113300883172,-122.2140550798807,2392,47.884636,-122.261059
+2393.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.90234060597739,-122.21816675322846,2393,47.884636,-122.261059
+2394.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.913074845196455,-122.23073740350016,2394,47.884636,-122.261059
+2395.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.902047867706564,-122.23058189447936,2395,47.884636,-122.261059
+2396.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.899983734976644,-122.24371110047537,2396,47.884636,-122.261059
+2397.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.919033469062654,-122.23554408498836,2397,47.884636,-122.261059
+2398.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.91921229367436,-122.25078264254776,2398,47.884636,-122.261059
+2399.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.911575946830034,-122.24818272508568,2399,47.884636,-122.261059
+2400.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.900889196780994,-122.25284663729148,2400,47.884636,-122.261059
+2401.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.91456894566575,-122.26532538427338,2401,47.884636,-122.261059
+2402.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.90420036830386,-122.26043235397607,2402,47.884636,-122.261059
+2403.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.90619306365898,-122.28098280485789,2403,47.884636,-122.261059
+2404.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.91787496415004,-122.29574832048516,2404,47.884636,-122.261059
+2405.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.91509724707093,-122.30865224644168,2405,47.884636,-122.261059
+2406.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.904061500998424,-122.31069307476454,2406,47.884636,-122.261059
+2407.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.90035040557541,-122.29789715050741,2407,47.884636,-122.261059
+2408.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.88721092715922,-122.31852009577734,2408,47.884636,-122.261059
+2409.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.891014039350004,-122.30396300237284,2409,47.884636,-122.261059
+2410.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.89287106892908,-122.29246309061486,2410,47.884636,-122.261059
+2411.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.882330155603675,-122.29174491543621,2411,47.884636,-122.261059
+2412.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.87720438767765,-122.28189798247809,2412,47.884636,-122.261059
+2413.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.88135707800983,-122.27312782053669,2413,47.884636,-122.261059
+2414.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.89012868287993,-122.26690868289239,2414,47.884636,-122.261059
+2415.0,74.0,Snohomish,2,Everett-Lynwood-Edmonds,47.89410128062064,-122.257915371061,2415,47.884636,-122.261059
+2416.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.89343269335363,-122.24661757890615,2416,47.884636,-122.261059
+2417.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.8873972418325,-122.25183094833048,2417,47.884636,-122.261059
+2418.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.882983878052556,-122.2598435531036,2418,47.884636,-122.261059
+2419.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.88600440690092,-122.2435703666128,2419,47.884636,-122.261059
+2420.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.89279260804655,-122.23402083839979,2420,47.884636,-122.261059
+2421.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.886509132976855,-122.23286939362221,2421,47.884636,-122.261059
+2422.0,75.0,Snohomish,2,Everett-Lynwood-Edmonds,47.89370427283515,-122.22399572473007,2422,47.884636,-122.261059
+2423.0,79.0,Snohomish,1,Suburban Snohomish,47.88532608729349,-122.22344256388769,2423,,
+2424.0,79.0,Snohomish,1,Suburban Snohomish,47.89255052369282,-122.21127264164404,2424,,
+2425.0,79.0,Snohomish,1,Suburban Snohomish,47.88339368419734,-122.212774483174,2425,,
+2426.0,76.0,Snohomish,1,Suburban Snohomish,47.895535205129526,-122.19971225270679,2426,,
+2427.0,76.0,Snohomish,1,Suburban Snohomish,47.885365053426064,-122.20253160970215,2427,,
+2428.0,76.0,Snohomish,1,Suburban Snohomish,47.88520932808638,-122.19195017369036,2428,,
+2429.0,76.0,Snohomish,1,Suburban Snohomish,47.89572771932861,-122.17983890019958,2429,,
+2430.0,76.0,Snohomish,1,Suburban Snohomish,47.88945530083864,-122.18017435372549,2430,,
+2431.0,76.0,Snohomish,1,Suburban Snohomish,47.88217536501547,-122.17957952354892,2431,,
+2432.0,76.0,Snohomish,1,Suburban Snohomish,47.8843099189099,-122.16943319655708,2432,,
+2433.0,76.0,Snohomish,1,Suburban Snohomish,47.888898966565854,-122.15656804592454,2433,,
+2434.0,80.0,Snohomish,1,Suburban Snohomish,47.88156949731743,-122.14791607243272,2434,,
+2435.0,80.0,Snohomish,1,Suburban Snohomish,47.872956044683725,-122.12536225308321,2435,,
+2436.0,86.0,Snohomish,1,Suburban Snohomish,47.86706172250815,-122.10435934927341,2436,,
+2437.0,86.0,Snohomish,1,Suburban Snohomish,47.85498146496518,-122.09612378642824,2437,,
+2438.0,77.0,Snohomish,1,Suburban Snohomish,47.86541579748191,-122.06221699590981,2438,,
+2439.0,77.0,Snohomish,1,Suburban Snohomish,47.870067043497286,-122.0253724781501,2439,,
+2440.0,77.0,Snohomish,1,Suburban Snohomish,47.85961189959156,-122.00175846755803,2440,,
+2441.0,77.0,Snohomish,1,Suburban Snohomish,47.85569130203703,-121.9843514530924,2441,,
+2442.0,77.0,Snohomish,1,Suburban Snohomish,47.852998649472106,-121.96442565593595,2442,,
+2443.0,87.0,Snohomish,1,Suburban Snohomish,47.81358321742402,-121.90569590290701,2443,,
+2444.0,77.0,Snohomish,1,Suburban Snohomish,47.846871696558296,-121.98167865685032,2444,,
+2445.0,77.0,Snohomish,1,Suburban Snohomish,47.843543447505176,-122.00283398765215,2445,,
+2446.0,77.0,Snohomish,1,Suburban Snohomish,47.84241924404722,-122.04864611713751,2446,,
+2447.0,86.0,Snohomish,1,Suburban Snohomish,47.83876174409127,-122.09519700154556,2447,,
+2448.0,86.0,Snohomish,1,Suburban Snohomish,47.85367125579362,-122.10788320585678,2448,,
+2449.0,86.0,Snohomish,1,Suburban Snohomish,47.84464774200741,-122.110760726289,2449,,
+2450.0,86.0,Snohomish,1,Suburban Snohomish,47.837479897917284,-122.11470863807352,2450,,
+2451.0,80.0,Snohomish,1,Suburban Snohomish,47.85647211970566,-122.12430058364184,2451,,
+2452.0,80.0,Snohomish,1,Suburban Snohomish,47.84242576804322,-122.1276641529298,2452,,
+2453.0,80.0,Snohomish,1,Suburban Snohomish,47.867555792468565,-122.14777069244316,2453,,
+2454.0,80.0,Snohomish,1,Suburban Snohomish,47.871519378314474,-122.16327434591663,2454,,
+2455.0,80.0,Snohomish,1,Suburban Snohomish,47.85751222786274,-122.14692567156206,2455,,
+2456.0,80.0,Snohomish,1,Suburban Snohomish,47.85968330322047,-122.16224894601366,2456,,
+2457.0,80.0,Snohomish,1,Suburban Snohomish,47.84869221867715,-122.15556213173453,2457,,
+2458.0,80.0,Snohomish,1,Suburban Snohomish,47.840043445907796,-122.14235364592858,2458,,
+2459.0,80.0,Snohomish,1,Suburban Snohomish,47.83840940898532,-122.16213256199372,2459,,
+2460.0,80.0,Snohomish,1,Suburban Snohomish,47.87136373773158,-122.17935909880686,2460,,
+2461.0,80.0,Snohomish,1,Suburban Snohomish,47.86756091294082,-122.17311445254515,2461,,
+2462.0,80.0,Snohomish,1,Suburban Snohomish,47.85951523371176,-122.17733786761185,2462,,
+2463.0,80.0,Snohomish,1,Suburban Snohomish,47.850487159549246,-122.17818400419307,2463,,
+2464.0,80.0,Snohomish,1,Suburban Snohomish,47.83947563489724,-122.1777752458822,2464,,
+2465.0,79.0,Snohomish,1,Suburban Snohomish,47.870654079619854,-122.19366840035015,2465,,
+2466.0,79.0,Snohomish,1,Suburban Snohomish,47.87325241291972,-122.20747161713238,2466,,
+2467.0,79.0,Snohomish,1,Suburban Snohomish,47.85725437847183,-122.19695355998087,2467,,
+2468.0,79.0,Snohomish,1,Suburban Snohomish,47.85934925229196,-122.20686489564642,2468,,
+2469.0,79.0,Snohomish,1,Suburban Snohomish,47.86421698736135,-122.21552324760486,2469,,
+2470.0,79.0,Snohomish,1,Suburban Snohomish,47.849360432885426,-122.1962812380353,2470,,
+2471.0,79.0,Snohomish,1,Suburban Snohomish,47.84821616719015,-122.21145438771536,2471,,
+2472.0,79.0,Snohomish,1,Suburban Snohomish,47.839474419074534,-122.19108201505115,2472,,
+2473.0,79.0,Snohomish,1,Suburban Snohomish,47.83937327008427,-122.20599612631206,2473,,
+2474.0,79.0,Snohomish,1,Suburban Snohomish,47.87600009260434,-122.21857196716707,2474,,
+2475.0,79.0,Snohomish,1,Suburban Snohomish,47.86539802145965,-122.22453955358351,2475,,
+2476.0,79.0,Snohomish,1,Suburban Snohomish,47.854001274911155,-122.22484314456257,2476,,
+2477.0,79.0,Snohomish,1,Suburban Snohomish,47.87722287908656,-122.23004112021357,2477,,
+2478.0,79.0,Snohomish,1,Suburban Snohomish,47.8696511645281,-122.23784252263269,2478,,
+2479.0,79.0,Snohomish,1,Suburban Snohomish,47.857553935871366,-122.24320045933487,2479,,
+2480.0,79.0,Snohomish,1,Suburban Snohomish,47.85335198188467,-122.23576837212843,2480,,
+2481.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.87733146277432,-122.24488990567549,2481,47.884636,-122.261059
+2482.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.874616054640505,-122.25452357577528,2482,47.884636,-122.261059
+2483.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.86649523980911,-122.25341080023442,2483,47.884636,-122.261059
+2484.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.86786980673472,-122.26333978106537,2484,47.884636,-122.261059
+2485.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.85797647699243,-122.25817402506529,2485,47.884636,-122.261059
+2486.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.87167174095578,-122.26953181155028,2486,47.884636,-122.261059
+2487.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.85706620155703,-122.26959634818633,2487,47.884636,-122.261059
+2488.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.862635048014056,-122.27782561881304,2488,47.884636,-122.261059
+2489.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.85422042847321,-122.27738691219406,2489,47.884636,-122.261059
+2490.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.8601522281475,-122.28304423587166,2490,47.884636,-122.261059
+2491.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.853227852255046,-122.28669749005712,2491,47.884636,-122.261059
+2492.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.871060920304615,-122.2846928261462,2492,47.884636,-122.261059
+2493.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.86646146788189,-122.29285180350917,2493,47.884636,-122.261059
+2494.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.860253159949686,-122.28917427823488,2494,47.884636,-122.261059
+2495.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.86141978084186,-122.29733944712996,2495,47.884636,-122.261059
+2496.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.85479419638182,-122.29871903004653,2496,47.884636,-122.261059
+2497.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.875279914368626,-122.30380139938471,2497,47.884636,-122.261059
+2498.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.86919531090742,-122.30791056406122,2498,47.884636,-122.261059
+2499.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.85742079625743,-122.30881622338623,2499,47.884636,-122.261059
+2500.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.8717166562626,-122.32287785284971,2500,47.884636,-122.261059
+2501.0,73.0,Snohomish,2,Everett-Lynwood-Edmonds,47.86006076793353,-122.32257807063496,2501,47.884636,-122.261059
+2502.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.85285247788264,-122.32478497837684,2502,47.884636,-122.261059
+2503.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84728304455793,-122.32818473994504,2503,47.884636,-122.261059
+2504.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84007512020688,-122.34440978071927,2504,47.884636,-122.261059
+2505.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.83815071518077,-122.33159522363606,2505,47.884636,-122.261059
+2506.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.8329854032576,-122.32412455728223,2506,47.884636,-122.261059
+2507.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.848711600550025,-122.30986512283621,2507,47.884636,-122.261059
+2508.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84263492680906,-122.31264612249164,2508,47.884636,-122.261059
+2509.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.83383230389066,-122.31699216938196,2509,47.884636,-122.261059
+2510.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.837453020578394,-122.30761560404329,2510,47.884636,-122.261059
+2511.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.83243843088485,-122.3098215619194,2511,47.884636,-122.261059
+2512.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.848414625562675,-122.2987430999822,2512,47.884636,-122.261059
+2513.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.843320402116746,-122.29995214945872,2513,47.884636,-122.261059
+2514.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.839712619083116,-122.29583454702691,2514,47.884636,-122.261059
+2515.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.831652055294896,-122.29922875230862,2515,47.884636,-122.261059
+2516.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84648886457772,-122.28715830699736,2516,47.884636,-122.261059
+2517.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.836687997526376,-122.28638839915222,2517,47.884636,-122.261059
+2518.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.831649518093826,-122.28959834784462,2518,47.884636,-122.261059
+2519.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84426459458421,-122.27674983477013,2519,47.884636,-122.261059
+2520.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.836288442304784,-122.27698703605192,2520,47.884636,-122.261059
+2521.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84597230621693,-122.26938947516857,2521,47.884636,-122.261059
+2522.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.84087739002131,-122.26381583236716,2522,47.884636,-122.261059
+2523.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.83083197975788,-122.2670958239924,2523,47.884636,-122.261059
+2524.0,85.0,Snohomish,1,Suburban Snohomish,47.842791433090106,-122.2529864538933,2524,,
+2525.0,85.0,Snohomish,1,Suburban Snohomish,47.83288159804271,-122.25412869593173,2525,,
+2526.0,85.0,Snohomish,1,Suburban Snohomish,47.83685453832615,-122.24258194054686,2526,,
+2527.0,85.0,Snohomish,1,Suburban Snohomish,47.83839160806511,-122.22964652692622,2527,,
+2528.0,85.0,Snohomish,1,Suburban Snohomish,47.82411639106122,-122.22845139446044,2528,,
+2529.0,85.0,Snohomish,1,Suburban Snohomish,47.81496380221302,-122.22648563628351,2529,,
+2530.0,85.0,Snohomish,1,Suburban Snohomish,47.834814506021566,-122.21691235635558,2530,,
+2531.0,85.0,Snohomish,1,Suburban Snohomish,47.82060235116203,-122.21649225697678,2531,,
+2532.0,85.0,Snohomish,1,Suburban Snohomish,47.81322888889513,-122.21899315777893,2532,,
+2533.0,85.0,Snohomish,1,Suburban Snohomish,47.81490704860922,-122.2107191387076,2533,,
+2534.0,85.0,Snohomish,1,Suburban Snohomish,47.82897516511734,-122.20501562081228,2534,,
+2535.0,85.0,Snohomish,1,Suburban Snohomish,47.83093801022133,-122.19501389602026,2535,,
+2536.0,85.0,Snohomish,1,Suburban Snohomish,47.82386306887268,-122.19571905142172,2536,,
+2537.0,85.0,Snohomish,1,Suburban Snohomish,47.816411799062,-122.19647132922151,2537,,
+2538.0,80.0,Snohomish,1,Suburban Snohomish,47.82632706591785,-122.17450043803059,2538,,
+2539.0,80.0,Snohomish,1,Suburban Snohomish,47.81410099446194,-122.16900168872007,2539,,
+2540.0,80.0,Snohomish,1,Suburban Snohomish,47.822059048366526,-122.14825248306194,2540,,
+2541.0,86.0,Snohomish,1,Suburban Snohomish,47.822761261408864,-122.12048310331177,2541,,
+2542.0,86.0,Snohomish,1,Suburban Snohomish,47.81038982918285,-122.12900191708201,2542,,
+2543.0,86.0,Snohomish,1,Suburban Snohomish,47.826611612277965,-122.1016526219405,2543,,
+2544.0,86.0,Snohomish,1,Suburban Snohomish,47.815567589899594,-122.10222792273451,2544,,
+2545.0,86.0,Snohomish,1,Suburban Snohomish,47.824751050617174,-122.08483853280252,2545,,
+2546.0,86.0,Snohomish,1,Suburban Snohomish,47.82631338240442,-122.07001379179144,2546,,
+2547.0,77.0,Snohomish,1,Suburban Snohomish,47.83108396574596,-122.01372368016344,2547,,
+2548.0,87.0,Snohomish,1,Suburban Snohomish,47.8171910789,-121.99237404909503,2548,,
+2549.0,86.0,Snohomish,1,Suburban Snohomish,47.794403723900395,-122.0205242796636,2549,,
+2550.0,86.0,Snohomish,1,Suburban Snohomish,47.80969491949625,-122.05892065151954,2550,,
+2551.0,86.0,Snohomish,1,Suburban Snohomish,47.7855719955309,-122.06602773861694,2551,,
+2552.0,86.0,Snohomish,1,Suburban Snohomish,47.8018822890905,-122.0887039199548,2552,,
+2553.0,86.0,Snohomish,1,Suburban Snohomish,47.78632409613762,-122.09747775203093,2553,,
+2554.0,86.0,Snohomish,1,Suburban Snohomish,47.79815935254936,-122.1300863159602,2554,,
+2555.0,86.0,Snohomish,1,Suburban Snohomish,47.78431188182388,-122.12255703545506,2555,,
+2556.0,86.0,Snohomish,1,Suburban Snohomish,47.782327984966116,-122.13920643471322,2556,,
+2557.0,86.0,Snohomish,1,Suburban Snohomish,47.7997533398745,-122.16099013639706,2557,,
+2558.0,86.0,Snohomish,1,Suburban Snohomish,47.78530238137589,-122.15061512482114,2558,,
+2559.0,86.0,Snohomish,1,Suburban Snohomish,47.78337820551089,-122.16432481455972,2559,,
+2560.0,86.0,Snohomish,1,Suburban Snohomish,47.79815431928621,-122.17696928852371,2560,,
+2561.0,86.0,Snohomish,1,Suburban Snohomish,47.78354997292432,-122.17933013277195,2561,,
+2562.0,86.0,Snohomish,1,Suburban Snohomish,47.80951632939771,-122.18844433254758,2562,,
+2563.0,86.0,Snohomish,1,Suburban Snohomish,47.79711870376278,-122.18545474806105,2563,,
+2564.0,86.0,Snohomish,1,Suburban Snohomish,47.80005084911998,-122.20006629347652,2564,,
+2565.0,86.0,Snohomish,1,Suburban Snohomish,47.78541821173993,-122.19248030645693,2565,,
+2566.0,85.0,Snohomish,1,Suburban Snohomish,47.803470187972536,-122.21501160045423,2566,,
+2567.0,85.0,Snohomish,1,Suburban Snohomish,47.81248440781425,-122.23016113954728,2567,,
+2568.0,85.0,Snohomish,1,Suburban Snohomish,47.82385632337098,-122.24611246761023,2568,,
+2569.0,84.0,Snohomish,1,Suburban Snohomish,47.82446047545434,-122.26405085197077,2569,,
+2570.0,78.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82804170646161,-122.2751579873748,2570,47.884636,-122.261059
+2571.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82466373307882,-122.28000154090358,2571,47.884636,-122.261059
+2572.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.824740240757954,-122.28450899453264,2572,47.884636,-122.261059
+2573.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82459889116986,-122.28974528562695,2573,47.884636,-122.261059
+2574.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82464598220714,-122.29513359105196,2574,47.884636,-122.261059
+2575.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82470664464202,-122.30056444237957,2575,47.884636,-122.261059
+2576.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.824388035997146,-122.3078308250318,2576,47.884636,-122.261059
+2577.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82633957988936,-122.31245787352594,2577,47.884636,-122.261059
+2578.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.824856785786295,-122.31697651293214,2578,47.884636,-122.261059
+2579.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82490089057165,-122.32237048238085,2579,47.884636,-122.261059
+2580.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.825845342507506,-122.33135931056341,2580,47.884636,-122.261059
+2581.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.82861304533914,-122.34465409713984,2581,47.884636,-122.261059
+2582.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.825685578476076,-122.36182182546615,2582,47.884636,-122.261059
+2583.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81469584193793,-122.37874636605477,2583,47.884636,-122.261059
+2584.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81384610405345,-122.37216807896128,2584,47.884636,-122.261059
+2585.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.816131200869684,-122.36054753380311,2585,47.884636,-122.261059
+2586.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81453185498745,-122.34442303313172,2586,47.884636,-122.261059
+2587.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81594815728271,-122.33036024791572,2587,47.884636,-122.261059
+2588.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.808786225016405,-122.3304235399176,2588,47.884636,-122.261059
+2589.0,81.0,Snohomish,2,Everett-Lynwood-Edmonds,47.817483785221704,-122.32160692952914,2589,47.884636,-122.261059
+2590.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81715934462503,-122.31600944554646,2590,47.884636,-122.261059
+2591.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81000065471953,-122.31893095200412,2591,47.884636,-122.261059
+2592.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81928797609915,-122.30658120371963,2592,47.884636,-122.261059
+2593.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.8157490247904,-122.30802308452436,2593,47.884636,-122.261059
+2594.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.810418256288784,-122.30850143724129,2594,47.884636,-122.261059
+2595.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.819216028431185,-122.29643067277729,2595,47.884636,-122.261059
+2596.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81373619658226,-122.29832363628651,2596,47.884636,-122.261059
+2597.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80890827881629,-122.2967846899502,2597,47.884636,-122.261059
+2598.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.81854894225661,-122.28779974795016,2598,47.884636,-122.261059
+2599.0,84.0,Snohomish,1,Suburban Snohomish,47.81667646797971,-122.2789214759023,2599,,
+2600.0,84.0,Snohomish,1,Suburban Snohomish,47.81025144710714,-122.28601162225945,2600,,
+2601.0,84.0,Snohomish,1,Suburban Snohomish,47.811140856208105,-122.27577995968129,2601,,
+2602.0,84.0,Snohomish,1,Suburban Snohomish,47.817030361407966,-122.26456314749468,2602,,
+2603.0,84.0,Snohomish,1,Suburban Snohomish,47.811277039448655,-122.26480239326125,2603,,
+2604.0,84.0,Snohomish,1,Suburban Snohomish,47.81551744322638,-122.25588168023502,2604,,
+2605.0,84.0,Snohomish,1,Suburban Snohomish,47.81620483545823,-122.24896238846384,2605,,
+2606.0,84.0,Snohomish,1,Suburban Snohomish,47.812107656953394,-122.2369698841873,2606,,
+2607.0,84.0,Snohomish,1,Suburban Snohomish,47.80716215836057,-122.24035772563194,2607,,
+2608.0,84.0,Snohomish,1,Suburban Snohomish,47.79467938570857,-122.22010967830414,2608,,
+2609.0,84.0,Snohomish,1,Suburban Snohomish,47.7986081786584,-122.23234808669292,2609,,
+2610.0,84.0,Snohomish,1,Suburban Snohomish,47.80512817938655,-122.24701269396186,2610,,
+2611.0,84.0,Snohomish,1,Suburban Snohomish,47.79637496704144,-122.24404511723377,2611,,
+2612.0,84.0,Snohomish,1,Suburban Snohomish,47.79674904094296,-122.25301257575724,2612,,
+2613.0,84.0,Snohomish,1,Suburban Snohomish,47.802584385154894,-122.260141240176,2613,,
+2614.0,84.0,Snohomish,1,Suburban Snohomish,47.79472227991969,-122.26318674477957,2614,,
+2615.0,84.0,Snohomish,1,Suburban Snohomish,47.80680502262405,-122.26985838843605,2615,,
+2616.0,84.0,Snohomish,1,Suburban Snohomish,47.79784062145001,-122.27636151405632,2616,,
+2617.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.7997522647176,-122.28685483460785,2617,47.884636,-122.261059
+2618.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.802274280418736,-122.29765046756542,2618,47.884636,-122.261059
+2619.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79443092975994,-122.30071458463335,2619,47.884636,-122.261059
+2620.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80230805183338,-122.30757393449692,2620,47.884636,-122.261059
+2621.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79471542668804,-122.3119598550046,2621,47.884636,-122.261059
+2622.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80362449735652,-122.31616200196972,2622,47.884636,-122.261059
+2623.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.795931106958285,-122.31859907791343,2623,47.884636,-122.261059
+2624.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.802747125053436,-122.32567133862136,2624,47.884636,-122.261059
+2625.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79629712310767,-122.3253796974507,2625,47.884636,-122.261059
+2626.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79547639095888,-122.33178310987302,2626,47.884636,-122.261059
+2627.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80377051549529,-122.33221493830679,2627,47.884636,-122.261059
+2628.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80330676281401,-122.34112203278872,2628,47.884636,-122.261059
+2629.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79627126166454,-122.34028458655916,2629,47.884636,-122.261059
+2630.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.808133781678976,-122.35658434140421,2630,47.884636,-122.261059
+2631.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.802924817521685,-122.35725535626824,2631,47.884636,-122.261059
+2632.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.795979187905104,-122.35304884239618,2632,47.884636,-122.261059
+2633.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.795220013203256,-122.36334942798368,2633,47.884636,-122.261059
+2634.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80687851191023,-122.37226587453506,2634,47.884636,-122.261059
+2635.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79833685245865,-122.37152635299246,2635,47.884636,-122.261059
+2636.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80550341239669,-122.37887685722393,2636,47.884636,-122.261059
+2637.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.806743397166564,-122.38168479447461,2637,47.884636,-122.261059
+2638.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.80682507912514,-122.38773855688213,2638,47.884636,-122.261059
+2639.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.79712267658885,-122.38549044373886,2639,47.884636,-122.261059
+2640.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.783882231044785,-122.38586541406305,2640,47.884636,-122.261059
+2641.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.785565408562974,-122.37205736306791,2641,47.884636,-122.261059
+2642.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.783760958255215,-122.35839269943571,2642,47.884636,-122.261059
+2643.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.789111264727964,-122.35106629218971,2643,47.884636,-122.261059
+2644.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78321555735357,-122.34750017477734,2644,47.884636,-122.261059
+2645.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.7892030919579,-122.34277742877256,2645,47.884636,-122.261059
+2646.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78864828516277,-122.33657848036141,2646,47.884636,-122.261059
+2647.0,82.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78120261572703,-122.33707127558607,2647,47.884636,-122.261059
+2648.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78348115220288,-122.32535973508169,2648,47.884636,-122.261059
+2649.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.789039183304475,-122.3222140087594,2649,47.884636,-122.261059
+2650.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.787637808780644,-122.31234185839088,2650,47.884636,-122.261059
+2651.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78108196093916,-122.31260158062643,2651,47.884636,-122.261059
+2652.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78772261192179,-122.30403292834328,2652,47.884636,-122.261059
+2653.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78113303428602,-122.30570357719542,2653,47.884636,-122.261059
+2654.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78108918847434,-122.30032027780064,2654,47.884636,-122.261059
+2655.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.78815901299218,-122.29464265801448,2655,47.884636,-122.261059
+2656.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.780999543350106,-122.29382693979936,2656,47.884636,-122.261059
+2657.0,83.0,Snohomish,2,Everett-Lynwood-Edmonds,47.7877782668269,-122.28631283581775,2657,47.884636,-122.261059
+2658.0,84.0,Snohomish,1,Suburban Snohomish,47.78814292179064,-122.27736880181412,2658,,
+2659.0,84.0,Snohomish,1,Suburban Snohomish,47.780522685422476,-122.28119125969702,2659,,
+2660.0,84.0,Snohomish,1,Suburban Snohomish,47.7879509735024,-122.26715577750393,2660,,
+2661.0,84.0,Snohomish,1,Suburban Snohomish,47.78120401622561,-122.26547792458106,2661,,
+2662.0,84.0,Snohomish,1,Suburban Snohomish,47.78336829074482,-122.25464745441609,2662,,
+2663.0,84.0,Snohomish,1,Suburban Snohomish,47.78602194177335,-122.24748395653944,2663,,
+2664.0,84.0,Snohomish,1,Suburban Snohomish,47.77860329425194,-122.24812143816679,2664,,
+2665.0,84.0,Snohomish,1,Suburban Snohomish,47.784202165424574,-122.23825190474771,2665,,
+2666.0,84.0,Snohomish,1,Suburban Snohomish,47.78396283522154,-122.22602398293144,2666,,
+2667.0,84.0,Snohomish,1,Suburban Snohomish,47.78380910766165,-122.21329648873531,2667,,
+2668.0,84.0,Snohomish,1,Suburban Snohomish,47.78218360105134,-122.20112813049231,2668,,
+2669.0,88.0,Pierce,11,S.Kitsap,47.39648869298482,-122.78282239569502,2669,47.427932,-122.67538799999998
+2670.0,88.0,Pierce,11,S.Kitsap,47.39626273512837,-122.72983490636216,2670,47.427932,-122.67538799999998
+2671.0,88.0,Pierce,11,S.Kitsap,47.39526864247024,-122.68036730792136,2671,47.427932,-122.67538799999998
+2672.0,88.0,Pierce,11,S.Kitsap,47.392931935845965,-122.64852625812814,2672,47.427932,-122.67538799999998
+2673.0,88.0,Pierce,11,S.Kitsap,47.37697018242736,-122.78494916285898,2673,47.427932,-122.67538799999998
+2674.0,88.0,Pierce,11,S.Kitsap,47.3824265727458,-122.745480663143,2674,47.427932,-122.67538799999998
+2675.0,88.0,Pierce,11,S.Kitsap,47.38286125576639,-122.71064380503212,2675,47.427932,-122.67538799999998
+2676.0,88.0,Pierce,11,S.Kitsap,47.38242474159782,-122.68527374395704,2676,47.427932,-122.67538799999998
+2677.0,88.0,Pierce,11,S.Kitsap,47.37568598724117,-122.67382568226564,2677,47.427932,-122.67538799999998
+2678.0,88.0,Pierce,11,S.Kitsap,47.355335368979894,-122.77692940100084,2678,47.427932,-122.67538799999998
+2679.0,88.0,Pierce,11,S.Kitsap,47.361485470900824,-122.74495503497246,2679,47.427932,-122.67538799999998
+2680.0,88.0,Pierce,11,S.Kitsap,47.35796618262989,-122.71483001286086,2680,47.427932,-122.67538799999998
+2681.0,88.0,Pierce,11,S.Kitsap,47.31397435551777,-122.78011167883345,2681,47.427932,-122.67538799999998
+2682.0,88.0,Pierce,11,S.Kitsap,47.3190884434873,-122.76069361872416,2682,47.427932,-122.67538799999998
+2683.0,88.0,Pierce,11,S.Kitsap,47.32545168103727,-122.74184871705653,2683,47.427932,-122.67538799999998
+2684.0,88.0,Pierce,11,S.Kitsap,47.2740854070101,-122.79576744456692,2684,47.427932,-122.67538799999998
+2685.0,88.0,Pierce,11,S.Kitsap,47.28923827387242,-122.75723477955114,2685,47.427932,-122.67538799999998
+2686.0,88.0,Pierce,11,S.Kitsap,47.24610567775178,-122.79906762791978,2686,47.427932,-122.67538799999998
+2687.0,88.0,Pierce,11,S.Kitsap,47.26537581285932,-122.75765875051756,2687,47.427932,-122.67538799999998
+2688.0,88.0,Pierce,11,S.Kitsap,47.23115846113391,-122.77527661207807,2688,47.427932,-122.67538799999998
+2689.0,88.0,Pierce,11,S.Kitsap,47.23518045364769,-122.74566723685659,2689,47.427932,-122.67538799999998
+2690.0,88.0,Pierce,11,S.Kitsap,47.20035846273859,-122.77780337515742,2690,47.427932,-122.67538799999998
+2691.0,88.0,Pierce,11,S.Kitsap,47.190643392287186,-122.75329245386395,2691,47.427932,-122.67538799999998
+2692.0,88.0,Pierce,11,S.Kitsap,47.209692904367905,-122.68405185170558,2692,47.427932,-122.67538799999998
+2693.0,88.0,Pierce,11,S.Kitsap,47.15842538309552,-122.70389793199732,2693,47.427932,-122.67538799999998
+2694.0,88.0,Pierce,11,S.Kitsap,47.394517001258976,-122.62785782531445,2694,47.427932,-122.67538799999998
+2695.0,89.0,Pierce,11,S.Kitsap,47.39389326542131,-122.62259232031471,2695,47.427932,-122.67538799999998
+2696.0,89.0,Pierce,11,S.Kitsap,47.39662427452946,-122.60490143289482,2696,47.427932,-122.67538799999998
+2697.0,89.0,Pierce,11,S.Kitsap,47.39651438389762,-122.5802000025498,2697,47.427932,-122.67538799999998
+2698.0,89.0,Pierce,11,S.Kitsap,47.37685568066011,-122.56564919110436,2698,47.427932,-122.67538799999998
+2699.0,89.0,Pierce,11,S.Kitsap,47.3798888846548,-122.55072014110128,2699,47.427932,-122.67538799999998
+2700.0,89.0,Pierce,11,S.Kitsap,47.376198648514574,-122.58293470675993,2700,47.427932,-122.67538799999998
+2701.0,89.0,Pierce,11,S.Kitsap,47.3823608288208,-122.59917559770749,2701,47.427932,-122.67538799999998
+2702.0,89.0,Pierce,11,S.Kitsap,47.38161301964218,-122.61323511665155,2702,47.427932,-122.67538799999998
+2703.0,89.0,Pierce,11,S.Kitsap,47.381917880693436,-122.62050961228142,2703,47.427932,-122.67538799999998
+2704.0,88.0,Pierce,11,S.Kitsap,47.36127056035076,-122.6301762444663,2704,47.427932,-122.67538799999998
+2705.0,89.0,Pierce,11,S.Kitsap,47.37001347138956,-122.60482252434295,2705,47.427932,-122.67538799999998
+2706.0,89.0,Pierce,11,S.Kitsap,47.362675635710964,-122.60166129935018,2706,47.427932,-122.67538799999998
+2707.0,89.0,Pierce,11,S.Kitsap,47.35283092474842,-122.60040689781832,2707,47.427932,-122.67538799999998
+2708.0,89.0,Pierce,11,S.Kitsap,47.353449975254094,-122.58399651764952,2708,47.427932,-122.67538799999998
+2709.0,89.0,Pierce,11,S.Kitsap,47.34489333733778,-122.56881304451952,2709,47.427932,-122.67538799999998
+2710.0,89.0,Pierce,11,S.Kitsap,47.342111455217676,-122.59199253232616,2710,47.427932,-122.67538799999998
+2711.0,89.0,Pierce,11,S.Kitsap,47.333040101248024,-122.59186963374852,2711,47.427932,-122.67538799999998
+2712.0,89.0,Pierce,11,S.Kitsap,47.33937616815993,-122.60107174451672,2712,47.427932,-122.67538799999998
+2713.0,88.0,Pierce,11,S.Kitsap,47.34175215546645,-122.61232157100414,2713,47.427932,-122.67538799999998
+2714.0,88.0,Pierce,11,S.Kitsap,47.34050116952815,-122.63421257592665,2714,47.427932,-122.67538799999998
+2715.0,88.0,Pierce,11,S.Kitsap,47.34447449628242,-122.6564336039568,2715,47.427932,-122.67538799999998
+2716.0,88.0,Pierce,11,S.Kitsap,47.32423950749405,-122.650802466656,2716,47.427932,-122.67538799999998
+2717.0,88.0,Pierce,11,S.Kitsap,47.31329444860111,-122.62995054557314,2717,47.427932,-122.67538799999998
+2718.0,88.0,Pierce,11,S.Kitsap,47.322288258429325,-122.61976546068364,2718,47.427932,-122.67538799999998
+2719.0,88.0,Pierce,11,S.Kitsap,47.322914796141355,-122.60536609202214,2719,47.427932,-122.67538799999998
+2720.0,88.0,Pierce,11,S.Kitsap,47.32199052624691,-122.59673225210054,2720,47.427932,-122.67538799999998
+2721.0,89.0,Pierce,11,S.Kitsap,47.32714586453202,-122.59278248347724,2721,47.427932,-122.67538799999998
+2722.0,89.0,Pierce,11,S.Kitsap,47.328165031989975,-122.58499366778344,2722,47.427932,-122.67538799999998
+2723.0,89.0,Pierce,11,S.Kitsap,47.32575006733058,-122.58194956693451,2723,47.427932,-122.67538799999998
+2724.0,89.0,Pierce,11,S.Kitsap,47.31999928136008,-122.5836948479283,2724,47.427932,-122.67538799999998
+2725.0,89.0,Pierce,11,S.Kitsap,47.3207301750121,-122.57654837482114,2725,47.427932,-122.67538799999998
+2726.0,89.0,Pierce,11,S.Kitsap,47.30634395744256,-122.56732025470751,2726,47.427932,-122.67538799999998
+2727.0,89.0,Pierce,11,S.Kitsap,47.31223659901478,-122.57605460265644,2727,47.427932,-122.67538799999998
+2728.0,89.0,Pierce,11,S.Kitsap,47.312072514118,-122.58046562274349,2728,47.427932,-122.67538799999998
+2729.0,89.0,Pierce,11,S.Kitsap,47.31170637720586,-122.58574959368427,2729,47.427932,-122.67538799999998
+2730.0,89.0,Pierce,11,S.Kitsap,47.31136127900311,-122.597500639056,2730,47.427932,-122.67538799999998
+2731.0,88.0,Pierce,11,S.Kitsap,47.313272101405715,-122.6072467388197,2731,47.427932,-122.67538799999998
+2732.0,88.0,Pierce,11,S.Kitsap,47.30333869232786,-122.6521071944096,2732,47.427932,-122.67538799999998
+2733.0,88.0,Pierce,11,S.Kitsap,47.31553250606034,-122.67172549421065,2733,47.427932,-122.67538799999998
+2734.0,88.0,Pierce,11,S.Kitsap,47.29121150039664,-122.68114231008828,2734,47.427932,-122.67538799999998
+2735.0,88.0,Pierce,11,S.Kitsap,47.2879081816407,-122.64911708202835,2735,47.427932,-122.67538799999998
+2736.0,88.0,Pierce,11,S.Kitsap,47.29725889779933,-122.62530572394752,2736,47.427932,-122.67538799999998
+2737.0,89.0,Pierce,11,S.Kitsap,47.30081284368535,-122.60026901580143,2737,47.427932,-122.67538799999998
+2738.0,89.0,Pierce,11,S.Kitsap,47.30077329471713,-122.58508080767069,2738,47.427932,-122.67538799999998
+2739.0,89.0,Pierce,11,S.Kitsap,47.296314439814786,-122.57398095755366,2739,47.427932,-122.67538799999998
+2740.0,89.0,Pierce,11,S.Kitsap,47.30283236273492,-122.5706985342663,2740,47.427932,-122.67538799999998
+2741.0,89.0,Pierce,11,S.Kitsap,47.28621372199542,-122.55439787140708,2741,47.427932,-122.67538799999998
+2742.0,89.0,Pierce,11,S.Kitsap,47.289922692057196,-122.56137986027493,2742,47.427932,-122.67538799999998
+2743.0,89.0,Pierce,11,S.Kitsap,47.283115494304504,-122.57013024503878,2743,47.427932,-122.67538799999998
+2744.0,89.0,Pierce,11,S.Kitsap,47.28767978239309,-122.59029749847923,2744,47.427932,-122.67538799999998
+2745.0,89.0,Pierce,11,S.Kitsap,47.288056195461294,-122.6099969692738,2745,47.427932,-122.67538799999998
+2746.0,89.0,Pierce,11,S.Kitsap,47.29053824005701,-122.62442850644423,2746,47.427932,-122.67538799999998
+2747.0,89.0,Pierce,11,S.Kitsap,47.28100292830346,-122.63071730228629,2747,47.427932,-122.67538799999998
+2748.0,89.0,Pierce,11,S.Kitsap,47.2834914425312,-122.61536388454121,2748,47.427932,-122.67538799999998
+2749.0,89.0,Pierce,11,S.Kitsap,47.27519594615825,-122.6106035554312,2749,47.427932,-122.67538799999998
+2750.0,89.0,Pierce,11,S.Kitsap,47.26680629456029,-122.58958170623673,2750,47.427932,-122.67538799999998
+2751.0,89.0,Pierce,11,S.Kitsap,47.267690297497865,-122.57397012696876,2751,47.427932,-122.67538799999998
+2752.0,89.0,Pierce,11,S.Kitsap,47.25713568892432,-122.635954401819,2752,47.427932,-122.67538799999998
+2753.0,89.0,Pierce,11,S.Kitsap,47.23244592230876,-122.60852260089018,2753,47.427932,-122.67538799999998
+2754.0,90.0,Pierce,8,Tacoma,47.30779880751229,-122.52950870591481,2754,47.25268,-122.45526799999999
+2755.0,90.0,Pierce,8,Tacoma,47.29469792511926,-122.52567236525536,2755,47.25268,-122.45526799999999
+2756.0,90.0,Pierce,8,Tacoma,47.29545025572761,-122.51821903258663,2756,47.25268,-122.45526799999999
+2757.0,90.0,Pierce,8,Tacoma,47.29822730365512,-122.51030868313734,2757,47.25268,-122.45526799999999
+2758.0,90.0,Pierce,8,Tacoma,47.2925617761341,-122.51041741329912,2758,47.25268,-122.45526799999999
+2759.0,90.0,Pierce,8,Tacoma,47.294921991298246,-122.50254849492642,2759,47.25268,-122.45526799999999
+2760.0,91.0,Pierce,8,Tacoma,47.2842858952076,-122.48284975124376,2760,47.25268,-122.45526799999999
+2761.0,91.0,Pierce,8,Tacoma,47.28529098773389,-122.49141269237838,2761,47.25268,-122.45526799999999
+2762.0,91.0,Pierce,8,Tacoma,47.288550251115005,-122.50023374546986,2762,47.25268,-122.45526799999999
+2763.0,91.0,Pierce,8,Tacoma,47.283593065832996,-122.50086716010182,2763,47.25268,-122.45526799999999
+2764.0,90.0,Pierce,8,Tacoma,47.28892583752228,-122.51043559963207,2764,47.25268,-122.45526799999999
+2765.0,90.0,Pierce,8,Tacoma,47.28382191192484,-122.51007795287173,2765,47.25268,-122.45526799999999
+2766.0,90.0,Pierce,8,Tacoma,47.28635592640847,-122.51840807331031,2766,47.25268,-122.45526799999999
+2767.0,90.0,Pierce,8,Tacoma,47.28184642464901,-122.52638392716084,2767,47.25268,-122.45526799999999
+2768.0,90.0,Pierce,8,Tacoma,47.27627617700778,-122.51945377293436,2768,47.25268,-122.45526799999999
+2769.0,90.0,Pierce,8,Tacoma,47.277533332932606,-122.51106688291893,2769,47.25268,-122.45526799999999
+2770.0,90.0,Pierce,8,Tacoma,47.27267493768521,-122.51025887780628,2770,47.25268,-122.45526799999999
+2771.0,91.0,Pierce,8,Tacoma,47.27773779662851,-122.50065001631062,2771,47.25268,-122.45526799999999
+2772.0,91.0,Pierce,8,Tacoma,47.272982464951276,-122.50072275073569,2772,47.25268,-122.45526799999999
+2773.0,91.0,Pierce,8,Tacoma,47.27769170506559,-122.49247116830952,2773,47.25268,-122.45526799999999
+2774.0,91.0,Pierce,8,Tacoma,47.272967879164774,-122.492531533172,2774,47.25268,-122.45526799999999
+2775.0,91.0,Pierce,8,Tacoma,47.27754069528639,-122.4834043000328,2775,47.25268,-122.45526799999999
+2776.0,91.0,Pierce,8,Tacoma,47.272950582875254,-122.48648389200173,2776,47.25268,-122.45526799999999
+2777.0,91.0,Pierce,8,Tacoma,47.27288908507025,-122.48089469955653,2777,47.25268,-122.45526799999999
+2778.0,91.0,Pierce,8,Tacoma,47.27651156165987,-122.4734630708831,2778,47.25268,-122.45526799999999
+2779.0,91.0,Pierce,8,Tacoma,47.27128526500261,-122.47183016223728,2779,47.25268,-122.45526799999999
+2780.0,91.0,Pierce,8,Tacoma,47.26984269784502,-122.45149907534731,2780,47.25268,-122.45526799999999
+2781.0,91.0,Pierce,8,Tacoma,47.27077769580918,-122.45870585952349,2781,47.25268,-122.45526799999999
+2782.0,92.0,Pierce,8,Tacoma,47.265840425019256,-122.44795368584735,2782,47.25268,-122.45526799999999
+2783.0,92.0,Pierce,8,Tacoma,47.2641412487741,-122.45191417867979,2783,47.25268,-122.45526799999999
+2784.0,91.0,Pierce,8,Tacoma,47.265865008570316,-122.45775190325236,2784,47.25268,-122.45526799999999
+2785.0,91.0,Pierce,8,Tacoma,47.26793694649364,-122.46389866555904,2785,47.25268,-122.45526799999999
+2786.0,91.0,Pierce,8,Tacoma,47.26923169244557,-122.48092325662195,2786,47.25268,-122.45526799999999
+2787.0,91.0,Pierce,8,Tacoma,47.269208119350345,-122.48647301945202,2787,47.25268,-122.45526799999999
+2788.0,91.0,Pierce,8,Tacoma,47.26919779833143,-122.49252367668345,2788,47.25268,-122.45526799999999
+2789.0,91.0,Pierce,8,Tacoma,47.269191199678936,-122.5007091966184,2789,47.25268,-122.45526799999999
+2790.0,90.0,Pierce,8,Tacoma,47.26919664081074,-122.51059312395849,2790,47.25268,-122.45526799999999
+2791.0,90.0,Pierce,8,Tacoma,47.26846906827143,-122.52482007312372,2791,47.25268,-122.45526799999999
+2792.0,90.0,Pierce,8,Tacoma,47.26792663452433,-122.53640758421514,2792,47.25268,-122.45526799999999
+2793.0,90.0,Pierce,8,Tacoma,47.261966168875595,-122.53181564807943,2793,47.25268,-122.45526799999999
+2794.0,90.0,Pierce,8,Tacoma,47.262144793153496,-122.52090771997221,2794,47.25268,-122.45526799999999
+2795.0,90.0,Pierce,8,Tacoma,47.263619275325134,-122.51055879495468,2795,47.25268,-122.45526799999999
+2796.0,91.0,Pierce,8,Tacoma,47.26455146385343,-122.50069791089714,2796,47.25268,-122.45526799999999
+2797.0,91.0,Pierce,8,Tacoma,47.26455925739294,-122.49251526419651,2797,47.25268,-122.45526799999999
+2798.0,91.0,Pierce,8,Tacoma,47.26457519460453,-122.48647188637594,2798,47.25268,-122.45526799999999
+2799.0,91.0,Pierce,8,Tacoma,47.263643393272474,-122.48095370084556,2799,47.25268,-122.45526799999999
+2800.0,91.0,Pierce,8,Tacoma,47.265566120167215,-122.47299640207979,2800,47.25268,-122.45526799999999
+2801.0,91.0,Pierce,8,Tacoma,47.26174190405842,-122.47298936348948,2801,47.25268,-122.45526799999999
+2802.0,91.0,Pierce,8,Tacoma,47.26470366415649,-122.46537602263788,2802,47.25268,-122.45526799999999
+2803.0,91.0,Pierce,8,Tacoma,47.26277094300984,-122.45992694942042,2803,47.25268,-122.45526799999999
+2804.0,92.0,Pierce,8,Tacoma,47.26180488255246,-122.45482113280823,2804,47.25268,-122.45526799999999
+2805.0,92.0,Pierce,8,Tacoma,47.26218786574385,-122.4451724588896,2805,47.25268,-122.45526799999999
+2806.0,92.0,Pierce,8,Tacoma,47.25953262505346,-122.44396996385001,2806,47.25268,-122.45526799999999
+2807.0,92.0,Pierce,8,Tacoma,47.26042449542124,-122.44832962041949,2807,47.25268,-122.45526799999999
+2808.0,92.0,Pierce,8,Tacoma,47.2591930706305,-122.45247529951841,2808,47.25268,-122.45526799999999
+2809.0,92.0,Pierce,8,Tacoma,47.25786257816225,-122.45696638780407,2809,47.25268,-122.45526799999999
+2810.0,91.0,Pierce,8,Tacoma,47.259323062488384,-122.45952228842678,2810,47.25268,-122.45526799999999
+2811.0,91.0,Pierce,8,Tacoma,47.26071491427206,-122.46511627919828,2811,47.25268,-122.45526799999999
+2812.0,91.0,Pierce,8,Tacoma,47.25733395398846,-122.46534391450541,2812,47.25268,-122.45526799999999
+2813.0,91.0,Pierce,8,Tacoma,47.257619560818135,-122.47307296971672,2813,47.25268,-122.45526799999999
+2814.0,91.0,Pierce,8,Tacoma,47.25760542296082,-122.4807701890656,2814,47.25268,-122.45526799999999
+2815.0,91.0,Pierce,8,Tacoma,47.25844714381018,-122.48636781875472,2815,47.25268,-122.45526799999999
+2816.0,91.0,Pierce,8,Tacoma,47.258577508435046,-122.49249124626695,2816,47.25268,-122.45526799999999
+2817.0,91.0,Pierce,8,Tacoma,47.25855456463312,-122.50062476281187,2817,47.25268,-122.45526799999999
+2818.0,90.0,Pierce,8,Tacoma,47.257715092869816,-122.51034956920302,2818,47.25268,-122.45526799999999
+2819.0,90.0,Pierce,8,Tacoma,47.25676466758328,-122.5214968888919,2819,47.25268,-122.45526799999999
+2820.0,90.0,Pierce,8,Tacoma,47.25705363344427,-122.53168861712574,2820,47.25268,-122.45526799999999
+2821.0,90.0,Pierce,8,Tacoma,47.25478019954846,-122.54648864446031,2821,47.25268,-122.45526799999999
+2822.0,90.0,Pierce,8,Tacoma,47.246118422894156,-122.54897171255614,2822,47.25268,-122.45526799999999
+2823.0,90.0,Pierce,8,Tacoma,47.248180514326364,-122.54076487976238,2823,47.25268,-122.45526799999999
+2824.0,90.0,Pierce,8,Tacoma,47.252715493071356,-122.53167768657272,2824,47.25268,-122.45526799999999
+2825.0,90.0,Pierce,8,Tacoma,47.24630870310688,-122.53169485510588,2825,47.25268,-122.45526799999999
+2826.0,90.0,Pierce,8,Tacoma,47.25270939593924,-122.52112683536946,2826,47.25268,-122.45526799999999
+2827.0,90.0,Pierce,8,Tacoma,47.24634751642282,-122.52106165273544,2827,47.25268,-122.45526799999999
+2828.0,92.0,Pierce,8,Tacoma,47.252721492722216,-122.51447115135902,2828,47.25268,-122.45526799999999
+2829.0,92.0,Pierce,8,Tacoma,47.25288984126833,-122.50914680205295,2829,47.25268,-122.45526799999999
+2830.0,90.0,Pierce,8,Tacoma,47.24594880216989,-122.51167615989401,2830,47.25268,-122.45526799999999
+2831.0,92.0,Pierce,8,Tacoma,47.24762267134774,-122.50701290911628,2831,47.25268,-122.45526799999999
+2832.0,92.0,Pierce,8,Tacoma,47.25273826603325,-122.50055308147257,2832,47.25268,-122.45526799999999
+2833.0,92.0,Pierce,8,Tacoma,47.246489889237004,-122.50021163799464,2833,47.25268,-122.45526799999999
+2834.0,92.0,Pierce,8,Tacoma,47.25269143584386,-122.49249676067714,2834,47.25268,-122.45526799999999
+2835.0,92.0,Pierce,8,Tacoma,47.24653183874664,-122.4922817050133,2835,47.25268,-122.45526799999999
+2836.0,92.0,Pierce,8,Tacoma,47.25266463099518,-122.48630985381409,2836,47.25268,-122.45526799999999
+2837.0,92.0,Pierce,8,Tacoma,47.24627871211201,-122.48618352491889,2837,47.25268,-122.45526799999999
+2838.0,92.0,Pierce,8,Tacoma,47.252518422417964,-122.48023424678335,2838,47.25268,-122.45526799999999
+2839.0,92.0,Pierce,8,Tacoma,47.246493018826506,-122.47983377372108,2839,47.25268,-122.45526799999999
+2840.0,92.0,Pierce,8,Tacoma,47.25281771188961,-122.47257736205131,2840,47.25268,-122.45526799999999
+2841.0,92.0,Pierce,8,Tacoma,47.25281210281618,-122.46559797342691,2841,47.25268,-122.45526799999999
+2842.0,92.0,Pierce,8,Tacoma,47.24830659938214,-122.4696022640254,2842,47.25268,-122.45526799999999
+2843.0,92.0,Pierce,8,Tacoma,47.24464400441376,-122.46951891983039,2843,47.25268,-122.45526799999999
+2844.0,92.0,Pierce,8,Tacoma,47.25487360681269,-122.46129939465506,2844,47.25268,-122.45526799999999
+2845.0,92.0,Pierce,8,Tacoma,47.25205294946081,-122.4610444851134,2845,47.25268,-122.45526799999999
+2846.0,92.0,Pierce,8,Tacoma,47.24887305035098,-122.46066316616243,2846,47.25268,-122.45526799999999
+2847.0,92.0,Pierce,8,Tacoma,47.24475393118991,-122.46014710991345,2847,47.25268,-122.45526799999999
+2848.0,92.0,Pierce,8,Tacoma,47.255152769631955,-122.45683943156445,2848,47.25268,-122.45526799999999
+2849.0,92.0,Pierce,8,Tacoma,47.25257795244369,-122.45625143133988,2849,47.25268,-122.45526799999999
+2850.0,92.0,Pierce,8,Tacoma,47.25051879076956,-122.45578094379671,2850,47.25268,-122.45526799999999
+2851.0,92.0,Pierce,8,Tacoma,47.24845880393686,-122.45531516377808,2851,47.25268,-122.45526799999999
+2852.0,92.0,Pierce,8,Tacoma,47.2453701936709,-122.45461161401224,2852,47.25268,-122.45526799999999
+2853.0,92.0,Pierce,8,Tacoma,47.25566073501847,-122.4512317476936,2853,47.25268,-122.45526799999999
+2854.0,92.0,Pierce,8,Tacoma,47.253030981917455,-122.4519539354168,2854,47.25268,-122.45526799999999
+2855.0,92.0,Pierce,8,Tacoma,47.25097178104144,-122.45148499812748,2855,47.25268,-122.45526799999999
+2856.0,92.0,Pierce,8,Tacoma,47.24891267345978,-122.4510181262441,2856,47.25268,-122.45526799999999
+2857.0,92.0,Pierce,8,Tacoma,47.24574919034746,-122.4510311219544,2857,47.25268,-122.45526799999999
+2858.0,92.0,Pierce,8,Tacoma,47.24589966750261,-122.44959928190607,2858,47.25268,-122.45526799999999
+2859.0,92.0,Pierce,8,Tacoma,47.253332669892366,-122.44906913745164,2859,47.25268,-122.45526799999999
+2860.0,92.0,Pierce,8,Tacoma,47.25127447852163,-122.44859916506121,2860,47.25268,-122.45526799999999
+2861.0,92.0,Pierce,8,Tacoma,47.249215006349246,-122.44813179080042,2861,47.25268,-122.45526799999999
+2862.0,92.0,Pierce,8,Tacoma,47.2460419636901,-122.4472919522571,2862,47.25268,-122.45526799999999
+2863.0,92.0,Pierce,8,Tacoma,47.25631153166842,-122.44699407692407,2863,47.25268,-122.45526799999999
+2864.0,92.0,Pierce,8,Tacoma,47.25363895383976,-122.44614382151488,2864,47.25268,-122.45526799999999
+2865.0,92.0,Pierce,8,Tacoma,47.251580735387144,-122.44567529069862,2865,47.25268,-122.45526799999999
+2866.0,92.0,Pierce,8,Tacoma,47.2495223140167,-122.44520637276165,2866,47.25268,-122.45526799999999
+2867.0,92.0,Pierce,8,Tacoma,47.24655068426814,-122.44445244461089,2867,47.25268,-122.45526799999999
+2868.0,92.0,Pierce,8,Tacoma,47.256610244822674,-122.44382960967786,2868,47.25268,-122.45526799999999
+2869.0,92.0,Pierce,8,Tacoma,47.25394428342664,-122.44321994245013,2869,47.25268,-122.45526799999999
+2870.0,92.0,Pierce,8,Tacoma,47.2518877274134,-122.44275248999948,2870,47.25268,-122.45526799999999
+2871.0,92.0,Pierce,8,Tacoma,47.249830619438725,-122.4422832796812,2871,47.25268,-122.45526799999999
+2872.0,92.0,Pierce,8,Tacoma,47.247771398714065,-122.44181613811571,2872,47.25268,-122.45526799999999
+2873.0,92.0,Pierce,8,Tacoma,47.245712410953395,-122.44134773271492,2873,47.25268,-122.45526799999999
+2874.0,92.0,Pierce,8,Tacoma,47.25727698685767,-122.44145739414108,2874,47.25268,-122.45526799999999
+2875.0,92.0,Pierce,8,Tacoma,47.25421231137678,-122.44069487069784,2875,47.25268,-122.45526799999999
+2876.0,92.0,Pierce,8,Tacoma,47.25215278986648,-122.44022608420643,2876,47.25268,-122.45526799999999
+2877.0,92.0,Pierce,8,Tacoma,47.25009512245585,-122.43975804093279,2877,47.25268,-122.45526799999999
+2878.0,92.0,Pierce,8,Tacoma,47.247980569133,-122.43925200622377,2878,47.25268,-122.45526799999999
+2879.0,92.0,Pierce,8,Tacoma,47.24603743401424,-122.43928713523987,2879,47.25268,-122.45526799999999
+2880.0,92.0,Pierce,8,Tacoma,47.24592877749691,-122.43778342368198,2880,47.25268,-122.45526799999999
+2881.0,92.0,Pierce,8,Tacoma,47.256656728819465,-122.43890353553628,2881,47.25268,-122.45526799999999
+2882.0,92.0,Pierce,8,Tacoma,47.25436629059354,-122.439242603601,2882,47.25268,-122.45526799999999
+2883.0,92.0,Pierce,8,Tacoma,47.252307944125405,-122.43877405947664,2883,47.25268,-122.45526799999999
+2884.0,92.0,Pierce,8,Tacoma,47.25025671883058,-122.43830860156885,2884,47.25268,-122.45526799999999
+2885.0,92.0,Pierce,8,Tacoma,47.24840681040804,-122.43744026844143,2885,47.25268,-122.45526799999999
+2886.0,92.0,Pierce,8,Tacoma,47.25499925241278,-122.43824561772352,2886,47.25268,-122.45526799999999
+2887.0,92.0,Pierce,8,Tacoma,47.2539698051966,-122.43801328882287,2887,47.25268,-122.45526799999999
+2888.0,92.0,Pierce,8,Tacoma,47.25294186336031,-122.43777590467116,2888,47.25268,-122.45526799999999
+2889.0,92.0,Pierce,8,Tacoma,47.25191253703827,-122.4375417714613,2889,47.25268,-122.45526799999999
+2890.0,92.0,Pierce,8,Tacoma,47.25034756823194,-122.43717192976528,2890,47.25268,-122.45526799999999
+2891.0,92.0,Pierce,8,Tacoma,47.254478480854594,-122.43692995169873,2891,47.25268,-122.45526799999999
+2892.0,92.0,Pierce,8,Tacoma,47.251687014063215,-122.43622450395692,2892,47.25268,-122.45526799999999
+2893.0,93.0,Pierce,8,Tacoma,47.24596573561274,-122.43591604614583,2893,47.25268,-122.45526799999999
+2894.0,92.0,Pierce,8,Tacoma,47.25821319127612,-122.4380999390922,2894,47.25268,-122.45526799999999
+2895.0,92.0,Pierce,8,Tacoma,47.251461055040856,-122.43494964440922,2895,47.25268,-122.45526799999999
+2896.0,93.0,Pierce,8,Tacoma,47.24638373626294,-122.43410505467844,2896,47.25268,-122.45526799999999
+2897.0,93.0,Pierce,8,Tacoma,47.24185451530928,-122.43184377420653,2897,47.25268,-122.45526799999999
+2898.0,93.0,Pierce,8,Tacoma,47.23951424880336,-122.43091211765793,2898,47.25268,-122.45526799999999
+2899.0,93.0,Pierce,8,Tacoma,47.23596647144553,-122.43003475899981,2899,47.25268,-122.45526799999999
+2900.0,92.0,Pierce,8,Tacoma,47.24155663405686,-122.43483406302437,2900,47.25268,-122.45526799999999
+2901.0,92.0,Pierce,8,Tacoma,47.23908142414084,-122.43463315815329,2901,47.25268,-122.45526799999999
+2902.0,92.0,Pierce,8,Tacoma,47.239186814089344,-122.4332135459006,2902,47.25268,-122.45526799999999
+2903.0,92.0,Pierce,8,Tacoma,47.235866461381036,-122.43310116026713,2903,47.25268,-122.45526799999999
+2904.0,92.0,Pierce,8,Tacoma,47.2439398207121,-122.43798686642513,2904,47.25268,-122.45526799999999
+2905.0,92.0,Pierce,8,Tacoma,47.24197660622893,-122.43679402677708,2905,47.25268,-122.45526799999999
+2906.0,92.0,Pierce,8,Tacoma,47.24043196029388,-122.43644436375645,2906,47.25268,-122.45526799999999
+2907.0,92.0,Pierce,8,Tacoma,47.23940292620411,-122.43621176356348,2907,47.25268,-122.45526799999999
+2908.0,92.0,Pierce,8,Tacoma,47.24177255709238,-122.43836626017128,2908,47.25268,-122.45526799999999
+2909.0,92.0,Pierce,8,Tacoma,47.239722037186525,-122.43804077727243,2909,47.25268,-122.45526799999999
+2910.0,92.0,Pierce,8,Tacoma,47.23719096684369,-122.43786573850313,2910,47.25268,-122.45526799999999
+2911.0,92.0,Pierce,8,Tacoma,47.23524473005674,-122.43689036564345,2911,47.25268,-122.45526799999999
+2912.0,92.0,Pierce,8,Tacoma,47.23302199940353,-122.43777564657152,2912,47.25268,-122.45526799999999
+2913.0,92.0,Pierce,8,Tacoma,47.243655674128846,-122.44088021992576,2913,47.25268,-122.45526799999999
+2914.0,92.0,Pierce,8,Tacoma,47.241787060039215,-122.44054545359819,2914,47.25268,-122.45526799999999
+2915.0,92.0,Pierce,8,Tacoma,47.23971513169013,-122.440263876533,2915,47.25268,-122.45526799999999
+2916.0,92.0,Pierce,8,Tacoma,47.2373243894044,-122.440026310674,2916,47.25268,-122.45526799999999
+2917.0,92.0,Pierce,8,Tacoma,47.24128886591567,-122.44333467553612,2917,47.25268,-122.45526799999999
+2918.0,92.0,Pierce,8,Tacoma,47.23639890739833,-122.4422833520288,2918,47.25268,-122.45526799999999
+2919.0,92.0,Pierce,8,Tacoma,47.24098077412326,-122.44625837042831,2919,47.25268,-122.45526799999999
+2920.0,92.0,Pierce,8,Tacoma,47.2358712378174,-122.44511046728941,2920,47.25268,-122.45526799999999
+2921.0,97.0,Pierce,8,Tacoma,47.232919755045394,-122.44680226328785,2921,47.25268,-122.45526799999999
+2922.0,97.0,Pierce,8,Tacoma,47.2317188191873,-122.44464208840981,2922,47.25268,-122.45526799999999
+2923.0,92.0,Pierce,8,Tacoma,47.240750528865604,-122.44842713821163,2923,47.25268,-122.45526799999999
+2924.0,92.0,Pierce,8,Tacoma,47.240449172693395,-122.45129112861092,2924,47.25268,-122.45526799999999
+2925.0,92.0,Pierce,8,Tacoma,47.23979446258225,-122.45623985102364,2925,47.25268,-122.45526799999999
+2926.0,92.0,Pierce,8,Tacoma,47.235472176716826,-122.45240554160513,2926,47.25268,-122.45526799999999
+2927.0,97.0,Pierce,8,Tacoma,47.23323663380013,-122.45543694251312,2927,47.25268,-122.45526799999999
+2928.0,97.0,Pierce,8,Tacoma,47.23185911630043,-122.4572313326058,2928,47.25268,-122.45526799999999
+2929.0,92.0,Pierce,8,Tacoma,47.239611396414546,-122.46108509936373,2929,47.25268,-122.45526799999999
+2930.0,92.0,Pierce,8,Tacoma,47.235142918013786,-122.46171294751028,2930,47.25268,-122.45526799999999
+2931.0,92.0,Pierce,8,Tacoma,47.240006500746134,-122.46436244614452,2931,47.25268,-122.45526799999999
+2932.0,92.0,Pierce,8,Tacoma,47.23884771101065,-122.47074792605244,2932,47.25268,-122.45526799999999
+2933.0,92.0,Pierce,8,Tacoma,47.238707258105705,-122.47916626709669,2933,47.25268,-122.45526799999999
+2934.0,92.0,Pierce,8,Tacoma,47.23880393131463,-122.48779378268705,2934,47.25268,-122.45526799999999
+2935.0,92.0,Pierce,8,Tacoma,47.23932258284956,-122.4975530464374,2935,47.25268,-122.45526799999999
+2936.0,97.0,Pierce,8,Tacoma,47.23659762854906,-122.50089263349369,2936,47.25268,-122.45526799999999
+2937.0,96.0,Pierce,10,South Pierce,47.23935599414829,-122.50973995920224,2937,47.12806,-122.108131
+2938.0,96.0,Pierce,10,South Pierce,47.24073107920274,-122.52126404394487,2938,47.12806,-122.108131
+2939.0,96.0,Pierce,10,South Pierce,47.239241191045714,-122.52828366281356,2939,47.12806,-122.108131
+2940.0,96.0,Pierce,10,South Pierce,47.23904949062276,-122.53354540897959,2940,47.12806,-122.108131
+2941.0,96.0,Pierce,10,South Pierce,47.23898757083746,-122.54104442396944,2941,47.12806,-122.108131
+2942.0,96.0,Pierce,10,South Pierce,47.23895218769162,-122.54768192640013,2942,47.12806,-122.108131
+2943.0,96.0,Pierce,10,South Pierce,47.23935701572867,-122.556603201167,2943,47.12806,-122.108131
+2944.0,96.0,Pierce,10,South Pierce,47.23292484550762,-122.55648580730195,2944,47.12806,-122.108131
+2945.0,96.0,Pierce,10,South Pierce,47.23162382745842,-122.5478214634749,2945,47.12806,-122.108131
+2946.0,96.0,Pierce,10,South Pierce,47.23168630274786,-122.54114322394156,2946,47.12806,-122.108131
+2947.0,96.0,Pierce,10,South Pierce,47.23185632434128,-122.53159314790028,2947,47.12806,-122.108131
+2948.0,96.0,Pierce,10,South Pierce,47.23350418548386,-122.52120096426545,2948,47.12806,-122.108131
+2949.0,96.0,Pierce,10,South Pierce,47.232511506982156,-122.51124502819314,2949,47.12806,-122.108131
+2950.0,97.0,Pierce,8,Tacoma,47.2239193557741,-122.50302702172613,2950,47.25268,-122.45526799999999
+2951.0,97.0,Pierce,8,Tacoma,47.22354389688754,-122.49729546218114,2951,47.25268,-122.45526799999999
+2952.0,97.0,Pierce,8,Tacoma,47.23301692652046,-122.48811562014572,2952,47.25268,-122.45526799999999
+2953.0,97.0,Pierce,8,Tacoma,47.22874514908082,-122.48935770262199,2953,47.25268,-122.45526799999999
+2954.0,97.0,Pierce,8,Tacoma,47.23346393693488,-122.47986094015793,2954,47.25268,-122.45526799999999
+2955.0,97.0,Pierce,8,Tacoma,47.22972550927656,-122.47902346166737,2955,47.25268,-122.45526799999999
+2956.0,97.0,Pierce,8,Tacoma,47.23305246352546,-122.46951105506956,2956,47.25268,-122.45526799999999
+2957.0,97.0,Pierce,8,Tacoma,47.22984859552826,-122.46803826079159,2957,47.25268,-122.45526799999999
+2958.0,97.0,Pierce,8,Tacoma,47.225973131677854,-122.4678190203827,2958,47.25268,-122.45526799999999
+2959.0,97.0,Pierce,8,Tacoma,47.21832358976089,-122.46813704834604,2959,47.25268,-122.45526799999999
+2960.0,97.0,Pierce,8,Tacoma,47.210250482856424,-122.46730475694679,2960,47.25268,-122.45526799999999
+2961.0,97.0,Pierce,8,Tacoma,47.22526276772121,-122.47706313388343,2961,47.25268,-122.45526799999999
+2962.0,97.0,Pierce,8,Tacoma,47.218690994947615,-122.47711876912949,2962,47.25268,-122.45526799999999
+2963.0,97.0,Pierce,8,Tacoma,47.219108715680534,-122.48240541133282,2963,47.25268,-122.45526799999999
+2964.0,97.0,Pierce,8,Tacoma,47.21059441296608,-122.47810730632207,2964,47.25268,-122.45526799999999
+2965.0,97.0,Pierce,8,Tacoma,47.21638996130414,-122.48858890498785,2965,47.25268,-122.45526799999999
+2966.0,97.0,Pierce,8,Tacoma,47.20985039615394,-122.49980263973076,2966,47.25268,-122.45526799999999
+2967.0,96.0,Pierce,10,South Pierce,47.22465489521394,-122.51043666566794,2967,47.12806,-122.108131
+2968.0,96.0,Pierce,10,South Pierce,47.21375969738927,-122.5105753700788,2968,47.12806,-122.108131
+2969.0,96.0,Pierce,10,South Pierce,47.22471834712918,-122.52103575848491,2969,47.12806,-122.108131
+2970.0,96.0,Pierce,10,South Pierce,47.21740404184704,-122.52109322908274,2970,47.12806,-122.108131
+2971.0,96.0,Pierce,10,South Pierce,47.2110309566711,-122.52062055600204,2971,47.12806,-122.108131
+2972.0,96.0,Pierce,10,South Pierce,47.224510029667684,-122.53170778263149,2972,47.12806,-122.108131
+2973.0,96.0,Pierce,10,South Pierce,47.216386620172294,-122.53136938371796,2973,47.12806,-122.108131
+2974.0,96.0,Pierce,10,South Pierce,47.2244324285185,-122.54108226138096,2974,47.12806,-122.108131
+2975.0,96.0,Pierce,10,South Pierce,47.21715395041482,-122.54114093936626,2975,47.12806,-122.108131
+2976.0,96.0,Pierce,10,South Pierce,47.22373436920818,-122.54984629596842,2976,47.12806,-122.108131
+2977.0,96.0,Pierce,10,South Pierce,47.2168747720088,-122.55091703159927,2977,47.12806,-122.108131
+2978.0,96.0,Pierce,10,South Pierce,47.226606720059536,-122.55715529924484,2978,47.12806,-122.108131
+2979.0,96.0,Pierce,10,South Pierce,47.21591443104519,-122.5618260733952,2979,47.12806,-122.108131
+2980.0,96.0,Pierce,10,South Pierce,47.22161246063574,-122.56666190364945,2980,47.12806,-122.108131
+2981.0,96.0,Pierce,10,South Pierce,47.19991487054586,-122.57521905279577,2981,47.12806,-122.108131
+2982.0,96.0,Pierce,10,South Pierce,47.20419987493077,-122.56351168256687,2982,47.12806,-122.108131
+2983.0,96.0,Pierce,10,South Pierce,47.20718836384469,-122.55434009448628,2983,47.12806,-122.108131
+2984.0,96.0,Pierce,10,South Pierce,47.210666852528455,-122.54156886798971,2984,47.12806,-122.108131
+2985.0,96.0,Pierce,10,South Pierce,47.203933384171,-122.5420705285206,2985,47.12806,-122.108131
+2986.0,96.0,Pierce,10,South Pierce,47.19811366299783,-122.54382365725624,2986,47.12806,-122.108131
+2987.0,96.0,Pierce,10,South Pierce,47.20648828385676,-122.53080692501464,2987,47.12806,-122.108131
+2988.0,96.0,Pierce,10,South Pierce,47.20394538965761,-122.52219337044106,2988,47.12806,-122.108131
+2989.0,96.0,Pierce,10,South Pierce,47.20282195807957,-122.51233171149818,2989,47.12806,-122.108131
+2990.0,97.0,Pierce,8,Tacoma,47.20173992111671,-122.50028552183262,2990,47.25268,-122.45526799999999
+2991.0,97.0,Pierce,8,Tacoma,47.20189206728856,-122.488164516694,2991,47.25268,-122.45526799999999
+2992.0,97.0,Pierce,8,Tacoma,47.20189866095525,-122.47795958696658,2992,47.25268,-122.45526799999999
+2993.0,97.0,Pierce,8,Tacoma,47.1997154255437,-122.46845735145507,2993,47.25268,-122.45526799999999
+2994.0,97.0,Pierce,8,Tacoma,47.196221962393544,-122.46453727046485,2994,47.25268,-122.45526799999999
+2995.0,97.0,Pierce,8,Tacoma,47.19381385338362,-122.47805171235468,2995,47.25268,-122.45526799999999
+2996.0,97.0,Pierce,8,Tacoma,47.193916165016525,-122.48826567887586,2996,47.25268,-122.45526799999999
+2997.0,97.0,Pierce,8,Tacoma,47.19399006127086,-122.50078892589924,2997,47.25268,-122.45526799999999
+2998.0,100.0,Pierce,10,South Pierce,47.194221679508715,-122.51473207604616,2998,47.12806,-122.108131
+2999.0,99.0,Pierce,10,South Pierce,47.19326466124307,-122.5246243153,2999,47.12806,-122.108131
+3000.0,99.0,Pierce,10,South Pierce,47.19346692441924,-122.539201299479,3000,47.12806,-122.108131
+3001.0,99.0,Pierce,10,South Pierce,47.19377596429785,-122.55502785432994,3001,47.12806,-122.108131
+3002.0,99.0,Pierce,10,South Pierce,47.18765386956488,-122.55656515541477,3002,47.12806,-122.108131
+3003.0,99.0,Pierce,10,South Pierce,47.1876345719065,-122.56934675589994,3003,47.12806,-122.108131
+3004.0,99.0,Pierce,10,South Pierce,47.179814669823685,-122.58387374602741,3004,47.12806,-122.108131
+3005.0,99.0,Pierce,10,South Pierce,47.180092387654845,-122.56341481360457,3005,47.12806,-122.108131
+3006.0,99.0,Pierce,10,South Pierce,47.182985609109686,-122.55025833260572,3006,47.12806,-122.108131
+3007.0,99.0,Pierce,10,South Pierce,47.18428707748246,-122.54238894786717,3007,47.12806,-122.108131
+3008.0,99.0,Pierce,10,South Pierce,47.18487271337543,-122.53009521197778,3008,47.12806,-122.108131
+3009.0,100.0,Pierce,10,South Pierce,47.18044039904278,-122.52211941078457,3009,47.12806,-122.108131
+3010.0,100.0,Pierce,10,South Pierce,47.18272104419867,-122.51301048590548,3010,47.12806,-122.108131
+3011.0,100.0,Pierce,10,South Pierce,47.18330978720448,-122.50261009678042,3011,47.12806,-122.108131
+3012.0,100.0,Pierce,10,South Pierce,47.18450719316705,-122.49225517425155,3012,47.12806,-122.108131
+3013.0,100.0,Pierce,10,South Pierce,47.182032393885,-122.48598845773873,3013,47.12806,-122.108131
+3014.0,97.0,Pierce,8,Tacoma,47.18578540645663,-122.47850213922649,3014,47.25268,-122.45526799999999
+3015.0,97.0,Pierce,8,Tacoma,47.18568337461994,-122.46840436590256,3015,47.25268,-122.45526799999999
+3016.0,100.0,Pierce,10,South Pierce,47.176128053002024,-122.47556165724194,3016,47.12806,-122.108131
+3017.0,100.0,Pierce,10,South Pierce,47.1673895030587,-122.4794499559766,3017,47.12806,-122.108131
+3018.0,100.0,Pierce,10,South Pierce,47.17138933022579,-122.4877999506246,3018,47.12806,-122.108131
+3019.0,100.0,Pierce,10,South Pierce,47.171829004550396,-122.49945287468351,3019,47.12806,-122.108131
+3020.0,100.0,Pierce,10,South Pierce,47.17144316326002,-122.51121635449236,3020,47.12806,-122.108131
+3021.0,100.0,Pierce,10,South Pierce,47.17515820251761,-122.51526413039151,3021,47.12806,-122.108131
+3022.0,100.0,Pierce,10,South Pierce,47.168394387380296,-122.51666070840362,3022,47.12806,-122.108131
+3023.0,100.0,Pierce,10,South Pierce,47.17353151041525,-122.52252159424341,3023,47.12806,-122.108131
+3024.0,100.0,Pierce,10,South Pierce,47.167937618114394,-122.52303308548822,3024,47.12806,-122.108131
+3025.0,99.0,Pierce,10,South Pierce,47.17211518363208,-122.53257966657301,3025,47.12806,-122.108131
+3026.0,99.0,Pierce,10,South Pierce,47.17270206649356,-122.54244121237326,3026,47.12806,-122.108131
+3027.0,99.0,Pierce,10,South Pierce,47.1731760928769,-122.55022416793602,3027,47.12806,-122.108131
+3028.0,99.0,Pierce,10,South Pierce,47.16482042228464,-122.55454917319388,3028,47.12806,-122.108131
+3029.0,99.0,Pierce,10,South Pierce,47.17144128015714,-122.56359873797881,3029,47.12806,-122.108131
+3030.0,99.0,Pierce,10,South Pierce,47.17307289897589,-122.5786495564838,3030,47.12806,-122.108131
+3031.0,99.0,Pierce,10,South Pierce,47.17354113763368,-122.58795119949913,3031,47.12806,-122.108131
+3032.0,99.0,Pierce,10,South Pierce,47.17251880978504,-122.59957197427723,3032,47.12806,-122.108131
+3033.0,99.0,Pierce,10,South Pierce,47.16966425292889,-122.59928868966995,3033,47.12806,-122.108131
+3034.0,99.0,Pierce,10,South Pierce,47.16371676434045,-122.60883869215077,3034,47.12806,-122.108131
+3035.0,99.0,Pierce,10,South Pierce,47.1643847034943,-122.5956016731341,3035,47.12806,-122.108131
+3036.0,99.0,Pierce,10,South Pierce,47.16718377369386,-122.58770972344051,3036,47.12806,-122.108131
+3037.0,99.0,Pierce,10,South Pierce,47.166476607940815,-122.58091833587184,3037,47.12806,-122.108131
+3038.0,99.0,Pierce,10,South Pierce,47.158501409500026,-122.58382406000477,3038,47.12806,-122.108131
+3039.0,99.0,Pierce,10,South Pierce,47.1622132841076,-122.57624927026043,3039,47.12806,-122.108131
+3040.0,99.0,Pierce,10,South Pierce,47.15976416650486,-122.57187938942488,3040,47.12806,-122.108131
+3041.0,99.0,Pierce,10,South Pierce,47.16057587894762,-122.5668506531928,3041,47.12806,-122.108131
+3042.0,99.0,Pierce,10,South Pierce,47.15956594356634,-122.55875009933122,3042,47.12806,-122.108131
+3043.0,99.0,Pierce,10,South Pierce,47.15939422703872,-122.54845875464615,3043,47.12806,-122.108131
+3044.0,99.0,Pierce,10,South Pierce,47.15759949814292,-122.53671870287295,3044,47.12806,-122.108131
+3045.0,99.0,Pierce,10,South Pierce,47.162954845799646,-122.5282650631204,3045,47.12806,-122.108131
+3046.0,100.0,Pierce,10,South Pierce,47.16298829170032,-122.51327596758858,3046,47.12806,-122.108131
+3047.0,100.0,Pierce,10,South Pierce,47.15778252172725,-122.51290883129343,3047,47.12806,-122.108131
+3048.0,100.0,Pierce,10,South Pierce,47.16333008588999,-122.50029407505116,3048,47.12806,-122.108131
+3049.0,100.0,Pierce,10,South Pierce,47.16225920778322,-122.48972678293352,3049,47.12806,-122.108131
+3050.0,100.0,Pierce,10,South Pierce,47.15832903481119,-122.48752299296731,3050,47.12806,-122.108131
+3051.0,100.0,Pierce,10,South Pierce,47.1559870066511,-122.50040045434208,3051,47.12806,-122.108131
+3052.0,100.0,Pierce,10,South Pierce,47.15132010703545,-122.49976472902144,3052,47.12806,-122.108131
+3053.0,100.0,Pierce,10,South Pierce,47.15251626527814,-122.51116554168316,3053,47.12806,-122.108131
+3054.0,100.0,Pierce,10,South Pierce,47.14358484941926,-122.51356387461094,3054,47.12806,-122.108131
+3055.0,100.0,Pierce,10,South Pierce,47.146983354866286,-122.51942498093632,3055,47.12806,-122.108131
+3056.0,99.0,Pierce,10,South Pierce,47.14786693671337,-122.53060189589105,3056,47.12806,-122.108131
+3057.0,99.0,Pierce,10,South Pierce,47.15190692544964,-122.54621167471096,3057,47.12806,-122.108131
+3058.0,99.0,Pierce,10,South Pierce,47.1467676673679,-122.54494978458736,3058,47.12806,-122.108131
+3059.0,99.0,Pierce,10,South Pierce,47.136625466791706,-122.54001792039145,3059,47.12806,-122.108131
+3060.0,105.0,Pierce,10,South Pierce,47.12569549194766,-122.55178305864872,3060,47.12806,-122.108131
+3061.0,105.0,Pierce,10,South Pierce,47.12107045911507,-122.56184074751415,3061,47.12806,-122.108131
+3062.0,99.0,Pierce,10,South Pierce,47.152233647917264,-122.55468255863038,3062,47.12806,-122.108131
+3063.0,99.0,Pierce,10,South Pierce,47.14671817701392,-122.55557040499987,3063,47.12806,-122.108131
+3064.0,99.0,Pierce,10,South Pierce,47.142139274965714,-122.55522616592415,3064,47.12806,-122.108131
+3065.0,99.0,Pierce,10,South Pierce,47.15257796737904,-122.56347537874396,3065,47.12806,-122.108131
+3066.0,99.0,Pierce,10,South Pierce,47.14547028108633,-122.56649033084132,3066,47.12806,-122.108131
+3067.0,99.0,Pierce,10,South Pierce,47.14372316883974,-122.56241945179949,3067,47.12806,-122.108131
+3068.0,99.0,Pierce,10,South Pierce,47.15400748425378,-122.57082457305978,3068,47.12806,-122.108131
+3069.0,99.0,Pierce,10,South Pierce,47.151822532144344,-122.57384765170768,3069,47.12806,-122.108131
+3070.0,105.0,Pierce,10,South Pierce,47.126300875219506,-122.59628327466544,3070,47.12806,-122.108131
+3071.0,105.0,Pierce,10,South Pierce,47.1376611001712,-122.62557966371092,3071,47.12806,-122.108131
+3072.0,99.0,Pierce,10,South Pierce,47.156812953102865,-122.6340833233624,3072,47.12806,-122.108131
+3073.0,105.0,Pierce,10,South Pierce,47.116711085763356,-122.63924800062279,3073,47.12806,-122.108131
+3074.0,105.0,Pierce,10,South Pierce,47.10227825999358,-122.66398270516035,3074,47.12806,-122.108131
+3075.0,105.0,Pierce,10,South Pierce,47.09959775716853,-122.6343303888072,3075,47.12806,-122.108131
+3076.0,105.0,Pierce,10,South Pierce,47.084701747649056,-122.69318476222291,3076,47.12806,-122.108131
+3077.0,105.0,Pierce,10,South Pierce,47.089108779294136,-122.66155055397051,3077,47.12806,-122.108131
+3078.0,93.0,Pierce,8,Tacoma,47.31652522563367,-122.42331964589243,3078,47.25268,-122.45526799999999
+3079.0,93.0,Pierce,8,Tacoma,47.30368748334942,-122.43716720604236,3079,47.25268,-122.45526799999999
+3080.0,93.0,Pierce,8,Tacoma,47.30883674317207,-122.42718078405784,3080,47.25268,-122.45526799999999
+3081.0,93.0,Pierce,8,Tacoma,47.30611067267317,-122.4159864601305,3081,47.25268,-122.45526799999999
+3082.0,93.0,Pierce,8,Tacoma,47.31061526447532,-122.40953048060568,3082,47.25268,-122.45526799999999
+3083.0,93.0,Pierce,8,Tacoma,47.30290815215394,-122.40031734050557,3083,47.25268,-122.45526799999999
+3084.0,93.0,Pierce,8,Tacoma,47.283128238395264,-122.40866524062297,3084,47.25268,-122.45526799999999
+3085.0,93.0,Pierce,8,Tacoma,47.29420366613821,-122.41142211931609,3085,47.25268,-122.45526799999999
+3086.0,93.0,Pierce,8,Tacoma,47.2945023560145,-122.40289305699696,3086,47.25268,-122.45526799999999
+3087.0,93.0,Pierce,8,Tacoma,47.29735916471092,-122.39733960629351,3087,47.25268,-122.45526799999999
+3088.0,93.0,Pierce,8,Tacoma,47.29268403494896,-122.39223825758482,3088,47.25268,-122.45526799999999
+3089.0,93.0,Pierce,8,Tacoma,47.29240415234318,-122.38486617312284,3089,47.25268,-122.45526799999999
+3090.0,93.0,Pierce,8,Tacoma,47.28947862014494,-122.37760878517237,3090,47.25268,-122.45526799999999
+3091.0,93.0,Pierce,8,Tacoma,47.28074175251962,-122.3806075492252,3091,47.25268,-122.45526799999999
+3092.0,93.0,Pierce,8,Tacoma,47.2832111160342,-122.37189770973222,3092,47.25268,-122.45526799999999
+3093.0,93.0,Pierce,8,Tacoma,47.27336388169693,-122.3627725761618,3093,47.25268,-122.45526799999999
+3094.0,93.0,Pierce,8,Tacoma,47.260568470177496,-122.3459893149612,3094,47.25268,-122.45526799999999
+3095.0,93.0,Pierce,8,Tacoma,47.26884250429404,-122.374860495426,3095,47.25268,-122.45526799999999
+3096.0,93.0,Pierce,8,Tacoma,47.265499964880625,-122.38258142517662,3096,47.25268,-122.45526799999999
+3097.0,93.0,Pierce,8,Tacoma,47.26933877696369,-122.41455529432304,3097,47.25268,-122.45526799999999
+3098.0,93.0,Pierce,8,Tacoma,47.26302502754131,-122.39436120602907,3098,47.25268,-122.45526799999999
+3099.0,93.0,Pierce,8,Tacoma,47.262421619562,-122.40516421091615,3099,47.25268,-122.45526799999999
+3100.0,93.0,Pierce,8,Tacoma,47.25669258080928,-122.39570571967887,3100,47.25268,-122.45526799999999
+3101.0,93.0,Pierce,8,Tacoma,47.25683401727351,-122.4123151532513,3101,47.25268,-122.45526799999999
+3102.0,93.0,Pierce,8,Tacoma,47.26060041852642,-122.42856421922977,3102,47.25268,-122.45526799999999
+3103.0,93.0,Pierce,8,Tacoma,47.254435906639024,-122.4209197363167,3103,47.25268,-122.45526799999999
+3104.0,93.0,Pierce,8,Tacoma,47.25205112860717,-122.4280352626092,3104,47.25268,-122.45526799999999
+3105.0,93.0,Pierce,8,Tacoma,47.24560574913002,-122.4309100979508,3105,47.25268,-122.45526799999999
+3106.0,93.0,Pierce,8,Tacoma,47.24718804631385,-122.42258811687157,3106,47.25268,-122.45526799999999
+3107.0,93.0,Pierce,8,Tacoma,47.24273994674365,-122.42355524913783,3107,47.25268,-122.45526799999999
+3108.0,93.0,Pierce,8,Tacoma,47.23992253573187,-122.42708180577499,3108,47.25268,-122.45526799999999
+3109.0,93.0,Pierce,8,Tacoma,47.24089354125537,-122.42126996890444,3109,47.25268,-122.45526799999999
+3110.0,93.0,Pierce,8,Tacoma,47.23802634658143,-122.42331244294796,3110,47.25268,-122.45526799999999
+3111.0,93.0,Pierce,8,Tacoma,47.2433342189929,-122.41424376071664,3111,47.25268,-122.45526799999999
+3112.0,93.0,Pierce,8,Tacoma,47.24048866036852,-122.41395557282542,3112,47.25268,-122.45526799999999
+3113.0,93.0,Pierce,8,Tacoma,47.24694441424914,-122.41272701928726,3113,47.25268,-122.45526799999999
+3114.0,93.0,Pierce,8,Tacoma,47.2440386137855,-122.40965344140434,3114,47.25268,-122.45526799999999
+3115.0,93.0,Pierce,8,Tacoma,47.241629831632956,-122.406621655557,3115,47.25268,-122.45526799999999
+3116.0,93.0,Pierce,8,Tacoma,47.2400555058107,-122.40764556886226,3116,47.25268,-122.45526799999999
+3117.0,93.0,Pierce,8,Tacoma,47.25025859178986,-122.4063419815832,3117,47.25268,-122.45526799999999
+3118.0,93.0,Pierce,8,Tacoma,47.24516201620947,-122.40333712585802,3118,47.25268,-122.45526799999999
+3119.0,93.0,Pierce,8,Tacoma,47.24230203645068,-122.40081881005888,3119,47.25268,-122.45526799999999
+3120.0,93.0,Pierce,8,Tacoma,47.25046939740717,-122.39339938934816,3120,47.25268,-122.45526799999999
+3121.0,93.0,Pierce,8,Tacoma,47.24515869784265,-122.39311756780072,3121,47.25268,-122.45526799999999
+3122.0,93.0,Pierce,8,Tacoma,47.24233801870738,-122.39178329476894,3122,47.25268,-122.45526799999999
+3123.0,93.0,Pierce,8,Tacoma,47.251749703904025,-122.37962091538614,3123,47.25268,-122.45526799999999
+3124.0,93.0,Pierce,8,Tacoma,47.2450614896151,-122.37922263767449,3124,47.25268,-122.45526799999999
+3125.0,93.0,Pierce,8,Tacoma,47.241943324181264,-122.37947560180143,3125,47.25268,-122.45526799999999
+3126.0,93.0,Pierce,8,Tacoma,47.25362360857883,-122.36810843712357,3126,47.25268,-122.45526799999999
+3127.0,93.0,Pierce,8,Tacoma,47.25006073652165,-122.36224243091856,3127,47.25268,-122.45526799999999
+3128.0,93.0,Pierce,8,Tacoma,47.244684253344325,-122.36495882188984,3128,47.25268,-122.45526799999999
+3129.0,93.0,Pierce,8,Tacoma,47.2417824934636,-122.36568271706051,3129,47.25268,-122.45526799999999
+3130.0,93.0,Pierce,8,Tacoma,47.251418632505406,-122.35269371386585,3130,47.25268,-122.45526799999999
+3131.0,93.0,Pierce,8,Tacoma,47.24469150010929,-122.34723487640535,3131,47.25268,-122.45526799999999
+3132.0,93.0,Pierce,8,Tacoma,47.242189120330686,-122.346819514437,3132,47.25268,-122.45526799999999
+3133.0,94.0,Pierce,10,South Pierce,47.25386117961484,-122.33416550537953,3133,47.12806,-122.108131
+3134.0,94.0,Pierce,10,South Pierce,47.2541422302192,-122.32971277225442,3134,47.12806,-122.108131
+3135.0,94.0,Pierce,10,South Pierce,47.247817774238605,-122.32993119674896,3135,47.12806,-122.108131
+3136.0,94.0,Pierce,10,South Pierce,47.242302504757404,-122.3300180171682,3136,47.12806,-122.108131
+3137.0,94.0,Pierce,10,South Pierce,47.25235862811634,-122.3210030780328,3137,47.12806,-122.108131
+3138.0,94.0,Pierce,10,South Pierce,47.24515908251863,-122.31836124154282,3138,47.12806,-122.108131
+3139.0,94.0,Pierce,10,South Pierce,47.24111243330561,-122.32094682832405,3139,47.12806,-122.108131
+3140.0,94.0,Pierce,10,South Pierce,47.25355521809214,-122.30945843977614,3140,47.12806,-122.108131
+3141.0,94.0,Pierce,10,South Pierce,47.239598150574686,-122.30307892382244,3141,47.12806,-122.108131
+3142.0,94.0,Pierce,10,South Pierce,47.2463824088887,-122.30949764936615,3142,47.12806,-122.108131
+3143.0,94.0,Pierce,10,South Pierce,47.25364138044482,-122.29904214426114,3143,47.12806,-122.108131
+3144.0,94.0,Pierce,10,South Pierce,47.24640761368161,-122.29914107199144,3144,47.12806,-122.108131
+3145.0,94.0,Pierce,10,South Pierce,47.25532115428675,-122.28651559933209,3145,47.12806,-122.108131
+3146.0,94.0,Pierce,10,South Pierce,47.25191885222579,-122.28447734419608,3146,47.12806,-122.108131
+3147.0,94.0,Pierce,10,South Pierce,47.24620194429658,-122.28491740355402,3147,47.12806,-122.108131
+3148.0,94.0,Pierce,10,South Pierce,47.239229629253856,-122.2867858326396,3148,47.12806,-122.108131
+3149.0,94.0,Pierce,10,South Pierce,47.242524339214036,-122.27200660490314,3149,47.12806,-122.108131
+3150.0,94.0,Pierce,10,South Pierce,47.25507800965429,-122.26458069908571,3150,47.12806,-122.108131
+3151.0,94.0,Pierce,10,South Pierce,47.24963551425343,-122.26566427808916,3151,47.12806,-122.108131
+3152.0,94.0,Pierce,10,South Pierce,47.253915248139926,-122.25349435226036,3152,47.12806,-122.108131
+3153.0,94.0,Pierce,10,South Pierce,47.246722038239824,-122.25250498984896,3153,47.12806,-122.108131
+3154.0,94.0,Pierce,10,South Pierce,47.2541457285045,-122.24557121007747,3154,47.12806,-122.108131
+3155.0,94.0,Pierce,10,South Pierce,47.24567649703108,-122.24397954843504,3155,47.12806,-122.108131
+3156.0,94.0,Pierce,10,South Pierce,47.23927017103696,-122.24160133042541,3156,47.12806,-122.108131
+3157.0,94.0,Pierce,10,South Pierce,47.253592739988186,-122.23585117558581,3157,47.12806,-122.108131
+3158.0,94.0,Pierce,10,South Pierce,47.24496759368908,-122.23372568128329,3158,47.12806,-122.108131
+3159.0,95.0,Pierce,10,South Pierce,47.25107466385356,-122.21984063334499,3159,47.12806,-122.108131
+3160.0,95.0,Pierce,10,South Pierce,47.23879939076679,-122.2108139828446,3160,47.12806,-122.108131
+3161.0,95.0,Pierce,10,South Pierce,47.253014230048606,-122.20123241298992,3161,47.12806,-122.108131
+3162.0,95.0,Pierce,10,South Pierce,47.238475618428424,-122.18940624324611,3162,47.12806,-122.108131
+3163.0,95.0,Pierce,10,South Pierce,47.251487333347036,-122.15855181879387,3163,47.12806,-122.108131
+3164.0,95.0,Pierce,10,South Pierce,47.24263837662213,-122.1614028689764,3164,47.12806,-122.108131
+3165.0,95.0,Pierce,10,South Pierce,47.23100507983547,-122.13165401823325,3165,47.12806,-122.108131
+3166.0,95.0,Pierce,10,South Pierce,47.218893579162255,-122.15351838657692,3166,47.12806,-122.108131
+3167.0,95.0,Pierce,10,South Pierce,47.21311756456593,-122.185245493561,3167,47.12806,-122.108131
+3168.0,95.0,Pierce,10,South Pierce,47.22278967458045,-122.21572391852413,3168,47.12806,-122.108131
+3169.0,94.0,Pierce,10,South Pierce,47.229147694273614,-122.22961995554934,3169,47.12806,-122.108131
+3170.0,94.0,Pierce,10,South Pierce,47.2257379634721,-122.24216675428829,3170,47.12806,-122.108131
+3171.0,94.0,Pierce,10,South Pierce,47.22262146550792,-122.25101233560656,3171,47.12806,-122.108131
+3172.0,94.0,Pierce,10,South Pierce,47.226428009511935,-122.26018959406065,3172,47.12806,-122.108131
+3173.0,94.0,Pierce,10,South Pierce,47.230253927856296,-122.28055164188524,3173,47.12806,-122.108131
+3174.0,94.0,Pierce,10,South Pierce,47.21867522069423,-122.28198331959172,3174,47.12806,-122.108131
+3175.0,94.0,Pierce,10,South Pierce,47.2275818641751,-122.30831850247677,3175,47.12806,-122.108131
+3176.0,94.0,Pierce,10,South Pierce,47.23096093713482,-122.32863346102914,3176,47.12806,-122.108131
+3177.0,93.0,Pierce,8,Tacoma,47.24014365712454,-122.3455319862682,3177,47.25268,-122.45526799999999
+3178.0,93.0,Pierce,8,Tacoma,47.23373067664171,-122.34152120783364,3178,47.25268,-122.45526799999999
+3179.0,93.0,Pierce,8,Tacoma,47.235961445497416,-122.35147695016589,3179,47.25268,-122.45526799999999
+3180.0,93.0,Pierce,8,Tacoma,47.229452126607505,-122.34810744300208,3180,47.25268,-122.45526799999999
+3181.0,93.0,Pierce,8,Tacoma,47.236389441234344,-122.36338145028292,3181,47.25268,-122.45526799999999
+3182.0,93.0,Pierce,8,Tacoma,47.23837193182122,-122.38103016728657,3182,47.25268,-122.45526799999999
+3183.0,93.0,Pierce,8,Tacoma,47.23185464235058,-122.37288254279518,3183,47.25268,-122.45526799999999
+3184.0,102.0,Pierce,8,Tacoma,47.23175903655098,-122.40091109648031,3184,47.25268,-122.45526799999999
+3185.0,102.0,Pierce,8,Tacoma,47.23365363156578,-122.41239032787979,3185,47.25268,-122.45526799999999
+3186.0,102.0,Pierce,8,Tacoma,47.22570885999072,-122.4140919695573,3186,47.25268,-122.45526799999999
+3187.0,102.0,Pierce,8,Tacoma,47.2347377240665,-122.41996727961642,3187,47.25268,-122.45526799999999
+3188.0,102.0,Pierce,8,Tacoma,47.22997719233064,-122.4177190911575,3188,47.25268,-122.45526799999999
+3189.0,102.0,Pierce,8,Tacoma,47.22895536128537,-122.424651689228,3189,47.25268,-122.45526799999999
+3190.0,98.0,Pierce,8,Tacoma,47.22751222541145,-122.43070072558884,3190,47.25268,-122.45526799999999
+3191.0,98.0,Pierce,8,Tacoma,47.227807342901116,-122.43731663883742,3191,47.25268,-122.45526799999999
+3192.0,98.0,Pierce,8,Tacoma,47.22671377438208,-122.44262633253322,3192,47.25268,-122.45526799999999
+3193.0,98.0,Pierce,8,Tacoma,47.2267893611479,-122.44759301779726,3193,47.25268,-122.45526799999999
+3194.0,98.0,Pierce,8,Tacoma,47.22681247044821,-122.4568582010193,3194,47.25268,-122.45526799999999
+3195.0,98.0,Pierce,8,Tacoma,47.21870978774564,-122.46054380808016,3195,47.25268,-122.45526799999999
+3196.0,98.0,Pierce,8,Tacoma,47.21844733218603,-122.45446302501148,3196,47.25268,-122.45526799999999
+3197.0,98.0,Pierce,8,Tacoma,47.21020276522073,-122.45652689598793,3197,47.25268,-122.45526799999999
+3198.0,98.0,Pierce,8,Tacoma,47.218254488229334,-122.44772202003237,3198,47.25268,-122.45526799999999
+3199.0,98.0,Pierce,8,Tacoma,47.21005232869012,-122.44714951296513,3199,47.25268,-122.45526799999999
+3200.0,98.0,Pierce,8,Tacoma,47.220716384782065,-122.43942897259713,3200,47.25268,-122.45526799999999
+3201.0,98.0,Pierce,8,Tacoma,47.2160598707568,-122.43923847648334,3201,47.25268,-122.45526799999999
+3202.0,98.0,Pierce,8,Tacoma,47.210013392268465,-122.438600074599,3202,47.25268,-122.45526799999999
+3203.0,98.0,Pierce,8,Tacoma,47.218306182637704,-122.43005646966441,3203,47.25268,-122.45526799999999
+3204.0,98.0,Pierce,8,Tacoma,47.2096787860395,-122.42951994621954,3204,47.25268,-122.45526799999999
+3205.0,102.0,Pierce,8,Tacoma,47.218263580771605,-122.42359126215209,3205,47.25268,-122.45526799999999
+3206.0,102.0,Pierce,8,Tacoma,47.21044880919509,-122.42307933957672,3206,47.25268,-122.45526799999999
+3207.0,102.0,Pierce,8,Tacoma,47.21830677523782,-122.41427795823608,3207,47.25268,-122.45526799999999
+3208.0,102.0,Pierce,8,Tacoma,47.209922634888194,-122.4143662527479,3208,47.25268,-122.45526799999999
+3209.0,102.0,Pierce,8,Tacoma,47.215102515591795,-122.40143331287807,3209,47.25268,-122.45526799999999
+3210.0,102.0,Pierce,8,Tacoma,47.21110421037782,-122.39149014605749,3210,47.25268,-122.45526799999999
+3211.0,102.0,Pierce,8,Tacoma,47.21804819880003,-122.38069506838005,3211,47.25268,-122.45526799999999
+3212.0,102.0,Pierce,8,Tacoma,47.21745245316024,-122.36288706221329,3212,47.25268,-122.45526799999999
+3213.0,93.0,Pierce,8,Tacoma,47.22170107230285,-122.34582847992986,3213,47.25268,-122.45526799999999
+3214.0,94.0,Pierce,10,South Pierce,47.21705410691676,-122.32887524138052,3214,47.12806,-122.108131
+3215.0,94.0,Pierce,10,South Pierce,47.21014658621325,-122.3103540734281,3215,47.12806,-122.108131
+3216.0,94.0,Pierce,10,South Pierce,47.21171554283802,-122.30055983135055,3216,47.12806,-122.108131
+3217.0,94.0,Pierce,10,South Pierce,47.207353340219356,-122.28878666896657,3217,47.12806,-122.108131
+3218.0,94.0,Pierce,10,South Pierce,47.20973733231679,-122.26698887872669,3218,47.12806,-122.108131
+3219.0,94.0,Pierce,10,South Pierce,47.204179937590396,-122.27038249184156,3219,47.12806,-122.108131
+3220.0,94.0,Pierce,10,South Pierce,47.20102773656127,-122.25029655592049,3220,47.12806,-122.108131
+3221.0,94.0,Pierce,10,South Pierce,47.211919370594615,-122.23617906913029,3221,47.12806,-122.108131
+3222.0,94.0,Pierce,10,South Pierce,47.20620619681974,-122.23417754138559,3222,47.12806,-122.108131
+3223.0,94.0,Pierce,10,South Pierce,47.20795918357794,-122.22152173191677,3223,47.12806,-122.108131
+3224.0,95.0,Pierce,10,South Pierce,47.20350609685558,-122.20108084900541,3224,47.12806,-122.108131
+3225.0,95.0,Pierce,10,South Pierce,47.19893456175642,-122.1804335054165,3225,47.12806,-122.108131
+3226.0,95.0,Pierce,10,South Pierce,47.195243646614685,-122.15310794861156,3226,47.12806,-122.108131
+3227.0,95.0,Pierce,10,South Pierce,47.19982587049897,-122.1270958223338,3227,47.12806,-122.108131
+3228.0,95.0,Pierce,10,South Pierce,47.194361213218286,-122.09504779608869,3228,47.12806,-122.108131
+3229.0,95.0,Pierce,10,South Pierce,47.18607744024066,-122.17935051794659,3229,47.12806,-122.108131
+3230.0,94.0,Pierce,10,South Pierce,47.19891494858294,-122.22025193040622,3230,47.12806,-122.108131
+3231.0,94.0,Pierce,10,South Pierce,47.19806745089668,-122.23738222376056,3231,47.12806,-122.108131
+3232.0,104.0,Pierce,10,South Pierce,47.194644377954646,-122.26443323793109,3232,47.12806,-122.108131
+3233.0,94.0,Pierce,10,South Pierce,47.19856522736374,-122.27227211483243,3233,47.12806,-122.108131
+3234.0,94.0,Pierce,10,South Pierce,47.20032271717545,-122.28516501440686,3234,47.12806,-122.108131
+3235.0,94.0,Pierce,10,South Pierce,47.19830441673334,-122.29106098581923,3235,47.12806,-122.108131
+3236.0,94.0,Pierce,10,South Pierce,47.193869852704935,-122.28831433860151,3236,47.12806,-122.108131
+3237.0,94.0,Pierce,10,South Pierce,47.1913181028801,-122.28887847744974,3237,47.12806,-122.108131
+3238.0,94.0,Pierce,10,South Pierce,47.20164892201008,-122.3006398341534,3238,47.12806,-122.108131
+3239.0,94.0,Pierce,10,South Pierce,47.196285161934156,-122.296120826169,3239,47.12806,-122.108131
+3240.0,94.0,Pierce,10,South Pierce,47.19188576669664,-122.29649988530926,3240,47.12806,-122.108131
+3241.0,94.0,Pierce,10,South Pierce,47.1984118215948,-122.30643498711399,3241,47.12806,-122.108131
+3242.0,94.0,Pierce,10,South Pierce,47.192434697442806,-122.30470294041731,3242,47.12806,-122.108131
+3243.0,94.0,Pierce,10,South Pierce,47.20213417050354,-122.32160290317483,3243,47.12806,-122.108131
+3244.0,94.0,Pierce,10,South Pierce,47.19441938094614,-122.32087954519908,3244,47.12806,-122.108131
+3245.0,94.0,Pierce,10,South Pierce,47.20540941206434,-122.33568625159693,3245,47.12806,-122.108131
+3246.0,103.0,Pierce,10,South Pierce,47.19580998713909,-122.33735540065523,3246,47.12806,-122.108131
+3247.0,103.0,Pierce,10,South Pierce,47.1967497406405,-122.35166052012495,3247,47.12806,-122.108131
+3248.0,102.0,Pierce,8,Tacoma,47.202775728582886,-122.36685544006752,3248,47.25268,-122.45526799999999
+3249.0,102.0,Pierce,8,Tacoma,47.201582089948644,-122.38183822177277,3249,47.25268,-122.45526799999999
+3250.0,102.0,Pierce,8,Tacoma,47.202642583611144,-122.40188876091034,3250,47.25268,-122.45526799999999
+3251.0,102.0,Pierce,8,Tacoma,47.195147790701206,-122.40034213980287,3251,47.25268,-122.45526799999999
+3252.0,102.0,Pierce,8,Tacoma,47.20265048794836,-122.41441266118206,3252,47.25268,-122.45526799999999
+3253.0,102.0,Pierce,8,Tacoma,47.19537897272559,-122.41431142365148,3253,47.25268,-122.45526799999999
+3254.0,98.0,Pierce,8,Tacoma,47.2027178523133,-122.42740437882827,3254,47.25268,-122.45526799999999
+3255.0,98.0,Pierce,8,Tacoma,47.195481021027184,-122.4274618427056,3255,47.25268,-122.45526799999999
+3256.0,98.0,Pierce,8,Tacoma,47.202778669957574,-122.43857587695958,3256,47.25268,-122.45526799999999
+3257.0,98.0,Pierce,8,Tacoma,47.195535261596774,-122.4386029756279,3257,47.25268,-122.45526799999999
+3258.0,98.0,Pierce,8,Tacoma,47.202818066541965,-122.44780625704679,3258,47.25268,-122.45526799999999
+3259.0,98.0,Pierce,8,Tacoma,47.19556561600482,-122.44784283995467,3259,47.25268,-122.45526799999999
+3260.0,98.0,Pierce,8,Tacoma,47.1989916185481,-122.45713743007444,3260,47.25268,-122.45526799999999
+3261.0,98.0,Pierce,8,Tacoma,47.18631131438242,-122.45772623851052,3261,47.25268,-122.45526799999999
+3262.0,101.0,Pierce,10,South Pierce,47.1748884876309,-122.46599211612921,3262,47.12806,-122.108131
+3263.0,98.0,Pierce,8,Tacoma,47.175681648294756,-122.4576869446142,3263,47.25268,-122.45526799999999
+3264.0,98.0,Pierce,8,Tacoma,47.186445652071285,-122.44792430878263,3264,47.25268,-122.45526799999999
+3265.0,98.0,Pierce,8,Tacoma,47.175325034657966,-122.4476627026525,3265,47.25268,-122.45526799999999
+3266.0,98.0,Pierce,8,Tacoma,47.186452252655144,-122.43867489528877,3266,47.25268,-122.45526799999999
+3267.0,98.0,Pierce,8,Tacoma,47.17552256025389,-122.4386909942814,3267,47.25268,-122.45526799999999
+3268.0,98.0,Pierce,8,Tacoma,47.186397573728286,-122.42750269321795,3268,47.25268,-122.45526799999999
+3269.0,98.0,Pierce,8,Tacoma,47.17548921640315,-122.42752842718126,3269,47.25268,-122.45526799999999
+3270.0,101.0,Pierce,10,South Pierce,47.18586923278138,-122.41832248116184,3270,47.12806,-122.108131
+3271.0,101.0,Pierce,10,South Pierce,47.17502868336274,-122.41827848735741,3271,47.12806,-122.108131
+3272.0,101.0,Pierce,10,South Pierce,47.18585076145408,-122.41171417650554,3272,47.12806,-122.108131
+3273.0,101.0,Pierce,10,South Pierce,47.17415857112261,-122.41093098466844,3273,47.12806,-122.108131
+3274.0,102.0,Pierce,8,Tacoma,47.1861792485287,-122.39859462659271,3274,47.25268,-122.45526799999999
+3275.0,101.0,Pierce,10,South Pierce,47.175190362285754,-122.3978492836644,3275,47.12806,-122.108131
+3276.0,102.0,Pierce,8,Tacoma,47.186264487482575,-122.37322519656394,3276,47.25268,-122.45526799999999
+3277.0,102.0,Pierce,8,Tacoma,47.175399049550975,-122.37311779151815,3277,47.25268,-122.45526799999999
+3278.0,103.0,Pierce,10,South Pierce,47.18613250464061,-122.34258121019815,3278,47.12806,-122.108131
+3279.0,103.0,Pierce,10,South Pierce,47.17538380522607,-122.34203489962911,3279,47.12806,-122.108131
+3280.0,94.0,Pierce,10,South Pierce,47.17697980261904,-122.31811870405569,3280,47.12806,-122.108131
+3281.0,94.0,Pierce,10,South Pierce,47.18495565437084,-122.30977737985485,3281,47.12806,-122.108131
+3282.0,94.0,Pierce,10,South Pierce,47.18315447386432,-122.29759240553216,3282,47.12806,-122.108131
+3283.0,94.0,Pierce,10,South Pierce,47.17356213273211,-122.30054648090281,3283,47.12806,-122.108131
+3284.0,94.0,Pierce,10,South Pierce,47.18827854974746,-122.28845135125056,3284,47.12806,-122.108131
+3285.0,94.0,Pierce,10,South Pierce,47.183475234114596,-122.28876020278341,3285,47.12806,-122.108131
+3286.0,104.0,Pierce,10,South Pierce,47.17560986545373,-122.28226058590721,3286,47.12806,-122.108131
+3287.0,104.0,Pierce,10,South Pierce,47.18503469988463,-122.27530106228927,3287,47.12806,-122.108131
+3288.0,104.0,Pierce,10,South Pierce,47.190185771728366,-122.26811804308872,3288,47.12806,-122.108131
+3289.0,104.0,Pierce,10,South Pierce,47.1771122811797,-122.26182191028006,3289,47.12806,-122.108131
+3290.0,94.0,Pierce,10,South Pierce,47.190510639304065,-122.23660347104952,3290,47.12806,-122.108131
+3291.0,104.0,Pierce,10,South Pierce,47.187257926762285,-122.2454871913298,3291,47.12806,-122.108131
+3292.0,104.0,Pierce,10,South Pierce,47.17106387718561,-122.24466082055562,3292,47.12806,-122.108131
+3293.0,111.0,Pierce,10,South Pierce,47.18320650414954,-122.21937810307386,3293,47.12806,-122.108131
+3294.0,95.0,Pierce,10,South Pierce,47.182849026484696,-122.20327642487108,3294,47.12806,-122.108131
+3295.0,95.0,Pierce,10,South Pierce,47.17581796371813,-122.18054280614928,3295,47.12806,-122.108131
+3296.0,95.0,Pierce,10,South Pierce,47.18078361087003,-122.15877503375948,3296,47.12806,-122.108131
+3297.0,95.0,Pierce,10,South Pierce,47.17245075480194,-122.1580531115438,3297,47.12806,-122.108131
+3298.0,95.0,Pierce,10,South Pierce,47.176711544609915,-122.1308613720512,3298,47.12806,-122.108131
+3299.0,111.0,Pierce,10,South Pierce,47.17235927394864,-122.09014992556894,3299,47.12806,-122.108131
+3300.0,111.0,Pierce,10,South Pierce,47.166838665459004,-122.0432211717112,3300,47.12806,-122.108131
+3301.0,111.0,Pierce,10,South Pierce,47.160361035053135,-122.01284753738422,3301,47.12806,-122.108131
+3302.0,111.0,Pierce,10,South Pierce,47.142772433075656,-122.0005345966127,3302,47.12806,-122.108131
+3303.0,111.0,Pierce,10,South Pierce,47.15177641975466,-122.0505627469424,3303,47.12806,-122.108131
+3304.0,111.0,Pierce,10,South Pierce,47.15956827461205,-122.09307389129854,3304,47.12806,-122.108131
+3305.0,111.0,Pierce,10,South Pierce,47.148874842689494,-122.08723524397516,3305,47.12806,-122.108131
+3306.0,95.0,Pierce,10,South Pierce,47.157354260068814,-122.130944080825,3306,47.12806,-122.108131
+3307.0,95.0,Pierce,10,South Pierce,47.15754278548678,-122.15439609173524,3307,47.12806,-122.108131
+3308.0,95.0,Pierce,10,South Pierce,47.160387972346896,-122.17288294734541,3308,47.12806,-122.108131
+3309.0,95.0,Pierce,10,South Pierce,47.16062972274587,-122.18824766137152,3309,47.12806,-122.108131
+3310.0,95.0,Pierce,10,South Pierce,47.166293431906155,-122.20117237785023,3310,47.12806,-122.108131
+3311.0,111.0,Pierce,10,South Pierce,47.15261707240281,-122.22514876656795,3311,47.12806,-122.108131
+3312.0,104.0,Pierce,10,South Pierce,47.15533699325896,-122.24252523461159,3312,47.12806,-122.108131
+3313.0,104.0,Pierce,10,South Pierce,47.16282795036683,-122.26294666460564,3313,47.12806,-122.108131
+3314.0,104.0,Pierce,10,South Pierce,47.16183053583314,-122.27831453957904,3314,47.12806,-122.108131
+3315.0,104.0,Pierce,10,South Pierce,47.16422492912236,-122.29127888868521,3315,47.12806,-122.108131
+3316.0,103.0,Pierce,10,South Pierce,47.16484426679011,-122.30064827717705,3316,47.12806,-122.108131
+3317.0,103.0,Pierce,10,South Pierce,47.157550712944925,-122.29815418548357,3317,47.12806,-122.108131
+3318.0,103.0,Pierce,10,South Pierce,47.1643367439063,-122.31628710800621,3318,47.12806,-122.108131
+3319.0,103.0,Pierce,10,South Pierce,47.156834105300725,-122.31564432772885,3319,47.12806,-122.108131
+3320.0,103.0,Pierce,10,South Pierce,47.16592494720028,-122.34284094538287,3320,47.12806,-122.108131
+3321.0,103.0,Pierce,10,South Pierce,47.15998169174686,-122.34255753749149,3321,47.12806,-122.108131
+3322.0,103.0,Pierce,10,South Pierce,47.1563888686561,-122.34263459979263,3322,47.12806,-122.108131
+3323.0,102.0,Pierce,8,Tacoma,47.16595632915517,-122.37276642291643,3323,47.25268,-122.45526799999999
+3324.0,102.0,Pierce,8,Tacoma,47.16020268164769,-122.37366126189829,3324,47.25268,-122.45526799999999
+3325.0,102.0,Pierce,8,Tacoma,47.15655405460514,-122.37257589478504,3325,47.25268,-122.45526799999999
+3326.0,102.0,Pierce,8,Tacoma,47.165848558815746,-122.39685964633345,3326,47.25268,-122.45526799999999
+3327.0,102.0,Pierce,8,Tacoma,47.160400536432846,-122.39641255696121,3327,47.25268,-122.45526799999999
+3328.0,102.0,Pierce,8,Tacoma,47.15668666870679,-122.39558071094713,3328,47.25268,-122.45526799999999
+3329.0,101.0,Pierce,10,South Pierce,47.163944425691966,-122.41024062826249,3329,47.12806,-122.108131
+3330.0,101.0,Pierce,10,South Pierce,47.15707402457038,-122.40875477020121,3330,47.12806,-122.108131
+3331.0,101.0,Pierce,10,South Pierce,47.1656643325249,-122.42106077592527,3331,47.12806,-122.108131
+3332.0,101.0,Pierce,10,South Pierce,47.16250683315418,-122.42703957403441,3332,47.12806,-122.108131
+3333.0,101.0,Pierce,10,South Pierce,47.16406340163989,-122.43278437631851,3333,47.12806,-122.108131
+3334.0,101.0,Pierce,10,South Pierce,47.156943468619424,-122.42424507652464,3334,47.12806,-122.108131
+3335.0,101.0,Pierce,10,South Pierce,47.164850981209746,-122.43785172776266,3335,47.12806,-122.108131
+3336.0,101.0,Pierce,10,South Pierce,47.167400393800925,-122.44883267364777,3336,47.12806,-122.108131
+3337.0,101.0,Pierce,10,South Pierce,47.16148457363636,-122.44784271890279,3337,47.12806,-122.108131
+3338.0,101.0,Pierce,10,South Pierce,47.15695500868018,-122.44482771793123,3338,47.12806,-122.108131
+3339.0,101.0,Pierce,10,South Pierce,47.16755114879018,-122.46061946596669,3339,47.12806,-122.108131
+3340.0,101.0,Pierce,10,South Pierce,47.16195500545762,-122.4611345813686,3340,47.12806,-122.108131
+3341.0,101.0,Pierce,10,South Pierce,47.157407311260826,-122.46207355569166,3341,47.12806,-122.108131
+3342.0,101.0,Pierce,10,South Pierce,47.16790004123751,-122.4685664441188,3342,47.12806,-122.108131
+3343.0,101.0,Pierce,10,South Pierce,47.164419997727784,-122.4726009751041,3343,47.12806,-122.108131
+3344.0,105.0,Pierce,10,South Pierce,47.15850170944804,-122.4763508951297,3344,47.12806,-122.108131
+3345.0,100.0,Pierce,10,South Pierce,47.14361330080134,-122.50303364400801,3345,47.12806,-122.108131
+3346.0,105.0,Pierce,10,South Pierce,47.130457985903746,-122.51482918920351,3346,47.12806,-122.108131
+3347.0,105.0,Pierce,10,South Pierce,47.12305680787342,-122.53084176024788,3347,47.12806,-122.108131
+3348.0,105.0,Pierce,10,South Pierce,47.13436863555207,-122.48217140573105,3348,47.12806,-122.108131
+3349.0,105.0,Pierce,10,South Pierce,47.107662791810895,-122.52616543066765,3349,47.12806,-122.108131
+3350.0,105.0,Pierce,10,South Pierce,47.10325492789514,-122.56903755384704,3350,47.12806,-122.108131
+3351.0,105.0,Pierce,10,South Pierce,47.09607600083065,-122.59860587606607,3351,47.12806,-122.108131
+3352.0,105.0,Pierce,10,South Pierce,47.08748982080842,-122.60915203823895,3352,47.12806,-122.108131
+3353.0,105.0,Pierce,10,South Pierce,47.07851145033162,-122.57681359125029,3353,47.12806,-122.108131
+3354.0,105.0,Pierce,10,South Pierce,47.04915875316858,-122.63387154703452,3354,47.12806,-122.108131
+3355.0,105.0,Pierce,10,South Pierce,47.02807985165618,-122.56925329220964,3355,47.12806,-122.108131
+3356.0,105.0,Pierce,10,South Pierce,47.06779464833875,-122.48816606593412,3356,47.12806,-122.108131
+3357.0,105.0,Pierce,10,South Pierce,47.03004171931213,-122.44629790135664,3357,47.12806,-122.108131
+3358.0,101.0,Pierce,10,South Pierce,47.153561700462255,-122.46162145698496,3358,47.12806,-122.108131
+3359.0,101.0,Pierce,10,South Pierce,47.14796478166094,-122.4581081190226,3359,47.12806,-122.108131
+3360.0,101.0,Pierce,10,South Pierce,47.1533846361045,-122.44749924326702,3360,47.12806,-122.108131
+3361.0,101.0,Pierce,10,South Pierce,47.149320603559815,-122.44920717120944,3361,47.12806,-122.108131
+3362.0,101.0,Pierce,10,South Pierce,47.143856100353965,-122.44834998690628,3362,47.12806,-122.108131
+3363.0,101.0,Pierce,10,South Pierce,47.15110659901953,-122.43772054236472,3363,47.12806,-122.108131
+3364.0,101.0,Pierce,10,South Pierce,47.14310040141391,-122.4379270042769,3364,47.12806,-122.108131
+3365.0,101.0,Pierce,10,South Pierce,47.1514441721571,-122.4329841333932,3365,47.12806,-122.108131
+3366.0,101.0,Pierce,10,South Pierce,47.14294184810018,-122.43309409360012,3366,47.12806,-122.108131
+3367.0,101.0,Pierce,10,South Pierce,47.15132730522216,-122.42359263799969,3367,47.12806,-122.108131
+3368.0,102.0,Pierce,8,Tacoma,47.14227365077224,-122.4231339097407,3368,47.25268,-122.45526799999999
+3369.0,102.0,Pierce,8,Tacoma,47.14774260702,-122.40226655663076,3369,47.25268,-122.45526799999999
+3370.0,102.0,Pierce,8,Tacoma,47.14740960387864,-122.37313010364372,3370,47.25268,-122.45526799999999
+3371.0,103.0,Pierce,10,South Pierce,47.14739291433834,-122.34667831260572,3371,47.12806,-122.108131
+3372.0,103.0,Pierce,10,South Pierce,47.14756041667197,-122.32545016513508,3372,47.12806,-122.108131
+3373.0,103.0,Pierce,10,South Pierce,47.1475849363192,-122.3094093282682,3373,47.12806,-122.108131
+3374.0,103.0,Pierce,10,South Pierce,47.14753955034356,-122.29847781571978,3374,47.12806,-122.108131
+3375.0,104.0,Pierce,10,South Pierce,47.1505830270285,-122.27563754531559,3375,47.12806,-122.108131
+3376.0,104.0,Pierce,10,South Pierce,47.14288381918289,-122.2771455545502,3376,47.12806,-122.108131
+3377.0,108.0,Pierce,10,South Pierce,47.1360884786473,-122.25015548330258,3377,47.12806,-122.108131
+3378.0,104.0,Pierce,10,South Pierce,47.13627132867399,-122.27990113043751,3378,47.12806,-122.108131
+3379.0,108.0,Pierce,10,South Pierce,47.129212914462464,-122.27979869194756,3379,47.12806,-122.108131
+3380.0,108.0,Pierce,10,South Pierce,47.121951218252754,-122.27987097115854,3380,47.12806,-122.108131
+3381.0,103.0,Pierce,10,South Pierce,47.136610140328386,-122.29851923435244,3381,47.12806,-122.108131
+3382.0,107.0,Pierce,10,South Pierce,47.12568833851363,-122.29853485707987,3382,47.12806,-122.108131
+3383.0,103.0,Pierce,10,South Pierce,47.129330066736614,-122.3094312709499,3383,47.12806,-122.108131
+3384.0,103.0,Pierce,10,South Pierce,47.13297208604721,-122.32586079806947,3384,47.12806,-122.108131
+3385.0,103.0,Pierce,10,South Pierce,47.12023990735518,-122.32711442510879,3385,47.12806,-122.108131
+3386.0,103.0,Pierce,10,South Pierce,47.12539683144507,-122.34692441001232,3386,47.12806,-122.108131
+3387.0,102.0,Pierce,8,Tacoma,47.128672230696964,-122.3722823638527,3387,47.25268,-122.45526799999999
+3388.0,102.0,Pierce,8,Tacoma,47.13648058662079,-122.4019667349296,3388,47.25268,-122.45526799999999
+3389.0,102.0,Pierce,8,Tacoma,47.129421989283706,-122.40650374823991,3389,47.25268,-122.45526799999999
+3390.0,102.0,Pierce,8,Tacoma,47.12233948480904,-122.39728629531284,3390,47.25268,-122.45526799999999
+3391.0,102.0,Pierce,8,Tacoma,47.13448804064061,-122.42434311958992,3391,47.25268,-122.45526799999999
+3392.0,101.0,Pierce,10,South Pierce,47.13491913331417,-122.43329959307286,3392,47.12806,-122.108131
+3393.0,102.0,Pierce,8,Tacoma,47.12516973209938,-122.42502219677358,3393,47.25268,-122.45526799999999
+3394.0,101.0,Pierce,10,South Pierce,47.13595973495447,-122.44754514225164,3394,47.12806,-122.108131
+3395.0,101.0,Pierce,10,South Pierce,47.12668409509062,-122.4459703406718,3395,47.12806,-122.108131
+3396.0,101.0,Pierce,10,South Pierce,47.13794220608077,-122.46354179633906,3396,47.12806,-122.108131
+3397.0,106.0,Pierce,10,South Pierce,47.111017618459734,-122.45524890203691,3397,47.12806,-122.108131
+3398.0,106.0,Pierce,10,South Pierce,47.1085959046908,-122.44111067370392,3398,47.12806,-122.108131
+3399.0,106.0,Pierce,10,South Pierce,47.11475228869765,-122.42672532708995,3399,47.12806,-122.108131
+3400.0,106.0,Pierce,10,South Pierce,47.10496913077178,-122.429025576918,3400,47.12806,-122.108131
+3401.0,102.0,Pierce,8,Tacoma,47.11543772070149,-122.4107436039215,3401,47.25268,-122.45526799999999
+3402.0,106.0,Pierce,10,South Pierce,47.10402051103469,-122.41186260813141,3402,47.12806,-122.108131
+3403.0,102.0,Pierce,8,Tacoma,47.11234979615306,-122.38847201717087,3403,47.25268,-122.45526799999999
+3404.0,102.0,Pierce,8,Tacoma,47.110523201060886,-122.36983161735272,3404,47.25268,-122.45526799999999
+3405.0,107.0,Pierce,10,South Pierce,47.10135728657927,-122.37955085192377,3405,47.12806,-122.108131
+3406.0,107.0,Pierce,10,South Pierce,47.10380506457022,-122.33602806649799,3406,47.12806,-122.108131
+3407.0,107.0,Pierce,10,South Pierce,47.11462012141371,-122.31080460891087,3407,47.12806,-122.108131
+3408.0,107.0,Pierce,10,South Pierce,47.10390085939454,-122.30429498813318,3408,47.12806,-122.108131
+3409.0,108.0,Pierce,10,South Pierce,47.10805090881478,-122.28798541301407,3409,47.12806,-122.108131
+3410.0,108.0,Pierce,10,South Pierce,47.10766535681797,-122.274635787626,3410,47.12806,-122.108131
+3411.0,108.0,Pierce,10,South Pierce,47.11170112011424,-122.25364540954399,3411,47.12806,-122.108131
+3412.0,111.0,Pierce,10,South Pierce,47.103189383860645,-122.22649730031216,3412,47.12806,-122.108131
+3413.0,111.0,Pierce,10,South Pierce,47.10884189430438,-122.20246934275505,3413,47.12806,-122.108131
+3414.0,111.0,Pierce,10,South Pierce,47.13302528314011,-122.19308504493195,3414,47.12806,-122.108131
+3415.0,111.0,Pierce,10,South Pierce,47.1178386665171,-122.16013570918469,3415,47.12806,-122.108131
+3416.0,111.0,Pierce,10,South Pierce,47.135112967338074,-122.13037378223005,3416,47.12806,-122.108131
+3417.0,111.0,Pierce,10,South Pierce,47.11470048116991,-122.11233461215146,3417,47.12806,-122.108131
+3418.0,111.0,Pierce,10,South Pierce,47.128635785142976,-122.07150444976051,3418,47.12806,-122.108131
+3419.0,111.0,Pierce,10,South Pierce,47.11147117367734,-122.0550435835783,3419,47.12806,-122.108131
+3420.0,111.0,Pierce,10,South Pierce,47.083142598200375,-122.0606415969036,3420,47.12806,-122.108131
+3421.0,111.0,Pierce,10,South Pierce,47.08338726429322,-122.1165424311453,3421,47.12806,-122.108131
+3422.0,111.0,Pierce,10,South Pierce,47.08439465941149,-122.20625556227681,3422,47.12806,-122.108131
+3423.0,109.0,Pierce,10,South Pierce,47.06543421193914,-122.21840977742858,3423,47.12806,-122.108131
+3424.0,108.0,Pierce,10,South Pierce,47.08636013002429,-122.2496491421898,3424,47.12806,-122.108131
+3425.0,108.0,Pierce,10,South Pierce,47.08686018895806,-122.28179258959244,3425,47.12806,-122.108131
+3426.0,108.0,Pierce,10,South Pierce,47.063978493810865,-122.27275308389312,3426,47.12806,-122.108131
+3427.0,107.0,Pierce,10,South Pierce,47.08927775505375,-122.30949126171137,3427,47.12806,-122.108131
+3428.0,107.0,Pierce,10,South Pierce,47.0664757568039,-122.30453500539384,3428,47.12806,-122.108131
+3429.0,107.0,Pierce,10,South Pierce,47.072062680998535,-122.32176497567232,3429,47.12806,-122.108131
+3430.0,107.0,Pierce,10,South Pierce,47.08541423466584,-122.33846601373526,3430,47.12806,-122.108131
+3431.0,107.0,Pierce,10,South Pierce,47.0615709338035,-122.34122253556637,3431,47.12806,-122.108131
+3432.0,107.0,Pierce,10,South Pierce,47.08455933658672,-122.36455001265793,3432,47.12806,-122.108131
+3433.0,107.0,Pierce,10,South Pierce,47.063170858098495,-122.36764234011623,3433,47.12806,-122.108131
+3434.0,107.0,Pierce,10,South Pierce,47.081667307142496,-122.38889384694698,3434,47.12806,-122.108131
+3435.0,106.0,Pierce,10,South Pierce,47.089613282048596,-122.41786524689155,3435,47.12806,-122.108131
+3436.0,106.0,Pierce,10,South Pierce,47.07606716936032,-122.41224818260963,3436,47.12806,-122.108131
+3437.0,107.0,Pierce,10,South Pierce,47.06144782227703,-122.39260855518236,3437,47.12806,-122.108131
+3438.0,109.0,Pierce,10,South Pierce,47.04181639362497,-122.37799837183704,3438,47.12806,-122.108131
+3439.0,110.0,Pierce,10,South Pierce,46.99813098925404,-122.39686274317823,3439,47.12806,-122.108131
+3440.0,109.0,Pierce,10,South Pierce,47.03692439947064,-122.35325206442629,3440,47.12806,-122.108131
+3441.0,109.0,Pierce,10,South Pierce,47.0307191707173,-122.31678061321016,3441,47.12806,-122.108131
+3442.0,109.0,Pierce,10,South Pierce,46.99801576588898,-122.33280544685437,3442,47.12806,-122.108131
+3443.0,109.0,Pierce,10,South Pierce,47.0153868935476,-122.27151517321602,3443,47.12806,-122.108131
+3444.0,109.0,Pierce,10,South Pierce,47.02045033760746,-122.22481336395529,3444,47.12806,-122.108131
+3445.0,111.0,Pierce,10,South Pierce,46.979136524553354,-122.04804076125664,3445,47.12806,-122.108131
+3446.0,111.0,Pierce,10,South Pierce,47.07658755325343,-121.84596528849508,3446,47.12806,-122.108131
+3447.0,111.0,Pierce,10,South Pierce,47.037210862361576,-121.53647029499085,3447,47.12806,-122.108131
+3448.0,111.0,Pierce,10,South Pierce,46.87759720015136,-121.7198734756836,3448,47.12806,-122.108131
+3449.0,110.0,Pierce,10,South Pierce,46.855834727900934,-122.07305950912568,3449,47.12806,-122.108131
+3450.0,109.0,Pierce,10,South Pierce,46.96125417762771,-122.17861735889322,3450,47.12806,-122.108131
+3451.0,109.0,Pierce,10,South Pierce,46.94345765455064,-122.27156815421148,3451,47.12806,-122.108131
+3452.0,109.0,Pierce,10,South Pierce,46.957448420431575,-122.32545647181412,3452,47.12806,-122.108131
+3453.0,110.0,Pierce,10,South Pierce,46.958676960229816,-122.40011803250755,3453,47.12806,-122.108131
+3454.0,110.0,Pierce,10,South Pierce,46.966601990200154,-122.46340617968235,3454,47.12806,-122.108131
+3455.0,110.0,Pierce,10,South Pierce,47.00218480910406,-122.52717424950035,3455,47.12806,-122.108131
+3456.0,110.0,Pierce,10,South Pierce,46.9805592481075,-122.51526700341648,3456,47.12806,-122.108131
+3457.0,110.0,Pierce,10,South Pierce,46.95168719326272,-122.51960194782276,3457,47.12806,-122.108131
+3458.0,110.0,Pierce,10,South Pierce,46.99491717046056,-122.55138470102061,3458,47.12806,-122.108131
+3459.0,110.0,Pierce,10,South Pierce,46.95913316633374,-122.55935555835516,3459,47.12806,-122.108131
+3460.0,110.0,Pierce,10,South Pierce,46.900537195166436,-122.47090885241327,3460,47.12806,-122.108131
+3461.0,110.0,Pierce,10,South Pierce,46.89339346680213,-122.39341742030605,3461,47.12806,-122.108131
+3462.0,110.0,Pierce,10,South Pierce,46.91458130673322,-122.32399886779913,3462,47.12806,-122.108131
+3463.0,110.0,Pierce,10,South Pierce,46.86699505535138,-122.32528591799824,3463,47.12806,-122.108131
+3464.0,110.0,Pierce,10,South Pierce,46.83251680320685,-122.28857431293541,3464,47.12806,-122.108131
+3465.0,110.0,Pierce,10,South Pierce,46.86802870097301,-122.26906487005249,3465,47.12806,-122.108131
+3466.0,110.0,Pierce,10,South Pierce,46.80310909891904,-122.16790899283112,3466,47.12806,-122.108131
+3467.0,112.0,Kitsap,9,Kitsap,47.88872104188978,-122.53024050198812,3467,47.680028,-122.642429
+3468.0,112.0,Kitsap,9,Kitsap,47.901924887453966,-122.56940506598517,3468,47.680028,-122.642429
+3469.0,112.0,Kitsap,9,Kitsap,47.855962233651574,-122.5562547193874,3469,47.680028,-122.642429
+3470.0,112.0,Kitsap,9,Kitsap,47.853446027452144,-122.52563415272282,3470,47.680028,-122.642429
+3471.0,112.0,Kitsap,9,Kitsap,47.819747105710746,-122.514576438265,3471,47.680028,-122.642429
+3472.0,112.0,Kitsap,9,Kitsap,47.82183115507695,-122.55726465229652,3472,47.680028,-122.642429
+3473.0,112.0,Kitsap,9,Kitsap,47.831722256932366,-122.58971619374142,3473,47.680028,-122.642429
+3474.0,112.0,Kitsap,9,Kitsap,47.8284977518519,-122.61531125421646,3474,47.680028,-122.642429
+3475.0,112.0,Kitsap,9,Kitsap,47.806333345921146,-122.661396954311,3475,47.680028,-122.642429
+3476.0,112.0,Kitsap,9,Kitsap,47.775416635158145,-122.68906567473725,3476,47.680028,-122.642429
+3477.0,112.0,Kitsap,9,Kitsap,47.791272421660146,-122.66430561896549,3477,47.680028,-122.642429
+3478.0,112.0,Kitsap,9,Kitsap,47.77459408198253,-122.66870953071123,3478,47.680028,-122.642429
+3479.0,112.0,Kitsap,9,Kitsap,47.761670134328796,-122.66347441805569,3479,47.680028,-122.642429
+3480.0,112.0,Kitsap,9,Kitsap,47.78518075835904,-122.64193373355269,3480,47.680028,-122.642429
+3481.0,112.0,Kitsap,9,Kitsap,47.78314656290354,-122.62154006563179,3481,47.680028,-122.642429
+3482.0,112.0,Kitsap,9,Kitsap,47.79803715940647,-122.59476007587116,3482,47.680028,-122.642429
+3483.0,112.0,Kitsap,9,Kitsap,47.77952049305858,-122.59087239821878,3483,47.680028,-122.642429
+3484.0,112.0,Kitsap,9,Kitsap,47.7903463287641,-122.56289983241177,3484,47.680028,-122.642429
+3485.0,112.0,Kitsap,9,Kitsap,47.80154710921722,-122.52310144815763,3485,47.680028,-122.642429
+3486.0,112.0,Kitsap,9,Kitsap,47.76098916650036,-122.4977373047742,3486,47.680028,-122.642429
+3487.0,112.0,Kitsap,9,Kitsap,47.77333317155276,-122.53171416967852,3487,47.680028,-122.642429
+3488.0,112.0,Kitsap,9,Kitsap,47.75743030814597,-122.56827349838764,3488,47.680028,-122.642429
+3489.0,112.0,Kitsap,9,Kitsap,47.759841653417,-122.59921489108362,3489,47.680028,-122.642429
+3490.0,112.0,Kitsap,9,Kitsap,47.753757757870865,-122.62944269027832,3490,47.680028,-122.642429
+3491.0,112.0,Kitsap,9,Kitsap,47.7441888944,-122.63114566556688,3491,47.680028,-122.642429
+3492.0,112.0,Kitsap,9,Kitsap,47.74227736510599,-122.64482533627012,3492,47.680028,-122.642429
+3493.0,112.0,Kitsap,9,Kitsap,47.75250517419786,-122.65439334725905,3493,47.680028,-122.642429
+3494.0,112.0,Kitsap,9,Kitsap,47.7425858851146,-122.6635000545758,3494,47.680028,-122.642429
+3495.0,113.0,Kitsap,9,Kitsap,47.74655136805414,-122.68080051630665,3495,47.680028,-122.642429
+3496.0,113.0,Kitsap,9,Kitsap,47.733267869868826,-122.71327779948031,3496,47.680028,-122.642429
+3497.0,113.0,Kitsap,9,Kitsap,47.71744645875531,-122.68731079678152,3497,47.680028,-122.642429
+3498.0,113.0,Kitsap,9,Kitsap,47.71689543625944,-122.67009473771792,3498,47.680028,-122.642429
+3499.0,113.0,Kitsap,9,Kitsap,47.70750912655024,-122.64761535545841,3499,47.680028,-122.642429
+3500.0,112.0,Kitsap,9,Kitsap,47.728833256497964,-122.63892198076194,3500,47.680028,-122.642429
+3501.0,112.0,Kitsap,9,Kitsap,47.739278154406115,-122.62157738378721,3501,47.680028,-122.642429
+3502.0,112.0,Kitsap,9,Kitsap,47.726959307635184,-122.62510925167835,3502,47.680028,-122.642429
+3503.0,112.0,Kitsap,9,Kitsap,47.732095006882325,-122.60406674261769,3503,47.680028,-122.642429
+3504.0,112.0,Kitsap,9,Kitsap,47.727908415783844,-122.58290128211989,3504,47.680028,-122.642429
+3505.0,112.0,Kitsap,9,Kitsap,47.73175780074214,-122.56071073699688,3505,47.680028,-122.642429
+3506.0,112.0,Kitsap,9,Kitsap,47.70192769543978,-122.5901596998994,3506,47.680028,-122.642429
+3507.0,112.0,Kitsap,9,Kitsap,47.713752677303546,-122.61937606813068,3507,47.680028,-122.642429
+3508.0,113.0,Kitsap,9,Kitsap,47.68725271838584,-122.62265013274592,3508,47.680028,-122.642429
+3509.0,113.0,Kitsap,9,Kitsap,47.66079204348442,-122.62767974069321,3509,47.680028,-122.642429
+3510.0,113.0,Kitsap,9,Kitsap,47.685597307006084,-122.6399938443887,3510,47.680028,-122.642429
+3511.0,113.0,Kitsap,9,Kitsap,47.680474973534935,-122.65415255083164,3511,47.680028,-122.642429
+3512.0,113.0,Kitsap,9,Kitsap,47.6606993357256,-122.64842119350617,3512,47.680028,-122.642429
+3513.0,113.0,Kitsap,9,Kitsap,47.6907602408323,-122.66098832762593,3513,47.680028,-122.642429
+3514.0,113.0,Kitsap,9,Kitsap,47.66583709353484,-122.66361514400292,3514,47.680028,-122.642429
+3515.0,113.0,Kitsap,9,Kitsap,47.66867910674221,-122.67365537083185,3515,47.680028,-122.642429
+3516.0,113.0,Kitsap,9,Kitsap,47.68724933076286,-122.6779721418152,3516,47.680028,-122.642429
+3517.0,113.0,Kitsap,9,Kitsap,47.67926329611568,-122.69560805370962,3517,47.680028,-122.642429
+3518.0,113.0,Kitsap,9,Kitsap,47.694024958057184,-122.71771155524151,3518,47.680028,-122.642429
+3519.0,115.0,Kitsap,9,Kitsap,47.66763931447221,-122.71269570867382,3519,47.680028,-122.642429
+3520.0,115.0,Kitsap,9,Kitsap,47.6807526155927,-122.74147728496963,3520,47.680028,-122.642429
+3521.0,115.0,Kitsap,9,Kitsap,47.645931564765604,-122.75836114977129,3521,47.680028,-122.642429
+3522.0,115.0,Kitsap,9,Kitsap,47.65237047771159,-122.73442721157605,3522,47.680028,-122.642429
+3523.0,113.0,Kitsap,9,Kitsap,47.65136751594146,-122.71369421964222,3523,47.680028,-122.642429
+3524.0,113.0,Kitsap,9,Kitsap,47.641336689916066,-122.71417799346425,3524,47.680028,-122.642429
+3525.0,113.0,Kitsap,9,Kitsap,47.65563392698859,-122.69792139816694,3525,47.680028,-122.642429
+3526.0,113.0,Kitsap,9,Kitsap,47.64618980820242,-122.70229818570832,3526,47.680028,-122.642429
+3527.0,113.0,Kitsap,9,Kitsap,47.64553805199661,-122.69543090298296,3527,47.680028,-122.642429
+3528.0,113.0,Kitsap,9,Kitsap,47.65970713076218,-122.68907383329405,3528,47.680028,-122.642429
+3529.0,113.0,Kitsap,9,Kitsap,47.65858747749221,-122.68485549638052,3529,47.680028,-122.642429
+3530.0,113.0,Kitsap,9,Kitsap,47.65743866322346,-122.67874447666362,3530,47.680028,-122.642429
+3531.0,113.0,Kitsap,9,Kitsap,47.65223890357744,-122.68454738925924,3531,47.680028,-122.642429
+3532.0,113.0,Kitsap,9,Kitsap,47.64568958834657,-122.67908861843365,3532,47.680028,-122.642429
+3533.0,113.0,Kitsap,9,Kitsap,47.652548508085076,-122.67004341975077,3533,47.680028,-122.642429
+3534.0,113.0,Kitsap,9,Kitsap,47.646850278386346,-122.66890726020692,3534,47.680028,-122.642429
+3535.0,116.0,Kitsap,9,Kitsap,47.638142717193325,-122.66697737203228,3535,47.680028,-122.642429
+3536.0,116.0,Kitsap,9,Kitsap,47.641367147146006,-122.65562665898823,3536,47.680028,-122.642429
+3537.0,116.0,Kitsap,9,Kitsap,47.646022390139294,-122.64247277260618,3537,47.680028,-122.642429
+3538.0,116.0,Kitsap,9,Kitsap,47.636820068660924,-122.64406487869749,3538,47.680028,-122.642429
+3539.0,116.0,Kitsap,9,Kitsap,47.639058656443254,-122.63302779741724,3539,47.680028,-122.642429
+3540.0,117.0,Kitsap,9,Kitsap,47.641037087883504,-122.61925073621441,3540,47.680028,-122.642429
+3541.0,117.0,Kitsap,9,Kitsap,47.63467606718193,-122.5991477798296,3541,47.680028,-122.642429
+3542.0,117.0,Kitsap,9,Kitsap,47.62741270124281,-122.60423351794769,3542,47.680028,-122.642429
+3543.0,117.0,Kitsap,9,Kitsap,47.614219461626355,-122.60811067393223,3543,47.680028,-122.642429
+3544.0,117.0,Kitsap,9,Kitsap,47.62645076632672,-122.61997057421007,3544,47.680028,-122.642429
+3545.0,117.0,Kitsap,9,Kitsap,47.61419191227,-122.62477981156007,3545,47.680028,-122.642429
+3546.0,116.0,Kitsap,9,Kitsap,47.626879690072315,-122.63296677068716,3546,47.680028,-122.642429
+3547.0,116.0,Kitsap,9,Kitsap,47.61408921264356,-122.63406965082376,3547,47.680028,-122.642429
+3548.0,116.0,Kitsap,9,Kitsap,47.626771918856875,-122.64359852476271,3548,47.680028,-122.642429
+3549.0,116.0,Kitsap,9,Kitsap,47.61379539864562,-122.645209917642,3549,47.680028,-122.642429
+3550.0,116.0,Kitsap,9,Kitsap,47.62559622831842,-122.65578722009757,3550,47.680028,-122.642429
+3551.0,116.0,Kitsap,9,Kitsap,47.61091943389014,-122.65557088018551,3551,47.680028,-122.642429
+3552.0,116.0,Kitsap,9,Kitsap,47.626381775064964,-122.66587901478323,3552,47.680028,-122.642429
+3553.0,116.0,Kitsap,9,Kitsap,47.62443493851156,-122.67099313723612,3553,47.680028,-122.642429
+3554.0,115.0,Kitsap,9,Kitsap,47.625277756474,-122.7080801210147,3554,47.680028,-122.642429
+3555.0,115.0,Kitsap,9,Kitsap,47.6213375502777,-122.71830435969446,3555,47.680028,-122.642429
+3556.0,115.0,Kitsap,9,Kitsap,47.621590075838135,-122.74406710014314,3556,47.680028,-122.642429
+3557.0,115.0,Kitsap,9,Kitsap,47.6248639288283,-122.78203102491764,3557,47.680028,-122.642429
+3558.0,115.0,Kitsap,9,Kitsap,47.6424023822057,-122.82665260581852,3558,47.680028,-122.642429
+3559.0,115.0,Kitsap,9,Kitsap,47.61379595634408,-122.82094246335579,3559,47.680028,-122.642429
+3560.0,115.0,Kitsap,9,Kitsap,47.60001541296975,-122.89285258515032,3560,47.680028,-122.642429
+3561.0,115.0,Kitsap,9,Kitsap,47.54819102154657,-122.909605997539,3561,47.680028,-122.642429
+3562.0,115.0,Kitsap,9,Kitsap,47.57439413874321,-122.77849469521122,3562,47.680028,-122.642429
+3563.0,115.0,Kitsap,9,Kitsap,47.59453931677049,-122.7201717366817,3563,47.680028,-122.642429
+3564.0,115.0,Kitsap,9,Kitsap,47.56831452376014,-122.70694588288882,3564,47.680028,-122.642429
+3565.0,115.0,Kitsap,9,Kitsap,47.58931218840656,-122.7035038338314,3565,47.680028,-122.642429
+3566.0,115.0,Kitsap,9,Kitsap,47.602772668337444,-122.69654767153291,3566,47.680028,-122.642429
+3567.0,115.0,Kitsap,9,Kitsap,47.58935810266591,-122.69314014268343,3567,47.680028,-122.642429
+3568.0,118.0,Kitsap,9,Kitsap,47.58969220394216,-122.67019840248129,3568,47.680028,-122.642429
+3569.0,116.0,Kitsap,9,Kitsap,47.60190420093952,-122.6484002848583,3569,47.680028,-122.642429
+3570.0,116.0,Kitsap,9,Kitsap,47.5976388751229,-122.64444964561487,3570,47.680028,-122.642429
+3571.0,116.0,Kitsap,9,Kitsap,47.603515165935036,-122.63408137263298,3571,47.680028,-122.642429
+3572.0,116.0,Kitsap,9,Kitsap,47.59796416700308,-122.63507543808926,3572,47.680028,-122.642429
+3573.0,117.0,Kitsap,9,Kitsap,47.6012433425399,-122.62515786156807,3573,47.680028,-122.642429
+3574.0,117.0,Kitsap,9,Kitsap,47.60125076231062,-122.61714035031912,3574,47.680028,-122.642429
+3575.0,117.0,Kitsap,9,Kitsap,47.60127190460525,-122.60407881281134,3575,47.680028,-122.642429
+3576.0,117.0,Kitsap,9,Kitsap,47.592947475015926,-122.60278938180171,3576,47.680028,-122.642429
+3577.0,117.0,Kitsap,9,Kitsap,47.59190268308112,-122.61788221064552,3577,47.680028,-122.642429
+3578.0,117.0,Kitsap,9,Kitsap,47.59211185038078,-122.62591028936312,3578,47.680028,-122.642429
+3579.0,116.0,Kitsap,9,Kitsap,47.59178844099032,-122.63251971434364,3579,47.680028,-122.642429
+3580.0,116.0,Kitsap,9,Kitsap,47.59218291411073,-122.63907846314196,3580,47.680028,-122.642429
+3581.0,116.0,Kitsap,9,Kitsap,47.59207150711486,-122.64788573039921,3581,47.680028,-122.642429
+3582.0,116.0,Kitsap,9,Kitsap,47.58546285394062,-122.63693305009964,3582,47.680028,-122.642429
+3583.0,117.0,Kitsap,9,Kitsap,47.583073607922756,-122.62567017075588,3583,47.680028,-122.642429
+3584.0,117.0,Kitsap,9,Kitsap,47.58645665543386,-122.61842097735328,3584,47.680028,-122.642429
+3585.0,117.0,Kitsap,9,Kitsap,47.58022912214364,-122.61753249111878,3585,47.680028,-122.642429
+3586.0,117.0,Kitsap,9,Kitsap,47.58698487649407,-122.61084400852013,3586,47.680028,-122.642429
+3587.0,117.0,Kitsap,9,Kitsap,47.58042880389701,-122.61099625524628,3587,47.680028,-122.642429
+3588.0,117.0,Kitsap,9,Kitsap,47.58486327679568,-122.60258404470859,3588,47.680028,-122.642429
+3589.0,117.0,Kitsap,9,Kitsap,47.57536678035232,-122.60543712917502,3589,47.680028,-122.642429
+3590.0,117.0,Kitsap,9,Kitsap,47.575441528425124,-122.61109090565077,3590,47.680028,-122.642429
+3591.0,117.0,Kitsap,9,Kitsap,47.5720504690903,-122.61115298618986,3591,47.680028,-122.642429
+3592.0,117.0,Kitsap,9,Kitsap,47.56901124060912,-122.61202748280355,3592,47.680028,-122.642429
+3593.0,117.0,Kitsap,9,Kitsap,47.57538814233525,-122.61746631787966,3593,47.680028,-122.642429
+3594.0,117.0,Kitsap,9,Kitsap,47.572334530256775,-122.61639117686892,3594,47.680028,-122.642429
+3595.0,118.0,Kitsap,9,Kitsap,47.5740660470344,-122.63022553353704,3595,47.680028,-122.642429
+3596.0,118.0,Kitsap,9,Kitsap,47.57546265418231,-122.6368001761924,3596,47.680028,-122.642429
+3597.0,118.0,Kitsap,9,Kitsap,47.57192169772525,-122.63693192195817,3597,47.680028,-122.642429
+3598.0,118.0,Kitsap,9,Kitsap,47.577454016736795,-122.64473194292654,3598,47.680028,-122.642429
+3599.0,118.0,Kitsap,9,Kitsap,47.57283084294792,-122.64360753822577,3599,47.680028,-122.642429
+3600.0,118.0,Kitsap,9,Kitsap,47.58238917976343,-122.65461675405312,3600,47.680028,-122.642429
+3601.0,118.0,Kitsap,9,Kitsap,47.57650316324481,-122.64901325721563,3601,47.680028,-122.642429
+3602.0,118.0,Kitsap,9,Kitsap,47.57285715371597,-122.64978417481858,3602,47.680028,-122.642429
+3603.0,118.0,Kitsap,9,Kitsap,47.57661787245964,-122.65546555091295,3603,47.680028,-122.642429
+3604.0,118.0,Kitsap,9,Kitsap,47.573066324186705,-122.65649713178387,3604,47.680028,-122.642429
+3605.0,118.0,Kitsap,9,Kitsap,47.57540746981589,-122.66569019672968,3605,47.680028,-122.642429
+3606.0,118.0,Kitsap,9,Kitsap,47.5751931549308,-122.68314986970816,3606,47.680028,-122.642429
+3607.0,115.0,Kitsap,9,Kitsap,47.56693985799848,-122.68668360052985,3607,47.680028,-122.642429
+3608.0,118.0,Kitsap,9,Kitsap,47.566166590141584,-122.67744572577813,3608,47.680028,-122.642429
+3609.0,118.0,Kitsap,9,Kitsap,47.563204727628964,-122.67049050356479,3609,47.680028,-122.642429
+3610.0,118.0,Kitsap,9,Kitsap,47.56897943153653,-122.66137237788072,3610,47.680028,-122.642429
+3611.0,118.0,Kitsap,9,Kitsap,47.562884372822296,-122.66518789179165,3611,47.680028,-122.642429
+3612.0,118.0,Kitsap,9,Kitsap,47.56564154514638,-122.65775131472704,3612,47.680028,-122.642429
+3613.0,118.0,Kitsap,9,Kitsap,47.569313018446856,-122.65333165909551,3613,47.680028,-122.642429
+3614.0,118.0,Kitsap,9,Kitsap,47.569201246904676,-122.64871443113813,3614,47.680028,-122.642429
+3615.0,118.0,Kitsap,9,Kitsap,47.56642443223459,-122.64968484258956,3615,47.680028,-122.642429
+3616.0,118.0,Kitsap,9,Kitsap,47.56455426558115,-122.6498928750671,3616,47.680028,-122.642429
+3617.0,118.0,Kitsap,9,Kitsap,47.569185879914244,-122.64364343401256,3617,47.680028,-122.642429
+3618.0,118.0,Kitsap,9,Kitsap,47.566253303369024,-122.64247913973223,3618,47.680028,-122.642429
+3619.0,118.0,Kitsap,9,Kitsap,47.56443011365962,-122.64061896006264,3619,47.680028,-122.642429
+3620.0,118.0,Kitsap,9,Kitsap,47.56918116327729,-122.63691655426932,3620,47.680028,-122.642429
+3621.0,118.0,Kitsap,9,Kitsap,47.5661670823705,-122.63580628057224,3621,47.680028,-122.642429
+3622.0,118.0,Kitsap,9,Kitsap,47.56919867690458,-122.62989465012545,3622,47.680028,-122.642429
+3623.0,118.0,Kitsap,9,Kitsap,47.56605400861728,-122.62984553074983,3623,47.680028,-122.642429
+3624.0,118.0,Kitsap,9,Kitsap,47.57004161635004,-122.62511611818235,3624,47.680028,-122.642429
+3625.0,118.0,Kitsap,9,Kitsap,47.565422858512285,-122.62468174888252,3625,47.680028,-122.642429
+3626.0,118.0,Kitsap,9,Kitsap,47.56013103366623,-122.64184187504732,3626,47.680028,-122.642429
+3627.0,118.0,Kitsap,9,Kitsap,47.561705741786156,-122.65777401332012,3627,47.680028,-122.642429
+3628.0,118.0,Kitsap,9,Kitsap,47.558089117549066,-122.66095387277474,3628,47.680028,-122.642429
+3629.0,118.0,Kitsap,9,Kitsap,47.5535650777525,-122.66520209893828,3629,47.680028,-122.642429
+3630.0,118.0,Kitsap,9,Kitsap,47.55020249827615,-122.67535862635741,3630,47.680028,-122.642429
+3631.0,115.0,Kitsap,9,Kitsap,47.54949448366458,-122.69637028233014,3631,47.680028,-122.642429
+3632.0,120.0,Kitsap,9,Kitsap,47.53318519784243,-122.69403407341156,3632,47.680028,-122.642429
+3633.0,115.0,Kitsap,9,Kitsap,47.531659210312434,-122.77300746003591,3633,47.680028,-122.642429
+3634.0,120.0,Kitsap,9,Kitsap,47.505215535838936,-122.76324314163959,3634,47.680028,-122.642429
+3635.0,119.0,Kitsap,9,Kitsap,47.57869043832575,-122.55920855497676,3635,47.680028,-122.642429
+3636.0,119.0,Kitsap,9,Kitsap,47.558352378521434,-122.58297333454688,3636,47.680028,-122.642429
+3637.0,119.0,Kitsap,9,Kitsap,47.56197839764648,-122.55527477764149,3637,47.680028,-122.642429
+3638.0,119.0,Kitsap,9,Kitsap,47.543084844507284,-122.54630189263432,3638,47.680028,-122.642429
+3639.0,119.0,Kitsap,9,Kitsap,47.54313701828572,-122.55934544721848,3639,47.680028,-122.642429
+3640.0,119.0,Kitsap,9,Kitsap,47.541184369378655,-122.58302304870442,3640,47.680028,-122.642429
+3641.0,119.0,Kitsap,9,Kitsap,47.54094491358201,-122.604981624756,3641,47.680028,-122.642429
+3642.0,119.0,Kitsap,9,Kitsap,47.52601513561714,-122.59395271048791,3642,47.680028,-122.642429
+3643.0,119.0,Kitsap,9,Kitsap,47.54001543675177,-122.61469775172891,3643,47.680028,-122.642429
+3644.0,119.0,Kitsap,9,Kitsap,47.54044486008339,-122.62223656660721,3644,47.680028,-122.642429
+3645.0,119.0,Kitsap,9,Kitsap,47.5266960316857,-122.62025709877314,3645,47.680028,-122.642429
+3646.0,119.0,Kitsap,9,Kitsap,47.53871387091512,-122.63570724053093,3646,47.680028,-122.642429
+3647.0,119.0,Kitsap,9,Kitsap,47.529416718692566,-122.6362613900796,3647,47.680028,-122.642429
+3648.0,119.0,Kitsap,9,Kitsap,47.53181462289008,-122.65669057418415,3648,47.680028,-122.642429
+3649.0,119.0,Kitsap,9,Kitsap,47.520689998223325,-122.65197733769048,3649,47.680028,-122.642429
+3650.0,120.0,Kitsap,9,Kitsap,47.518569347363176,-122.66852050772553,3650,47.680028,-122.642429
+3651.0,120.0,Kitsap,9,Kitsap,47.51537835232982,-122.69220917689225,3651,47.680028,-122.642429
+3652.0,120.0,Kitsap,9,Kitsap,47.51291047273911,-122.71925139649584,3652,47.680028,-122.642429
+3653.0,120.0,Kitsap,9,Kitsap,47.48287117760974,-122.75361795333487,3653,47.680028,-122.642429
+3654.0,120.0,Kitsap,9,Kitsap,47.49123392436818,-122.69436339451862,3654,47.680028,-122.642429
+3655.0,119.0,Kitsap,9,Kitsap,47.51437518236969,-122.6456123186699,3655,47.680028,-122.642429
+3656.0,119.0,Kitsap,9,Kitsap,47.511754471196525,-122.63721847801264,3656,47.680028,-122.642429
+3657.0,119.0,Kitsap,9,Kitsap,47.51217746376133,-122.6206440183778,3657,47.680028,-122.642429
+3658.0,119.0,Kitsap,9,Kitsap,47.51265389504707,-122.60087884474605,3658,47.680028,-122.642429
+3659.0,119.0,Kitsap,9,Kitsap,47.517952136470136,-122.56601505246095,3659,47.680028,-122.642429
+3660.0,119.0,Kitsap,9,Kitsap,47.51981816278128,-122.51636056974844,3660,47.680028,-122.642429
+3661.0,119.0,Kitsap,9,Kitsap,47.48839798611821,-122.52979454775058,3661,47.680028,-122.642429
+3662.0,119.0,Kitsap,9,Kitsap,47.490122732280426,-122.55679661727824,3662,47.680028,-122.642429
+3663.0,119.0,Kitsap,9,Kitsap,47.468488160152035,-122.55874533914732,3663,47.680028,-122.642429
+3664.0,119.0,Kitsap,9,Kitsap,47.488022600923394,-122.58587416386852,3664,47.680028,-122.642429
+3665.0,120.0,Kitsap,9,Kitsap,47.49489958296834,-122.62221297397707,3665,47.680028,-122.642429
+3666.0,120.0,Kitsap,9,Kitsap,47.475858258841065,-122.61329937852508,3666,47.680028,-122.642429
+3667.0,120.0,Kitsap,9,Kitsap,47.45803069014384,-122.59797103885151,3667,47.680028,-122.642429
+3668.0,120.0,Kitsap,9,Kitsap,47.46874776181629,-122.64034695215307,3668,47.680028,-122.642429
+3669.0,120.0,Kitsap,9,Kitsap,47.467845495835725,-122.66445375098421,3669,47.680028,-122.642429
+3670.0,120.0,Kitsap,9,Kitsap,47.457117155317256,-122.70369973784919,3670,47.680028,-122.642429
+3671.0,120.0,Kitsap,9,Kitsap,47.439590347468396,-122.77324884902669,3671,47.680028,-122.642429
+3672.0,120.0,Kitsap,9,Kitsap,47.421162625355386,-122.72352732332484,3672,47.680028,-122.642429
+3673.0,120.0,Kitsap,9,Kitsap,47.425472262412434,-122.67799819976715,3673,47.680028,-122.642429
+3674.0,120.0,Kitsap,9,Kitsap,47.43779223254585,-122.64213259099495,3674,47.680028,-122.642429
+3675.0,120.0,Kitsap,9,Kitsap,47.43950449919117,-122.59904866151687,3675,47.680028,-122.642429
+3676.0,120.0,Kitsap,9,Kitsap,47.444247774357436,-122.55874552090708,3676,47.680028,-122.642429
+3677.0,120.0,Kitsap,9,Kitsap,47.44484321326577,-122.5395989557202,3677,47.680028,-122.642429
+3678.0,120.0,Kitsap,9,Kitsap,47.42461594423646,-122.57927476240236,3678,47.680028,-122.642429
+3679.0,120.0,Kitsap,9,Kitsap,47.4111282430097,-122.56747446853315,3679,47.680028,-122.642429
+3680.0,120.0,Kitsap,9,Kitsap,47.41470369827367,-122.60943231028796,3680,47.680028,-122.642429
+3681.0,120.0,Kitsap,9,Kitsap,47.416198251438104,-122.64592846318094,3681,47.680028,-122.642429
+3682.0,114.0,Kitsap,9,Kitsap,47.70681286812536,-122.54792017578801,3682,47.680028,-122.642429
+3683.0,114.0,Kitsap,9,Kitsap,47.68954577947794,-122.55653557388678,3683,47.680028,-122.642429
+3684.0,114.0,Kitsap,9,Kitsap,47.69306982270828,-122.5343166543441,3684,47.680028,-122.642429
+3685.0,114.0,Kitsap,9,Kitsap,47.69101374268608,-122.5175945198469,3685,47.680028,-122.642429
+3686.0,114.0,Kitsap,9,Kitsap,47.66738826024352,-122.51772423884569,3686,47.680028,-122.642429
+3687.0,114.0,Kitsap,9,Kitsap,47.67134023868247,-122.54750247816523,3687,47.680028,-122.642429
+3688.0,114.0,Kitsap,9,Kitsap,47.65808576153863,-122.57427085657676,3688,47.680028,-122.642429
+3689.0,114.0,Kitsap,9,Kitsap,47.65365175883606,-122.54369675849622,3689,47.680028,-122.642429
+3690.0,114.0,Kitsap,9,Kitsap,47.643382045548,-122.51302902907172,3690,47.680028,-122.642429
+3691.0,114.0,Kitsap,9,Kitsap,47.63039460463828,-122.50316797597885,3691,47.680028,-122.642429
+3692.0,114.0,Kitsap,9,Kitsap,47.64098606059412,-122.52413468700097,3692,47.680028,-122.642429
+3693.0,114.0,Kitsap,9,Kitsap,47.63179531470333,-122.52549494023356,3693,47.680028,-122.642429
+3694.0,114.0,Kitsap,9,Kitsap,47.6252081279445,-122.52480669538099,3694,47.680028,-122.642429
+3695.0,114.0,Kitsap,9,Kitsap,47.634905884572866,-122.54651777484337,3695,47.680028,-122.642429
+3696.0,114.0,Kitsap,9,Kitsap,47.632517804219,-122.56539487353494,3696,47.680028,-122.642429
+3697.0,114.0,Kitsap,9,Kitsap,47.610280140927784,-122.56391995061222,3697,47.680028,-122.642429
+3698.0,114.0,Kitsap,9,Kitsap,47.60811923218953,-122.54250166247371,3698,47.680028,-122.642429
+3699.0,114.0,Kitsap,9,Kitsap,47.60858693142804,-122.51923495353728,3699,47.680028,-122.642429
+3700.0,114.0,Kitsap,9,Kitsap,47.586038522014505,-122.51274167616907,3700,47.680028,-122.642429
diff --git a/scripts/summarize/inputs/calibration/survey.h5 b/scripts/summarize/inputs/calibration/survey.h5
new file mode 100644
index 00000000..37d8d702
Binary files /dev/null and b/scripts/summarize/inputs/calibration/survey.h5 differ
diff --git a/scripts/summarize/inputs/calibration/variable_labels.csv b/scripts/summarize/inputs/calibration/variable_labels.csv
new file mode 100644
index 00000000..21b3ef63
--- /dev/null
+++ b/scripts/summarize/inputs/calibration/variable_labels.csv
@@ -0,0 +1,160 @@
+table,field,value,text
+Tour,pdpurp,0,None/Home
+Tour,pdpurp,1,Work
+Tour,pdpurp,2,School
+Tour,pdpurp,3,Escort
+Tour,pdpurp,4,Personal Business
+Tour,pdpurp,5,Shop
+Tour,pdpurp,6,Meal
+Tour,pdpurp,7,Social
+Tour,pdpurp,8,Social
+Tour,pdpurp,9,Personal Business
+Tour,pdpurp,10,Change Mode Inserted Purpose
+Tour,toadtyp,1,Home
+Tour,toadtyp,2,Usual Workplace
+Tour,toadtyp,3,Usual School
+Tour,toadtyp,4,Other
+Tour,toadtyp,5,Missing
+Tour,toadtyp,6,Change Mode Inserted Location
+Tour,tdadtyp,1,Home
+Tour,tdadtyp,2,Usual Workplace
+Tour,tdadtyp,3,Usual School
+Tour,tdadtyp,4,Other
+Tour,tdadtyp,5,Missing
+Tour,tdadtyp,6,Change Mode Inserted Location
+Tour,tmodetp,1,Walk
+Tour,tmodetp,2,Bike
+Tour,tmodetp,3,SOV
+Tour,tmodetp,4,HOV2
+Tour,tmodetp,5,HOV3+
+Tour,tmodetp,6,Transit
+Tour,tmodetp,7,Park
+Tour,tmodetp,8,School Bus
+Tour,tmodetp,9,Other
+Tour,tpathtp,0,None
+Tour,tpathtp,1,Full Network
+Tour,tpathtp,2,No-Toll Network
+Tour,tpathtp,3,Local Bus
+Tour,tpathtp,4,Light Rail
+Tour,tpathtp,5,Premium Bus
+Tour,tpathtp,6,Commuter Rail
+Tour,tpathtp,7,Ferry
+Household,hownrent,1,Own
+Household,hownrent,2,Rent
+Household,hownrent,3,Other
+Household,hownrent,9,Missing
+Household,hrestype,1,Detached Single House
+Household,hrestype,2,Duplex/Triplex/Rowhouse
+Household,hrestype,3,Apartment/Condo
+Household,hrestype,4,Mobile Home/Trailer
+Household,hrestype,5,Dorm Room/Rented room
+Household,hrestype,6,Other
+Household,hrestype,9,Missing
+Household,samptype,1,Random Proportional
+Household,samptype,2,Transit Area Enrichment
+Household,samptype,3,Ferry Intercept
+Household,samptype,4,Park and Ride Intercept
+Person,pptyp,1,Full-Time Worker
+Person,pptyp,2,Part-Time Worker
+Person,pptyp,3,Non-Working Adult Age 65+
+Person,pptyp,4,Non-Working Adult Age <65
+Person,pptyp,5,University Student
+Person,pptyp,6,High School Student Age 16+
+Person,pptyp,7,Child Age 5-15
+Person,pptyp,8,Child Age 0-4
+Person,pgend,1,Male
+Person,pgend,2,Female
+Person,pgend,9,Missing
+Person,pwtyp,0,Not a Paid Worker
+Person,pwtyp,1,Paid Full-Time Worker
+Person,pwtyp,2,Paid Part-Time Worker
+Person,pstyp,0,Not a Student
+Person,pstyp,1,Full-Time Student
+Person,pstyp,2,Part-Time Student
+Person,puwmode,0,None 4+ Days/Week
+Person,puwmode,1,Walk
+Person,puwmode,2,Bike
+Person,puwmode,3,Drive Alone
+Person,puwmode,4,Shared Ride 2
+Person,puwmode,5,Shared Ride 3+
+Person,puwmode,6,Walk to Transit
+Person,puwmode,7,Park and Ride
+Person,puwarrp,1,Before 6 am
+Person,puwarrp,2,Between 6 and 6:30 am
+Person,puwarrp,3,Between 6:30 and 7 am
+Person,puwarrp,4,Between 7 and 7:30 am
+Person,puwarrp,5,Between 7:30 and 8 am
+Person,puwarrp,6,Between 8 and 8:30 am
+Person,puwarrp,7,Between 8:30 and 9 am
+Person,puwarrp,8,After 9 am
+Person,puwarrp,9,None 4+ Days/Week
+Person,puwdepp,1,Before 3:30 pm
+Person,puwdepp,2,Between 3:30 and 4 pm
+Person,puwdepp,3,Between 4 and 4:30 pm
+Person,puwdepp,4,Between 4:30 and 5 pm
+Person,puwdepp,5,Between 5 and 5:30 pm
+Person,puwdepp,6,Between 5:30 and 6 pm
+Person,puwdepp,7,Between 6 and 6:30 pm
+Person,puwdepp,8,After 6:30 pm
+Person,puwdepp,9,None 4+ Days/Week
+Person,pproxy,1,No Proxy
+Person,pproxy,2,Proxy
+Person,pproxy,3,Mailed Diary
+Person,pproxy,8,Missing
+HouseholdDay,dow,0,Sunday
+HouseholdDay,dow,1,Monday
+HouseholdDay,dow,2,Tuesday
+HouseholdDay,dow,3,Wednesday
+HouseholdDay,dow,4,Thursday
+HouseholdDay,dow,5,Friday
+HouseholdDay,dow,6,Saturday
+Trip,opurp,0,None/Home
+Trip,opurp,1,Work
+Trip,opurp,2,School
+Trip,opurp,3,Escort
+Trip,opurp,4,Personal Business
+Trip,opurp,5,Shop
+Trip,opurp,6,Meal
+Trip,opurp,7,Social
+Trip,opurp,8,Recreational
+Trip,opurp,9,medical
+Trip,opurp,10,Change Mode Inserted Purpose
+Trip,dpurp,0,None/Home
+Trip,dpurp,1,Work
+Trip,dpurp,2,School
+Trip,dpurp,3,Escort
+Trip,dpurp,4,Personal Business
+Trip,dpurp,5,Shop
+Trip,dpurp,6,Meal
+Trip,dpurp,7,Social
+Trip,dpurp,8,Recreational
+Trip,dpurp,9,medical
+Trip,dpurp,10,Change Mode Inserted Purpose
+Trip,oadtyp,1,Home
+Trip,oadtyp,2,Usual Workplace
+Trip,oadtyp,3,Usual School
+Trip,oadtyp,4,Other
+Trip,oadtyp,5,Missing
+Trip,oadtyp,6,Change Mode Inserted Location
+Trip,dadtyp,1,Home
+Trip,dadtyp,2,Usual Workplace
+Trip,dadtyp,3,Usual School
+Trip,dadtyp,4,Other
+Trip,dadtyp,5,Missing
+Trip,dadtyp,6,Change Mode Inserted Location
+Trip,mode,1,Walk
+Trip,mode,2,Bike
+Trip,mode,3,SOV
+Trip,mode,4,HOV2
+Trip,mode,5,HOV3+
+Trip,mode,6,Transit
+Trip,mode,8,School Bus
+Trip,mode,9,Other
+Trip,pathtype,0,None
+Trip,pathtype,1,Full Network
+Trip,pathtype,2,No-Toll Network
+Trip,pathtype,3,Local Bus
+Trip,pathtype,4,Light Rail
+Trip,pathtype,5,Premium Bus
+Trip,pathtype,6,Commuter Rail
+Trip,pathtype,7,Ferry
diff --git a/scripts/summarize/inputs/county_taz.csv b/scripts/summarize/inputs/county_taz.csv
new file mode 100644
index 00000000..763f8dce
--- /dev/null
+++ b/scripts/summarize/inputs/county_taz.csv
@@ -0,0 +1,3701 @@
+taz,geog_name,district,lat_1,lon_1
+1,King,North Seattle-Shoreline,47.51978,-122.138064
+2,King,North Seattle-Shoreline,47.51978,-122.138064
+3,King,North Seattle-Shoreline,47.51978,-122.138064
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+2538,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2539,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2540,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2541,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2542,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2543,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2544,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2545,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2546,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2547,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2548,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2549,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2550,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2551,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2552,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2553,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2554,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2555,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2556,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2557,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2558,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2559,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2560,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2561,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2562,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2563,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2564,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2565,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2566,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2567,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2568,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2569,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2570,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2571,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2572,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2573,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2574,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2575,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2576,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2577,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2578,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2579,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2580,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2581,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2582,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2583,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2584,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2585,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2586,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2587,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2588,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2589,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2590,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2591,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2592,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2593,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2594,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2595,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2596,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2597,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2598,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2599,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2600,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2601,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2602,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2603,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2604,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2605,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2606,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2607,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2608,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2609,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2610,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2611,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2612,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2613,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2614,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2615,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2616,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2617,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2618,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2619,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2620,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2621,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2622,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2623,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2624,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2625,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2626,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2627,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2628,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2629,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2630,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2631,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2632,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2633,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2634,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2635,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2636,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2637,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2638,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2639,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2640,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2641,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2642,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2643,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2644,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2645,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2646,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2647,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2648,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2649,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2650,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2651,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2652,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2653,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2654,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2655,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2656,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2657,Snohomish,Everett-Lynwood-Edmonds,47.973024,-122.130877
+2658,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2659,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2660,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2661,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2662,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2663,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2664,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2665,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2666,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2667,Snohomish,Suburban Snohomish,47.973024,-122.130877
+2668,Snohomish,Suburban Snohomish,47.973024,-122.130877
diff --git a/scripts/summarize/inputs/income_tiers.csv b/scripts/summarize/inputs/income_tiers.csv
new file mode 100644
index 00000000..c07c676b
--- /dev/null
+++ b/scripts/summarize/inputs/income_tiers.csv
@@ -0,0 +1,21 @@
+hhsize,income_threshold
+1,11670
+2,15730
+3,19790
+4,23850
+5,27910
+6,31970
+7,36030
+8,40090
+9,40090
+10,40090
+11,40090
+12,40090
+13,40090
+14,40090
+15,40090
+16,40090
+17,40090
+18,40090
+19,40090
+20,40090
diff --git a/scripts/summarize/inputs/mic_taz.csv b/scripts/summarize/inputs/mic_taz.csv
new file mode 100644
index 00000000..676afab8
--- /dev/null
+++ b/scripts/summarize/inputs/mic_taz.csv
@@ -0,0 +1,124 @@
+taz,lat,lon,center
+3434,47.08166731,-122.3888938,Frederickson
+3432,47.08455934,-122.36455,Frederickson
+3430,47.08541423,-122.338466,Frederickson
+3221,47.21191937,-122.2361791,Sumner Pacific
+3171,47.22262147,-122.2510123,Sumner Pacific
+3170,47.22573796,-122.2421668,Sumner Pacific
+3172,47.22642801,-122.2601896,Sumner Pacific
+3156,47.23927017,-122.2416013,Sumner Pacific
+3116,47.24005551,-122.4076456,Port of Tacoma
+3112,47.24048866,-122.4139556,Port of Tacoma
+3115,47.24162983,-122.4066217,Port of Tacoma
+3119,47.24230204,-122.4008188,Port of Tacoma
+3107,47.24273995,-122.4235552,Port of Tacoma
+3111,47.24333422,-122.4142438,Port of Tacoma
+3155,47.2456765,-122.2439795,Sumner Pacific
+3114,47.24403861,-122.4096534,Port of Tacoma
+3153,47.24672204,-122.252505,Sumner Pacific
+3121,47.2451587,-122.3931176,Port of Tacoma
+3118,47.24516202,-122.4033371,Port of Tacoma
+3113,47.24694441,-122.412727,Port of Tacoma
+3106,47.24718805,-122.4225881,Port of Tacoma
+3127,47.25006074,-122.3622424,Port of Tacoma
+3117,47.25025859,-122.406342,Port of Tacoma
+3120,47.2504694,-122.3933994,Port of Tacoma
+3157,47.25359274,-122.2358512,Sumner Pacific
+3123,47.2517497,-122.3796209,Port of Tacoma
+3152,47.25391525,-122.2534944,Sumner Pacific
+3154,47.25414573,-122.2455712,Sumner Pacific
+3104,47.25205113,-122.4280353,Port of Tacoma
+3126,47.25362361,-122.3681084,Port of Tacoma
+3103,47.25443591,-122.4209197,Port of Tacoma
+3100,47.25669258,-122.3957057,Port of Tacoma
+3101,47.25683402,-122.4123152,Port of Tacoma
+3102,47.26060042,-122.4285642,Port of Tacoma
+3099,47.26242162,-122.4051642,Port of Tacoma
+3098,47.26302503,-122.3943612,Port of Tacoma
+3096,47.26549996,-122.3825814,Port of Tacoma
+3095,47.2688425,-122.3748605,Port of Tacoma
+3097,47.26933878,-122.4145553,Port of Tacoma
+3084,47.28312824,-122.4086652,Port of Tacoma
+1050,47.39459473,-122.2381924,Kent MIC
+1049,47.4043448,-122.2382154,Kent MIC
+1051,47.40487585,-122.2314989,Kent MIC
+1024,47.41855877,-122.2395533,Kent MIC
+1023,47.41943069,-122.2327728,Kent MIC
+1022,47.42128254,-122.2244917,Kent MIC
+1019,47.43425213,-122.2367103,Kent MIC
+1021,47.43491856,-122.2223582,Kent MIC
+1020,47.43500093,-122.229122,Kent MIC
+3653,47.48287118,-122.753618,Puget Sound Industrial Center- Bremerton
+907,47.49233523,-122.2836225,North Tukwila
+906,47.49240007,-122.2890987,North Tukwila
+878,47.50345155,-122.2924006,North Tukwila
+879,47.50752012,-122.2982879,North Tukwila
+843,47.51283823,-122.2820003,Duwamish
+842,47.51320464,-122.2884617,Duwamish
+826,47.52053255,-122.3208335,Duwamish
+874,47.52103696,-122.2968428,North Tukwila
+873,47.52162093,-122.3034906,North Tukwila
+824,47.52467618,-122.3122783,Duwamish
+805,47.53035998,-122.3312341,Duwamish
+807,47.53490546,-122.3183119,Duwamish
+809,47.53551347,-122.3060922,Duwamish
+804,47.53541671,-122.3287764,Duwamish
+777,47.53735458,-122.3014773,Duwamish
+808,47.53981592,-122.3166473,Duwamish
+780,47.54094895,-122.3278737,Duwamish
+779,47.54430465,-122.3254437,Duwamish
+757,47.54831267,-122.3428511,Duwamish
+758,47.54993073,-122.3368414,Duwamish
+763,47.55056648,-122.3167283,Duwamish
+761,47.55056458,-122.3256064,Duwamish
+760,47.55069847,-122.3317924,Duwamish
+759,47.55324092,-122.3355335,Duwamish
+727,47.55788341,-122.3327327,Duwamish
+725,47.56010458,-122.3250643,Duwamish
+728,47.56162004,-122.3367309,Duwamish
+726,47.56275113,-122.3315111,Duwamish
+704,47.56433943,-122.341931,Duwamish
+703,47.56553595,-122.3515846,Duwamish
+700,47.56812651,-122.3669494,Duwamish
+707,47.56908147,-122.3244897,Duwamish
+706,47.56945608,-122.3316432,Duwamish
+705,47.56943698,-122.3367663,Duwamish
+672,47.57358118,-122.3367621,Duwamish
+673,47.57521411,-122.3411181,Duwamish
+670,47.57567873,-122.3246561,Duwamish
+671,47.57568505,-122.3316255,Duwamish
+675,47.57770114,-122.3659956,Duwamish
+674,47.57964387,-122.3514401,Duwamish
+655,47.58078921,-122.3367354,Duwamish
+657,47.582664,-122.324241,Duwamish
+656,47.58296159,-122.3316255,Duwamish
+654,47.5856368,-122.3406874,Duwamish
+652,47.5882274,-122.3316079,Duwamish
+653,47.58868085,-122.336025,Duwamish
+650,47.58939177,-122.3247647,Duwamish
+651,47.59135395,-122.3315884,Duwamish
+634,47.59394988,-122.3262475,Duwamish
+635,47.59468138,-122.3233448,Duwamish
+633,47.5948158,-122.3381217,Duwamish
+632,47.59573536,-122.3348792,Duwamish
+444,47.62245268,-122.3646754,Ballard-Interbay
+394,47.62883088,-122.3745132,Ballard-Interbay
+390,47.63474539,-122.3836407,Ballard-Interbay
+342,47.65197127,-122.3802632,Ballard-Interbay
+336,47.65460546,-122.3693519,Ballard-Interbay
+329,47.65638785,-122.3637439,Ballard-Interbay
+335,47.65846911,-122.3851057,Ballard-Interbay
+330,47.66033337,-122.3684599,Ballard-Interbay
+259,47.6618627,-122.3731328,Ballard-Interbay
+261,47.66357635,-122.3679412,Ballard-Interbay
+257,47.66372398,-122.3793843,Ballard-Interbay
+255,47.6640398,-122.3828961,Ballard-Interbay
+258,47.66615675,-122.3734914,Ballard-Interbay
+253,47.66766665,-122.3898314,Ballard-Interbay
+2415,47.89410128,-122.2579154,Paine Field / Boeing Everett
+2403,47.90619306,-122.2809828,Paine Field / Boeing Everett
+2401,47.91456895,-122.2653254,Paine Field / Boeing Everett
+2311,47.93144972,-122.2549986,Paine Field / Boeing Everett
+2309,47.93353519,-122.2774941,Paine Field / Boeing Everett
+2313,47.9385288,-122.246513,Paine Field / Boeing Everett
+2310,47.94658706,-122.2658844,Paine Field / Boeing Everett
diff --git a/scripts/summarize/inputs/network_summary/ObservedBoardings.xlsx b/scripts/summarize/inputs/network_summary/ObservedBoardings.xlsx
deleted file mode 100644
index 15235a83..00000000
Binary files a/scripts/summarize/inputs/network_summary/ObservedBoardings.xlsx and /dev/null differ
diff --git a/scripts/summarize/inputs/network_summary/TransitRouteKey.xlsx b/scripts/summarize/inputs/network_summary/TransitRouteKey.xlsx
deleted file mode 100644
index 9d5f4166..00000000
Binary files a/scripts/summarize/inputs/network_summary/TransitRouteKey.xlsx and /dev/null differ
diff --git a/scripts/summarize/inputs/network_summary/corridor_travel_times.csv b/scripts/summarize/inputs/network_summary/corridor_travel_times.csv
new file mode 100644
index 00000000..34de60f6
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/corridor_travel_times.csv
@@ -0,0 +1,45 @@
+corridor_name,description,corridor,direction_id,direction,tod,observed_time_2010,observed_time_2014
+Everett to Seattle via I-5,Seattle to Everett via I-5,2,1,N,7to8,26.8,
+Everett to Seattle via I-5,Everett to Seattle via I-5,2,2,S,7to8,55.4,
+Federal Way to Seattle via I-5,Federal Way to Seattle via I-5,3,1,N,7to8,50.3,
+Federal Way to Seattle via I-5,Seattle to Federal Way via I-5,3,2,S,7to8,25,
+Lynnwood to Bellevue via I-405,Bellevue to Lynnwood via I-405,4,1,N,7to8,17.7,
+Lynnwood to Bellevue via I-405,Lynnwood to Bellevue via I-405,4,2,S,7to8,47.9,
+Tukwila to Bellevue via I-405,Tukwila to Bellevue via I-405,5,1,N,7to8,31.1,
+Tukwila to Bellevue via I-405,Bellevue to Tukwila via I-405,5,2,S,7to8,21.9,
+Renton to Auburn via SR 167,Auburn to Renton via SR 167,6,1,N,7to8,21.1,
+Renton to Auburn via SR 167,Renton to Auburn via SR 167,6,2,S,7to8,13.3,
+Seattle to Redmond via SR 520,Seattle to Redmond via SR 520,7,1,E,7to8,24.3,
+Seattle to Redmond via SR 520,Redmond to Seattle via SR 520,7,2,W,7to8,21.5,
+Bellevue to Redmond via SR 520,Bellevue to Redmond via SR 520,8,1,N,7to8,7.6,
+Bellevue to Redmond via SR 520,Redmond to Bellevue via SR 520,8,2,S,7to8,8.2,
+Bellevue to Issaquah via I-90,Bellevue to Issaquah via I-90,9,1,E,7to8,10.5,
+Bellevue to Issaquah via I-90,Issaquah to Bellevue via I-90,9,2,W,7to8,15.7,
+Bellevue to Seattle via SR 520,Seattle to Bellevue via SR 520,10,1,E,7to8,22.1,
+Bellevue to Seattle via SR 520,Bellevue to Seattle via SR 520,10,2,W,7to8,21.2,
+Bellevue to Seattle via I-90,Seattle to Bellevue via I-90,11,1,E,7to8,16.8,
+Bellevue to Seattle via I-90,Bellevue to Seattle via I-90,11,2,W,7to8,15.4,
+Issaquah to Seattle via I-90,Seattle to Issaquah via I-90,12,1,E,7to8,20.4,
+Issaquah to Seattle via I-90,Issaquah to Seattle via I-90,12,2,W,7to8,24.8,
+Everett to Seattle via I-5,Seattle to Everett via I-5,2,1,N,15to16,51.3,
+Everett to Seattle via I-5,Everett to Seattle via I-5,2,2,S,15to16,55.2,
+Federal Way to Seattle via I-5,Federal Way to Seattle via I-5,3,1,N,15to16,38.9,
+Federal Way to Seattle via I-5,Seattle to Federal Way via I-5,3,2,S,15to16,39.9,
+Lynnwood to Bellevue via I-405,Bellevue to Lynnwood via I-405,4,1,N,15to16,28.7,
+Lynnwood to Bellevue via I-405,Lynnwood to Bellevue via I-405,4,2,S,15to16,27.2,
+Tukwila to Bellevue via I-405,Tukwila to Bellevue via I-405,5,1,N,15to16,28.2,
+Tukwila to Bellevue via I-405,Bellevue to Tukwila via I-405,5,2,S,15to16,37.7,
+Renton to Auburn via SR 167,Auburn to Renton via SR 167,6,1,N,15to16,17.4,
+Renton to Auburn via SR 167,Renton to Auburn via SR 167,6,2,S,15to16,28.6,
+Seattle to Redmond via SR 520,Seattle to Redmond via SR 520,7,1,E,15to16,31.1,
+Seattle to Redmond via SR 520,Redmond to Seattle via SR 520,7,2,W,15to16,45.7,
+Bellevue to Redmond via SR 520,Bellevue to Redmond via SR 520,8,1,N,15to16,11.2,
+Bellevue to Redmond via SR 520,Redmond to Bellevue via SR 520,8,2,S,15to16,23.8,
+Bellevue to Issaquah via I-90,Bellevue to Issaquah via I-90,9,1,E,15to16,19.6,
+Bellevue to Issaquah via I-90,Issaquah to Bellevue via I-90,9,2,W,15to16,14.6,
+Bellevue to Seattle via SR 520,Seattle to Bellevue via SR 520,10,1,E,15to16,29.9,
+Bellevue to Seattle via SR 520,Bellevue to Seattle via SR 520,10,2,W,15to16,36.5,
+Bellevue to Seattle via I-90,Seattle to Bellevue via I-90,11,1,E,15to16,21.6,
+Bellevue to Seattle via I-90,Bellevue to Seattle via I-90,11,2,W,15to16,30.2,
+Issaquah to Seattle via I-90,Seattle to Issaquah via I-90,12,1,E,15to16,25.5,
+Issaquah to Seattle via I-90,Issaquah to Seattle via I-90,12,2,W,15to16,34.8,
diff --git a/scripts/summarize/inputs/network_summary/light_rail_boardings.csv b/scripts/summarize/inputs/network_summary/light_rail_boardings.csv
new file mode 100644
index 00000000..8aa61b82
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/light_rail_boardings.csv
@@ -0,0 +1,86 @@
+station,id,observed_boardings,observed_transfer_rate
+Tacoma Dome,151798,,
+East Tacoma,199013,,
+Fife,199012,,
+S Federal Way (SR-18),199010,,
+Federal Way TC,199009,,
+Star Lake (272nd),199008,,
+Kent-Des Moines Rd,199007,,
+South SeaTac,120350,,
+SeaTac Airport,186180,4659.905272,0.264
+International Blvd,114752,2213.135731,0.264
+Boeing Access,107638,,
+Rainier Beach,104962,1059.240105,0.362
+Othello,101780,1529.512678,0.123
+Graham,100100,,
+Columbia City,183600,1416.076315,0.125
+Mt. Baker,92684,1598.626221,0.324
+Beacon Hill,91596,1505.473189,0.353
+West Seattle Junction,198980,,
+Avalon & Genesee,198979,,
+Delridge,198981,,
+Issaquah,198988,,
+Lakemont,198987,,
+Eastgate P&R,199068,,
+Richards Road,199069,,
+Main St (Bellevue),198655,,
+Downtown Redmond,71174,,
+SE Redmond,198992,,
+Overlake TC,198662,,
+148th Ave NE,198661,,
+Bel Red/132nd,198660,,
+120th/16th,198659,,
+Overlake Med Ctr,198658,,
+Bellevue TC,198657,,
+S Kirkland P&R,198993,,
+S Belleuve P&R,90271,,
+Mercer Island P&R,198653,,
+Rainier Ave,185807,,
+SODO,183604,979.6092943,0.313
+Stadium,88671,982.6142203,0.119
+International District,199119,2300.278883,0.450
+Pioneer Square,199118,1554.303399,0.159
+ Midtown (5th & Madison),198975,,
+University St,199116,2019.317326,0.200
+Westlake-2,198973,,
+Westlake,199115,5269.907366,0.364
+Denny,198972,,
+South Lake Union,198971,,
+Seattle Center,198983,,
+Capitol Hill,198149,,
+Smith Cove,198970,,
+Interbay,198968,,
+Ballard,198967,,
+Husky Stadium,183595,,
+Brooklyn,198995,,
+Roosevelt,183795,,
+Northgate TC,64343,,
+130th St,198997,,
+145th St,186448,,
+185th St,53083,,
+Mountlake Terrace P&R,48891,,
+Lynnwood P&R,43078,,
+West Alderwood Mall,198999,,
+Ash Way P&R,199000,,
+Mariner P&R,199001,,
+SR 99/Airport Rd,199002,,
+SW Evt Industrial Complex,199003,,
+SR-526/Evergreen,199004,,
+Everett Station,199005,,
+S 25th St Station,151922,,
+Union Station,150772,,
+Convention Center Station,149887,,
+Commerce,149191,,
+Theater District,148900,,
+4th & Broadway,198571,,
+Division & St Helens,198570,,
+Division & MLK,198569,,
+6th & MLK,199015,,
+11th & MLK,199016,,
+18th & MLK,199017,,
+Hilltop,199019,,
+Sprague,199020,,
+Union,199021,,
+Stevens,199022,,
+Pearl,199023,,
+Tacoma CC,199024,,
diff --git a/scripts/summarize/inputs/network_summary/od_travel_times.csv b/scripts/summarize/inputs/network_summary/od_travel_times.csv
new file mode 100644
index 00000000..ee5f8df3
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/od_travel_times.csv
@@ -0,0 +1,41 @@
+origin,destination,best_guess,optimistic,pessimistic,time,date,d_city,o_city,dtaz,otaz
+"2349 Dakota St, Everett, WA 98201, USA","3200 184th St SW, Lynnwood, WA 98037, USA",20.15,18.2833333333,23.1666666667,am,10/18/2017,Lynnwood,Everett,2570,2286
+"2349 Dakota St, Everett, WA 98201, USA","501-505 E 25th St, Tacoma, WA 98421, USA",80.1166666667,69.7166666667,105.5,am,10/18/2017,Tacoma,Everett,3108,2286
+"2349 Dakota St, Everett, WA 98201, USA","505 Madison St, Seattle, WA 98104, USA",49.9666666667,44.3166666667,76.1166666667,am,10/18/2017,Seattle,Everett,532,2286
+"2349 Dakota St, Everett, WA 98201, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",45.0166666667,37.2,67.9,am,10/18/2017,Bellevue,Everett,1552,2286
+"3200 184th St SW, Lynnwood, WA 98037, USA","2349 Dakota St, Everett, WA 98201, USA",19.4666666667,17.85,21.8,am,10/18/2017,Everett,Lynnwood,2286,2570
+"3200 184th St SW, Lynnwood, WA 98037, USA","501-505 E 25th St, Tacoma, WA 98421, USA",73.6666666667,63.8833333333,103.083333333,am,10/18/2017,Tacoma,Lynnwood,3108,2570
+"3200 184th St SW, Lynnwood, WA 98037, USA","505 Madison St, Seattle, WA 98104, USA",43.6666666667,36.25,70.7166666667,am,10/18/2017,Seattle,Lynnwood,532,2570
+"3200 184th St SW, Lynnwood, WA 98037, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",39.7,31.3,62.1333333333,am,10/18/2017,Bellevue,Lynnwood,1552,2570
+"501-505 E 25th St, Tacoma, WA 98421, USA","2349 Dakota St, Everett, WA 98201, USA",86.7,74.6166666667,124.666666667,am,10/18/2017,Everett,Tacoma,2286,3108
+"501-505 E 25th St, Tacoma, WA 98421, USA","3200 184th St SW, Lynnwood, WA 98037, USA",77.45,65.3333333333,115.483333333,am,10/18/2017,Lynnwood,Tacoma,2570,3108
+"501-505 E 25th St, Tacoma, WA 98421, USA","505 Madison St, Seattle, WA 98104, USA",56.1666666667,46.9333333333,89.4,am,10/18/2017,Seattle,Tacoma,532,3108
+"501-505 E 25th St, Tacoma, WA 98421, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",62.2166666667,49.1166666667,101.083333333,am,10/18/2017,Bellevue,Tacoma,1552,3108
+"505 Madison St, Seattle, WA 98104, USA","2349 Dakota St, Everett, WA 98201, USA",34.25,30.7666666667,40.6166666667,am,10/18/2017,Everett,Seattle,2286,532
+"505 Madison St, Seattle, WA 98104, USA","3200 184th St SW, Lynnwood, WA 98037, USA",24.6,21.3,30.7833333333,am,10/18/2017,Lynnwood,Seattle,2570,532
+"505 Madison St, Seattle, WA 98104, USA","501-505 E 25th St, Tacoma, WA 98421, USA",34.5333333333,31.5833333333,40.7,am,10/18/2017,Tacoma,Seattle,3108,532
+"505 Madison St, Seattle, WA 98104, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",22.0333333333,17.1333333333,29.3,am,10/18/2017,Bellevue,Seattle,1552,532
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","2349 Dakota St, Everett, WA 98201, USA",32.65,30.1166666667,37.25,am,10/18/2017,Everett,Bellevue,2286,1552
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","3200 184th St SW, Lynnwood, WA 98037, USA",25.1333333333,22.7333333333,29.0666666667,am,10/18/2017,Lynnwood,Bellevue,2570,1552
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","501-505 E 25th St, Tacoma, WA 98421, USA",40.3666666667,36.75,47.8333333333,am,10/18/2017,Tacoma,Bellevue,3108,1552
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","505 Madison St, Seattle, WA 98104, USA",25.6333333333,19.4833333333,41.4333333333,am,10/18/2017,Seattle,Bellevue,532,1552
+"2349 Dakota St, Everett, WA 98201, USA","3200 184th St SW, Lynnwood, WA 98037, USA",21.1333333333,18.4166666667,26.0333333333,pm,10/18/2017,Lynnwood,Everett,2570,2286
+"2349 Dakota St, Everett, WA 98201, USA","501-505 E 25th St, Tacoma, WA 98421, USA",91.1166666667,80.7333333333,109.233333333,pm,10/18/2017,Tacoma,Everett,3108,2286
+"2349 Dakota St, Everett, WA 98201, USA","505 Madison St, Seattle, WA 98104, USA",52.5333333333,44.4833333333,69.5166666667,pm,10/18/2017,Seattle,Everett,532,2286
+"2349 Dakota St, Everett, WA 98201, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",34.2166666667,30.35,40.9833333333,pm,10/18/2017,Bellevue,Everett,1552,2286
+"3200 184th St SW, Lynnwood, WA 98037, USA","2349 Dakota St, Everett, WA 98201, USA",27.6333333333,22.4166666667,37.4166666667,pm,10/18/2017,Everett,Lynnwood,2286,2570
+"3200 184th St SW, Lynnwood, WA 98037, USA","501-505 E 25th St, Tacoma, WA 98421, USA",84.45,74.5166666667,103.366666667,pm,10/18/2017,Tacoma,Lynnwood,3108,2570
+"3200 184th St SW, Lynnwood, WA 98037, USA","505 Madison St, Seattle, WA 98104, USA",44.1166666667,35.5833333333,60.7,pm,10/18/2017,Seattle,Lynnwood,532,2570
+"3200 184th St SW, Lynnwood, WA 98037, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",26.8833333333,23.0166666667,34.55,pm,10/18/2017,Bellevue,Lynnwood,1552,2570
+"501-505 E 25th St, Tacoma, WA 98421, USA","2349 Dakota St, Everett, WA 98201, USA",78.1666666667,68.4,93.8166666667,pm,10/18/2017,Everett,Tacoma,2286,3108
+"501-505 E 25th St, Tacoma, WA 98421, USA","3200 184th St SW, Lynnwood, WA 98037, USA",67.4333333333,58.0666666667,84.2333333333,pm,10/18/2017,Lynnwood,Tacoma,2570,3108
+"501-505 E 25th St, Tacoma, WA 98421, USA","505 Madison St, Seattle, WA 98104, USA",40.45,34.9833333333,52.0666666667,pm,10/18/2017,Seattle,Tacoma,532,3108
+"501-505 E 25th St, Tacoma, WA 98421, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",46.4666666667,39.1666666667,58.25,pm,10/18/2017,Bellevue,Tacoma,1552,3108
+"505 Madison St, Seattle, WA 98104, USA","2349 Dakota St, Everett, WA 98201, USA",52.3666666667,42.0333333333,68.3833333333,pm,10/18/2017,Everett,Seattle,2286,532
+"505 Madison St, Seattle, WA 98104, USA","3200 184th St SW, Lynnwood, WA 98037, USA",39.3,30.2666666667,54.2166666667,pm,10/18/2017,Lynnwood,Seattle,2570,532
+"505 Madison St, Seattle, WA 98104, USA","501-505 E 25th St, Tacoma, WA 98421, USA",58.5833333333,49.6166666667,75.6333333333,pm,10/18/2017,Tacoma,Seattle,3108,532
+"505 Madison St, Seattle, WA 98104, USA","940-1002 110th Ave NE, Bellevue, WA 98004, USA",21.6333333333,16.45,31.2166666667,pm,10/18/2017,Bellevue,Seattle,1552,532
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","2349 Dakota St, Everett, WA 98201, USA",48.65,38.4,64.4,pm,10/18/2017,Everett,Bellevue,2286,1552
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","3200 184th St SW, Lynnwood, WA 98037, USA",37.2666666667,27.7833333333,53.2666666667,pm,10/18/2017,Lynnwood,Bellevue,2570,1552
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","501-505 E 25th St, Tacoma, WA 98421, USA",74.0666666667,61.3666666667,98.2166666667,pm,10/18/2017,Tacoma,Bellevue,3108,1552
+"940-1002 110th Ave NE, Bellevue, WA 98004, USA","505 Madison St, Seattle, WA 98104, USA",33.8166666667,23.7166666667,51.4666666667,pm,10/18/2017,Seattle,Bellevue,532,1552
diff --git a/scripts/summarize/inputs/network_summary/screenlines_2014.csv b/scripts/summarize/inputs/network_summary/screenlines_2014.csv
new file mode 100644
index 00000000..7833454f
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/screenlines_2014.csv
@@ -0,0 +1,26 @@
+Primary,Screenline,ScreenlineName,Observed_Volume
+1,14,Auburn,540866
+1,30,Bellevue/Redmond,362151
+1,44,Bothell,268625
+1,37,Kirkland/Redmond,375697
+1,43,Lynnwood/Bothell,255467
+1,46,Mill Creek,365342
+1,23,Renton,85258
+1,41,Seattle - North,338825
+1,29,Seattle - South of CBD,469433
+1,35,Ship Canal,507450
+1,4,Tacoma - East of CBD,283164
+1,32,TransLake,234150
+1,22,Tukwila,245433
+1,58,Agate Pass,21000
+0,60,Cross-Sound,17412
+0,54,Gig Harbor,61503
+0,20,Kent,557842
+0,57,Kitsap - North,100890
+0,18,Maple Valley,75982
+0,2,Parkland,275733
+0,66,"Preston, Issaquah",91451
+0,3,Puyallup,119401
+0,19,SeaTac,72335
+0,7,Tacoma Narrows,80000
+0,71,Woodinville,87944
diff --git a/scripts/summarize/inputs/network_summary/transit_boardings_2014.csv b/scripts/summarize/inputs/network_summary/transit_boardings_2014.csv
index c5a34dd5..2bc43852 100644
--- a/scripts/summarize/inputs/network_summary/transit_boardings_2014.csv
+++ b/scripts/summarize/inputs/network_summary/transit_boardings_2014.csv
@@ -1,384 +1,390 @@
-,PSRC_Rte_ID,SignRt,hour_10,hour_11,hour_12,hour_13,hour_14,hour_15,hour_16,hour_17,hour_18,hour_19,hour_20,hour_21,hour_22,hour_23,hour_24,hour_1,hour_2,hour_3,hour_4,hour_5,hour_6,hour_7,hour_8,hour_9
-0,1001,1,96,106,92,100,103,180,246,265,164,90,67,32,24,21,0,0,0,0,0,21,131,296,262,128
-1,1002,2,319,283,294,287,417,405,486,447,269,208,162,130,102,60,54,9,0,0,25,118,284,486,452,316
-2,1003,3,455,452,485,447,517,538,443,531,278,153,118,111,43,39,13,10,0,0,0,59,361,471,628,422
-3,1004,4,258,252,284,260,320,400,448,312,275,169,142,116,90,57,22,6,0,0,9,146,255,615,288,270
-4,1005,5,287,407,443,436,404,602,786,618,484,294,231,146,121,67,55,0,0,0,41,146,532,796,516,457
-5,1007,7,867,889,944,905,1128,1095,1105,897,561,511,372,299,207,143,54,42,52,22,57,235,700,765,788,829
-6,1008,8,536,608,583,591,776,785,871,767,527,370,373,181,105,110,48,0,0,0,12,164,562,943,795,609
-7,1009,9,177,186,196,182,202,235,259,145,67,0,0,0,0,0,0,0,0,0,0,48,174,413,216,282
-8,1010,10,257,260,283,260,206,372,445,514,293,128,119,79,73,64,24,5,0,0,0,45,162,396,427,296
-9,1011,11,157,180,192,222,327,332,352,332,232,155,88,106,40,22,19,11,0,0,25,62,125,343,250,172
-10,1012,12,170,177,180,167,227,270,314,290,96,68,41,30,9,6,0,0,0,0,0,27,213,529,422,234
-11,1013,13,127,144,162,155,222,269,393,306,177,149,77,96,54,39,6,0,0,0,0,67,151,380,161,106
-12,1014,14,137,136,132,146,203,175,238,254,142,106,108,58,23,20,12,7,0,0,0,62,162,245,151,135
-13,1015,15,0,0,0,0,0,67,252,212,0,0,0,0,0,0,0,0,0,0,0,0,81,343,83,0
-14,1016,16,238,285,272,292,353,343,354,407,211,169,163,108,78,57,20,9,0,0,26,116,199,483,333,268
-15,1017,17,0,0,0,0,0,36,140,173,0,0,0,0,0,0,0,0,0,0,0,0,99,275,0,0
-16,1018,18,0,0,0,0,0,46,218,172,42,0,0,0,0,0,0,0,0,0,0,0,118,235,78,0
-17,1019,19,0,0,0,0,0,31,72,65,0,0,0,0,0,0,0,0,0,0,0,0,44,39,38,0
-18,1021,21,209,225,238,234,280,408,535,501,264,169,96,72,66,33,18,10,0,0,28,138,387,553,344,222
-19,1022,22,13,11,11,10,19,15,14,10,9,8,4,0,0,0,0,0,0,0,0,0,5,28,6,13
-20,1024,24,91,103,111,116,149,173,297,182,157,82,60,25,22,0,0,0,0,0,0,117,199,223,176,114
-21,1025,25,36,40,37,44,51,52,34,34,43,0,0,0,0,0,0,0,0,0,0,0,70,53,60,34
-22,1026,26,107,101,117,124,126,266,399,440,208,111,100,85,65,40,4,10,0,0,0,59,258,547,357,150
-23,1027,27,65,79,87,64,127,97,123,119,50,6,27,25,9,0,0,0,0,0,0,35,78,165,123,79
-24,1028,28,104,110,100,143,154,238,551,485,197,142,102,82,59,20,22,0,0,0,24,120,405,519,417,159
-25,1029,29,25,0,0,0,0,35,194,215,143,0,0,0,0,0,0,0,0,0,0,0,151,290,164,29
-26,1030,30,82,70,74,89,101,88,98,100,77,63,66,57,52,33,11,0,0,0,0,7,56,75,55,54
-27,1031,31,110,134,132,121,165,231,182,189,138,45,0,0,0,0,0,0,0,0,0,12,59,205,221,140
-28,1032,32,136,124,129,147,165,222,314,191,146,160,138,86,66,31,11,0,0,0,0,21,132,210,187,167
-29,1033,33,57,60,54,61,77,120,169,246,54,37,25,23,16,0,0,0,0,0,0,51,194,198,148,85
-30,1036,36,624,713,726,745,935,988,1083,717,463,291,222,188,131,65,37,30,19,0,64,232,528,599,594,630
-31,1037,37,0,0,0,0,0,0,44,34,0,0,0,0,0,0,0,0,0,0,0,19,51,33,0,0
-32,1040,40,430,505,473,563,644,623,721,529,423,272,194,144,114,15,33,0,0,0,0,141,412,642,591,466
-33,1041,41,461,497,451,425,590,833,991,966,515,265,247,195,127,57,40,16,0,0,26,181,557,1004,847,444
-34,1043,43,361,417,388,423,499,630,719,723,580,374,219,163,119,121,57,22,5,0,0,132,313,518,567,398
-35,1044,44,360,385,422,469,434,504,712,712,509,322,241,207,148,91,54,46,0,0,0,81,239,570,440,498
-36,1047,47,16,30,34,16,32,25,64,108,76,22,26,15,11,0,0,0,0,0,1,16,58,120,112,57
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-208,1901,901,18,19,34,28,27,26,26,27,17,13,13,8,0,0,0,0,0,0,0,16,15,13,21,15
-209,1903,903,34,34,44,48,37,43,36,43,21,17,16,12,0,0,0,0,0,0,0,14,26,23,28,30
-210,1906,906,26,27,30,31,34,35,32,25,22,0,0,0,0,0,0,0,0,0,0,0,19,21,29,26
-211,1907,907,14,0,17,15,0,9,3,0,0,0,0,0,0,0,0,0,0,0,0,0,5,3,5,15
-212,1908,908,6,6,6,10,10,10,10,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,6
-213,1909,909,12,12,12,12,12,12,12,12,12,6,0,0,0,0,0,0,0,0,0,6,12,12,12,12
-214,1910,910,12,11,10,14,16,13,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,9,8
-215,1913,913,0,0,0,0,0,38,33,19,8,0,0,0,0,0,0,0,0,0,0,15,27,34,7,0
-216,1914,914,38,29,40,40,22,31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,35
-217,1915,915,7,16,28,18,9,22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16
-218,1916,916,31,22,24,36,27,30,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24
-219,1917,917,10,9,9,10,9,13,17,14,6,0,0,0,0,0,0,0,0,0,8,5,11,16,8,4
-220,1919,919,15,13,12,17,20,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,12,10
-221,1927,927,12,21,18,16,28,9,10,7,0,0,0,0,0,0,0,0,0,0,0,0,0,13,17,9
-222,1930,930,0,0,0,0,0,24,28,12,0,0,0,0,0,0,0,0,0,0,0,0,19,26,19,0
-223,1931,931,22,16,20,18,14,35,38,30,11,12,0,0,0,0,0,0,0,0,0,0,30,31,25,12
-224,1935,935,0,0,0,0,4,12,18,16,4,0,0,0,0,0,0,0,0,0,0,11,16,18,8,0
-225,1952,952,0,0,0,0,97,0,132,0,0,0,0,0,0,0,0,0,0,0,84,85,0,0,0,0
-226,1973,973,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-227,1975,975,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-228,1980,980,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0
-229,1981,981,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-230,1982,982,0,0,0,0,0,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,27,0,0
-231,1984,984,0,0,0,0,0,0,0,0,13,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-232,1986,986,0,0,0,0,0,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,30,0,0
-233,1987,987,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,44,0,0,0
-234,1988,988,0,0,0,0,0,28,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,0,0
-235,1989,989,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,46,0,0,0
-236,1994,994,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,29,0,0,0
-237,1995,995,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,0,0
-238,2001,1,402,433,473,487,524,605,492,445,315,251,193,112,80,14,0,0,0,0,29,144,329,410,364,373
-239,2002,2,181,200,217,238,300,283,243,214,139,108,75,56,31,0,0,0,0,0,0,15,156,196,195,203
-240,2003,3,151,185,197,184,218,203,174,147,98,79,59,37,1,0,0,0,0,0,0,27,104,175,149,145
-241,2010,10,42,41,53,49,82,83,65,55,28,1,0,0,0,0,0,0,0,0,0,0,37,75,67,49
-242,2011,11,31,24,26,36,51,48,41,15,18,0,0,0,0,0,0,0,0,0,0,0,39,41,32,25
-243,2013,13,10,9,11,13,12,20,18,10,0,0,0,0,0,0,0,0,0,0,0,10,15,22,10,12
-244,2014,14,10,11,11,15,25,27,14,16,18,0,0,0,0,0,0,0,0,0,0,0,16,31,11,9
-245,2016,16,34,39,38,43,87,50,38,36,33,32,13,0,0,0,0,0,0,0,0,7,40,56,34,36
-246,2028,28,41,38,52,43,68,90,65,52,23,21,7,0,0,0,0,0,0,0,0,0,23,73,53,40
-247,2041,41,50,49,69,60,80,108,103,82,42,44,21,0,0,0,0,0,0,0,0,32,62,81,69,52
-248,2042,42,34,38,38,41,35,48,36,26,29,13,0,0,0,0,0,0,0,0,0,0,24,27,23,29
-249,2045,45,37,35,41,45,41,49,41,32,21,2,0,0,0,0,0,0,0,0,0,9,21,46,42,31
-250,2048,48,52,59,63,87,115,126,115,90,49,48,37,7,0,0,0,0,0,0,0,35,73,95,68,51
-251,2051,51,43,47,51,52,54,54,45,42,27,18,0,0,0,0,0,0,0,0,0,2,31,39,39,40
-252,2052,52,76,78,80,87,122,103,87,72,52,23,18,0,0,0,0,0,0,0,0,0,53,82,89,79
-253,2053,53,81,87,94,97,106,117,94,81,66,59,0,0,0,0,0,0,0,0,0,0,47,78,66,81
-254,2054,54,52,51,48,55,83,76,63,63,29,22,22,0,0,0,0,0,0,0,0,0,34,58,38,42
-255,2055,55,72,72,86,81,94,95,90,74,37,35,27,1,0,0,0,0,0,0,0,0,55,67,63,63
-256,2056,56,26,28,29,29,45,34,32,25,23,1,0,0,0,0,0,0,0,0,0,0,0,33,21,24
-257,2057,57,98,123,123,128,151,127,103,82,45,36,14,0,0,0,0,0,0,0,0,0,44,61,78,93
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+371,6555,0,0,0,0,30,152,59,57,0,0,0,0,0,36,106,103,30,21,0,0,0,0,0,0
+372,6556,0,0,0,0,47,126,123,110,0,0,0,0,0,75,116,179,39,34,0,0,0,0,0,0
+373,6560,0,0,0,49,114,157,133,111,111,84,99,104,97,118,127,132,133,91,41,52,38,35,0,0
+374,6566,0,0,0,67,101,256,174,73,52,45,21,22,53,82,177,222,136,47,69,31,15,0,0,0
+375,6567,0,0,0,0,38,122,58,47,0,0,0,0,0,48,103,85,37,0,0,0,0,0,0,0
+376,6574,0,112,70,127,98,124,120,104,114,111,104,177,149,166,147,183,80,103,77,91,50,36,46,0
+377,6577,0,0,0,0,126,331,352,150,0,0,0,0,0,97,159,256,153,0,0,0,0,0,0,0
+378,6578,0,0,0,12,0,16,38,110,150,136,147,156,174,129,63,49,44,169,151,103,92,33,0,0
+379,6586,0,0,0,0,49,105,60,41,33,28,0,31,26,33,71,77,73,27,0,0,0,0,0,0
+380,6590,0,0,0,149,402,548,410,206,49,35,35,18,0,108,287,449,341,103,0,0,0,0,0,0
+381,6592,0,0,0,151,158,129,68,0,0,0,0,0,0,62,132,130,113,45,0,0,0,0,0,0
+382,6594,0,0,0,0,0,37,63,116,129,121,126,150,266,169,60,51,42,106,148,122,96,57,17,0
+383,6595,0,0,0,48,115,50,0,0,0,0,0,0,0,34,88,31,38,0,0,0,0,0,0,0
+384,6596,0,0,0,26,33,87,44,22,0,0,0,0,0,0,32,61,92,47,0,0,0,0,0,0
+385,6999,0,0,0,698,1635,2247,579,401,0,0,0,0,0,0,1132,2629,2602,438,0,0,0,0,0,0
+386,6998,0,0,0,38,89,342,88,61,0,0,0,0,0,0,133,309,183,0,0,0,0,0,0,0
+387,6996,90,0,0,22,301,1113,2089,2381,1783,1400,1492,1582,1622,1635,2220,2578,2908,2397,1587,1091,989,802,707,416
+388,6995,0,0,0,0,52,101,330,195,207,184,281,221,273,231,340,317,260,190,106,70,43,10,0,0
diff --git a/scripts/summarize/inputs/network_summary/transit_route_groups.csv b/scripts/summarize/inputs/network_summary/transit_route_groups.csv
new file mode 100644
index 00000000..34c79d41
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/transit_route_groups.csv
@@ -0,0 +1,8 @@
+RouteGroupID,RouteGroupName
+1,Metro
+2,Pierce
+3,CT
+4,Kitsap
+5,WSF
+6,Sound Transit
+7,ET
diff --git a/scripts/summarize/inputs/network_summary/transit_special_routes.csv b/scripts/summarize/inputs/network_summary/transit_special_routes.csv
new file mode 100644
index 00000000..47e9cbab
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/transit_special_routes.csv
@@ -0,0 +1,11 @@
+description,id,mode,route_code,RouteGroupName
+A-Line Rapid Ride,5347,b,1671,RapidRideA
+B Line B-Line Rapid*,4087,b,1672,RapidRideB
+C Line test,5301,b,1673,RapidRideC
+D Line test,5299,b,1674,RapidRideD
+E-Line E-Line Rapid*,4093,b,1675,RapidRideE
+LITE RL TCOMA CBD,4946,r,6995,ST Light Rail - Tacoma
+LightRail SeaTac-Sea,4950,r,6996,ST Light Rail -Central Link
+COMMTR RL TAC-SEA NB,4944,c,6998,ST Commuter Rail South
+COMMTR RL SEA-EV NB,4945,c,6999,ST Commuter Rail North
+Swift,4985,b,3701,Swift
diff --git a/scripts/summarize/inputs/network_summary/transit_time_groups.csv b/scripts/summarize/inputs/network_summary/transit_time_groups.csv
new file mode 100644
index 00000000..d859d6ce
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/transit_time_groups.csv
@@ -0,0 +1,16 @@
+Hour,ModelPeriod,TimeGroup,BigTimeGroup,All,tod
+5,5to6_board,EV/NT,Off-Peak,All,5to6
+6,6to7_board,AM,Peak,All,6to7
+7,7to8_board,AM,Peak,All,7to8
+8,8to9_board,AM,Peak,All,8to9
+9,9to10_board,MD,Off-Peak,All,10to14
+10,10to14_board,MD,Off-Peak,All,10to14
+11,10to14_board,MD,Off-Peak,All,10to14
+12,10to14_board,MD,Off-Peak,All,10to14
+13,10to14_board,MD,Off-Peak,All,10to14
+14,14to15_board,MD,Off-Peak,All,14to15
+15,15to16_board,PM,Peak,All,15to16
+16,16to17_board,PM,Peak,All,16to17
+17,17to18_board,PM,Peak,All,17to18
+18,18to20_board,EV/NT,Off-Peak,All,18to20
+19,18to20_board,EV/NT,Off-Peak,All,18to20
diff --git a/scripts/summarize/inputs/network_summary/truck_counts_2014.csv b/scripts/summarize/inputs/network_summary/truck_counts_2014.csv
new file mode 100644
index 00000000..791a129b
--- /dev/null
+++ b/scripts/summarize/inputs/network_summary/truck_counts_2014.csv
@@ -0,0 +1,398 @@
+,i,j,ij_id,CountID,Location,LocationDetail,FacilityType,observedTot,observedMed,observedHvy,LARGE_AREA,county,lat,lon
+0,6436,6827,6436-6827,1,SR-9,n/o 53rd Ave NE,6,218.5,176,43,NW Snohomish,Snohomish,48.24432143,-122.161116
+1,6827,6436,6827-6436,1,SR-9,n/o 53rd Ave NE,6,218.5,176,43,NW Snohomish,Snohomish,48.24432143,-122.161116
+2,7784,7940,7784-7940,2,I-5,@ 236th St.  NE,1,8165,4083,4083,NW Snohomish,Snohomish,48.21006851,-122.2166785
+3,7997,7744,7997-7744,2,I-5,@ 236th St.  NE,1,8165,4083,4083,NW Snohomish,Snohomish,48.20940118,-122.215634
+4,8786,8836,8786-8836,3,SR-9,n/o Highland Dr.,2,1200,960,240,Everett,Snohomish,48.18847346,-122.1283739
+5,8836,8786,8836-8786,3,SR-9,n/o Highland Dr.,2,1200,960,240,Everett,Snohomish,48.18847346,-122.1283739
+6,8920,9282,8920-9282,4,I-5,s/o SR-530,1,8715,4358,4358,NW Snohomish,Snohomish,48.18107477,-122.1973348
+7,9277,8917,9277-8917,4,I-5,s/o SR-530,1,8715,4358,4358,NW Snohomish,Snohomish,48.18114113,-122.1970157
+8,21557,21656,21557-21656,5,SR-2,e/o I-5,1,8690,7900,790,Everett,Snohomish,47.9787135,-122.1812156
+9,21630,21366,21630-21366,5,SR-2,e/o I-5,1,8690,7900,790,Everett,Snohomish,47.97958772,-122.1773421
+10,3748,35642,3748-35642,6,SR-104,@ Hood Canal Bridge,5,1350,900,450,North Kitsap,Kitsap,47.85583172,-122.6166002
+11,35642,3748,35642-3748,6,SR-104,@ Hood Canal Bridge,5,1350,900,450,North Kitsap,Kitsap,47.85583172,-122.6166002
+12,51826,52483,51826-52483,8,SR-307,s/o NE Gunderson Rd.,6,1020,850,170,North Kitsap,Kitsap,47.76423955,-122.6149398
+13,52483,51826,52483-51826,8,SR-307,s/o NE Gunderson Rd.,6,1020,850,170,North Kitsap,Kitsap,47.76423955,-122.6149398
+14,50024,52834,50024-52834,9,SR-522,w/o SR-9,1,4250,3125,1125,Eastside King,King,47.77135525,-122.153782
+15,50024,52834,50024-52834,9,SR-522,w/o SR-9,1,4250,3125,1125,Eastside King,King,47.77891628,-122.1487935
+16,50024,52834,50024-52834,9,SR-522,w/o SR-9,1,4250,3125,1125,SW Snohomish,Snohomish,47.77135525,-122.153782
+17,50024,52834,50024-52834,9,SR-522,w/o SR-9,1,4250,3125,1125,SW Snohomish,Snohomish,47.77891628,-122.1487935
+18,52901,50140,52901-50140,9,SR-522,w/o SR-9,1,4250,3125,1125,Eastside King,King,47.76688907,-122.1564881
+19,52901,50140,52901-50140,9,SR-522,w/o SR-9,1,4250,3125,1125,Eastside King,King,47.77855267,-122.1493711
+20,52901,50140,52901-50140,9,SR-522,w/o SR-9,1,4250,3125,1125,SW Snohomish,Snohomish,47.76688907,-122.1564881
+21,52901,50140,52901-50140,9,SR-522,w/o SR-9,1,4250,3125,1125,SW Snohomish,Snohomish,47.77855267,-122.1493711
+22,44762,64063,44762-64063,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,King Other,King,47.74457963,-121.4293236
+23,44762,64063,44762-64063,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,King Other,King,47.79554401,-121.5340482
+24,44762,64063,44762-64063,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,Snohomish Other,Snohomish,47.74457963,-121.4293236
+25,44762,64063,44762-64063,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,Snohomish Other,Snohomish,47.79554401,-121.5340482
+26,64063,44762,64063-44762,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,King Other,King,47.74457963,-121.4293236
+27,64063,44762,64063-44762,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,King Other,King,47.79554401,-121.5340482
+28,64063,44762,64063-44762,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,Snohomish Other,Snohomish,47.74457963,-121.4293236
+29,64063,44762,64063-44762,10,SR-2,e/o NE Old Cascade Hwy,6,297,215,83,Snohomish Other,Snohomish,47.79554401,-121.5340482
+30,76611,184102,76611-184102,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Eastside King,King,47.63870639,-122.2488432
+31,76611,184102,76611-184102,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Eastside King,King,47.64253222,-122.2745434
+32,76611,184102,76611-184102,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Seattle,King,47.63870639,-122.2488432
+33,76611,184102,76611-184102,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Seattle,King,47.64253222,-122.2745434
+34,78764,184158,78764-184158,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Eastside King,King,47.63880793,-122.2488601
+35,78764,184158,78764-184158,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Eastside King,King,47.64261882,-122.2744792
+36,78764,184158,78764-184158,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Seattle,King,47.63880793,-122.2488601
+37,78764,184158,78764-184158,11,SR-520,w/o 76th Ave NE,1,3710,2915,795,Seattle,King,47.64261882,-122.2744792
+38,91400,92880,91400-92880,12,SR-99,n/o Spokane St. Bridge,1,3320,2490,830,Seattle,King,47.57709903,-122.3394091
+39,92882,91402,92882-91402,12,SR-99,n/o Spokane St. Bridge,1,3320,2490,830,Seattle,King,47.57709812,-122.3391069
+40,97728,98432,97728-98432,13,I-405,@ 112th Ave SE,1,9685,6333,3353,Eastside King,King,47.55657644,-122.1904161
+41,98611,97531,98611-97531,13,I-405,@ 112th Ave SE,1,9685,6333,3353,Eastside King,King,47.55656871,-122.1901979
+42,108230,108773,108230-108773,14,SR-18,s/o I-90,6,3570,1365,2205,SE King,King,47.5062705,-121.8842036
+43,108975,183447,108975-183447,14,SR-18,s/o I-90,6,3570,1365,2205,King Other,King,47.50530299,-121.8843295
+44,108975,183447,108975-183447,14,SR-18,s/o I-90,6,3570,1365,2205,King Other,King,47.50376816,-121.884654
+45,108975,183447,108975-183447,14,SR-18,s/o I-90,6,3570,1365,2205,SE King,King,47.50530299,-121.8843295
+46,108975,183447,108975-183447,14,SR-18,s/o I-90,6,3570,1365,2205,SE King,King,47.50376816,-121.884654
+47,109313,110836,109313-110836,15,SR-599,e/o SR-99,1,5280,2880,2400,Green River,King,47.49257857,-122.2861553
+48,110925,109232,110925-109232,15,SR-599,e/o SR-99,1,5280,2880,2400,Green River,King,47.49255711,-122.2840728
+49,111466,112966,111466-112966,16,SR-3,s/o Lake Flora Rd.,3,1360,1020,340,South Kitsap,Kitsap,47.4734142,-122.7965678
+50,112966,111466,112966-111466,16,SR-3,s/o Lake Flora Rd.,3,1360,1020,340,South Kitsap,Kitsap,47.4734142,-122.7965678
+51,111007,112527,111007-112527,17,SR-509,n/o SR-518,1,1525,1373,153,SW King,King,47.4820869,-122.3281266
+52,113079,111152,113079-111152,17,SR-509,n/o SR-518,1,1525,1373,153,SW King,King,47.47446013,-122.3299639
+53,113725,113756,113725-113756,18,SR-518,e/o SR-509,1,2320,1740,580,SW King,King,47.47021178,-122.3206642
+54,113778,113754,113778-113754,18,SR-518,e/o SR-509,1,2320,1740,580,SW King,King,47.47005834,-122.3204543
+55,114189,115398,114189-115398,19,I-90,w/o 468th Ave SE,1,5880,1680,4200,King Other,King,47.46984428,-121.7376741
+56,115378,114124,115378-114124,19,I-90,w/o 468th Ave SE,1,5880,1680,4200,King Other,King,47.47009966,-121.736757
+57,119546,121652,119546-121652,20,SR-167,n/o S 212th St.,1,9920,6200,3720,Green River,King,47.41457445,-122.2206981
+58,119546,121652,119546-121652,20,SR-167,n/o S 212th St.,1,9920,6200,3720,Green River,King,47.4223761,-122.2205408
+59,119546,121652,119546-121652,20,SR-167,n/o S 212th St.,1,9920,6200,3720,SE King,King,47.41457445,-122.2206981
+60,119546,121652,119546-121652,20,SR-167,n/o S 212th St.,1,9920,6200,3720,SE King,King,47.4223761,-122.2205408
+61,121845,119548,121845-119548,20,SR-167,n/o S 212th St.,1,9920,6200,3720,SE King,King,47.42150964,-122.2203043
+62,132978,183897,132978-183897,21,SR-18,@ 180th Ave SE,1,4320,1840,2480,SE King,King,47.34598132,-122.1419415
+63,133686,131998,133686-131998,21,SR-18,@ 180th Ave SE,1,4320,1840,2480,SE King,King,47.34948009,-122.1351216
+64,140916,142006,140916-142006,22,SR-18,e/o SR-164,1,6720,3360,3360,Green River,King,47.30085977,-122.2030428
+65,141993,140899,141993-140899,22,SR-18,e/o SR-164,1,6720,3360,3360,Green River,King,47.29950476,-122.1937452
+66,145118,145421,145118-145421,23,SR-16,w/o TNB,1,5810,4150,1660,Peninsula,Pierce,47.27507199,-122.5587672
+67,145416,145214,145416-145214,23,SR-16,w/o TNB,1,5810,4150,1660,Peninsula,Pierce,47.27479036,-122.5583018
+68,199310,199311,199310-199311,23,SR-16,w/o TNB,1,5810,4150,1660,Peninsula,Pierce,47.27507733,-122.5587597
+69,155418,155778,155418-155778,24,I-5,n/o S 56th St.,1,13300,7600,5700,Tacoma,Pierce,47.2148969,-122.4628021
+70,155715,155389,155715-155389,24,I-5,n/o S 56th St.,1,13300,7600,5700,Tacoma,Pierce,47.21517401,-122.462629
+71,166519,166652,166519-166652,25,SR-512,w/o SR-7,1,8550,4750,3800,Pierce Other,Pierce,47.15839244,-122.4509507
+72,166618,166420,166618-166420,25,SR-512,w/o SR-7,1,8550,4750,3800,Pierce Other,Pierce,47.15869599,-122.4524684
+73,166803,168071,166803-168071,26,I-5,s/o SR-512,1,15000,6000,9000,SW Pierce,Pierce,47.15362186,-122.4934068
+74,168120,166347,168120-166347,26,I-5,s/o SR-512,1,15000,6000,9000,SW Pierce,Pierce,47.1545328,-122.4915119
+75,174478,175503,174478-175503,27,I-5,n/o Dupont I/C,1,14640,6100,8540,SW Pierce,Pierce,47.09890203,-122.6072188
+76,175560,174492,175560-174492,27,I-5,n/o Dupont I/C,1,14640,6100,8540,SW Pierce,Pierce,47.09852019,-122.607741
+77,185855,185857,185855-185857,28,SR-3,n/o Newberry Hill Rd.,1,3290,2350,940,Central Kitsap,Kitsap,47.65009471,-122.7033318
+78,185900,185902,185900-185902,28,SR-3,n/o Newberry Hill Rd.,1,3290,2350,940,Central Kitsap,Kitsap,47.65536687,-122.7005031
+79,185987,185994,185987-185994,29,SR-16,n/o SR-302 spur,1,3680,2760,920,South Kitsap,Kitsap,47.41449486,-122.6235877
+80,185993,185988,185993-185988,29,SR-16,n/o SR-302 spur,1,3680,2760,920,South Kitsap,Kitsap,47.4129282,-122.624101
+81,42013,47082,42013-47082,30,I-405,n/o SR-527,1,4800,2400,2400,SW Snohomish,Snohomish,47.80815821,-122.2298933
+82,47057,186111,47057-186111,30,I-405,n/o SR-527,1,4800,2400,2400,SW Snohomish,Snohomish,47.80833796,-122.2296158
+83,58263,58277,58263-58277,31,145th St.,US-99 to I-5 (e/o Meridian),3,2425,1318,1107,Shoreline,King,47.73413451,-122.3369814
+84,58277,58263,58277-58263,31,145th St.,US-99 to I-5 (e/o Meridian),3,2425,1318,1107,Shoreline,King,47.73413451,-122.3369814
+85,62054,62366,62054-62366,32,US-99,85th to 145th (n/o 115th),3,3268,2500,768,Seattle,King,47.71322068,-122.3448289
+86,62366,62054,62366-62054,32,US-99,85th to 145th (n/o 115th),3,3268,2500,768,Seattle,King,47.71322068,-122.3448289
+87,65278,65635,65278-65635,33,Greenwood,80th to 105th (n/o 90th),3,580,313,267,Seattle,King,47.6951223,-122.355349
+88,65635,65278,65635-65278,33,Greenwood,80th to 105th (n/o 90th),3,580,313,267,Seattle,King,47.6951223,-122.355349
+89,60822,61135,60822-61135,34,SR-522,115th to 145th (n/o 125th),3,1748,1070,678,Seattle,King,47.72018508,-122.2946969
+90,61135,60822,61135-60822,34,SR-522,115th to 145th (n/o 125th),3,1748,1070,678,Seattle,King,47.72018508,-122.2946969
+91,65062,65393,65062-65393,35,5th Ave N,80th to Ngate Way (n/o 90th),3,756,302,454,Seattle,King,47.69677883,-122.3230744
+92,65393,65062,65393-65062,35,5th Ave N,80th to Ngate Way (n/o 90th),3,756,302,454,Seattle,King,47.69677883,-122.3230744
+93,66441,66448,66441-66448,36,N 85th,US-99 to I-5 (e/o Meridian),3,1428,826,602,Seattle,King,47.69047183,-122.3326558
+94,66448,66441,66448-66441,36,N 85th,US-99 to I-5 (e/o Meridian),3,1428,826,602,Seattle,King,47.69047183,-122.3326558
+95,63036,63043,63036-63043,37,Northgate,1st to Roosevelt (w/o 5th),3,1044,650,394,Seattle,King,47.70860281,-122.3246151
+96,63043,63036,63043-63036,37,Northgate,1st to Roosevelt (w/o 5th),3,1044,650,394,Seattle,King,47.70860281,-122.3246151
+97,66308,66311,66308-66311,38,NW 85th,3rd NW to 15th NW (w/o 8th),3,1457,701,756,Seattle,King,47.69063442,-122.3709213
+98,66311,66308,66311-66308,38,NW 85th,3rd NW to 15th NW (w/o 8th),3,1457,701,756,Seattle,King,47.69063442,-122.3709213
+99,69934,70277,69934-70277,39,24th Ave NW,65th to Market (n/o Market),3,1483,535,948,Seattle,King,47.67237402,-122.3875868
+100,70277,69934,70277-69934,39,24th Ave NW,65th to Market (n/o Market),3,1483,535,948,Seattle,King,47.67237402,-122.3875868
+101,72379,72721,72379-72721,40,Market/50th,8th to Stone (w/o Phinney),3,1505,1006,499,Seattle,King,47.66286711,-122.3567303
+102,72721,72379,72721-72379,40,Market/50th,8th to Stone (w/o Phinney),3,1505,1006,499,Seattle,King,47.66286711,-122.3567303
+103,73836,74473,73836-74473,41,Stone,45th to 34th (n/o 40th),3,1104,659,445,Seattle,King,47.65669945,-122.3423769
+104,74473,73836,74473-73836,41,Stone,45th to 34th (n/o 40th),3,1104,659,445,Seattle,King,47.65669945,-122.3423769
+105,72100,72112,72100-72112,42,50th,Stone to I-5 (e/o Meridian),3,1122,790,332,Seattle,King,47.66501297,-122.3298225
+106,72112,72100,72112-72100,42,50th,Stone to I-5 (e/o Meridian),3,1122,790,332,Seattle,King,47.66501297,-122.3298225
+107,68950,69471,68950-69471,43,Sandpoint,65th to 75th (n/o 65th),3,550,372,178,Seattle,King,47.67801866,-122.2635874
+108,69471,68950,69471-68950,43,Sandpoint,65th to 75th (n/o 65th),3,550,372,178,Seattle,King,47.67801866,-122.2635874
+109,69225,69597,69225-69597,44,35th Ave NE,65th to 75th (n/o 65th),3,897,516,381,Seattle,King,47.67669149,-122.2902992
+110,69597,69225,69597-69225,44,35th Ave NE,65th to 75th (n/o 65th),3,897,516,381,Seattle,King,47.67669149,-122.2902992
+111,69545,69547,69545-69547,45,65th,12th to 25th (w/o 20th),3,837,430,407,Seattle,King,47.67578006,-122.3081786
+112,69547,69545,69547-69545,45,65th,12th to 25th (w/o 20th),3,837,430,407,Seattle,King,47.67578006,-122.3081786
+113,74648,75163,74648-75163,46,Montlake,Pacific to 45th (n/o Pacific),3,908,735,173,Seattle,King,47.65369463,-122.3027385
+114,75163,74648,75163-74648,46,Montlake,Pacific to 45th (n/o Pacific),3,908,735,173,Seattle,King,47.65369463,-122.3027385
+115,76064,76424,76064-76424,47,15th Ave NW,Nickerson to Magnolia Br. (s/o Dravus),3,1376,827,549,Seattle,King,47.64566404,-122.3762635
+116,76424,76064,76424-76064,47,15th Ave NW,Nickerson to Magnolia Br. (s/o Dravus),3,1376,827,549,Seattle,King,47.64566404,-122.3762635
+117,75145,75324,75145-75324,48,Nickerson,15th Ave NW to Fremont Br. (w/o 3rd),3,957,807,150,Seattle,King,47.65123341,-122.3626695
+118,75324,75145,75324-75145,48,Nickerson,15th Ave NW to Fremont Br. (w/o 3rd),3,957,807,150,Seattle,King,47.65123341,-122.3626695
+119,77834,79348,77834-79348,49,Westlake,Fremont Br. to Valley (n/o Galer),3,763,553,210,Seattle,King,47.63670071,-122.3409782
+120,79348,77834,79348-77834,49,Westlake,Fremont Br. to Valley (n/o Galer),3,763,553,210,Seattle,King,47.63670071,-122.3409782
+121,81456,81447,81456-81447,50,Mercer/Valley,Dexter to Fairview (e/o Westlake),3,1643,852,791,Seattle,King,47.62579263,-122.337878
+122,81796,81801,81796-81801,50,Mercer/Valley,Dexter to Fairview (e/o Westlake),3,1643,852,791,Seattle,King,47.62455005,-122.3377835
+123,82910,82914,82910-82914,51,John,12th to Summit (w/o Broadway),3,1606,648,958,Seattle,King,47.61990301,-122.3215394
+124,82914,82910,82914-82910,51,John,12th to Summit (w/o Broadway),3,1606,648,958,Seattle,King,47.61990301,-122.3215394
+125,79065,79592,79065-79592,52,10th,Delmar to Roy (n/o Galer),3,795,352,443,Seattle,King,47.63403735,-122.3199373
+126,79592,79065,79592-79065,52,10th,Delmar to Roy (n/o Galer),3,795,352,443,Seattle,King,47.63403735,-122.3199373
+127,77604,78027,77604-78027,53,23rd,SR-520 to Boyer (n/o Lynn),3,832,398,434,Seattle,King,47.64025926,-122.3020728
+128,78027,77604,78027-77604,53,23rd,SR-520 to Boyer (n/o Lynn),3,832,398,434,Seattle,King,47.64025926,-122.3020728
+129,82197,82311,82197-82311,54,Madison,Lk Wash Blvd to 23rd (w/o MLK),3,787,508,279,Seattle,King,47.62303042,-122.296849
+130,82311,82197,82311-82197,54,Madison,Lk Wash Blvd to 23rd (w/o MLK),3,787,508,279,Seattle,King,47.62303042,-122.296849
+131,87388,87392,87388-87392,55,Jackson,Rainier to 21st (w/o 20th),3,905,534,371,Seattle,King,47.59924681,-122.3076622
+132,87392,87388,87392-87388,55,Jackson,Rainier to 21st (w/o 20th),3,905,534,371,Seattle,King,47.59924681,-122.3076622
+133,88052,88432,88052-88432,56,MLK,Yesler to Massachusetts (n/o Judkins),3,907,553,354,Seattle,King,47.59442404,-122.2977112
+134,88432,88052,88432-88052,56,MLK,Yesler to Massachusetts (n/o Judkins),3,907,553,354,Seattle,King,47.59442404,-122.2977112
+135,89878,90406,89878-90406,57,4th Ave,Dearborn to Spokane (s/o Holgate),3,2355,1520,835,Seattle,King,47.58507377,-122.329058
+136,90406,89878,90406-89878,57,4th Ave,Dearborn to Spokane (s/o Holgate),3,2355,1520,835,Seattle,King,47.58507377,-122.329058
+137,93859,93881,93859-93881,58,Spokane,Delridge to Marginal (w/o Marginal),3,3210,1056,2154,Seattle,King,47.57141055,-122.3499786
+138,93881,93859,93881-93859,58,Spokane,Delridge to Marginal (w/o Marginal),3,3210,1056,2154,Seattle,King,47.57141055,-122.3499786
+139,88088,88525,88088-88525,59,1st Ave,Jackson to Royal Brougham (n/o RB),3,868,600,268,Seattle,King,47.59362546,-122.3341939
+140,88525,88088,88525-88088,59,1st Ave,Jackson to Royal Brougham (n/o RB),3,868,600,268,Seattle,King,47.59362546,-122.3341939
+141,87934,87936,87934-87936,60,Dearborn,4th to Rainier (e/o 7th),3,732,493,239,Seattle,King,47.59583225,-122.323111
+142,87936,87934,87936-87934,60,Dearborn,4th to Rainier (e/o 7th),3,732,493,239,Seattle,King,47.59583225,-122.323111
+143,91439,91441,91439-91441,61,Lander,4th to Airport (e/o 6th),3,1518,1047,471,Seattle,King,47.579809,-122.3255918
+144,91441,91439,91441-91439,61,Lander,4th to Airport (e/o 6th),3,1518,1047,471,Seattle,King,47.579809,-122.3255918
+145,95005,96603,95005-96603,62,4th Ave,Spokane to Lucile (s/o Industrial),3,1860,1396,464,Seattle,King,47.56459387,-122.329266
+146,96603,95005,96603-95005,62,4th Ave,Spokane to Lucile (s/o Industrial),3,1860,1396,464,Seattle,King,47.56459387,-122.329266
+147,94654,96008,94654-96008,63,Rainier,Alaska to MLK (n/o Genesee),3,826,457,369,Seattle,King,47.56702279,-122.289467
+148,96008,94654,96008-94654,63,Rainier,Alaska to MLK (n/o Genesee),3,826,457,369,Seattle,King,47.56702279,-122.289467
+149,98333,98894,98333-98894,64,MLK,Graham to Alaska (n/o Orcas),3,580,346,234,Seattle,King,47.5527453,-122.2887866
+150,98894,98333,98894-98333,64,MLK,Graham to Alaska (n/o Orcas),3,580,346,234,Seattle,King,47.5527453,-122.2887866
+151,95591,100738,95591-100738,65,Marginal,Highland Park to Spokane (s/o Puget),3,1698,694,1004,Seattle,King,47.55379454,-122.3485307
+152,100738,95591,100738-95591,65,Marginal,Highland Park to Spokane (s/o Puget),3,1698,694,1004,Seattle,King,47.55379454,-122.3485307
+153,97948,100395,97948-100395,66,Delridge,Spokane to Orchard (n/o Brandon),3,1134,680,454,Seattle,King,47.54940508,-122.3620721
+154,100395,97948,100395-97948,66,Delridge,Spokane to Orchard (n/o Brandon),3,1134,680,454,Seattle,King,47.54940508,-122.3620721
+155,90943,91787,90943-91787,67,Admiral,California to Harbor (s/o Olga),3,1383,995,388,Seattle,King,47.57945553,-122.3770007
+156,91787,90943,91787-90943,67,Admiral,California to Harbor (s/o Olga),3,1383,995,388,Seattle,King,47.57945553,-122.3770007
+157,101852,102715,101852-102715,68,Fauntleroy,California to Wildwood (s/o Lincoln Park),3,1039,508,531,Seattle,King,47.53368224,-122.3926375
+158,102715,101852,102715-101852,68,Fauntleroy,California to Wildwood (s/o Lincoln Park),3,1039,508,531,Seattle,King,47.53368224,-122.3926375
+159,101745,102119,101745-102119,69,Marginal,16th to Ellis (n/o Webster),3,1370,828,542,Seattle,King,47.53641643,-122.3167642
+160,102119,101745,102119-101745,69,Marginal,16th to Ellis (n/o Webster),3,1370,828,542,Seattle,King,47.53641643,-122.3167642
+161,99840,101785,99840-101785,70,Airport,Hardy to ~Cloverdale (n/o airport access rd),3,628,422,206,Seattle,King,47.54193078,-122.3076195
+162,101785,99840,101785-99840,70,Airport,Hardy to ~Cloverdale (n/o airport access rd),3,628,422,206,Seattle,King,47.54193078,-122.3076195
+163,104807,104813,104807-104813,71,Henderson,MLK to Rainier (e/o Renton Ave),3,853,388,465,Seattle,King,47.52331109,-122.2753227
+164,104813,104807,104813-104807,71,Henderson,MLK to Rainier (e/o Renton Ave),3,853,388,465,Seattle,King,47.52331109,-122.2753227
+165,106302,107104,106302-107104,72,Renton Ave,Roxbury to Bangor (n/o Prentice),3,425,203,222,Seattle,King,47.51247511,-122.262971
+166,107104,106302,107104-106302,72,Renton Ave,Roxbury to Bangor (n/o Prentice),3,425,203,222,Seattle,King,47.51247511,-122.262971
+167,105288,106451,105288-106451,73,MLK,Boeing Access to Henderson (n/o Norfolk),3,797,586,211,Seattle,King,47.51705691,-122.2793436
+168,106451,105288,106451-105288,73,MLK,Boeing Access to Henderson (n/o Norfolk),3,797,586,211,Seattle,King,47.51705691,-122.2793436
+169,99129,99259,99129-99259,75,Corson,Michigan to Lucille (w/o I-5 ramp),3,2968,2008,960,Seattle,King,47.54922433,-122.3209168
+170,99259,99129,99259-99129,75,Corson,Michigan to Lucille (w/o I-5 ramp),3,2968,2008,960,Seattle,King,47.54922433,-122.3209168
+171,91303,91323,91303-91323,76,Admiral,California to 59th (w/o 49th),3,551,463,88,Seattle,King,47.57931917,-122.3957131
+172,91323,91303,91323-91303,76,Admiral,California to 59th (w/o 49th),3,551,463,88,Seattle,King,47.57931917,-122.3957131
+173,90892,91413,90892-91413,77,1st Ave,Royal Brougham to Spokane (n/o Lander),3,1691,1580,111,Seattle,King,47.58085319,-122.3341985
+174,91413,90892,91413-90892,77,1st Ave,Royal Brougham to Spokane (n/o Lander),3,1691,1580,111,Seattle,King,47.58085319,-122.3341985
+175,96181,185695,96181-185695,78,Columbian,15th to Beacon (w/o VA Hospital),3,1000,834,166,Seattle,King,47.56262405,-122.3119071
+176,185695,96181,185695-96181,78,Columbian,15th to Beacon (w/o VA Hospital),3,1000,834,166,Seattle,King,47.56262405,-122.3119071
+177,105669,105679,105669-105679,79,Roxbury,16th to 26th (w/o 20th),3,603,427,176,Seattle,King,47.51736323,-122.3605244
+178,105679,105669,105679-105669,79,Roxbury,16th to 26th (w/o 20th),3,603,427,176,Seattle,King,47.51736323,-122.3605244
+179,79245,79249,79245-79249,80,Magnolia Bridge,West of 15th,3,978,765,213,Seattle,King,47.63348784,-122.3828438
+180,79249,79245,79249-79245,80,Magnolia Bridge,West of 15th,3,978,765,213,Seattle,King,47.63348784,-122.3828438
+181,71799,72222,71799-72222,81,Leary,15th to 20th (w/o 17th),3,1317,875,442,Seattle,King,47.66477448,-122.3798749
+182,72222,71799,72222-71799,81,Leary,15th to 20th (w/o 17th),3,1317,875,442,Seattle,King,47.66477448,-122.3798749
+183,84032,84037,84032-84037,82,Pine,I-5 to Broadway (e/o Bellevue),3,784,461,323,Seattle,King,47.6152185,-122.3261668
+184,84037,84032,84037-84032,82,Pine,I-5 to Broadway (e/o Bellevue),3,784,461,323,Seattle,King,47.6152185,-122.3261668
+185,177515,177985,177515-177985,83,008 AV E,S/O SR-7,6,496.5,451,46,Pierce Other,Pierce,47.07372506,-122.4217475
+186,177515,177985,177515-177985,83,008 AV E,S/O SR-7,6,496.5,451,46,Pierce Other,Pierce,47.07082357,-122.4217118
+187,177515,177985,177515-177985,83,008 AV E,S/O SR-7,6,496.5,451,46,SW Pierce,Pierce,47.07372506,-122.4217475
+188,177515,177985,177515-177985,83,008 AV E,S/O SR-7,6,496.5,451,46,SW Pierce,Pierce,47.07082357,-122.4217118
+189,177985,177515,177985-177515,83,008 AV E,S/O SR-7,6,496.5,451,46,Pierce Other,Pierce,47.07372506,-122.4217475
+190,177985,177515,177985-177515,83,008 AV E,S/O SR-7,6,496.5,451,46,Pierce Other,Pierce,47.07082357,-122.4217118
+191,177985,177515,177985-177515,83,008 AV E,S/O SR-7,6,496.5,451,46,SW Pierce,Pierce,47.07372506,-122.4217475
+192,177985,177515,177985-177515,83,008 AV E,S/O SR-7,6,496.5,451,46,SW Pierce,Pierce,47.07082357,-122.4217118
+193,177975,179257,177975-179257,84,008 AV S,S/O SR-507,6,323,274,50,SW Pierce,Pierce,47.05334216,-122.443088
+194,179257,177975,179257-177975,84,008 AV S,S/O SR-507,6,323,274,50,SW Pierce,Pierce,47.05334216,-122.443088
+195,181956,182472,181956-182472,85,008 AV S,S/O SR-702,6,132,107,25,Pierce Other,Pierce,46.91852433,-122.4431691
+196,182472,181956,182472-181956,85,008 AV S,S/O SR-702,6,132,107,25,Pierce Other,Pierce,46.91852433,-122.4431691
+197,176957,177453,176957-177453,86,038 AV E,S/O 192 ST E (EAST LEG),3,281,247,34,Pierce Other,Pierce,47.07849557,-122.3764335
+198,177453,176957,177453-176957,86,038 AV E,S/O 192 ST E (EAST LEG),3,281,247,34,Pierce Other,Pierce,47.07849557,-122.3764335
+199,160057,160392,160057-160392,87,072 ST E,W/O PIONEER WY E,3,390,335,55,Pierce Other,Pierce,47.19211526,-122.341759
+200,160392,160057,160392-160057,87,072 ST E,W/O PIONEER WY E,3,390,335,55,Pierce Other,Pierce,47.19211526,-122.341759
+201,160128,160153,160128-160153,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Pierce Other,Pierce,47.19174439,-122.3933109
+202,160128,160153,160128-160153,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Pierce Other,Pierce,47.19173872,-122.3976719
+203,160128,160153,160128-160153,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Tacoma,Pierce,47.19174439,-122.3933109
+204,160128,160153,160128-160153,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Tacoma,Pierce,47.19173872,-122.3976719
+205,160153,160128,160153-160128,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Pierce Other,Pierce,47.19174439,-122.3933109
+206,160153,160128,160153-160128,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Pierce Other,Pierce,47.19173872,-122.3976719
+207,160153,160128,160153-160128,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Tacoma,Pierce,47.19174439,-122.3933109
+208,160153,160128,160153-160128,88,072 ST E ,W/O WALLER RD E,3,655.6666667,511,145,Tacoma,Pierce,47.19173872,-122.3976719
+209,162179,162214,162179-162214,89,084 ST E,W/O CANYON RD E,3,215.75,190,26,Pierce Other,Pierce,47.18086944,-122.3655037
+210,162214,162179,162214-162179,89,084 ST E,W/O CANYON RD E,3,215.75,190,26,Pierce Other,Pierce,47.18086944,-122.3655037
+211,168709,168992,168709-168992,90,094 AV E,N/O 122 ST E,3,1146,718,428,Pierce Other,Pierce,47.14845287,-122.3039944
+212,168992,168709,168992-168709,90,094 AV E,N/O 122 ST E,3,1146,718,428,Pierce Other,Pierce,47.14845287,-122.3039944
+213,168992,170071,168992-170071,91,094 AV E,N/O 128 ST E,3,1061.5,986,76,Pierce Other,Pierce,47.14426025,-122.3040111
+214,170071,168992,170071-168992,91,094 AV E,N/O 128 ST E,3,1061.5,986,76,Pierce Other,Pierce,47.14426025,-122.3040111
+215,165855,165885,165855-165885,92,104 ST E ,E/O WALLER RD E,3,212,192,20,Pierce Other,Pierce,47.16222638,-122.3925062
+216,165885,165855,165885-165855,92,104 ST E ,E/O WALLER RD E,3,212,192,20,Pierce Other,Pierce,47.16222638,-122.3925062
+217,167622,167701,167622-167701,93,112 ST E,E/O CANYON RD E,3,966.5,864,103,Pierce Other,Pierce,47.15457926,-122.3519741
+218,167701,167622,167701-167622,93,112 ST E,E/O CANYON RD E,3,966.5,864,103,Pierce Other,Pierce,47.15457926,-122.3519741
+219,167312,167331,167312-167331,94,112 ST E ,E/O A ST S,3,759,605,154,Pierce Other,Pierce,47.15535793,-122.4288646
+220,167331,167312,167331-167312,94,112 ST E ,E/O A ST S,3,759,605,154,Pierce Other,Pierce,47.15535793,-122.4288646
+221,167516,167547,167516-167547,95,112 ST E ,E/O WALLER RD E,3,921.5,735,187,Pierce Other,Pierce,47.15483556,-122.3835493
+222,167547,167516,167547-167516,95,112 ST E ,E/O WALLER RD E,3,921.5,735,187,Pierce Other,Pierce,47.15483556,-122.3835493
+223,167117,167182,167117-167182,96,112 ST S,E/O STEELE ST S,3,379,320,60,Pierce Other,Pierce,47.15576658,-122.4641699
+224,167182,167117,167182-167117,96,112 ST S,E/O STEELE ST S,3,379,320,60,Pierce Other,Pierce,47.15576658,-122.4641699
+225,174074,174503,174074-174503,97,122 AV E,S/0 160 ST CT E,3,474,448,26,Pierce Other,Pierce,47.10909905,-122.2667153
+226,174503,174074,174503-174074,97,122 AV E,S/0 160 ST CT E,3,474,448,26,Pierce Other,Pierce,47.10909905,-122.2667153
+227,173192,174074,173192-174074,98,122 AV E,S/O 152 ST E,3,987,908,79,Pierce Other,Pierce,47.11430676,-122.2667053
+228,174074,173192,174074-173192,98,122 AV E,S/O 152 ST E,3,987,908,79,Pierce Other,Pierce,47.11430676,-122.2667053
+229,170102,170132,170102-170132,99,128 ST E,E/O CANYON RD E,3,646.5,597,50,Pierce Other,Pierce,47.13999249,-122.3520527
+230,170132,170102,170132-170102,99,128 ST E,E/O CANYON RD E,3,646.5,597,50,Pierce Other,Pierce,47.13999249,-122.3520527
+231,169872,169956,169872-169956,100,128 ST E,E/O GOLDEN GIVEN RD E,3,174,166,8,Pierce Other,Pierce,47.14056148,-122.4081801
+232,169956,169872,169956-169872,100,128 ST E,E/O GOLDEN GIVEN RD E,3,174,166,8,Pierce Other,Pierce,47.14056148,-122.4081801
+233,172091,172106,172091-172106,101,144 ST E,E/O 94 AV E,3,297,271,26,Pierce Other,Pierce,47.12566173,-122.3000587
+234,172106,172091,172106-172091,101,144 ST E,E/O 94 AV E,3,297,271,26,Pierce Other,Pierce,47.12566173,-122.3000587
+235,172072,172091,172072-172091,102,144 ST E,W/O 94 AV E,3,302,275,27,Pierce Other,Pierce,47.12567096,-122.3093998
+236,172091,172072,172091-172072,102,144 ST E,W/O 94 AV E,3,302,275,27,Pierce Other,Pierce,47.12567096,-122.3093998
+237,173089,173112,173089-173112,103,152 ST E,E/O 110 AV E,3,449.6666667,418,32,Pierce Other,Pierce,47.11840244,-122.2772694
+238,173112,173089,173112-173089,103,152 ST E,E/O 110 AV E,3,449.6666667,418,32,Pierce Other,Pierce,47.11840244,-122.2772694
+239,173052,173068,173052-173068,104,152 ST E,E/O 94 AV E,3,516,449,67,Pierce Other,Pierce,47.11838021,-122.3010177
+240,173068,173052,173068-173052,104,152 ST E,E/O 94 AV E,3,516,449,67,Pierce Other,Pierce,47.11838021,-122.3010177
+241,173972,173981,173972-173981,105,160 ST E,W/O GEM HEIGHTS DR E,3,910.5,806,105,Pierce Other,Pierce,47.11109806,-122.3099294
+242,173981,173972,173981-173972,105,160 ST E,W/O GEM HEIGHTS DR E,3,910.5,806,105,Pierce Other,Pierce,47.11109806,-122.3099294
+243,175779,175787,175779-175787,106,176 ST E,E/O 78 AV E,3,1740,1081,659,Pierce Other,Pierce,47.0964446,-122.3225576
+244,175787,175779,175787-175779,106,176 ST E,E/O 78 AV E,3,1740,1081,659,Pierce Other,Pierce,47.0964446,-122.3225576
+245,175740,175757,175740-175757,107,176 ST E,E/O CANYON RD E,3,2158,1406,752,Pierce Other,Pierce,47.09646389,-122.346781
+246,175757,175740,175757-175740,107,176 ST E,E/O CANYON RD E,3,2158,1406,752,Pierce Other,Pierce,47.09646389,-122.346781
+247,175658,175686,175658-175686,108,176 ST E,E/O WALLER RD E,3,1551,1225,326,Pierce Other,Pierce,47.09670032,-122.3851038
+248,175686,175658,175686-175658,108,176 ST E,E/O WALLER RD E,3,1551,1225,326,Pierce Other,Pierce,47.09670032,-122.3851038
+249,176974,176976,176974-176976,109,192 ST E,W/O CANYON RD E,3,714.5,326,389,Pierce Other,Pierce,47.08183286,-122.3606817
+250,176976,176974,176976-176974,109,192 ST E,W/O CANYON RD E,3,714.5,326,389,Pierce Other,Pierce,47.08183286,-122.3606817
+251,178040,178056,178040-178056,110,208 ST E,W/O 66 AV E,3,125,114,11,Pierce Other,Pierce,47.06765816,-122.3475226
+252,178056,178040,178056-178040,110,208 ST E,W/O 66 AV E,3,125,114,11,Pierce Other,Pierce,47.06765816,-122.3475226
+253,167477,168134,167477-168134,111,214 AV E,N/O 112 ST E,3,949.6666667,891,59,Pierce Other,Pierce,47.15640769,-122.1443055
+254,168134,167477,168134-167477,111,214 AV E,N/O 112 ST E,3,949.6666667,891,59,Pierce Other,Pierce,47.15640769,-122.1443055
+255,152980,154241,152980-154241,112,214 AV E,N/O ISLAND PARK WAY,6,574,524,50,Pierce Other,Pierce,47.23232904,-122.1442973
+256,154241,152980,154241-152980,112,214 AV E,N/O ISLAND PARK WAY,6,574,524,50,Pierce Other,Pierce,47.23232904,-122.1442973
+257,162706,163430,162706-163430,113,214 AV E,N/O SUMNER-BUCKLEY HWY E,3,882,795,87,Pierce Other,Pierce,47.17920055,-122.1440762
+258,163430,162706,163430-162706,113,214 AV E,N/O SUMNER-BUCKLEY HWY E,3,882,795,87,Pierce Other,Pierce,47.17920055,-122.1440762
+259,173472,174104,173472-174104,114,22 AV E,N/O MILITARY RD E,3,315,282,33,Pierce Other,Pierce,47.11127776,-122.3999173
+260,174104,173472,174104-173472,114,22 AV E,N/O MILITARY RD E,3,315,282,33,Pierce Other,Pierce,47.11127776,-122.3999173
+261,174104,174678,174104-174678,115,22 AV E,S/O MILITARY RD E,3,438,401,37,Pierce Other,Pierce,47.10648575,-122.3999687
+262,174678,174104,174678-174104,115,22 AV E,S/O MILITARY RD E,3,438,401,37,Pierce Other,Pierce,47.10648575,-122.3999687
+263,178758,178767,178758-178767,116,224 ST E,E/O 90 AV E,6,1151,1043,108,Pierce Other,Pierce,47.05327457,-122.3120745
+264,178767,178758,178767-178758,116,224 ST E,E/O 90 AV E,6,1151,1043,108,Pierce Other,Pierce,47.05327457,-122.3120745
+265,178820,178832,178820-178832,117,224 ST E,W/O ORTING KAPOWSIN HY,6,628.6666667,583,45,Pierce Other,Pierce,47.05267907,-122.257921
+266,178832,178820,178832-178820,117,224 ST E,W/O ORTING KAPOWSIN HY,6,628.6666667,583,45,Pierce Other,Pierce,47.05267907,-122.257921
+267,178727,178749,178727-178749,118,224 ST E ,E/O 70 AV E,6,1066,962,104,Pierce Other,Pierce,47.05338058,-122.3306106
+268,178749,178727,178749-178727,118,224 ST E ,E/O 70 AV E,6,1066,962,104,Pierce Other,Pierce,47.05338058,-122.3306106
+269,178703,178717,178703-178717,119,224 ST E ,W/O 38 AV E,6,1074,979,96,Pierce Other,Pierce,47.0532223,-122.3845934
+270,178717,178703,178717-178703,119,224 ST E ,W/O 38 AV E,6,1074,979,96,Pierce Other,Pierce,47.0532223,-122.3845934
+271,181046,181067,181046-181067,120,304 ST E,W/O 8 AV E,6,148,117,32,Pierce Other,Pierce,46.98061419,-122.4323043
+272,181067,181046,181067-181046,120,304 ST E,W/O 8 AV E,6,148,117,32,Pierce Other,Pierce,46.98061419,-122.4323043
+273,156684,156712,156684-156712,121,52 ST E,W/O 66 AV E,3,450,383,67,Pierce Other,Pierce,47.20985872,-122.3451482
+274,156712,156684,156712-156684,121,52 ST E,W/O 66 AV E,3,450,383,67,Pierce Other,Pierce,47.20985872,-122.3451482
+275,181516,182028,181516-182028,122,56 AV S,N/O SR-705,6,72,67,5,Pierce Other,Pierce,46.9465969,-122.3574824
+276,182028,181516,182028-181516,122,56 AV S,N/O SR-705,6,72,67,5,Pierce Other,Pierce,46.9465969,-122.3574824
+277,156075,156712,156075-156712,123,66 AV E,S/O SR-167,3,653,569,84,Pierce Other,Pierce,47.21170312,-122.3419663
+278,156712,156075,156712-156075,123,66 AV E,S/O SR-167,3,653,569,84,Pierce Other,Pierce,47.21170312,-122.3419663
+279,180062,180258,180062-180258,124,70 AV E,S/O 260 ST E,6,138,133,5,Pierce Other,Pierce,47.0148153,-122.3349266
+280,180258,180062,180258-180062,124,70 AV E,S/O 260 ST E,6,138,133,5,Pierce Other,Pierce,47.0148153,-122.3349266
+281,175779,176845,175779-176845,125,78 AV E,S/O 176 ST E,3,521,488,33,Pierce Other,Pierce,47.09029985,-122.3256097
+282,176845,175779,176845-175779,125,78 AV E,S/O 176 ST E,3,521,488,33,Pierce Other,Pierce,47.09029985,-122.3256097
+283,176995,177255,176995-177255,126,78 AV E,S/O 192 ST E,3,416,378,38,Pierce Other,Pierce,47.08002611,-122.3257008
+284,177255,176995,177255-176995,126,78 AV E,S/O 192 ST E,3,416,378,38,Pierce Other,Pierce,47.08002611,-122.3257008
+285,181067,181439,181067-181439,127,8 AV S,S/O 304 ST S,6,324,252,72,Pierce Other,Pierce,46.97058158,-122.4215856
+286,181439,181067,181439-181067,127,8 AV S,S/O 304 ST S,6,324,252,72,Pierce Other,Pierce,46.97058158,-122.4215856
+287,150777,151240,150777-151240,128,9 ST E,W/0 198 AV E,6,664,626,38,Pierce Other,Pierce,47.24793235,-122.1709821
+288,151240,150777,151240-150777,128,9 ST E,W/0 198 AV E,6,664,626,38,Pierce Other,Pierce,47.24793235,-122.1709821
+289,171286,172091,171286-172091,129,94 AV E,S/O 136 ST E,3,790,717,73,Pierce Other,Pierce,47.12931197,-122.303963
+290,172091,171286,172091-171286,129,94 AV E,S/O 136 ST E,3,790,717,73,Pierce Other,Pierce,47.12931197,-122.303963
+291,172091,172465,172091-172465,130,94 AV E,S/O 144 ST E,3,464,419,45,Pierce Other,Pierce,47.12422952,-122.3039608
+292,172465,172091,172465-172091,130,94 AV E,S/O 144 ST E,3,464,419,45,Pierce Other,Pierce,47.12422952,-122.3039608
+293,182929,183064,182929-183064,131,ALDER-CUTTOFF,S/O MASHELL RIVER,6,458,331,127,Pierce Other,Pierce,46.83514745,-122.259674
+294,183064,182929,183064-182929,131,ALDER-CUTTOFF,S/O MASHELL RIVER,6,458,331,127,Pierce Other,Pierce,46.83514745,-122.259674
+295,182105,182418,182105-182418,132,ALLEN RD S,37600 BLOCK,6,16,16,0,Pierce Other,Pierce,46.91893077,-122.478882
+296,182418,182105,182418-182105,132,ALLEN RD S,37600 BLOCK,6,16,16,0,Pierce Other,Pierce,46.91893077,-122.478882
+297,172947,173171,172947-173171,133,BROOKEDALE ST E,SE/O 152 ST E,3,748.3333333,666,82,Pierce Other,Pierce,47.11744717,-122.374483
+298,173171,172947,173171-172947,133,BROOKEDALE ST E,SE/O 152 ST E,3,748.3333333,666,82,Pierce Other,Pierce,47.11744717,-122.374483
+299,168080,168704,168080-168704,134,C ST S,N/O 117 ST S,3,339,294,45,Pierce Other,Pierce,47.14935369,-122.4370229
+300,168704,168080,168704-168080,134,C ST S,N/O 117 ST S,3,339,294,45,Pierce Other,Pierce,47.14935369,-122.4370229
+301,176059,176425,176059-176425,135,CANYON RD E,N/O 184 ST E,3,2007,1366,641,Pierce Other,Pierce,47.09121854,-122.3573604
+302,176425,176059,176425-176059,135,CANYON RD E,N/O 184 ST E,3,2007,1366,641,Pierce Other,Pierce,47.09121854,-122.3573604
+303,176976,177510,176976-177510,136,CANYON RD E,S/O 192 ST E,3,1433,1068,365,Pierce Other,Pierce,47.07824635,-122.3585798
+304,177510,176976,177510-176976,136,CANYON RD E,S/O 192 ST E,3,1433,1068,365,Pierce Other,Pierce,47.07824635,-122.3585798
+305,163248,164275,163248-164275,137,CANYON RD E ,N/O 96 ST E,3,1368.333333,1055,313,Pierce Other,Pierce,47.17276,-122.3571015
+306,164275,163248,164275-163248,137,CANYON RD E ,N/O 96 ST E,3,1368.333333,1055,313,Pierce Other,Pierce,47.17276,-122.3571015
+307,157574,160244,157574-160244,138,CANYON RD E ,SE/O PIONEER WY E,3,940,718,222,Pierce Other,Pierce,47.1984833,-122.3580993
+308,160244,157574,160244-157574,138,CANYON RD E ,SE/O PIONEER WY E,3,940,718,222,Pierce Other,Pierce,47.1984833,-122.3580993
+309,131157,132588,131157-132588,139,CRESCENT VALLEY RD,N/O 96 ST NW,6,243,226,17,Peninsula,Pierce,47.34963811,-122.5785146
+310,132588,131157,132588-131157,139,CRESCENT VALLEY RD,N/O 96 ST NW,6,243,226,17,Peninsula,Pierce,47.34963811,-122.5785146
+311,182029,182349,182029-182349,140,EATONVILLE CUTOFF,SE/O 352 ST E,6,475,400,76,Pierce Other,Pierce,46.92777899,-122.339439
+312,182349,182029,182349-182029,140,EATONVILLE CUTOFF,SE/O 352 ST E,6,475,400,76,Pierce Other,Pierce,46.92777899,-122.339439
+313,167097,167392,167097-167392,141,GOLDEN GIVEN RD E,N/O 112 ST E,3,377.5,342,36,Pierce Other,Pierce,47.1559184,-122.4154311
+314,167392,167097,167392-167097,141,GOLDEN GIVEN RD E,N/O 112 ST E,3,377.5,342,36,Pierce Other,Pierce,47.1559184,-122.4154311
+315,125665,126463,125665-126463,142,GOODNOUGH DR ,E/O S END 302 SPUR,6,255.6666667,220,36,Peninsula,Pierce,47.38026717,-122.6230119
+316,126463,125665,126463-125665,142,GOODNOUGH DR ,E/O S END 302 SPUR,6,255.6666667,220,36,Peninsula,Pierce,47.38026717,-122.6230119
+317,181878,182348,181878-182348,143,HARTS LK RD S,SE/O SR-702,6,186,143,44,Pierce Other,Pierce,46.92711604,-122.5233531
+318,182348,181878,182348-181878,143,HARTS LK RD S,SE/O SR-702,6,186,143,44,Pierce Other,Pierce,46.92711604,-122.5233531
+319,143737,144450,143737-144450,144,JAHN AV NW,N/O 24 ST NW,6,189,177,12,Peninsula,Pierce,47.28176284,-122.5632912
+320,144450,143737,144450-143737,144,JAHN AV NW,N/O 24 ST NW,6,189,177,12,Peninsula,Pierce,47.28176284,-122.5632912
+321,131464,132858,131464-132858,145,KEY PENINSULA HY,N/O OLSEN/CRAMER RD,6,622,552,71,Peninsula,Pierce,47.34527198,-122.741326
+322,132858,131464,132858-131464,145,KEY PENINSULA HY,N/O OLSEN/CRAMER RD,6,622,552,71,Peninsula,Pierce,47.34527198,-122.741326
+323,174104,174258,174104-174258,146,MILITARY RD E,E/O 22 AV E,3,324,296,28,Pierce Other,Pierce,47.10827146,-122.3966589
+324,174258,174104,174258-174104,146,MILITARY RD E,E/O 22 AV E,3,324,296,28,Pierce Other,Pierce,47.10827146,-122.3966589
+325,173747,174104,173747-174104,147,MILITARY RD E,W/O 22 AV E,3,278,264,14,Pierce Other,Pierce,47.11014744,-122.403569
+326,174104,173747,174104-173747,147,MILITARY RD E,W/O 22 AV E,3,278,264,14,Pierce Other,Pierce,47.11014744,-122.403569
+327,168590,168616,168590-168616,148,MILITARY RD E,W/O SR 162,6,780,708,72,Pierce Other,Pierce,47.15072449,-122.2360182
+328,168616,168590,168616-168590,148,MILITARY RD E,W/O SR 162,6,780,708,72,Pierce Other,Pierce,47.15072449,-122.2360182
+329,177321,177665,177321-177665,149,ORTING KAPOWSIN HY,N/O 200 ST E,6,727.6666667,618,109,Pierce Other,Pierce,47.07646325,-122.2369177
+330,177665,177321,177665-177321,149,ORTING KAPOWSIN HY,N/O 200 ST E,6,727.6666667,618,109,Pierce Other,Pierce,47.07646325,-122.2369177
+331,180164,181014,180164-181014,150,ORTING KAPOWSIN HY,S/O 264 ST E,6,213,177,36,Pierce Other,Pierce,47.00062683,-122.2411004
+332,181014,180164,181014-180164,150,ORTING KAPOWSIN HY,S/O 264 ST E,6,213,177,36,Pierce Other,Pierce,47.00062683,-122.2411004
+333,177115,178510,177115-178510,151,ORVILLE RD E,S/O SR 162,6,777.3333333,518,259,Pierce Other,Pierce,47.07116187,-122.1881966
+334,178510,177115,178510-177115,151,ORVILLE RD E,S/O SR 162,6,777.3333333,518,259,Pierce Other,Pierce,47.07116187,-122.1881966
+335,124494,127097,124494-127097,152,PEACOCK HILL RD NW,S/O 144 ST NW,6,275.6666667,248,28,Peninsula,Pierce,47.38224715,-122.5890774
+336,127097,124494,127097-124494,152,PEACOCK HILL RD NW,S/O 144 ST NW,6,275.6666667,248,28,Peninsula,Pierce,47.38224715,-122.5890774
+337,162551,162553,162551-162553,153,PIONEER WAY E,W/O SR-162,6,479,414,66,Pierce Other,Pierce,47.18064256,-122.2317206
+338,162553,162551,162553-162551,153,PIONEER WAY E,W/O SR-162,6,479,414,66,Pierce Other,Pierce,47.18064256,-122.2317206
+339,159527,160392,159527-160392,154,PIONEER WY E,NW/O 72 ST E,3,422,390,32,Pierce Other,Pierce,47.19354455,-122.3411096
+340,160392,159527,160392-159527,154,PIONEER WY E,NW/O 72 ST E,3,422,390,32,Pierce Other,Pierce,47.19354455,-122.3411096
+341,153460,153844,153460-153844,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Pierce Other,Pierce,47.22889019,-122.3908856
+342,153460,153844,153460-153844,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Pierce Other,Pierce,47.23001563,-122.393481
+343,153460,153844,153460-153844,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Tacoma,Pierce,47.22889019,-122.3908856
+344,153460,153844,153460-153844,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Tacoma,Pierce,47.23001563,-122.393481
+345,153844,153460,153844-153460,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Pierce Other,Pierce,47.22889019,-122.3908856
+346,153844,153460,153844-153460,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Pierce Other,Pierce,47.23001563,-122.393481
+347,153844,153460,153844-153460,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Tacoma,Pierce,47.22889019,-122.3908856
+348,153844,153460,153844-153460,155,PIONEER WY E,NW/O WALLER RD E,3,1378.666667,1034,345,Tacoma,Pierce,47.23001563,-122.393481
+349,165131,165820,165131-165820,156,PORTLAND AV E,N/O 104 ST E,3,1025.666667,827,199,Pierce Other,Pierce,47.16377567,-122.4053549
+350,165820,165131,165820-165131,156,PORTLAND AV E,N/O 104 ST E,3,1025.666667,827,199,Pierce Other,Pierce,47.16377567,-122.4053549
+351,161542,198211,161542-198211,157,PORTLAND AV E,N/O 80 ST E,3,827,696,131,Pierce Other,Pierce,47.18627536,-122.4077644
+352,198211,161542,198211-161542,157,PORTLAND AV E,N/O 80 ST E,3,827,696,131,Pierce Other,Pierce,47.18627536,-122.4077644
+353,142403,145336,142403-145336,158,PT FOSDICK DR NW,N/O 34 AV NW,6,184,162,22,Peninsula,Pierce,47.28256522,-122.5810948
+354,145336,142403,145336-142403,158,PT FOSDICK DR NW,N/O 34 AV NW,6,184,162,22,Peninsula,Pierce,47.28256522,-122.5810948
+355,173339,175160,173339-175160,159,SPANAWAY LOOP,N/O FAIR OAKS,3,1694,1229,465,Pierce Other,Pierce,47.1069368,-122.4532357
+356,175160,173339,175160-173339,159,SPANAWAY LOOP,N/O FAIR OAKS,3,1694,1229,465,Pierce Other,Pierce,47.1069368,-122.4532357
+357,170068,171170,170068-171170,160,SPANAWAY LP RD S,N/O 138 ST S,3,1826,1363,463,Pierce Other,Pierce,47.13523528,-122.4589353
+358,171170,170068,171170-170068,160,SPANAWAY LP RD S,N/O 138 ST S,3,1826,1363,463,Pierce Other,Pierce,47.13523528,-122.4589353
+359,163943,164210,163943-164210,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Pierce Other,Pierce,47.16950178,-122.4657464
+360,163943,164210,163943-164210,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Pierce Other,Pierce,47.17017884,-122.4656453
+361,163943,164210,163943-164210,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Tacoma,Pierce,47.16950178,-122.4657464
+362,163943,164210,163943-164210,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Tacoma,Pierce,47.17017884,-122.4656453
+363,164210,163943,164210-163943,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Pierce Other,Pierce,47.16950178,-122.4657464
+364,164210,163943,164210-163943,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Pierce Other,Pierce,47.17017884,-122.4656453
+365,164210,163943,164210-163943,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Tacoma,Pierce,47.16950178,-122.4657464
+366,164210,163943,164210-163943,161,STEELE ST S ,S/O 96 ST S,3,616,507,109,Tacoma,Pierce,47.17017884,-122.4656453
+367,173735,174402,173735-174402,162,STEILACOOM DUPONT,S/O CENTER RD,3,1060,758,303,SW Pierce,Pierce,47.10574261,-122.6225115
+368,174402,173735,174402-173735,162,STEILACOOM DUPONT,S/O CENTER RD,3,1060,758,303,SW Pierce,Pierce,47.10574261,-122.6225115
+369,166012,170506,166012-170506,163,STEILACOOM DUPONT,S/O STEILACOOM C/L,3,395,348,48,SW Pierce,Pierce,47.14608281,-122.6078187
+370,170506,166012,170506-166012,163,STEILACOOM DUPONT,S/O STEILACOOM C/L,3,395,348,48,SW Pierce,Pierce,47.14608281,-122.6078187
+371,145034,145336,145034-145336,164,STONE DR NW,E/O POINT FOSDICK DR,6,341,313,28,Peninsula,Pierce,47.27525567,-122.5791726
+372,145336,145034,145336-145034,164,STONE DR NW,E/O POINT FOSDICK DR,6,341,313,28,Peninsula,Pierce,47.27525567,-122.5791726
+373,163383,164030,163383-164030,165,SUMNER BUCKLEY HWY E,W/O 198 AV E,3,607,544,63,Pierce Other,Pierce,47.17547728,-122.1710194
+374,164030,163383,164030-163383,165,SUMNER BUCKLEY HWY E,W/O 198 AV E,3,607,544,63,Pierce Other,Pierce,47.17547728,-122.1710194
+375,162525,163430,162525-163430,166,SUMNER BUCKLEY HY ,E/O 214 AV E,6,293.6666667,254,40,Pierce Other,Pierce,47.17976892,-122.1334072
+376,163430,162525,163430-162525,166,SUMNER BUCKLEY HY ,E/O 214 AV E,6,293.6666667,254,40,Pierce Other,Pierce,47.17976892,-122.1334072
+377,163430,163445,163430-163445,167,SUMNER-BUCKLEY HWY E,W/O 214 AV E,3,508,454,54,Pierce Other,Pierce,47.17725896,-122.1494329
+378,163445,163430,163445-163430,167,SUMNER-BUCKLEY HWY E,W/O 214 AV E,3,508,454,54,Pierce Other,Pierce,47.17725896,-122.1494329
+379,154999,155246,154999-155246,168,SUMNER-TAPPS HY E,S/O SOUTH-TAPPS HY,6,835.6666667,765,71,Pierce Other,Pierce,47.22116672,-122.2072943
+380,155246,154999,155246-154999,168,SUMNER-TAPPS HY E,S/O SOUTH-TAPPS HY,6,835.6666667,765,71,Pierce Other,Pierce,47.22116672,-122.2072943
+381,165885,167516,165885-167516,169,WALLER RD E,N/O 112 ST E,3,634.5,540,95,Pierce Other,Pierce,47.15853091,-122.388893
+382,167516,165885,167516-165885,169,WALLER RD E,N/O 112 ST E,3,634.5,540,95,Pierce Other,Pierce,47.15853091,-122.388893
+383,175248,175658,175248-175658,170,WALLER RD E,N/O 176 ST E,3,337,305,32,Pierce Other,Pierce,47.09840956,-122.3894696
+384,175658,175248,175658-175248,170,WALLER RD E,N/O 176 ST E,3,337,305,32,Pierce Other,Pierce,47.09840956,-122.3894696
+385,158765,160153,158765-160153,171,WALLER RD E,N/O 72 ST E,3,618.6666667,537,82,Pierce Other,Pierce,47.19536103,-122.3890804
+386,160153,158765,160153-158765,171,WALLER RD E,N/O 72 ST E,3,618.6666667,537,82,Pierce Other,Pierce,47.19536103,-122.3890804
+387,174384,175248,174384-175248,172,WALLER RD E,S/O MILITARY RD E,3,198,184,14,Pierce Other,Pierce,47.10343706,-122.3894631
+388,175248,174384,175248-174384,172,WALLER RD E,S/O MILITARY RD E,3,198,184,14,Pierce Other,Pierce,47.10343706,-122.3894631
+389,139091,140294,139091-140294,173,WOLLOCHET DR NW,S/O FILLMORE DR NW,6,1108.5,852,257,Peninsula,Pierce,47.30354035,-122.6118025
+390,140294,139091,140294-139091,173,WOLLOCHET DR NW,S/O FILLMORE DR NW,6,1108.5,852,257,Peninsula,Pierce,47.30354035,-122.6118025
+391,169537,170111,169537-170111,174,WOODLAND AV E,N/O 128 ST E,3,382,337,45,Pierce Other,Pierce,47.14206855,-122.3361214
+392,170111,169537,170111-169537,174,WOODLAND AV E,N/O 128 ST E,3,382,337,45,Pierce Other,Pierce,47.14206855,-122.3361214
+393,170111,171620,170111-171620,175,WOODLAND AV E,N/O 136 ST E,3,269,248,21,Pierce Other,Pierce,47.13496171,-122.3367226
+394,171620,170111,171620-170111,175,WOODLAND AV E,N/O 136 ST E,3,269,248,21,Pierce Other,Pierce,47.13496171,-122.3367226
+395,173003,173918,173003-173918,176,WOODLAND AV E,S/O 152 ST E,3,182,172,10,Pierce Other,Pierce,47.11476483,-122.3360237
+396,173918,173003,173918-173003,176,WOODLAND AV E,S/O 152 ST E,3,182,172,10,Pierce Other,Pierce,47.11476483,-122.3360237
diff --git a/scripts/summarize/inputs/network_summary/truck_counts_2014.xlsx b/scripts/summarize/inputs/network_summary/truck_counts_2014.xlsx
deleted file mode 100644
index e24c8de8..00000000
Binary files a/scripts/summarize/inputs/network_summary/truck_counts_2014.xlsx and /dev/null differ
diff --git a/scripts/summarize/inputs/rgc_taz.csv b/scripts/summarize/inputs/rgc_taz.csv
new file mode 100644
index 00000000..7a3b93d2
--- /dev/null
+++ b/scripts/summarize/inputs/rgc_taz.csv
@@ -0,0 +1,390 @@
+taz,geog_name,lat_1,lon_1
+3317,Puyallup South Hill,47.15623502,-122.2867533
+3051,Lakewood,47.16198611,-122.5100593
+3314,Puyallup South Hill,47.15623502,-122.2867533
+3315,Puyallup South Hill,47.15623502,-122.2867533
+3046,Lakewood,47.16198611,-122.5100593
+3022,Lakewood,47.16198611,-122.5100593
+3284,Puyallup Downtown,47.19040648,-122.2940926
+3237,Puyallup Downtown,47.19040648,-122.2940926
+3240,Puyallup Downtown,47.19040648,-122.2940926
+2959,Tacoma Mall,47.21978404,-122.4725101
+2962,Tacoma Mall,47.21978404,-122.4725101
+2963,Tacoma Mall,47.21978404,-122.4725101
+2961,Tacoma Mall,47.21978404,-122.4725101
+2958,Tacoma Mall,47.21978404,-122.4725101
+2947,University Place,47.22977705,-122.5360806
+2911,Tacoma Downtown,47.24845203,-122.4403625
+2903,Tacoma Downtown,47.24845203,-122.4403625
+2899,Tacoma Downtown,47.24845203,-122.4403625
+2920,Tacoma Downtown,47.24845203,-122.4403625
+2918,Tacoma Downtown,47.24845203,-122.4403625
+2910,Tacoma Downtown,47.24845203,-122.4403625
+2916,Tacoma Downtown,47.24845203,-122.4403625
+3110,Tacoma Downtown,47.24845203,-122.4403625
+2901,Tacoma Downtown,47.24845203,-122.4403625
+2902,Tacoma Downtown,47.24845203,-122.4403625
+2907,Tacoma Downtown,47.24845203,-122.4403625
+2898,Tacoma Downtown,47.24845203,-122.4403625
+2909,Tacoma Downtown,47.24845203,-122.4403625
+2915,Tacoma Downtown,47.24845203,-122.4403625
+3108,Tacoma Downtown,47.24845203,-122.4403625
+2906,Tacoma Downtown,47.24845203,-122.4403625
+2939,University Place,47.22977705,-122.5360806
+3109,Tacoma Downtown,47.24845203,-122.4403625
+2923,Tacoma Downtown,47.24845203,-122.4403625
+2919,Tacoma Downtown,47.24845203,-122.4403625
+2917,Tacoma Downtown,47.24845203,-122.4403625
+2900,Tacoma Downtown,47.24845203,-122.4403625
+2897,Tacoma Downtown,47.24845203,-122.4403625
+2908,Tacoma Downtown,47.24845203,-122.4403625
+2914,Tacoma Downtown,47.24845203,-122.4403625
+2905,Tacoma Downtown,47.24845203,-122.4403625
+2913,Tacoma Downtown,47.24845203,-122.4403625
+2904,Tacoma Downtown,47.24845203,-122.4403625
+3105,Tacoma Downtown,47.24845203,-122.4403625
+2873,Tacoma Downtown,47.24845203,-122.4403625
+2880,Tacoma Downtown,47.24845203,-122.4403625
+2893,Tacoma Downtown,47.24845203,-122.4403625
+2879,Tacoma Downtown,47.24845203,-122.4403625
+2858,Tacoma Downtown,47.24845203,-122.4403625
+2862,Tacoma Downtown,47.24845203,-122.4403625
+2896,Tacoma Downtown,47.24845203,-122.4403625
+2867,Tacoma Downtown,47.24845203,-122.4403625
+2872,Tacoma Downtown,47.24845203,-122.4403625
+2878,Tacoma Downtown,47.24845203,-122.4403625
+2885,Tacoma Downtown,47.24845203,-122.4403625
+2856,Tacoma Downtown,47.24845203,-122.4403625
+2861,Tacoma Downtown,47.24845203,-122.4403625
+2866,Tacoma Downtown,47.24845203,-122.4403625
+2871,Tacoma Downtown,47.24845203,-122.4403625
+2877,Tacoma Downtown,47.24845203,-122.4403625
+2884,Tacoma Downtown,47.24845203,-122.4403625
+2890,Tacoma Downtown,47.24845203,-122.4403625
+2855,Tacoma Downtown,47.24845203,-122.4403625
+2895,Tacoma Downtown,47.24845203,-122.4403625
+2860,Tacoma Downtown,47.24845203,-122.4403625
+2892,Tacoma Downtown,47.24845203,-122.4403625
+2865,Tacoma Downtown,47.24845203,-122.4403625
+2889,Tacoma Downtown,47.24845203,-122.4403625
+2870,Tacoma Downtown,47.24845203,-122.4403625
+2876,Tacoma Downtown,47.24845203,-122.4403625
+2883,Tacoma Downtown,47.24845203,-122.4403625
+2888,Tacoma Downtown,47.24845203,-122.4403625
+2854,Tacoma Downtown,47.24845203,-122.4403625
+2859,Tacoma Downtown,47.24845203,-122.4403625
+2864,Tacoma Downtown,47.24845203,-122.4403625
+2887,Tacoma Downtown,47.24845203,-122.4403625
+2869,Tacoma Downtown,47.24845203,-122.4403625
+2875,Tacoma Downtown,47.24845203,-122.4403625
+2882,Tacoma Downtown,47.24845203,-122.4403625
+2891,Tacoma Downtown,47.24845203,-122.4403625
+2886,Tacoma Downtown,47.24845203,-122.4403625
+2853,Tacoma Downtown,47.24845203,-122.4403625
+2863,Tacoma Downtown,47.24845203,-122.4403625
+2881,Tacoma Downtown,47.24845203,-122.4403625
+2868,Tacoma Downtown,47.24845203,-122.4403625
+2874,Tacoma Downtown,47.24845203,-122.4403625
+2894,Tacoma Downtown,47.24845203,-122.4403625
+2808,Tacoma Downtown,47.24845203,-122.4403625
+2806,Tacoma Downtown,47.24845203,-122.4403625
+2807,Tacoma Downtown,47.24845203,-122.4403625
+2805,Tacoma Downtown,47.24845203,-122.4403625
+2783,Tacoma Downtown,47.24845203,-122.4403625
+2782,Tacoma Downtown,47.24845203,-122.4403625
+1178,Auburn,47.30667679,-122.2302469
+1176,Auburn,47.30667679,-122.2302469
+1179,Auburn,47.30667679,-122.2302469
+1125,Federal Way,47.31499096,-122.3062854
+1255,Kent,47.38248083,-122.2360012
+1259,Kent,47.38248083,-122.2360012
+1251,Kent,47.38248083,-122.2360012
+1260,Kent,47.38248083,-122.2360012
+1256,Kent,47.38248083,-122.2360012
+1252,Kent,47.38248083,-122.2360012
+1031,SeaTac,47.43608603,-122.2944662
+1010,SeaTac,47.43608603,-122.2944662
+1009,SeaTac,47.43608603,-122.2944662
+1011,SeaTac,47.43608603,-122.2944662
+982,SeaTac,47.43608603,-122.2944662
+974,Tukwila,47.45190196,-122.2534033
+972,Tukwila,47.45190196,-122.2534033
+980,SeaTac,47.43608603,-122.2944662
+971,Tukwila,47.45190196,-122.2534033
+973,Tukwila,47.45190196,-122.2534033
+995,Burien,47.46773149,-122.3402886
+992,Burien,47.46773149,-122.3402886
+988,Burien,47.46773149,-122.3402886
+991,Burien,47.46773149,-122.3402886
+994,Burien,47.46773149,-122.3402886
+1351,Renton,47.48987648,-122.2069477
+1352,Renton,47.48987648,-122.2069477
+1354,Renton,47.48987648,-122.2069477
+1353,Renton,47.48987648,-122.2069477
+1410,Renton,47.48987648,-122.2069477
+1413,Renton,47.48987648,-122.2069477
+1411,Renton,47.48987648,-122.2069477
+1412,Renton,47.48987648,-122.2069477
+1443,Issaquah,47.5467951,-122.0571043
+1447,Issaquah,47.5467951,-122.0571043
+1448,Issaquah,47.5467951,-122.0571043
+1444,Issaquah,47.5467951,-122.0571043
+3625,Bremerton,47.56815949,-122.6277584
+3623,Bremerton,47.56815949,-122.6277584
+3622,Bremerton,47.56815949,-122.6277584
+3624,Bremerton,47.56815949,-122.6277584
+631,Seattle Downtown,47.60763232,-122.3360987
+629,Seattle Downtown,47.60763232,-122.3360987
+626,Seattle Downtown,47.60763232,-122.3360987
+627,Seattle Downtown,47.60763232,-122.3360987
+628,Seattle Downtown,47.60763232,-122.3360987
+614,Seattle Downtown,47.60763232,-122.3360987
+616,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+611,Seattle Downtown,47.60763232,-122.3360987
+613,Seattle Downtown,47.60763232,-122.3360987
+615,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+610,Seattle Downtown,47.60763232,-122.3360987
+612,Seattle Downtown,47.60763232,-122.3360987
+545,Seattle Downtown,47.60763232,-122.3360987
+540,Seattle Downtown,47.60763232,-122.3360987
+541,Seattle Downtown,47.60763232,-122.3360987
+608,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+546,Seattle Downtown,47.60763232,-122.3360987
+533,Seattle Downtown,47.60763232,-122.3360987
+542,Seattle Downtown,47.60763232,-122.3360987
+603,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+534,Seattle Downtown,47.60763232,-122.3360987
+609,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+543,Seattle Downtown,47.60763232,-122.3360987
+535,Seattle Downtown,47.60763232,-122.3360987
+605,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+536,Seattle Downtown,47.60763232,-122.3360987
+544,Seattle Downtown,47.60763232,-122.3360987
+526,Seattle Downtown,47.60763232,-122.3360987
+537,Seattle Downtown,47.60763232,-122.3360987
+527,Seattle Downtown,47.60763232,-122.3360987
+607,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+538,Seattle Downtown,47.60763232,-122.3360987
+528,Seattle Downtown,47.60763232,-122.3360987
+529,Seattle Downtown,47.60763232,-122.3360987
+518,Seattle Downtown,47.60763232,-122.3360987
+539,Seattle Downtown,47.60763232,-122.3360987
+578,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+530,Seattle Downtown,47.60763232,-122.3360987
+606,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+519,Seattle Downtown,47.60763232,-122.3360987
+602,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+531,Seattle Downtown,47.60763232,-122.3360987
+604,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+478,Seattle Downtown,47.60763232,-122.3360987
+520,Seattle Downtown,47.60763232,-122.3360987
+580,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+521,Seattle Downtown,47.60763232,-122.3360987
+532,Seattle Downtown,47.60763232,-122.3360987
+577,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+508,Seattle Downtown,47.60763232,-122.3360987
+522,Seattle Downtown,47.60763232,-122.3360987
+582,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+509,Seattle Downtown,47.60763232,-122.3360987
+523,Seattle Downtown,47.60763232,-122.3360987
+579,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+510,Seattle Downtown,47.60763232,-122.3360987
+524,Seattle Downtown,47.60763232,-122.3360987
+583,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+572,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+511,Seattle Downtown,47.60763232,-122.3360987
+525,Seattle Downtown,47.60763232,-122.3360987
+512,Seattle Downtown,47.60763232,-122.3360987
+513,Seattle Downtown,47.60763232,-122.3360987
+573,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+498,Seattle Downtown,47.60763232,-122.3360987
+479,Seattle Downtown,47.60763232,-122.3360987
+581,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+584,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+514,Seattle Downtown,47.60763232,-122.3360987
+499,Seattle Downtown,47.60763232,-122.3360987
+1553,Bellevue,47.61532598,-122.1984286
+1550,Bellevue,47.61532598,-122.1984286
+1547,Bellevue,47.61532598,-122.1984286
+515,Seattle Downtown,47.60763232,-122.3360987
+1544,Bellevue,47.61532598,-122.1984286
+575,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+500,Seattle Downtown,47.60763232,-122.3360987
+585,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+571,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
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+501,Seattle Downtown,47.60763232,-122.3360987
+502,Seattle Downtown,47.60763232,-122.3360987
+481,Seattle Downtown,47.60763232,-122.3360987
+576,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
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+517,Seattle Downtown,47.60763232,-122.3360987
+503,Seattle Downtown,47.60763232,-122.3360987
+574,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+482,Seattle Downtown,47.60763232,-122.3360987
+504,Seattle Downtown,47.60763232,-122.3360987
+468,Seattle Downtown,47.60763232,-122.3360987
+570,Seattle First Hill/Capitol Hill,47.61415235,-122.3207212
+483,Seattle Downtown,47.60763232,-122.3360987
+505,Seattle Downtown,47.60763232,-122.3360987
+457,Seattle Downtown,47.60763232,-122.3360987
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+469,Seattle Downtown,47.60763232,-122.3360987
+506,Seattle Downtown,47.60763232,-122.3360987
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diff --git a/scripts/summarize/inputs/screenlines.csv b/scripts/summarize/inputs/screenlines.csv
index cabb037a..cbf2a3a7 100644
--- a/scripts/summarize/inputs/screenlines.csv
+++ b/scripts/summarize/inputs/screenlines.csv
@@ -1,27 +1,27 @@
-id,name,type,2010,2014
-4,Tacoma - East of CBD,primary,271777,
-20,Kent,secondary,508617,
-60,Cross-Sound,secondary,17466,
-23,Renton,primary,81758,
-44,Bothell,primary,255590,
-19,SeaTac,secondary,71364,
-2,Parkland,secondary,285859,
-37,Kirkland/Redmond,primary,381331,
-57,Kitsap - North,secondary,97177,
-30,Bellevue/Redmond,primary,354612,
-3,Puyallup,secondary,118726,
-58,Agate Pass,secondary,21000,
-22,Tukwila,primary,239527,
-35,Ship Canal,primary,521155,
-71,Woodinville,secondary,98331,
-46,Mill Creek,primary,350492,
-66,"Preston, Issaquah",secondary,93227,
-18,Maple Valley,secondary,61921,
-54,Gig Harbor,secondary,58503,
-32,TransLake,primary,250220,
-43,Lynnwood/Bothell,primary,231368,
-29,Seattle - South of CBD,primary,490806,
-14,Auburn,primary,534811,
-15,Auburn,primary,0,
-41,Seattle - North,primary,327021,
-7,Tacoma Narrows,secondary,79000,
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+2,Parkland,secondary,47.1499021,-122.388954,285859,275700
+3,Puyallup,secondary,47.16224777,-122.3961683,118726,119400
+4,Tacoma - East of CBD,primary,47.24166823,-122.3983112,271777,283200
+7,Tacoma Narrows,secondary,47.27428979,-122.5578442,79000,80000
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+41,Seattle - North,primary,47.76910905,-122.3904434,327021,338800
+43,Lynnwood/Bothell,primary,47.82190222,-122.2465237,231368,255500
+44,Bothell,primary,47.75073046,-122.1556681,255590,268600
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+54,Gig Harbor,secondary,47.41582607,-122.6230682,58503,61500
+57,Kitsap - North,secondary,47.6714518,-122.6918139,97177,100900
+58,Agate Pass,secondary,47.71251654,-122.56615,21000,21000
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+66,"Preston, Issaquah",secondary,47.53918525,-121.9101173,93227,91500
+71,Woodinville,secondary,47.78154056,-121.998505,98331,87900
diff --git a/scripts/summarize/inputs/special_needs_taz.csv b/scripts/summarize/inputs/special_needs_taz.csv
new file mode 100644
index 00000000..ef9c9c45
--- /dev/null
+++ b/scripts/summarize/inputs/special_needs_taz.csv
@@ -0,0 +1,3701 @@
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diff --git a/scripts/summarize/mapping/format_logsums.py b/scripts/summarize/mapping/format_logsums.py
new file mode 100644
index 00000000..6ada7835
--- /dev/null
+++ b/scripts/summarize/mapping/format_logsums.py
@@ -0,0 +1,33 @@
+import pandas as pd
+
+agg_logsum_file = r'outputs\daysim\aggregate_logsums.1.dat'
+
+logsums = pd.read_csv(agg_logsum_file, sep = '\t', skiprows=1, header=None)
+
+auto_list = ['child', 'nocars', 'lsonecradult', 'onepluscarsperadult']
+inc_list = ['low', 'med',  'high']
+transit_list = ['lsqtmil', 'qtmitohalfmi', 'morehfmi']
+
+
+
+logsums_columns = ['TAZ']
+
+# the file has some junk in the columns after 29 so we can't use it
+logsums.drop(logsums.columns[[29,30,31,32,33,34]], axis =1, inplace=True)
+
+for auto in auto_list:
+    for inc in inc_list:
+        for transit in transit_list:
+            if len(logsums_columns)<29:
+              logsums_columns.append(auto + '_'+ inc + '_'+ transit)
+
+logsums.columns = logsums_columns
+
+# Use this logsum as the one:
+#lsonecradult_med_qtmitohalfmi
+
+accessibility_file = r'outputs\daysim\accessibility.csv'
+accessibility = logsums.filter(['TAZ', 'lsonecradult_med_qtmitohalfmi'], axis=1)
+accessibility.columns = ['TAZ', 'Accessibility']
+accessibility.to_csv(accessibility_file, index = False)
+
diff --git a/scripts/summarize/notebooks/arc_maps.ipynb b/scripts/summarize/notebooks/arc_maps.ipynb
deleted file mode 100644
index 806a2f59..00000000
--- a/scripts/summarize/notebooks/arc_maps.ipynb
+++ /dev/null
@@ -1,352 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import arcpy\n",
-    "from arcpy import env\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set main model directory to parent directory\n",
-    "model_dir = os.path.dirname(os.getcwd()) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Model Scenario Results\n",
-    "scen = h5py.File(model_dir + r'/outputs/daysim_outputs.h5','r+')\n",
-    "scen_name = 'Model: 2040'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Base Data\n",
-    "base_file = r'/inputs/hh_and_persons.h5'\n",
-    "\n",
-    "base = h5py.File(model_dir + base_file ,'r+')\n",
-    "base_name = '2006 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Results based on Household Location\n",
-    "df_hh = pd.DataFrame({'TAZ': np.asarray(scen['Household']['zone_id']), \n",
-    "                      'hhsize': np.asarray(scen['Household']['hhsize']),\n",
-    "                      'hhincome': np.asarray(scen['Household']['hhincome']),\n",
-    "                      'hhvehs': np.asarray(scen['Household']['hhvehs']),\n",
-    "                      })\n",
-    "\n",
-    "# Results based on Work Location\n",
-    "df_work = pd.DataFrame({'TAZ': np.asarray(scen['Person']['pwtaz']),\n",
-    "                        'transit_pass': np.asarray(scen['Person']['ptpass']),\n",
-    "                        'paid_work_parking': np.asarray(scen['Person']['ppaidprk']),\n",
-    "                        'auto_time_work': np.asarray(scen['Person']['pwautime']),\n",
-    "                        'auto_time_dist': np.asarray(scen['Person']['pwaudist']),\n",
-    "                      })\n",
-    "\n",
-    "\n",
-    "# Calculate average household size, income, and number of vehicles\n",
-    "df_hh_mean = df_hh.groupby('TAZ').mean()\n",
-    "df_work_mean = df_work.groupby('TAZ').mean()\n",
-    "df_work_mean = df_work_mean.query('TAZ > 0')    # Filter our -1 zone\n",
-    "\n",
-    "# Export to csv\n",
-    "df_hh_mean.to_csv('hh_souncast_taz.csv', index=True)\n",
-    "df_work_mean.to_csv('work_souncast_taz.csv', index=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Create comparison maps, scenario versus some stated base or census/survey data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Start ArcMap document and map results\n",
-    "\n",
-    "RunPath = \".\"\n",
-    "env.workspace = RunPath\n",
-    "env.qualifiedFieldNames = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def createTazMap(data, inputlayer, outputdataset, outputlayer):\n",
-    "    ''' Add data to TAZ layer '''\n",
-    "    if arcpy.Exists(outputdataset):\n",
-    "        arcpy.Delete_management(outputdataset)\n",
-    "    if arcpy.Exists(outputlayer):\n",
-    "        arcpy.Delete_management(outputlayer)\n",
-    "\n",
-    "    try:\n",
-    "        inputdataset = \"TAZ.shp\"\n",
-    "        InField = \"TAZ\"\n",
-    "        JoinField = \"TAZ\"\n",
-    "        arcpy.MakeFeatureLayer_management(inputdataset, inputlayer)\n",
-    "        arcpy.AddJoin_management(inputlayer, InField, data, JoinField)\n",
-    "        arcpy.CopyFeatures_management(inputlayer, outputdataset)\n",
-    "        arcpy.Delete_management(inputlayer)\n",
-    "    except Exception, e:\n",
-    "        print \"Unable to join data\"\n",
-    "        print e\n",
-    "        \n",
-    "    # Output the layer as map\n",
-    "    try:\n",
-    "        arcpy.MakeFeatureLayer_management(outputdataset, inputlayer)\n",
-    "        arcpy.SaveToLayerFile_management(inputlayer, outputlayer)\n",
-    "        arcpy.Delete_management(inputlayer)  \n",
-    "    except Exception, e:\n",
-    "        print \"Unable to save layer as map\"\n",
-    "        print e\n",
-    "        \n",
-    "    return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create a shapefile for household-level results\n",
-    "createTazMap(data='hh_souncast_taz.csv', \n",
-    "             inputlayer='Household Location', \n",
-    "             outputdataset='home_loc.shp', \n",
-    "             'home_loc.lyr')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create a shapefile for work-level results\n",
-    "createTazMap(data='work_souncast_taz.csv', \n",
-    "             inputlayer='Work Location', \n",
-    "             outputdataset='work_loc.shp', \n",
-    "             outputlayer='work_loc.lyr')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "###### Try to do all this without writing out to a CSV, reading data in memory ######"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "narray.fields require",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-139-03859ea8a61d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# Export the numpy array to a feature class using the XY field to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m#  represent the output point feature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0marcpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mda\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNumPyArrayToFeatureClass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_fc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'XY'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspatial_ref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m: narray.fields require"
-     ]
-    }
-   ],
-   "source": [
-    "out_fc = 'test.gdb/numpy_hh_out'\n",
-    "\n",
-    "# Create a numpy array with an id field, and a field with a tuple \n",
-    "#  of x,y coordinates\n",
-    "# arr = np.array([(1, (471316.3835861763, 5000448.782036674)),\n",
-    "#                    (2, (470402.49348005146, 5000049.216449278))],\n",
-    "#                   np.dtype([('idfield', np.int32),('XY', '<f8', 2)]))\n",
-    "\n",
-    "arr = test\n",
-    "\n",
-    "# Define a spatial reference for the output feature class\n",
-    "spatial_ref = arcpy.Describe('TAZ.shp').spatialReference\n",
-    "\n",
-    "# Export the numpy array to a feature class using the XY field to\n",
-    "#  represent the output point feature\n",
-    "arcpy.da.NumPyArrayToFeatureClass(arr, out_fc, ['XY'], spatial_ref)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2972, 2989, 2986, ..., 2364, 2365, 2365])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "test = np.array([a,np.dtype([('idfield', np.int32),('XY', '<f8', 2)])])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([array([2972, 2989, 2986, ..., 2364, 2365, 2365]),\n",
-       "       dtype([('idfield', '<i4'), ('XY', '<f8', (2,))])], dtype=object)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/bike.ipynb b/scripts/summarize/notebooks/bike.ipynb
index c2f2e3ed..757cba20 100644
--- a/scripts/summarize/notebooks/bike.ipynb
+++ b/scripts/summarize/notebooks/bike.ipynb
@@ -10,9 +10,7 @@
   {
    "cell_type": "code",
    "execution_count": 216,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -44,9 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 217,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Load daysim outputs results\n",
@@ -56,9 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": 218,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Load survey data\n",
@@ -68,9 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": 219,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def get_df(h5file, table, fields, nested=False):\n",
@@ -100,9 +92,7 @@
   {
    "cell_type": "code",
    "execution_count": 221,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "survey_trip = get_df(h5file=survey, table='Trip', fields=trip_fields + ['trexpfac'], nested=True)\n",
@@ -112,9 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 222,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model_trip = get_df(h5file=model, table='Trip', fields=trip_fields, nested=False)\n",
@@ -147,9 +135,7 @@
   {
    "cell_type": "code",
    "execution_count": 225,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def join_od_rgc(df,taz_lookup):\n",
@@ -170,9 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": 226,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# survey_bike_trips = join_od_rgc(df=survey_bike_trips, taz_lookup=taz_lookup)\n",
@@ -182,9 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": 227,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dest_survey_bike_trips = survey_bike_trips.merge(district_lookup,left_on='dtaz',right_on='TAZ')\n",
@@ -216,9 +198,7 @@
   {
    "cell_type": "code",
    "execution_count": 229,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -290,9 +270,7 @@
   {
    "cell_type": "code",
    "execution_count": 230,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -329,9 +307,7 @@
   {
    "cell_type": "code",
    "execution_count": 231,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -434,9 +410,7 @@
   {
    "cell_type": "code",
    "execution_count": 232,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -473,9 +447,7 @@
   {
    "cell_type": "code",
    "execution_count": 274,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pd.reset_option('float_format')"
@@ -484,9 +456,7 @@
   {
    "cell_type": "code",
    "execution_count": 275,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = pd.read_csv(r'D:\\soundcast\\soundcast\\outputs\\bike_volumes.csv')"
@@ -495,9 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": 276,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = df.groupby(['EmmeINode','EmmeJNode']).mean()"
@@ -506,9 +474,7 @@
   {
    "cell_type": "code",
    "execution_count": 277,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# AM counts correspond to 7 to 9 AM for WSDOT data\n",
@@ -518,9 +484,7 @@
   {
    "cell_type": "code",
    "execution_count": 278,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Associate each link with a counter location\n",
@@ -530,9 +494,7 @@
   {
    "cell_type": "code",
    "execution_count": 279,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "am_counts = counts_intersect[counts_intersect['CountTime'] == 'AM']\n",
@@ -542,18 +504,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": 280,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = am_counts.merge(df,left_on=['INode','JNode'],right_on=['gdbINode','gdbeJNode'])"
@@ -562,9 +520,7 @@
   {
    "cell_type": "code",
    "execution_count": 281,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "am_total = pd.DataFrame(df.groupby('OBJECTID').sum()['AM'])\n",
@@ -574,9 +530,7 @@
   {
    "cell_type": "code",
    "execution_count": 282,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Seattle only\n",
@@ -587,9 +541,7 @@
   {
    "cell_type": "code",
    "execution_count": 283,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "am_total = am_total.merge(am_counts[['OBJECTID','LocationDe','CountDate','Latitude','Longitude','Bike_Total']],on='OBJECTID')\n",
@@ -600,9 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": 284,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -632,18 +582,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -657,9 +603,7 @@
   {
    "cell_type": "code",
    "execution_count": 242,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Sum all trips by destination zone and fill zones with zero trips\n",
@@ -693,9 +637,7 @@
   {
    "cell_type": "code",
    "execution_count": 243,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -722,9 +664,7 @@
   {
    "cell_type": "code",
    "execution_count": 244,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -762,9 +702,7 @@
   {
    "cell_type": "code",
    "execution_count": 245,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -803,9 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 246,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -831,9 +767,7 @@
   {
    "cell_type": "code",
    "execution_count": 247,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -853,9 +787,7 @@
   {
    "cell_type": "code",
    "execution_count": 248,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df.fillna(0,inplace=True)"
@@ -897,9 +829,7 @@
   {
    "cell_type": "code",
    "execution_count": 251,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tazlookup;"
@@ -925,6 +855,184 @@
     "# Extract link values and compare to counts"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Daily Counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def daily_count_sum(location, year, month):\n",
+    "    df = pd.read_csv(r'R:\\Bike\\Bike count Data\\seattle counters\\data\\\\'+location+'.csv')\n",
+    "    df['datetime'] = pd.to_datetime(df['Date'])\n",
+    "    df['weekday_value'] = df['datetime'].apply(lambda row: row.weekday())\n",
+    "    df['month'] = df['datetime'].apply(lambda row: row.month)\n",
+    "    df['year'] = df['datetime'].apply(lambda row: row.year)\n",
+    "    df['day'] = df['datetime'].apply(lambda row: row.day)\n",
+    "\n",
+    "    # Get all spring months, march through may\n",
+    "    df = df[df['month'].isin([3,4,5])]\n",
+    "\n",
+    "    # Get only weekdays\n",
+    "    df = df[df['weekday_value'].isin([0,1,2,3,4])]\n",
+    "\n",
+    "    # Get daily totals\n",
+    "    df = df[df['year'].isin([2014])]\n",
+    "    \n",
+    "    # Average daily total for time period\n",
+    "    return df.groupby(['month','day']).sum().mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BGT North of NE 70th Total     1497.507692\n",
+       "Ped South                       195.538462\n",
+       "Ped North                       180.353846\n",
+       "Bike North                      548.030769\n",
+       "Bike South                      573.584615\n",
+       "weekday_value                    48.000000\n",
+       "year                          48336.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = daily_count_sum(location='burke',year=[2014],month=[3,4,5])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fremont Bridge West Sidewalk     1806.584615\n",
+       "Fremont Bridge East Sidewalk     1718.846154\n",
+       "weekday_value                      48.000000\n",
+       "year                            48336.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = daily_count_sum(location='fremont',year=[2014],month=[3,4,5])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Spokane St. Bridge Total      991.107692\n",
+       "West                          482.261538\n",
+       "East                          508.846154\n",
+       "weekday_value                  48.000000\n",
+       "year                        48336.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = daily_count_sum(location='spokane',year=[2014],month=[3,4,5])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26th Ave SW Greenway at SW Oregon St Total      602.276923\n",
+       "North                                           125.061538\n",
+       "South                                           477.215385\n",
+       "weekday_value                                    48.000000\n",
+       "year                                          48336.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = daily_count_sum(location='26sw_greenway',year=[2014],month=[3,4,5])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Elliott Bay Trail in Myrtle Edwards Park Total     3782.338462\n",
+       "Ped North                                          1283.661538\n",
+       "Ped South                                          1140.276923\n",
+       "Bike North                                          731.538462\n",
+       "Bike South                                          626.861538\n",
+       "weekday_value                                        48.000000\n",
+       "year                                              48336.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = daily_count_sum(location='elliot_bay_trail',year=[2014],month=[3,4,5])\n",
+    "df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -951,9 +1059,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.9"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/scripts/summarize/notebooks/calibration.ipynb b/scripts/summarize/notebooks/calibration.ipynb
new file mode 100644
index 00000000..717f1533
--- /dev/null
+++ b/scripts/summarize/notebooks/calibration.ipynb
@@ -0,0 +1,432 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:0,.1%}'.format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Relative path between notebooks and goruped output directories\n",
+    "relative_path = '../../../outputs/grouped'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".output {\n",
+       "    flex-direction: row;\n",
+       "}\n",
+       "</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "CSS = \"\"\"\n",
+    ".output {\n",
+    "    flex-direction: row;\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "HTML('<style>{}</style>'.format(CSS))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calibration Sheet\n",
+    "For base scenario validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tour Mode Share"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survey</th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bike</th>\n",
+       "      <td>1.4%</td>\n",
+       "      <td>1.7%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV2</th>\n",
+       "      <td>21.9%</td>\n",
+       "      <td>22.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV3+</th>\n",
+       "      <td>19.7%</td>\n",
+       "      <td>19.4%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Park</th>\n",
+       "      <td>0.9%</td>\n",
+       "      <td>0.6%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOV</th>\n",
+       "      <td>33.6%</td>\n",
+       "      <td>32.8%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>School Bus</th>\n",
+       "      <td>3.5%</td>\n",
+       "      <td>3.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit</th>\n",
+       "      <td>5.8%</td>\n",
+       "      <td>4.8%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Walk</th>\n",
+       "      <td>12.2%</td>\n",
+       "      <td>15.5%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            survey  sc_2025_20_5\n",
+       "Bike          1.4%          1.7%\n",
+       "HOV2         21.9%         22.1%\n",
+       "HOV3+        19.7%         19.4%\n",
+       "Park          0.9%          0.6%\n",
+       "SOV          33.6%         32.8%\n",
+       "School Bus    3.5%          3.1%\n",
+       "Transit       5.8%          4.8%\n",
+       "Walk         12.2%         15.5%"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FEXFVPeeNqqqO0w9mpSiXm8qiRdenHYa1I0lEhJou2bCONh3XmP8GLpfUF0DS\ngcBk4FrIZyLgNvIjoHsKEpCAnwEr60tAZmaWno42HVff6rgAiIi7JA0AHpYUwHpgYkSsLSg/H/gO\ncF7BsTHAqcCTyX0mgPMjYlExLsDMzJqvQyWhiOhdZ38OBfd2IuI64LpG6j8BdK1z7CFKa8RnZrbD\n8F/OZmaWGichMzNLTYeajktTLjc17RDMSlrGK7PtQ+hQS7TTIincD2ZmLbOjLdE2M7NOxknIzMxS\n4yRkZmapcRIyM7PUOAmZmVlqnITMzCw1TkJmZpYaJyEzM0uNk5CZmaXGScjMzFLjJGRmZqlxEjIz\ns9T4KdqJ8SeNb1X9TEWG6jnVbRSNmdmOwUkoMfjUwa2qn5uXa6NIzMx2HCUzHSfpPUlLJS2T9Jik\nQ5Pj/SXdnmxPkTQ73UjNzKy5Smkk9E5EjAKQNA64FBgbES8DJyZl/FIgM7MSUjIjoTr6AOsAJO0l\naXlyvPblSpKOlfSwpJ0ljUu2H5P0C0k90gjazMy2V0ojoUpJS4EKYA/gMw0VlPQl4FvAMUAZMBP4\nbERslDQNOAf4XvFDNjOzxpRSEtpYMB33SeBmYP96yn0GOBg4KiI2SDoOGA48LAlgJ+Dh9gnZzMwa\nU0pJqFZE/EnSLpJ2qfsV8BywN/BR4LHk+G8j4pTG2pw7eX7tdlllOWWVFY3GUNYNDjhgRO1+piLT\n7PjNzEpRNpslm822aZuKKI17+ZJqIqJXsv0xYDGwGzAYuCsiRkqaAhwE/AT4JfkFC68BS4DPRMRz\nyf2g/hHxbEHbUVXVsn7I5aayaNH1rb8wM7MSJYmIUNMlG1ZKI6Ft94QgvwDhtIiIZIptWwYJICLi\nGUkTgduB44ApwHxJ5Um5mUBtEjIzs3SUTBKKiHpjjYjVwMeT7TnAnGR7GbBtvuxvwCHFj9LMzFqi\nVJdom5lZJ+AkZGZmqXESMjOz1JTMPaFiy+Wmtqh8xiuyzcxarWSWaBeTpHA/mJm1TFss0fZ0nJmZ\npcZJqBNo699gbm+OP12lHH8pxw6lH39bcBLqBEr9B9nxp6uU4y/l2KH0428LTkJmZpYaJyEzM0uN\nV8eRXx2XdgxmZqWotavjnITMzCw1no4zM7PUOAmZmVlqOn0SkjRe0tOSnpU0rYEyP06+f0LSqJbU\nLbZWxr9a0pOSlkp6tP2irj1/o7FL+pikP0r6p6Rvt6Rue2hl/Kn2fRJDU/FPTH5mnpT0B0kfb27d\n9tDK+EtMpn8sAAAC6klEQVSh/7+YxL9U0mOSPtPcuu2hlfE3v/8jotN+gK7AX4G9gDJgGTCsTpl/\nBe5Jtj8B/Km5dTty/Mn+34C+HbjvdwUOBr4PfLsldTty/Gn3fQviPxTok2yPL8Gf/XrjL6H+71Gw\nPRL4a4n1f73xt7T/O/tI6BDyHbM6IjYD1cAX65T5Au+/CO8RICOpXzPrFtuHjX/3gu9btXKlFZqM\nPSJei4glwOaW1m0HrYl/m7T6HpoX/x8j4q1k9xFgz+bWbQetiX+bjt7/bxfs9gT+0dy67aA18W/T\nrP7v7EloAPBCwf6LybHmlOnfjLrF1pr4If+6899JWiLpP4oWZf2aE3sx6raV1saQZt9Dy+M/Hbjn\nQ9YthtbEDyXS/5KOl/QUsBA4qyV1i6w18UML+r+zv8qhuevP0/wXU2NaG//hEfGypF2B30p6OiIW\nt1FsTWnN2v+O8HsDrY1hTET8PaW+hxbEL+lI4GvAmJbWLaLWxA8l0v8RsQBYIOlTwFxJHytuWM32\noeIHPpp81ez+7+wjoZeAgQX7A8ln9MbK7JmUaU7dYvuw8b8EEBEvJ3++BtxBfojdXlrTf6XS9w2K\niL8nf6bR99DM+JOb+f8DfCEi3mhJ3SJrTfwl0//bJH9BdwP6JuVKov+32Ra/pJ2T/eb3f3ve7Grv\nD/n/qM+Rv7m2E03f2P8k79+cbbJuB4+/O9Ar2e4B/AEY15FiLyh7EdsvTCiJvm8k/lT7vgU/O4PI\n33z+5Ie99g4af6n0/z68/8CA0cBzJdb/DcXfov5vt4tK6wMcAzyT/LCenxybCkwtKPOT5PsngNGN\n1S2V+IEhyQ/OMuAvacTfVOxAP/Lzzm8BbwBrgJ6l0vcNxd8R+r6Z8f8UeB1YmnweLaWf/YbiL6H+\nPy+JbymwGPiXEuv/euNvaf/7sT1mZpaazn5PyMzMOjAnITMzS42TkJmZpcZJyMzMUuMkZGZmqXES\nMjOz1DgJmZlZapyEzMwsNf8f+1aTRChcIkgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf2084a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'tours_tlvdest.csv'))\n",
+    "# Save results by source as seperate df\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "dfplot = pd.DataFrame([df_dict[i].groupby('tmodetp').sum()['toexpfac']/df_dict[i].sum()['toexpfac'] for i in df_dict.keys()]).T\n",
+    "dfplot.columns = df_dict.keys()\n",
+    "dfplot = dfplot.drop('Other', axis=0)\n",
+    "dfplot.plot(kind='barh', alpha=0.6)\n",
+    "display(dfplot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tour Purpose Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survey</th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pdpurp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Escort</th>\n",
+       "      <td>10.8%</td>\n",
+       "      <td>10.6%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meal</th>\n",
+       "      <td>5.0%</td>\n",
+       "      <td>7.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Personal Business</th>\n",
+       "      <td>12.1%</td>\n",
+       "      <td>12.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>School</th>\n",
+       "      <td>13.3%</td>\n",
+       "      <td>10.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Shop</th>\n",
+       "      <td>11.9%</td>\n",
+       "      <td>12.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Social</th>\n",
+       "      <td>18.2%</td>\n",
+       "      <td>20.4%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Work</th>\n",
+       "      <td>28.7%</td>\n",
+       "      <td>25.8%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   survey  sc_2025_20_5\n",
+       "pdpurp                                 \n",
+       "Escort              10.8%         10.6%\n",
+       "Meal                 5.0%          7.0%\n",
+       "Personal Business   12.1%         12.3%\n",
+       "School              13.3%         10.9%\n",
+       "Shop                11.9%         12.9%\n",
+       "Social              18.2%         20.4%\n",
+       "Work                28.7%         25.8%"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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j7ceOHctBBx3EsGHD6sqefPJJqqqqePXVV9l111359re/zbp167Lu52233cbs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\nJEL2o3CU0wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd9d8cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Reformat to plot\n",
+    "dfplot = pd.DataFrame([df_dict[i].groupby('pdpurp').sum()['toexpfac']/df_dict[i].sum()['toexpfac'] for i in df_dict.keys()]).T\n",
+    "dfplot.columns = df_dict.keys()\n",
+    "dfplot.plot(kind='barh', alpha=0.6)\n",
+    "display(dfplot)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Screenlines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+ecq/FVgkaR3ZVPkZqaxtkq4BnkjHzY80Hd/6rhSsrrvulj0BpzyAHejB4r1j\nZsuqWWUzKzg/pNwDP+fVO71dKLe7Ma296xSOJmt1ebFds6Ib8A8p1zsHr945nIVy9w18qxgy5Hbe\n8Y638fnPz3PgMiuoagSvup5taANPd62vpUsX5vLudNAys4Nyy6sHbnn1Tl+6Df0eLrOBz92GNebg\n1Xu9eUbL7+EyGxzcbWiF0dzc7BaUmVWNg5dVjd/DZWb9xd2GPXC3Yf/zElBmA5/HvGrMwcvMrO8G\nw/u8zMzM+szBy8zMCsfBy8zMCsfBy8zMCsfBaxDzm43NrKg827AHA3W2YVtbG/PmXcPTT69O79Dy\nUk1m1n88Vb7GBmLw2ru+4DjgcrxUk5n1N0+Vt3639+WPJ9W6KmZmh8zLQw1al7G31eWlmsysWNzy\nGmRmz76MhoY5wGbgDxkyZDZnnnmbx7vMrFAG9ZiXpKnA3wNHAP8YEQvK9g+4MS/w+oJmVlmesFFB\nko4A/h34ENAFPAFcFBHP5o4ZkMHLzKySPGGjsiYB6yNiQ0TsAr4BnFvjOpmZWS8M5uB1MrAx93lT\nyjMzszo3mIOX+wPNzApqME+V7wLG5D6PIWt97aO1tXVPurGxkcbGxkrXy8ysUDo7O+ns7KzqNQfz\nhI2hZBM2zgGeA1bgCRtmZoetGhM2Bm3LKyJ2S/oU0EY2Vf7WfOAyM7P6NWhbXr3hlpeZWd95qryZ\nmVk3HLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxw\nHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwahK8\nJP2BpGckvSrprLJ98yStk7RGUlMuf6KkVWnfDbn8YZLuSvmPSTott69F0tq0fSKXP07S4+mcb0g6\nstL3bGZm/adWLa9VwHTgkXympAnAhcAEYCpwkySl3TcDMyNiPDBe0tSUPxPYmvKvBxakskYCnwUm\npe1qScelcxYA16VzfpHKGHA6OztrXYXDUuT6F7nu4PrXWtHrXw01CV4RsSYi1naz61xgcUTsiogN\nwHpgsqQTgeERsSIddwdwXkpPAxam9D3AOSndDLRHxPaI2A50AB9OwfADwN3puIW5sgaUov8DKHL9\ni1x3cP1rrej1r4Z6G/M6CdiU+7wJOLmb/K6UT/q6ESAidgM7JJ3QQ1kjge0R8Vo3ZZmZWQEMrVTB\nkjqA0d3wSE3OAAAHuElEQVTsuioivlWp6x5E1Oi6ZmbWnyKiZhvwMHBW7vNcYG7u83JgMlkQfDaX\nfxFwc+6Yd6f0UOCFlJ4BfDl3zj+QjacJeAEYkvJ/G1h+gPqFN2/evHnr+1bp+FGxllcfKJdeBnxd\n0pfIuvLGAysiIiS9KGkysAK4GLgxd04L8BhwPvBgym8HrpU0Il1jCjAnlfUw8AfAXence7urWESo\nu3wzM6stpRZGdS8qTScLPm8AdgArI+LDad9VwKXAbuDKiGhL+ROB24EG4P6IuCLlDwMWAWcCW4EZ\nabIHki4BrkqX/VxELEz544BvkI1/fR/4w4jYVdm7NjOz/lKT4GVmZnY46m22Yb8bjA9ES5qa7mmd\npDmVvl7Ztb8qaYukVbm8kZI60vemPXXllvZV/GfQx/qPkfRw+p35oaRSC78Q9yDpden37SlJqyV9\nvkj1z5VzhKSVkr5VtPpL2iDpB6n+K4pUf0kjJN0t6dn0+zO5buteywkbVZoU8mbgdPafHDIBeAo4\nEhhL9kxZqSW6ApiU0vcDU1N6FnBTSl8IfCOlRwI/Akak7UfAcWnfEuCClL4ZuLzC93tEupex6d6e\nAt5Sxe/3+8i6cFfl8r4I/M+UngN8oUo/gxGHUP/RwDtT+hjg34G3FOweXp++DiUbCz67SPVPZX0a\nuBNYVsDfoZ8AI8vyClF/sudeL839/hxXr3Wvyh+0etjYP3jNI5vAUfq8HHg3cCL7zmzcM2sxHTM5\n94MtzWzcM/sxff5yOq98ZuO7OcDMxn68z31mT1I2g7NK3+ux7Bu81gCjUno0sKZaP4N+uJd7gQ8V\n8R6A1wNPAG8tUv2BU4AHyBYT+FbRfofIgtcJZXl1X3+yQPXjbvLrsu4DvtuwBwP1geg9dSyrSy2N\niogtKb0FGJXS1fgZHDJJY8lakY8X6R4kDZH0VKrnwxHxTJHqT7bM218Cr+XyilT/AB6Q9D1Jf1yg\n+o8DXpB0m6TvS/qKpKPrte71MFX+sMkPRNfDdXslIkJSXdcRQNIxZMuNXRkRL0l7n5qo93tI/1l6\np7K1PNskfaBsf93WX9JHgOcjYqWkxu6Oqef6J++NiJ9JeiPQIWlNfmcd138ocBbwqYh4QtLfk/Xc\n7FFPdR8QLa+ImBIRZ3Sz9RS4uoAxuc+nkEX7rpQuzy+dcyqApKFk41pbuylrTMrbBoyQNCRXVtch\n3WTvdVeXTQc4tlq2SBoNoGydyudTfqV/Bod038om1dwDLIqI0jOAhboHgIjYAXwbmFig+r8HmCbp\nJ8Bi4IOSFhWo/kTEz9LXF4ClZAuDF6H+m4BNEfFE+nw3WTDbXJd172t/blE3sjGvibnPpcHGo8ia\nyz9i72Dj42Qre4j9BxtLK3vMYN/Bxh+TDTQeX0qnfUuAC3P9uJWesDE03cvYdG9VnbCR6jCW/Sds\nzEnpuew/4FvRn0Ef6y6yhZ+vL8svxD2QPTtZ+t1rIHtzwzlFqX/ZvbyfvWNehag/2Tjj8JQ+GvgX\noKlA9X8EOD2lW1O967LuVfuDVquN7NUrG4GdwGbgO7l9V5HNkFkDNOfyJ5K9tmU9cGMufxhZMFpH\nNotrbG7fJSl/HdCSyx+XfpDryFb0OLIK9/xhslly64F5Vf5+LwaeA36Vvu+XpF/MB4C1ZCufjMgd\nX/GfQR/rfzbZWMtTwMq0TS3KPQBnkD14/xTwA+AvU34h6l92L+9n72zDQtSf7N/7U2n7IenfX4Hq\n/w6yST5PA98km8RRl3X3Q8pmZlY4A2LMy8zMBhcHLzMzKxwHLzMzKxwHLzMzKxwHLzMzKxwHLzMz\nKxwHL7MqkzRWuVfG1AtJncpe+mpW9xy8zAaAtNTO4QrqfG1MsxIHL7MKk/Tp9GK+VZKuJAsQQyV9\nLb3w758kNaRjv6DsRZhPS/rblPfG9ILAFWl7T8pvlbRI0qPAHZL+TdKE3HU7JZ0l6WhlLwl9PK0W\nPi3tb1D2gtTVkr5JtpyUMCuAAbGqvFm9St1wf0S2OOsQsqXC/hl4E9lL//5N0q3ALEm3AedFxJvT\nucemYm4gW2vxXySdSvZOpFKQejNwdkS8IunPgAuA1rSA6uiI+L6ka4EHI+LS9BbcxyU9AFwOvBwR\nEySVlpVyy8sKwS0vs8o6G/hmROyMiF+SrRf3O8DGiPi3dMzX0nE7gP+SdKuk6WTrcUL2Msz/I2kl\ncB8wPL1nKcjW/nslHbcEOD+lLwD+KaWbgLnp/IfJ1pc7leyt118DiIhVZGshmhWCW15mlRXs3xVX\nPrYkslclvSppEtkq8OcDn0ppkb199lf5QtI7xv5zT6ERz0namlpRFwB/kjv8oxGxrpvz3U1oheSW\nl1llfRc4L40vHU32loPvAqdKenc65uPAd9P+ERHxHeDTZCt8Q7aS9xWlAiW9gwO7C5gDHBsRP0x5\nbWXnn5mSj6RrI+ltwNsP+S7NqszBy6yCImIlcDuwguwVEF8BfkH2yppPSlpN9tqJm4FjgW9Jepos\nwP15KuYK4F1pEscz7NuiKh+juhu4kKwLseQa4EhJP5D0Q2B+yr8ZOCbVYT7wvcO/Y7Pq8CtRzMys\ncNzyMjOzwnHwMjOzwnHwMjOzwnHwMjOzwnHwMjOzwnHwMjOzwnHwMjOzwnHwMjOzwvn/Yk91LRIv\nP7UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf213dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'screenlines.csv'))\n",
+    "# Save results by source as seperate df\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "for source in df_dict.keys():\n",
+    "    df_dict[source][['model','observed']].plot(kind='scatter', x='observed',y='model', title=source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Traffic Counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TX18MLAzt3wRMBcYCr4ba5wArQ/uc7ddLgT/49fOBu0LHrATmRPSpZ0/KMIyckGzW6+r2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\nDMMwChITKMMwDKMgMYEyDMMwCpL/Hx4RuzV9NXlPAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd83ff60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'traffic_counts.csv'))\n",
+    "# Save results by source as seperate df\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "for source in df_dict.keys():\n",
+    "    df_dict[source][['model','observed']].plot(kind='scatter', x='observed',y='model', title=source)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/summarize/notebooks/calibration/1-school-workplace.ipynb b/scripts/summarize/notebooks/calibration/1-school-workplace.ipynb
deleted file mode 100644
index 28a2e1ca..00000000
--- a/scripts/summarize/notebooks/calibration/1-school-workplace.ipynb
+++ /dev/null
@@ -1,1662 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These notebooks are used to compare a base and scenario, from surveys or model outputs, in H5 format. To run: from the menu bar above, choose **Cell -> Run All ** or run lines individually. Use the toggle button below to hide/show the raw Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    " ## School and Workplace Location Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "    - Workplace Location\n",
-    "        - by County\n",
-    "        - by District\n",
-    "    - School Location\n",
-    "        - by County\n",
-    "        - by District\n",
-    "    - Workers Paying to Park at Work\n",
-    "        - by Workplace County\n",
-    "        - by Workplace District\n",
-    "    - Transit Pass Ownership\n",
-    "       - by Workplace County \n",
-    "       - by Workplace District\n",
-    "       - by Home County \n",
-    "       - by Home District\n",
-    "    - Auto Ownership\n",
-    "       - by County\n",
-    "       - by District\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>\n",
-       "code_show=true; \n",
-       "function code_toggle() {\n",
-       " if (code_show){\n",
-       " $('div.input').hide();\n",
-       " } else {\n",
-       " $('div.input').show();\n",
-       " }\n",
-       " code_show = !code_show\n",
-       "} \n",
-       "$( document ).ready(code_toggle);\n",
-       "</script>\n",
-       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "HTML('''<script>\n",
-    "code_show=true; \n",
-    "function code_toggle() {\n",
-    " if (code_show){\n",
-    " $('div.input').hide();\n",
-    " } else {\n",
-    " $('div.input').show();\n",
-    " }\n",
-    " code_show = !code_show\n",
-    "} \n",
-    "$( document ).ready(code_toggle);\n",
-    "</script>\n",
-    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "from IPython.display import display, display_pretty, Javascript, HTML\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "matplotlib.style.use('ggplot')\n",
-    "from matplotlib.backends.backend_pdf import PdfPages\n",
-    "\n",
-    "# Change working directory; only run this once since its a relative path change\n",
-    "default_path = r'../../../..'\n",
-    "os.chdir(default_path)\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Load h5 or daysim outputs records\n",
-    "# Refering to 2 datasets as BASE and SCEN (scenario)\n",
-    "\n",
-    "survey_loc = r'R:\\SoundCast\\estimation\\2014\\P5'\n",
-    "\n",
-    "base = h5py.File(survey_loc + r'\\survey14.h5','r+')\n",
-    "base_name = '2014 Survey'\n",
-    "\n",
-    "# Note that expansion factor on daysim_outputs = 1 for each record, to allow direct comparison between survey records w/ exp. factor\n",
-    "scen = h5py.File(r'outputs\\daysim_outputs.h5','r+')\n",
-    "scen_name = 'Daysim'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def build_df(h5file, h5table, fields, nested=False):\n",
-    "    '''return all fields from h5 table'''\n",
-    "    data = {}\n",
-    "    for field in fields:\n",
-    "        if nested:\n",
-    "            data[field] = [i[0] for i in h5file[h5table][field][:]]\n",
-    "        else:   \n",
-    "            data[field] = [i for i in h5file[h5table][field][:]]\n",
-    "        \n",
-    "    return pd.DataFrame(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load trip, person, and household files from h5 files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "trip_fields = ['dorp','dtaz','otaz','opurp','dpurp','mode','dpcl','opcl','arrtm','deptm','travdist','trexpfac']\n",
-    "trip_base = build_df(h5file=base, h5table='Trip', fields=trip_fields)\n",
-    "trip_scen = build_df(h5file=scen, h5table='Trip', fields=trip_fields)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "person_fields = ['hhno','pno','ptpass','pwautime','pwaudist','pwtyp','pstyp','pwtaz','pstaz','pagey','pptyp','psexpfac']\n",
-    "person_scen = build_df(h5file=scen, h5table='Person', fields=person_fields)\n",
-    "person_base = build_df(h5file=base, h5table='Person', fields=person_fields)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create unique ID for person by concatenating household ID and person number \n",
-    "person_scen['personID'] = (person_scen['hhno'].astype('str')+person_scen['pno'].astype('str')).astype('int')\n",
-    "person_base['personID'] = (person_base['hhno'].astype('str')+person_base['pno'].astype('str')).astype('int')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hh_fields = ['hhno','hhsize','hhvehs','hhwkrs','hhincome','hhtaz','hhexpfac']\n",
-    "hh_scen = build_df(h5file=scen, h5table='Household', fields=hh_fields)\n",
-    "hh_base = build_df(h5file=base, h5table='Household', fields=hh_fields)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Join household records to person records\n",
-    "hh_per_scen = pd.merge(left=person_scen, right=hh_scen,on='hhno',suffixes=('_p','_h'))\n",
-    "hh_per_base = pd.merge(left=person_base, right=hh_base,on='hhno',suffixes=('_p','_h'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Join household geography\n",
-    "taz_geog = pd.read_csv(r'scripts/summarize/inputs/calibration/TAZ_TAD_County.csv')\n",
-    "taz_geog.reindex\n",
-    "hh_per_scen_home_geog = pd.merge(hh_per_scen, taz_geog, left_on='hhtaz', right_on='TAZ')\n",
-    "hh_per_base_home_geog = pd.merge(hh_per_base, taz_geog, left_on='hhtaz', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Join workplace geography\n",
-    "hh_per_scen_work_geog = pd.merge(hh_per_scen, taz_geog, left_on='pwtaz', right_on='TAZ')\n",
-    "hh_per_base_work_geog = pd.merge(hh_per_base, taz_geog, left_on='pwtaz', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set some formatting options\n",
-    "pd.options.display.float_format = '{:.1f}%'.format    # set float format as percent, until further notice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def report_field(field, expfac, df_base, df_scen, title=None, figsize=(5,5)):\n",
-    "    '''print table summary of totals, distribution, and plot of distribution, for given field'''\n",
-    "    \n",
-    "    df = pd.DataFrame([df_base.groupby(field).sum()[expfac].astype('int'),\n",
-    "                   df_scen.groupby(field).sum()[expfac].astype('int')]).T\n",
-    "    df.columns=([scen_name,base_name])\n",
-    "    df.loc['Total'] = df.sum(axis=0)\n",
-    "    df['% Difference'] = ((df[scen_name] - df[base_name])/df[base_name])*100    # relative to base case\n",
-    "    \n",
-    "    print '--------Totals--------'\n",
-    "    print '' \n",
-    "    print df\n",
-    "    \n",
-    "    print ''\n",
-    "    print ''\n",
-    "    print '-----Distribution-----'\n",
-    "    print ''\n",
-    "\n",
-    "    \n",
-    "    df_new = pd.DataFrame([df[scen_name]/(df[scen_name].loc['Total']),\n",
-    "             df[base_name]/(df[base_name].loc['Total'])]).T*100\n",
-    "    df_new['% Difference'] = ((df_new[scen_name] - df_new[base_name])/df_new[base_name])*100    # relative to base case\n",
-    "    print df_new\n",
-    "    print ''\n",
-    "    df_new.drop(df_new.tail(1).index, inplace=True)    # do not plot totals columnn\n",
-    "    df_new[[scen_name,base_name]].plot(kind='bar', alpha=0.8, title=title, figsize=figsize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Workplace Location"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### by County"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------Totals--------\n",
-      "\n",
-      "            Daysim  2014 Survey  % Difference\n",
-      "County                                       \n",
-      "King       1121029      1140109         -1.7%\n",
-      "Kitsap       79118       101795        -22.3%\n",
-      "Pierce      254531       285553        -10.9%\n",
-      "Snohomish   219565       258854        -15.2%\n",
-      "Total      1674243      1786311         -6.3%\n",
-      "\n",
-      "\n",
-      "-----Distribution-----\n",
-      "\n",
-      "           Daysim  2014 Survey  % Difference\n",
-      "County                                      \n",
-      "King        67.0%        63.8%          4.9%\n",
-      "Kitsap       4.7%         5.7%        -17.1%\n",
-      "Pierce      15.2%        16.0%         -4.9%\n",
-      "Snohomish   13.1%        14.5%         -9.5%\n",
-      "Total      100.0%       100.0%          0.0%\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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GCx8KXO3up5vZzcCZwIT477RGlq8BatJGjautrd3c+AFos7aOdevqm5+xOSF5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\nAZDZTRRc38EHAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16cd1be80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "report_field(field='County', expfac='hhexpfac',\n",
-    "             df_base=hh_per_base_work_geog, \n",
-    "             df_scen=hh_per_scen_work_geog, \n",
-    "             title='Workplace Distribution by County')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "### Workplace Location by District"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------Totals--------\n",
-      "\n",
-      "                          Daysim  2014 Survey  % Difference\n",
-      "New DistrictName                                           \n",
-      "East Side                 341627       331061          3.2%\n",
-      "Everett-Lynwood-Edmonds   134170       152032        -11.7%\n",
-      "Kitsap                     79118       101795        -22.3%\n",
-      "North Seattle-Shoreline   145550       142736          2.0%\n",
-      "Renton-FedWay-Kent        236524       288721        -18.1%\n",
-      "S.Kitsap                   14534        19895        -26.9%\n",
-      "Seattle CBD               280301       247207         13.4%\n",
-      "South Pierce              122157       144052        -15.2%\n",
-      "Suburban Snohomish         85395       106822        -20.1%\n",
-      "Tacoma                    117838       121606         -3.1%\n",
-      "West-South Seattle        117025       130384        -10.2%\n",
-      "Total                    1674239      1786311         -6.3%\n",
-      "\n",
-      "\n",
-      "-----Distribution-----\n",
-      "\n",
-      "                         Daysim  2014 Survey  % Difference\n",
-      "New DistrictName                                          \n",
-      "East Side                 20.4%        18.5%         10.1%\n",
-      "Everett-Lynwood-Edmonds    8.0%         8.5%         -5.8%\n",
-      "Kitsap                     4.7%         5.7%        -17.1%\n",
-      "North Seattle-Shoreline    8.7%         8.0%          8.8%\n",
-      "Renton-FedWay-Kent        14.1%        16.2%        -12.6%\n",
-      "S.Kitsap                   0.9%         1.1%        -22.1%\n",
-      "Seattle CBD               16.7%        13.8%         21.0%\n",
-      "South Pierce               7.3%         8.1%         -9.5%\n",
-      "Suburban Snohomish         5.1%         6.0%        -14.7%\n",
-      "Tacoma                     7.0%         6.8%          3.4%\n",
-      "West-South Seattle         7.0%         7.3%         -4.2%\n",
-      "Total                    100.0%       100.0%          0.0%\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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SJEnZMPxKkiQpG4ZfSZIkZcPwK0mSpGwYfiVJkpQNw68kSZKyYfiVJElSNgy/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\n/wO0GVuW/LSBqQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ba53c88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "report_field('New DistrictName', expfac='hhexpfac',\n",
-    "             df_base=hh_per_base_work_geog, \n",
-    "             df_scen=hh_per_scen_work_geog, \n",
-    "             title='Workplace Distribution by District', figsize=(12,8))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distance to Work"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Select all home -> work and work -> trips\n",
-    "def get_work_trips(df):\n",
-    "    \n",
-    "    # home to work trips\n",
-    "    work2home = df[df['opurp'] == 1]    # select all work destination\n",
-    "    work2home = work2home[work2home['dpurp'] == 0]    # select all home origins\n",
-    "\n",
-    "    # work to home trips\n",
-    "    home2work = df[df['opurp'] == 0]    # select all work destination\n",
-    "    home2work = home2work[home2work['dpurp'] == 1]   # select all home origins\n",
-    "\n",
-    "    # all work trips\n",
-    "    work_trips = work2home.append(home2work)\n",
-    "    \n",
-    "    return work_trips"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def wt_mean(df, measure_col, weight_col, groupby_field=None):\n",
-    "    '''Compute weighted statistic for a dataframe'''\n",
-    "    \n",
-    "    if groupby_field:\n",
-    "        df['weighted'] = df[measure_col]*df[weight_col]\n",
-    "        result_df = df.groupby(groupby_field).sum()['weighted']/df.sum()[weight_col]\n",
-    "    else:\n",
-    "        result_df = (df[measure_col]*df[weight_col]).sum()/df[weight_col].sum()\n",
-    "    \n",
-    "    return result_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "pd.options.display.float_format = '{:.1f}'.format "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "work_trips_base = get_work_trips(trip_base)\n",
-    "work_trips_scen = get_work_trips(trip_scen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Exclude all -1 trips\n",
-    "work_trips_base = work_trips_base[work_trips_base['travdist'] > 0]\n",
-    "work_trips_scen = work_trips_scen[work_trips_scen['travdist'] > 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "** Average Distance to Work/Home**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Daysim: 13.489142888\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print base_name + \": \" + str(wt_mean(df=work_trips_base, \n",
-    "#                                      measure_col='travdist', \n",
-    "#                                      weight_col='trexpfac'))\n",
-    "print scen_name + \": \" + str(wt_mean(df=work_trips_scen, \n",
-    "                                     measure_col='travdist', \n",
-    "                                     weight_col='trexpfac'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x16a998438>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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6+w4zC8ANwOPuvrTSSomIyNAqG/zdvdPMFgGriad63uDuG8xsYbJ9ubvfZWbn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\nUvAXEckhBX8RkRz6/wxh1bfVJ9SHAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x460c320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# (work_trips_base['travdist']/100).hist(bins=100, normed=True,\n",
-    "#                                               weights=work_trips_base['trexpfac'].values,\n",
-    "#                                              alpha=0.5, range=[0,50],\n",
-    "#                                              label=base_name)\n",
-    "\n",
-    "work_trips_scen['travdist'].hist(bins=100, normed=True, \n",
-    "                                        alpha=0.5, range=[0,50],\n",
-    "                                       label=scen_name)\n",
-    "\n",
-    "pyplot.legend(loc='upper right')\n",
-    "pyplot.title('Distance to Work, All Modes')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## School Location"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Join school geography\n",
-    "hh_per_scen_school_geog = pd.merge(hh_per_scen, taz_geog, left_on='pstaz', right_on='TAZ')\n",
-    "hh_per_base_school_geog = pd.merge(hh_per_base, taz_geog, left_on='pstaz', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### By County"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------Totals--------\n",
-      "\n",
-      "            Daysim  2014 Survey  % Difference\n",
-      "County                                       \n",
-      "King        692792       509961          35.9\n",
-      "Kitsap       63814        64244          -0.7\n",
-      "Pierce      239216       208352          14.8\n",
-      "Snohomish   187979       189855          -1.0\n",
-      "Total      1183801       972412          21.7\n",
-      "\n",
-      "\n",
-      "-----Distribution-----\n",
-      "\n",
-      "           Daysim  2014 Survey  % Difference\n",
-      "County                                      \n",
-      "King         58.5         52.4          11.6\n",
-      "Kitsap        5.4          6.6         -18.4\n",
-      "Pierce       20.2         21.4          -5.7\n",
-      "Snohomish    15.9         19.5         -18.7\n",
-      "Total       100.0        100.0           0.0\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ba65630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "report_field(field='County', expfac='psexpfac',\n",
-    "             df_base=hh_per_base_school_geog, \n",
-    "             df_scen=hh_per_scen_school_geog, \n",
-    "             title='School Distribution by County', figsize=(8,8))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "### School Location by District"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------Totals--------\n",
-      "\n",
-      "                          Daysim  2014 Survey  % Difference\n",
-      "New DistrictName                                           \n",
-      "East Side                 239550       165228          45.0\n",
-      "Everett-Lynwood-Edmonds    89072        74502          19.6\n",
-      "Kitsap                     63814        64244          -0.7\n",
-      "North Seattle-Shoreline   156919        78851          99.0\n",
-      "Renton-FedWay-Kent        135589       161415         -16.0\n",
-      "S.Kitsap                   14677        13659           7.5\n",
-      "Seattle CBD               102559        49515         107.1\n",
-      "South Pierce              149147       128679          15.9\n",
-      "Suburban Snohomish         98907       115353         -14.3\n",
-      "Tacoma                     75392        66014          14.2\n",
-      "West-South Seattle         58174        54952           5.9\n",
-      "Total                    1183800       972412          21.7\n",
-      "\n",
-      "\n",
-      "-----Distribution-----\n",
-      "\n",
-      "                         Daysim  2014 Survey  % Difference\n",
-      "New DistrictName                                          \n",
-      "East Side                  20.2         17.0          19.1\n",
-      "Everett-Lynwood-Edmonds     7.5          7.7          -1.8\n",
-      "Kitsap                      5.4          6.6         -18.4\n",
-      "North Seattle-Shoreline    13.3          8.1          63.5\n",
-      "Renton-FedWay-Kent         11.5         16.6         -31.0\n",
-      "S.Kitsap                    1.2          1.4         -11.7\n",
-      "Seattle CBD                 8.7          5.1          70.1\n",
-      "South Pierce               12.6         13.2          -4.8\n",
-      "Suburban Snohomish          8.4         11.9         -29.6\n",
-      "Tacoma                      6.4          6.8          -6.2\n",
-      "West-South Seattle          4.9          5.7         -13.0\n",
-      "Total                     100.0        100.0           0.0\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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CLl1p6lzFvkECnatYr2vXrlU/d2vq1q1blOddHnmvMe/1AXSaMzvX70PI/+uY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\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10870d630>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "report_field(field='New DistrictName', expfac='psexpfac',\n",
-    "             df_base=hh_per_base_school_geog, \n",
-    "             df_scen=hh_per_scen_school_geog, \n",
-    "             title='School Distribution by District', figsize=(8,8))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Transit Pass Ownership"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Brice\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:8: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "C:\\Users\\Brice\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 2014 survey has multiple fields for transit pass ownership. Sum all non-zero fields into 1, for yes/no\n",
-    "\n",
-    "# Drop -1 (no answer rows for transit pass)\n",
-    "df_pass_base_home = hh_per_base_home_geog[hh_per_base_home_geog['ptpass'] >= 0]\n",
-    "df_pass_base_work = hh_per_base_work_geog[hh_per_base_work_geog['ptpass'] >= 0]\n",
-    "\n",
-    "# Convert categorical responses (different pass types) to binary yes/no\n",
-    "df_pass_base_home['ptpass'] = df_pass_base_home['ptpass'].apply(lambda x: 1 if x >=1 else 0)\n",
-    "df_pass_base_work['ptpass'] = df_pass_base_work['ptpass'].apply(lambda x: 1 if x >=1 else 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Region Wide"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------Totals--------\n",
-      "\n",
-      "         Daysim  2014 Survey  % Difference\n",
-      "ptpass                                    \n",
-      "0       2299667      3088967         -25.6\n",
-      "1        467002       507572          -8.0\n",
-      "Total   2766669      3596539         -23.1\n",
-      "\n",
-      "\n",
-      "-----Distribution-----\n",
-      "\n",
-      "        Daysim  2014 Survey  % Difference\n",
-      "ptpass                                   \n",
-      "0         83.1         85.9          -3.2\n",
-      "1         16.9         14.1          19.6\n",
-      "Total    100.0        100.0           0.0\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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W9wZ+ApzvnKsLDAb2xu+tSmloxmBy84CnnXM7AKy1DwNPAYuL2f5j59y8YNvH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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1087689b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "report_field('ptpass', expfac='psexpfac',\n",
-    "             df_base=df_pass_base_home, \n",
-    "             df_scen=hh_per_scen_home_geog, \n",
-    "             title='Transit Pass', figsize=(4,4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## By Home County"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def transit_pass(df_base, expfac, df_scen, geography, title=None):\n",
-    "    df_result = pd.DataFrame()\n",
-    "\n",
-    "    # base\n",
-    "    df = pd.pivot_table(data=df_base, columns=['ptpass'], index=geography, \n",
-    "                   values=expfac, aggfunc='sum').astype('int')\n",
-    "    df_result[base_name] = (df[1]/df.sum(axis=1))*100\n",
-    "    df_result\n",
-    "\n",
-    "    # scenario\n",
-    "    df = pd.pivot_table(data=df_scen, columns=['ptpass'], index=geography, \n",
-    "                   values=expfac, aggfunc='sum').astype('int')\n",
-    "    df_result[scen_name] = (df[1]/df.sum(axis=1))*100\n",
-    "    \n",
-    "    print df_result\n",
-    "    print \"\"\n",
-    "    print \"--------------------------\"\n",
-    "    print \"\"\n",
-    "    print df_result.plot(kind='bar', title=title, alpha=0.8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           2014 Survey  Daysim\n",
-      "County                        \n",
-      "King              22.1    17.1\n",
-      "Kitsap            17.5     7.0\n",
-      "Pierce             8.4    10.8\n",
-      "Snohomish         11.0    12.2\n",
-      "\n",
-      "--------------------------\n",
-      "\n",
-      "Axes(0.125,0.125;0.775x0.775)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Jl1lWVkbS770hNMT2LC8vT8V7T4uioiJtz4SkZVsWFRVhZiW5ptWmG+FjwBHA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\nAheRpqLMzEblO4gkqQ1cRJo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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x16cc01278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "transit_pass(df_base=df_pass_base_home, expfac='hhexpfac',\n",
-    "             df_scen=hh_per_scen_home_geog, \n",
-    "             geography='County', title='Transit Pass Ownership by Home County')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                         2014 Survey  Daysim\n",
-      "New DistrictName                            \n",
-      "East Side                       17.5    12.2\n",
-      "Everett-Lynwood-Edmonds         14.3    15.7\n",
-      "Kitsap                          17.5     7.0\n",
-      "North Seattle-Shoreline         31.3    23.9\n",
-      "Renton-FedWay-Kent              10.1    12.3\n",
-      "S.Kitsap                         5.9     5.8\n",
-      "Seattle CBD                     38.7    34.5\n",
-      "South Pierce                     6.0     9.5\n",
-      "Suburban Snohomish               8.9    10.0\n",
-      "Tacoma                          13.5    14.9\n",
-      "West-South Seattle              32.4    19.5\n",
-      "\n",
-      "--------------------------\n",
-      "\n",
-      "Axes(0.125,0.125;0.775x0.775)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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CznadjFWnYBS4YbRa5n1H1f1jGlzd/phTYD1T4MaqYzZwwzCMIsUUuGEYRpFiCtwwDKNI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\nMQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x13060d2e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "transit_pass(df_base=df_pass_base_home, expfac='hhexpfac',\n",
-    "             df_scen=hh_per_scen_home_geog, \n",
-    "             geography='New DistrictName', title='Transit Pass Ownership by Home District')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### By Work Location"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## County Work Location"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           2014 Survey  Daysim\n",
-      "County                        \n",
-      "King              29.9    22.5\n",
-      "Kitsap            18.0     5.1\n",
-      "Pierce             6.6     8.8\n",
-      "Snohomish          8.4     8.3\n",
-      "\n",
-      "--------------------------\n",
-      "\n",
-      "Axes(0.125,0.125;0.775x0.775)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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ZXl5O0tueFkVFRS1225uC9mdy0rIvi4qKMLOSmqZttqrpvXfAvcBsM7sjY9KT\nwI/i9z8CJmcvKyIiTauuGvjhwCjgbe/9jHjcVcCvAfPe/5i4G2GTRSgiIjXabAI3sxepvZZ+TPLh\niIhIfelKTBGRlFICFxFJKSVwEZGUUgIXEUkpJXARkZRSAhcRSSklcBGRlFICFxFJKSVwEZGUUgIX\nEUmpet1OVlqGvGVLYOniRMtc37krtEvHLTtF0kYJXDZYupjyR+5OtMjCM86H3ZTARZqCmlBERFJK\nCVxEJKWUwEVEUkoJXEQkpZTARURSSglcRCSllMBFRFJKCVxEJKWUwEVEUkoJXEQkpZTARURSSglc\nRCSllMBFRFJKCVxEJKWUwEVEUkoJXEQkpep8oIP3/o/A8cAiM9s/HlcCnA1UPb7lKjP7a1MFKSIi\nm6rPE3nuA+4EHsgYFwG3mdltTRKViIjUqc4mFDN7Afi6hkku+XBERKS+GvNMzAu992cAbwCXmdmy\nhGISEZF6aOhJzHHAnkB/YD7wm8QiEhGRemlQDdzMFlW9997fAzyVPY/3vhgozliGoqJkn06+rqCQ\nyvyEO9I4yE+4zMLCwsS3vSk0xf7My8tLxbanRatWrbQ/E5KmfRl3HKlSamal0MAE7r3vYmbz48Hh\nwDvZ88QrKM0YNbasrKwhq6tV3vpyKioqEy2TiMTLLC8vJ+ltbwpNsT8rKytTse1pUVRUpP2ZkLTs\ny6KiIsyspKZp9elG+CdgMLCT9/5zYCxQ7L3vT+iN8inwk+TCFRGR+qgzgZvZqTWM/mMTxCIiIltA\nV2KKiKSUEriISEopgYuIpJQSuIhISimBi4iklBK4iEhKKYGLiKSUEriISEopgYuIpJQSuIhISimB\ni4iklBK4iEhKKYGLiKSUEriISEopgYuIpJQSuIhISimBi4iklBK4iEhKKYGLiKSUEriISEopgYuI\npJQSuIhISimBi4iklBK4iEhKKYGLiKSUEriISEopgYuIpJQSuIhIShXUNYP3/o/A8cAiM9s/Hrcj\nMBHYHZgLeDNb1oRxiohIlvrUwO8Dvpc17kpgipn1BKbFwyIishXVmcDN7AXg66zRQ4H74/f3A8MS\njktEROrQ0DbwTma2MH6/EOiUUDwiIlJPjT6JaWYRECUQi4iIbIE6T2LWYqH3vrOZLfDedwEWZc/g\nvS8GiquGzYyioqIGrq5m6woKqcxPuCONg/yEyywsLEx825tCU+zPvLy8VGx7WrRq1Ur7MyFp2pfe\n+5KMwVKWZ4vbAAAPMElEQVQzK4WGJ/AngR8BN8V/J2fPEK+gNGPU2LKysgaurmZ568upqKhMtEwi\nEi+zvLycpLe9KTTF/qysrEzFtqdFUVGR9mdC0rIvi4qKMLOSmqbVpxvhn4DBwE7e+8+B64FfA+a9\n/zFxN8LEohURkXqpM4Gb2am1TDom4VhERGQL6EpMEZGUUgIXEUkpJXARkZRSAhcRSSklcBGRlGpo\nP3ARyYH5y1cz7+u1iZa583aFdGyjulwaKYGLpMjCFeu4+/X5iZZ57sFd6NimdaJlytahr10RkZRS\nDVxEtnl5y5bA0sWJlrm+c1dol457odRGCVxEtn1LF1P+yN2JFvn1Odcwf3W6zycogYtIi7RoLdz9\nZrrPJ6gNXEQkpZTARURSSglcRCSllMBFRFJKCVxEJKWUwEVEUkoJXEQkpZTARURSSglcRCSllMBF\nRFJKl9KLNJGmuAFT5XbdEy1P0k0JXKSpNMENmDjjmmTLk1RTE4qISEopgYuIpJQSuIhISimBi4ik\nlBK4iEhKNaoXivd+LvANUAGUm9nBSQQlIiJ1a2w3wggoNrOlSQQjIiL1l0QTikugDBER2UKNTeAR\nMNV7/4b3/pwkAhIRkfppbBPK4WY233u/MzDFe/++mb2QRGDSPCxq3YH5S9YmWubO2xXSsY3Ov4s0\nKoGb2fz472Lv/RPAwcALAN77YqA4Y16Kiooas7pNrCsopDI/4Q+yg/yEyywsLEx825tCU+zPRevg\n3pkLEy3zvEO7scfOLXN/ttTjs6XvS+99ScZgqZmVQiMSuPe+HZBvZmXe++2AIcDPqqbHKyjNWGRs\nWVlZQ1dXo7z15VRUVCZaJhGJl1leXk7S294UtD+Tpf2ZnJa8L4uKijCzkpqmNaYG3gl4wntfVc7D\nZvZcI8oTEZEt0OAEbmafAv0TjEVERLaAzgSJiKSUEriISEopgYuIpJQSuIhISimBi4iklBK4iEhK\nKYGLiKSUEriISEopgYuIpJQSuIhISimBi4iklBK4iEhKKYGLiKSUEriISEopgYuIpJQSuIhISimB\ni4iklBK4iEhKKYGLiKSUEriISEopgYuIpJQSuIhISimBi4iklBK4iEhKKYGLiKSUEriISEopgYuI\npJQSuIhIShU0dEHv/feAO4B84B4zuymxqEREpE4NqoF77/OBu4DvAb2BU733+yYZmIiIbF5Dm1AO\nBj4ys7lmVg78GTgpubBERKQuDU3gXYHPM4bnxeNERGQraWgCjxKNQkREtlhDT2J+AXTPGO5OqIVX\n894XA8VVw2bGrrvu2sDV1WLXXWHQUYkW2R0YfGiiRaaH9meytD+T08L3pfe+JGOw1MxKAVwUbXll\n2ntfAHwAHA18CbwOnGpm7zU60hzz3peYWUmu42gutD+Tpf2ZnOawLxvUhGJm64ELgL8Bs4GJzSF5\ni4ikSYP7gZvZs8CzCcYiIiJbQFdibqo01wE0M6W5DqCZKc11AM1Iaa4DaKwGtYGLiEjuqQYuIpJS\nSuAiIimlBC4iklIN7oXSXHjvD2DTK0uXA/+Ku0vKFvLedyHcL6cS+KeZLchxSKnlvd8OuBTYzczO\n8d7vDfQys6dzHFoqee+7AnsQ7qLqgMjM/pHToBpBNXD4HfAa8If49SrwGDDHe//dXAaWRt77swn7\n82RgBPCa9/7HuY0q1e4D1gGHxcNfAjfmLpz08t7fBLwEXAP8FLg8/ptaLb4GTvhA/NjMZgF473sD\nPweuACYRLlaS+rsCGGBmSwC89x2BV4B7cxpVevUwM++9/wGAma303uc6prQaTvj1sjbXgSRFNfDw\nD51VNWBms4F9zOxjdNOuhvgKWJExvCIeJw2z1nvftmrAe98DaDYJaCv7GGiV6yCS1OL7gXvvDVhC\nuKe5AzywMzAKeNHMDspheKnjvX8Q2A/4SzzqJODt+BWZ2W25ii2NvPdDCD/5ewNTgMOB0WY2PaeB\npYj3/s747a5Af2AaG74EIzMbk5PAEqAmFBgN/CdwcTz8EqFtrBxI9vZnLcPH8auqZvCX+H37nEWU\nYmb2nPf+TaDqvnljzEy/aLbM/xGOQQc8lfE+9bXXFl8DF9mWee9PBv5uZsvi4Q5AsZlNzm1k6ea9\n3xHobmYzcx1LY7T4BO69HwSMJXQtqvpFEpnZv+UsqBTz3u9COJHZG6hqu43MTL9mGsB7P9PM+mWN\ne8vM+ucqprTy3pcCQwmf8/8DFgMvmdkluYyrMXQSM/SOuA0YBBwUvw7OaUTp9jDwPvBvQAkwF3gj\nh/GknathXP5Wj6J56GBm3xC6uD5gZgcDx+Q4pkZRGzgsi2+NK8noaGb3eO/HmNnzwPPeeyXwhvs/\n7/1thOsVHHA+ofYoWy4/vsjMA9fG41LdBKEEDtO997cQ+nxXd88yszdzF1KqrYv/LvDen0DoZ/+t\nHMaTdhcA1wMT4+EphCQuW+4GwnUdL5nZ63GXzA9zHFOjqA08tIttshPM7MitH036ee9PBF4gPHLw\nTmB7oMTMnsxpYCkUP7pwio5FqU2LT+Ai2zLv/TTglKpeKLLlvPf/ZWY3xf3Bq7oQVlE/8DTy3v/Q\nzB703l/GxjXwqhvc6IKTBoibo34OrAb+CvQDLjGzB3MaWHqtBN7x3k+J30PKk04OzI7/1nTuINU1\n2BabwIF28d8iakjgWz+cZmOImf3Uez+c0APlZEKTihJ4w0yKX1XHpI7PLWRmT8V/J+Q4lMS15AT+\nLICZlWRPiNtxpWGqjqkTgMfMbLn3Xgmngcxsgve+HeF2su/nOp40894fBFzNptd89M1ZUI3UkvuB\nT/He75k90nt/FvDfOYinuXjKe/8+cAAwLb6wZ02OY0ot7/1QYAahOQrv/QDvvU4IN8zDhNvzngKc\nGL+G5jSiRmrJNfBLgOe898eb2RwA7/1VwOnAETmNLN1KgFuA5Wa23nu/knBDK2mYEuAQYDqAmc3w\n3usq4YZZ3Nx6Q7XYBG5mz3jv1wLPeu9PAs4mXIH5HTP7OrfRpdrLZjawaiC+f/ULwMDNLCO1Kzez\nZVn3AK/MVTAp9zPv/b3AVDZcrxCZ2aQcxtQoLTaBA5jZNO/9mcDzhLsQHmVm+rnfAPEVbrsC7bz3\nA9lwsm17Npwwli03y3t/OlAQP05tDPByjmNKqx8BvQh5L/NLUAk8bbz3K9hwNr8NcDSwOK7pRGa2\nfa5iS6nvEj4gXYHfZIwvI5w4koa5kHA/8LXAnwhXEv48pxGl14GEh7U0m5PqupBHEuW9P8XMHs91\nHCLZvPf3AbdmPoEr7ZTAJRG6MCpZ3vv/NrOLvPdP1TA5MrNU957Ihbh3VA/gUzZ+Ik9quxG22CYU\nSZwujErWA/HfW+O/G13+vZVjaS6+F//NvCgq1ZTAJSm6MCpZs733lwB7EZ4n+kczK89xTKlmZnO9\n9/2B7xCS+AtpfyJPS76QR5KlC6OSdT/hYqi3gePYUBOXBvLeXwQ8RHhoeSfgIe99qu8powQuSam6\nMKpn1Yj4wqhL0YVRDbGvmY0ys/HACLQPk3A2cIiZXW9m1xEeFH1OjmNqFCVwSYSZPQOcR7gwaj/v\n/R2ES5W/Y2bzchtdKq2vemNm6zc3o2yRylrep5J6oUiivPdHAE8QLozyujCqYbz3FcCqjFFtCbfo\nBV2n0CDe+0uB0YQLdxwwDJhgZrfnMq7GUAKXRNRwYdQ6NtRwlHBkm+C9P4DwAPOqk5gzchxSoyiB\ni0iL4b3PBzoTeuBFAGb2WU6DagR1IxSRFsF7fyEwFlgEVGRM2j83ETWeEriItBQXA73MbEmuA0mK\neqGISEvxGfBNroNIktrARaRZi+/PA9Ab2Ad4mo3vB57a+/SoCUVEmruq+/N8BnwOtIpfqb9Pj2rg\nItKieO+LAMysLNexNJYSuIi0CN77/Ql3eewYj1oM/MjM3s1dVI2jk5gi0lLcDVxqZruZ2W7AZfG4\n1FICF5GWop2ZTa8aMLNSYLvchdN4OokpIi3Fp97764AHCScwTwc+yW1IjaMauIi0FGcBuxBuZvU4\n4b7gZ+U0okbSSUwRkZRSE4qItAje+17A5cAebMh9kZkdlbOgGkkJXERaikeBccA9bLiZVaqbIJTA\nRaSlKDezcbkOIklqAxeRFsF7X0K4eGcSsLZqvJktzVVMjaUauIi0FKMJTSaXZ43fc+uHkgwlcBFp\n1rz3BwOfm9ke8fBo4BRgLlCSq7iSoH7gItLcjSduMokfuv0rYAKwPJ6WWqqBi0hzl5fRzv3vwHgz\nexx43Hs/M4dxNZpq4CLS3OV77wvj98cA0zOmpboSm+rgRUTq4U/A8977r4BVwAsA3vu9gWW5DKyx\nVAMXkWbNzG4k3Dr2PmCQmVXGkxxwYc4CS4D6gYuIpJRq4CIiKaUELiKSUkrgIiIppQQuIpJS6kYo\nqeW9Pw24FOgFlAFvATea2UtNuM5KYC8zS/WjuKR5UA1cUsl7fylwO/ALwmOyugO/A4ZuhdW7rbAO\nkTqpG6Gkjvd+B2AeMDq+JDp7emvgJmBkPMqA/zKzdfGNjH5sZt/JmL+6Vu29nwCsBHYHjgBmA6fF\n0/4BDCJcDFIJnA1cB1xlZk/HZRUC84GjzSzVl2nLtk81cEmjbwNtgCdqmX4NcDDQL34dDFy7BeX/\nO+Eudd8CPgJuBDCzI+Lpfc1sezMz4AFgVMay3we+UPKWrUEJXNKoI/BVxhV12U4DbjCzr8zsK+Bn\nwA/rWXYETDKzN8ysAngY6L+Z+R8Gjvfet4+Hfwg8WM91iTSKErik0RJgJ+99bcfvrsC/MoY/i8fV\n18KM96uB9rXNaGZfAi8BI7z3HYDvEZK6SJNTApc0eoVwf+fhtUz/kvDk8Sq7xeMgtG+3q5rgve+c\nQDz3E5pRRgIvm9n8BMoUqZO6EUrqmNly7/31wO+89+uBKUA54VahxYS7z13rvf9nvMj1bGjWmAn0\n8d73Az5g0yey1NXDZCHQA8jsRvgEoQdMJ8LJU5GtQjVwSSUzu43QB/xaYBGhmeQ/Ccn0F8AbwNvx\n6414HGY2B7gBmEpI4C8Q2r2rRFnDZA2XAPd777/23o+Iy1xDeFDuHvFfka1C3QhFEuC9vw7Y28zO\nyHUs0nKoCUWkkbz3OwJnUf+eLiKJUBOKSCN4788hNN88a2Yv5joeaVnUhCIiklKqgYuIpJQSuIhI\nSimBi4iklBK4iEhKKYGLiKSUEriISEr9P7F8+AeiuGTWAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1305d3710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "transit_pass(df_base=df_pass_base_work, expfac='hhexpfac',\n",
-    "             df_scen=hh_per_scen_work_geog, \n",
-    "             geography='County', title='Transit Pass Ownership by Work County')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## by Work District"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                         2014 Survey  Daysim\n",
-      "New DistrictName                            \n",
-      "East Side                       18.4    14.7\n",
-      "Everett-Lynwood-Edmonds         10.5    11.1\n",
-      "Kitsap                          18.0     5.1\n",
-      "North Seattle-Shoreline         33.2    23.7\n",
-      "Renton-FedWay-Kent              11.7    11.5\n",
-      "S.Kitsap                         4.4     5.3\n",
-      "Seattle CBD                     57.7    47.0\n",
-      "South Pierce                     2.5     5.3\n",
-      "Suburban Snohomish               5.2     4.1\n",
-      "Tacoma                          11.1    13.6\n",
-      "West-South Seattle              29.9    19.2\n",
-      "\n",
-      "--------------------------\n",
-      "\n",
-      "Axes(0.125,0.125;0.775x0.775)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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G1Lr+i+MvYfQHmWUP260b227UNpZ6FeK1bq6ymyp3s802gyoTdiV1mlBU9WJV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\nCB0qAngIIXSo/weCyrr6+cS92gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ddc7668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "transit_pass(df_base=df_pass_base_work, expfac='hhexpfac',\n",
-    "             df_scen=hh_per_scen_work_geog, \n",
-    "             geography='New DistrictName', title='Transit Pass Ownership by Work District')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Auto Ownership"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vehicles per Household\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Daysim</th>\n",
-       "      <th>2014 Survey</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>hhvehs</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>110375</td>\n",
-       "      <td>116720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>421679</td>\n",
-       "      <td>477848</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>562190</td>\n",
-       "      <td>560222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>240517</td>\n",
-       "      <td>218361</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>114591</td>\n",
-       "      <td>72856</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>22097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0</td>\n",
-       "      <td>2295</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>4625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1874</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1537</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Daysim  2014 Survey\n",
-       "hhvehs                     \n",
-       "0       110375       116720\n",
-       "1       421679       477848\n",
-       "2       562190       560222\n",
-       "3       240517       218361\n",
-       "4       114591        72856\n",
-       "5            0        22097\n",
-       "6            0         2295\n",
-       "7            0         4625\n",
-       "8            0         1874\n",
-       "10           0         1537"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print 'Vehicles per Household'\n",
-    "df = pd.DataFrame([(hh_scen.groupby('hhvehs').sum()['hhexpfac']),\n",
-    "                   hh_base.groupby('hhvehs').sum()['hhexpfac']]).T\n",
-    "df.columns=([scen_name,base_name])\n",
-    "df.fillna(0,inplace=True)\n",
-    "df[scen_name] = df[scen_name].astype('int')\n",
-    "df[base_name] = df[base_name].astype('int')\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Distribution of Vehilces per Household\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Daysim</th>\n",
-       "      <th>2014 Survey</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>hhvehs</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7.6</td>\n",
-       "      <td>7.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>29.1</td>\n",
-       "      <td>32.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>38.8</td>\n",
-       "      <td>37.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.6</td>\n",
-       "      <td>14.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.9</td>\n",
-       "      <td>4.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Daysim  2014 Survey\n",
-       "hhvehs                     \n",
-       "0          7.6          7.9\n",
-       "1         29.1         32.3\n",
-       "2         38.8         37.9\n",
-       "3         16.6         14.8\n",
-       "4          7.9          4.9\n",
-       "5          0.0          1.5\n",
-       "6          0.0          0.2\n",
-       "7          0.0          0.3\n",
-       "8          0.0          0.1\n",
-       "10         0.0          0.1"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print 'Distribution of Vehilces per Household'\n",
-    "df_new = pd.DataFrame([df[scen_name]/df[scen_name].sum(),\n",
-    "             df[base_name]/df[base_name].sum()]).T*100\n",
-    "df_new"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x70ff4208>"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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S1g6lVSiBx9SStXBlbcbn+DYZf8y36dKupc/OlbjI1WOpUK5rdzZ27pJzvoMOOohly5bR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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9b5925f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_new.plot(kind='bar', title='Auto Ownership Distribution Regionwide')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Daysim: 1.8808226021\n",
-      "2014 Survey: 1.84776763947\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Average autos per household\n",
-    "print scen_name + \": \" + str(sum(hh_scen['hhvehs']*hh_scen['hhexpfac'])/sum(hh_scen['hhexpfac']))\n",
-    "print base_name + \": \" + str(sum(hh_base['hhvehs']*hh_base['hhexpfac'])/sum(hh_base['hhexpfac']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Auto Ownership by Income\n",
-    "# Create common income ranges\n",
-    "def map_income(df, in_field, out_field):\n",
-    "    \n",
-    "    # Define categories\n",
-    "    incmap = {}\n",
-    "    for i in range(0, 20000):\n",
-    "        incmap.update({i: ' <20k'})\n",
-    "    for i in range(20000, 40000):\n",
-    "        incmap.update({i: '20k-40k'})\n",
-    "    for i in range(40000, 60000):\n",
-    "        incmap.update({i: '40k-60k'})\n",
-    "    for i in range(60000, 75000):\n",
-    "        incmap.update({i: '60k-75k'})\n",
-    "    for i in range(75000, 100000):\n",
-    "        incmap.update({i: '75k-100k'})\n",
-    "    for i in range(100000, 150000):\n",
-    "        incmap.update({i: '100k-150k'})\n",
-    "    for i in range(150000, int(df[in_field].max())+1):\n",
-    "        incmap.update({i: '>150k'})\n",
-    "\n",
-    "    df[out_field] = df[in_field].map(incmap)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "hh_scen = map_income(hh_scen, in_field='hhincome', out_field='Income')\n",
-    "hh_base = map_income(hh_base, in_field='hhincome', out_field='Income')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "df_scen = pd.pivot_table(data=hh_scen, index='hhvehs', columns='Income', values='hhexpfac', aggfunc='sum')\n",
-    "df_base = pd.pivot_table(data=hh_base, index='hhvehs', columns='Income', values='hhexpfac', aggfunc='sum')\n",
-    "# # Sort the columns\n",
-    "df_scen = df_scen[[' <20k','20k-40k','40k-60k','60k-75k',\n",
-    "                   '75k-100k','100k-150k','>150k']]\n",
-    "df_base = df_base[[' <20k','20k-40k','40k-60k','60k-75k',\n",
-    "                   '75k-100k','100k-150k','>150k']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "pd.options.display.float_format = '{:.1f}'.format    # set float format as percent, until further notice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Daysim</th>\n",
-       "      <th>2014 Survey</th>\n",
-       "      <th>% difference 2006-&gt;2014</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Income</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>&lt;20k</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20k-40k</th>\n",
-       "      <td>1.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>-0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40k-60k</th>\n",
-       "      <td>1.7</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>-0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60k-75k</th>\n",
-       "      <td>2.1</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>-0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75k-100k</th>\n",
-       "      <td>2.3</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>-0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>100k-150k</th>\n",
-       "      <td>2.4</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>-0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>&gt;150k</th>\n",
-       "      <td>2.4</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Daysim  2014 Survey  % difference 2006->2014\n",
-       "Income                                                 \n",
-       " <20k         1.0          1.0                      0.0\n",
-       "20k-40k       1.5          1.4                     -0.1\n",
-       "40k-60k       1.7          1.6                     -0.1\n",
-       "60k-75k       2.1          1.9                     -0.1\n",
-       "75k-100k      2.3          2.2                     -0.0\n",
-       "100k-150k     2.4          2.4                     -0.0\n",
-       ">150k         2.4          2.6                      0.1"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Calculate averages by income class\n",
-    "df = pd.DataFrame([[sum(df_scen[colname]*df_scen.index)/sum(df_scen[colname]) for colname in df_scen.columns],\n",
-    "                    [sum(df_base[colname]*df_base.index)/sum(df_base[colname]) for colname in df_base.columns]]).T\n",
-    "df.index=df_base.columns\n",
-    "df.columns=[scen_name,base_name]\n",
-    "# df[scen_name] = df[scen_name].astype('int')\n",
-    "# df[base_name] = df[base_name].astype('int')\n",
-    "df['% difference 2006->2014']= (df[base_name]-df[scen_name])/df[scen_name]\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x9d675d30>"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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IDiiZi4jkgJK5iEgOKJmLiOSArgCVkjRSj0jlUzKXkjRSj0jlUzOLiEgOKJmL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\nHFAyFxHJgf8PHG3hTWEJQPkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9d6759e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[[base_name, scen_name]].plot(kind='bar', title='Avg. Cars per Household by Income')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/calibration/2-activity-pattern.ipynb b/scripts/summarize/notebooks/calibration/2-activity-pattern.ipynb
deleted file mode 100644
index 002a008a..00000000
--- a/scripts/summarize/notebooks/calibration/2-activity-pattern.ipynb
+++ /dev/null
@@ -1,1450 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These notebooks are used to compare a base and scenario, from expanded surveys or model outputs, in H5 format. To run: from the menu bar above, choose **Cell -> Run All ** or run lines individually. Use the toggle button below to hide/show the raw Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Summaries:*\n",
-    "    - Day Activity Pattern\n",
-    "    - Number of Tours\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>\n",
-       "code_show=true; \n",
-       "function code_toggle() {\n",
-       " if (code_show){\n",
-       " $('div.input').hide();\n",
-       " } else {\n",
-       " $('div.input').show();\n",
-       " }\n",
-       " code_show = !code_show\n",
-       "} \n",
-       "$( document ).ready(code_toggle);\n",
-       "</script>\n",
-       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "HTML('''<script>\n",
-    "code_show=true; \n",
-    "function code_toggle() {\n",
-    " if (code_show){\n",
-    " $('div.input').hide();\n",
-    " } else {\n",
-    " $('div.input').show();\n",
-    " }\n",
-    " code_show = !code_show\n",
-    "} \n",
-    "$( document ).ready(code_toggle);\n",
-    "</script>\n",
-    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "from IPython.display import display, display_pretty, Javascript, HTML\n",
-    "from pandas_highcharts.core import serialize\n",
-    "from pandas_highcharts.display import display_charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "# Change working directory; only run this once since its a relative path change\n",
-    "default_path = r'../../../..'\n",
-    "os.chdir(default_path)\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'base_run' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-2efa54314a0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# Refering to 2 datasets as BASE and SCEN (scenario)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mbase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_run\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34mr'\\survey14.h5'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'r+'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mbase_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'2014 Survey'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'base_run' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "# Load h5 or daysim outputs records\n",
-    "# Refering to 2 datasets as BASE and SCEN (scenario)\n",
-    "\n",
-    "survey_loc = r'R:\\SoundCast\\estimation\\2014\\P5'\n",
-    "\n",
-    "base = h5py.File(survey_loc + r'\\survey14.h5','r+')\n",
-    "base_name = '2014 Survey'\n",
-    "\n",
-    "# Note that expansion factor on daysim_outputs = 1 for each record, to allow direct comparison between survey records w/ exp. factor\n",
-    "scen = h5py.File(r'outputs\\daysim_outputs.h5','r+')\n",
-    "scen_name = 'Daysim'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def build_df(h5file, h5table, fields, nested=False):\n",
-    "    '''return all fields from h5 table'''\n",
-    "    data = {}\n",
-    "    for field in fields:\n",
-    "        if nested:\n",
-    "            data[field] = [i[0] for i in h5file[h5table][field][:]]\n",
-    "        else:   \n",
-    "            data[field] = [i for i in h5file[h5table][field][:]]\n",
-    "        \n",
-    "    return pd.DataFrame(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load trip, person, and household files from h5 files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "trip_fields = ['dorp','dtaz','otaz','opurp','dpurp','mode','dpcl','opcl','arrtm','deptm','travdist','trexpfac']\n",
-    "trip_base = build_df(h5file=base, h5table='Trip', fields=trip_fields)\n",
-    "trip_scen = build_df(h5file=scen, h5table='Trip', fields=trip_fields)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "person_fields = ['hhno','pno','ptpass','pwautime','pwaudist','pwtyp','pstyp','pwtaz','pstaz','pagey','pptyp','psexpfac']\n",
-    "person_scen = build_df(h5file=scen, h5table='Person', fields=person_fields)\n",
-    "person_base = build_df(h5file=base, h5table='Person', fields=person_fields)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Create unique ID for person by concatenating household ID and person number \n",
-    "person_scen['personID'] = (person_scen['Household ID'].astype('str')+person_scen['Person Number'].astype('str')).astype('int')\n",
-    "person_base['personID'] = (person_base['Household ID'].astype('str')+person_base['Person Number'].astype('str')).astype('int')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hh_fields = ['hhno','hhsize','hhvehs','hhwkrs','hhincome','hhtaz','hhexpfac']\n",
-    "hh_scen = build_df(h5file=scen, h5table='Household', fields=hh_fields)\n",
-    "hh_base = build_df(h5file=base, h5table='Household', fields=hh_fields)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hh_scen = build_df(h5file=scen, h5table='Household', var_dict=hhdict, nested=False)\n",
-    "hh_base = build_df(h5file=base, h5table='Household', var_dict=hhdict, nested=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Join household records to person records\n",
-    "hh_per_scen = pd.merge(left=person_scen, right=hh_scen,on='hhno',suffixes=('_p','_h'))\n",
-    "hh_per_base = pd.merge(left=person_base, right=hh_base,on='hhno',suffixes=('_p','_h'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Join household geography\n",
-    "taz_geog = pd.read_csv(r'scripts/summarize/inputs/calibration/TAZ_TAD_County.csv')\n",
-    "taz_geog.reindex\n",
-    "hh_per_scen_home_geog = pd.merge(hh_per_scen, taz_geog, left_on='hhtaz', right_on='TAZ')\n",
-    "hh_per_base_home_geog = pd.merge(hh_per_base, taz_geog, left_on='hhtaz', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Join workplace geography\n",
-    "hh_per_scen_work_geog = pd.merge(hh_per_scen, taz_geog, left_on='pwtaz', right_on='TAZ')\n",
-    "hh_per_base_work_geog = pd.merge(hh_per_base, taz_geog, left_on='pwtaz', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Set some formatting options\n",
-    "pd.options.display.float_format = '{:.1f}%'.format    # set float format as percent, until further notice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "----"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# VMT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read network summary from latest run\n",
-    "net_sum_scen = pd.read_excel(r'R:\\SoundCast\\releases\\TransportationFutures2010\\outputs\\network_summary_detailed.xlsx', \n",
-    "                        sheetname ='Network Summary')\n",
-    "net_sum_base = pd.read_excel(r'R:\\SoundCast\\releases\\soundcast_release_c1\\outputs\\network_summary_detailed.xlsx',\n",
-    "                            sheetname='Network Summary')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# delete first empty row\n",
-    "net_sum_scen.drop('tod', inplace=True)\n",
-    "net_sum_base.drop('tod', inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame([net_sum_base['arterial_vmt'],net_sum_scen['arterial_vmt']]).T\n",
-    "df.columns = [scen_name,base_name]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "('arrays used as indices must be of integer (or boolean) type', u'occurred at index Daysim')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-49-bea5847d36e7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TOD_index'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\Users\\Brice\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[0;32m   3594\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3595\u001b[0m                         \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3596\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3597\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3598\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\Users\\Brice\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_apply_standard\u001b[1;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[0;32m   3684\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3685\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3686\u001b[1;33m                     \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3687\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3688\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-49-bea5847d36e7>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TOD_index'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\Users\\Brice\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\pandas\\core\\index.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    924\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    925\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 926\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    927\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mpromote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mIndexError\u001b[0m: ('arrays used as indices must be of integer (or boolean) type', u'occurred at index Daysim')"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0xefa3f60>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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hjDEmJkEYxnXqYq8Qrl27tsEr2SEsi2fxLF5TiZf14XuUPTxrj/IPL5zEzGXJ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\nxqTEEogxxpiUWAIxxhiTkv8PbvoMQQM4F7YAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xefaec50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.plot(kind='bar', alpha=0.8, title='Arterial VMT')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15386970.0"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Report VMT using skims and trip data\n",
-    "trip_scen[trip_scen['Travel Distance'] > 0].sum()['Expansion Factor']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Departure Time</th>\n",
-       "      <th>Expansion Factor</th>\n",
-       "      <th>Household ID</th>\n",
-       "      <th>Mode</th>\n",
-       "      <th>Person Number</th>\n",
-       "      <th>Purpose</th>\n",
-       "      <th>Travel Cost</th>\n",
-       "      <th>Travel Distance</th>\n",
-       "      <th>Travel Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0       </th>\n",
-       "      <td>  916</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415445</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  4</td>\n",
-       "      <td> 1.708000</td>\n",
-       "      <td> 17.080000</td>\n",
-       "      <td> 31.080000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1       </th>\n",
-       "      <td> 1060</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415445</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 1.708000</td>\n",
-       "      <td> 17.080000</td>\n",
-       "      <td> 31.340000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2       </th>\n",
-       "      <td>  814</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  424160</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  6</td>\n",
-       "      <td> 0.191220</td>\n",
-       "      <td>  1.912200</td>\n",
-       "      <td>  7.154266</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3       </th>\n",
-       "      <td>  944</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  424160</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.191220</td>\n",
-       "      <td>  1.912200</td>\n",
-       "      <td>  7.483955</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4       </th>\n",
-       "      <td>  954</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  483970</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  5</td>\n",
-       "      <td> 0.293725</td>\n",
-       "      <td>  2.937249</td>\n",
-       "      <td>  8.912339</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5       </th>\n",
-       "      <td>  108</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  483970</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.293725</td>\n",
-       "      <td>  2.937249</td>\n",
-       "      <td>  8.811748</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6       </th>\n",
-       "      <td>  679</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  483970</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  1</td>\n",
-       "      <td> 0.717000</td>\n",
-       "      <td>  7.170000</td>\n",
-       "      <td> 16.960000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7       </th>\n",
-       "      <td> 1243</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  483970</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  5</td>\n",
-       "      <td> 0.160625</td>\n",
-       "      <td>  1.606255</td>\n",
-       "      <td>  8.077966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8       </th>\n",
-       "      <td> 1272</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  483970</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.723000</td>\n",
-       "      <td>  7.230000</td>\n",
-       "      <td> 16.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9       </th>\n",
-       "      <td> 1035</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  365349</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  4</td>\n",
-       "      <td> 0.973000</td>\n",
-       "      <td>  9.730000</td>\n",
-       "      <td> 23.180000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10      </th>\n",
-       "      <td> 1071</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  365349</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.973000</td>\n",
-       "      <td>  9.730000</td>\n",
-       "      <td> 23.180000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11      </th>\n",
-       "      <td> 1374</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  365349</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  1</td>\n",
-       "      <td> 0.852000</td>\n",
-       "      <td>  8.520000</td>\n",
-       "      <td> 19.760000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12      </th>\n",
-       "      <td>  122</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  365349</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.852000</td>\n",
-       "      <td>  8.520000</td>\n",
-       "      <td> 19.760000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13      </th>\n",
-       "      <td>  919</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  682139</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  5</td>\n",
-       "      <td> 0.410000</td>\n",
-       "      <td>  4.100000</td>\n",
-       "      <td> 11.950000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14      </th>\n",
-       "      <td>  959</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  682139</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  5</td>\n",
-       "      <td> 0.046090</td>\n",
-       "      <td>  0.460904</td>\n",
-       "      <td>  7.262732</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15      </th>\n",
-       "      <td> 1028</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  682139</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.427000</td>\n",
-       "      <td>  4.270000</td>\n",
-       "      <td> 14.200000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16      </th>\n",
-       "      <td>  644</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  682139</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  6</td>\n",
-       "      <td> 0.166098</td>\n",
-       "      <td>  1.660985</td>\n",
-       "      <td>  6.965755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17      </th>\n",
-       "      <td> 1116</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  682139</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.166098</td>\n",
-       "      <td>  1.660985</td>\n",
-       "      <td>  6.976136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18      </th>\n",
-       "      <td>  861</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>       1</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  3</td>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td>  0.455372</td>\n",
-       "      <td>  9.107435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19      </th>\n",
-       "      <td>  871</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>       1</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td>  0.455372</td>\n",
-       "      <td>  9.107435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20      </th>\n",
-       "      <td>  815</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  5</td>\n",
-       "      <td> 0.170152</td>\n",
-       "      <td>  1.701519</td>\n",
-       "      <td> 10.232425</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21      </th>\n",
-       "      <td>  829</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  4</td>\n",
-       "      <td> 0.078203</td>\n",
-       "      <td>  0.782027</td>\n",
-       "      <td>  7.072747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22      </th>\n",
-       "      <td>  927</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.106518</td>\n",
-       "      <td>  1.065183</td>\n",
-       "      <td>  7.478706</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23      </th>\n",
-       "      <td>  578</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  4</td>\n",
-       "      <td> 0.947000</td>\n",
-       "      <td>  9.470000</td>\n",
-       "      <td> 22.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24      </th>\n",
-       "      <td>  639</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.947000</td>\n",
-       "      <td>  9.470000</td>\n",
-       "      <td> 22.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25      </th>\n",
-       "      <td>  675</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  6</td>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td>  0.840820</td>\n",
-       "      <td> 16.816405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26      </th>\n",
-       "      <td>  726</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1415446</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td>  0.840820</td>\n",
-       "      <td> 16.816405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27      </th>\n",
-       "      <td>  621</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  950917</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  4</td>\n",
-       "      <td> 0.649000</td>\n",
-       "      <td>  6.490000</td>\n",
-       "      <td> 18.130000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28      </th>\n",
-       "      <td>  644</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  950917</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.649000</td>\n",
-       "      <td>  6.490000</td>\n",
-       "      <td> 18.130000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29      </th>\n",
-       "      <td>  970</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  950917</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  7</td>\n",
-       "      <td> 0.121928</td>\n",
-       "      <td>  1.219276</td>\n",
-       "      <td>  7.315655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397746</th>\n",
-       "      <td>  548</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 5</td>\n",
-       "      <td>  2</td>\n",
-       "      <td> 0.020549</td>\n",
-       "      <td>  0.205492</td>\n",
-       "      <td>  1.426534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397747</th>\n",
-       "      <td>  912</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 5</td>\n",
-       "      <td>  3</td>\n",
-       "      <td> 0.878000</td>\n",
-       "      <td>  8.780000</td>\n",
-       "      <td> 24.370000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397748</th>\n",
-       "      <td>  937</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 5</td>\n",
-       "      <td>  3</td>\n",
-       "      <td> 0.114255</td>\n",
-       "      <td>  1.142546</td>\n",
-       "      <td>  5.467134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397749</th>\n",
-       "      <td>  945</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 5</td>\n",
-       "      <td>  3</td>\n",
-       "      <td> 0.103504</td>\n",
-       "      <td>  1.035038</td>\n",
-       "      <td>  5.529464</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397750</th>\n",
-       "      <td>  992</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 5</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.054941</td>\n",
-       "      <td>  0.549412</td>\n",
-       "      <td>  2.541671</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397751</th>\n",
-       "      <td>  588</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 6</td>\n",
-       "      <td>  2</td>\n",
-       "      <td> 0.054941</td>\n",
-       "      <td>  0.549412</td>\n",
-       "      <td>  2.572479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397752</th>\n",
-       "      <td>  608</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 6</td>\n",
-       "      <td>  2</td>\n",
-       "      <td> 0.132879</td>\n",
-       "      <td>  1.328788</td>\n",
-       "      <td>  7.098759</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397753</th>\n",
-       "      <td>  927</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 6</td>\n",
-       "      <td>  2</td>\n",
-       "      <td> 0.151344</td>\n",
-       "      <td>  1.513437</td>\n",
-       "      <td>  7.001414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397754</th>\n",
-       "      <td>  962</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 6</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.044886</td>\n",
-       "      <td>  0.448864</td>\n",
-       "      <td>  2.551042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397755</th>\n",
-       "      <td>  271</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 7</td>\n",
-       "      <td>  7</td>\n",
-       "      <td> 1.255000</td>\n",
-       "      <td> 12.550000</td>\n",
-       "      <td> 22.470000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397756</th>\n",
-       "      <td>  387</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 7</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 1.255000</td>\n",
-       "      <td> 12.550000</td>\n",
-       "      <td> 22.430000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397757</th>\n",
-       "      <td>  669</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 7</td>\n",
-       "      <td>  2</td>\n",
-       "      <td> 0.054941</td>\n",
-       "      <td>  0.549412</td>\n",
-       "      <td>  2.557075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397758</th>\n",
-       "      <td>  888</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597076</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 7</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.054941</td>\n",
-       "      <td>  0.549412</td>\n",
-       "      <td>  2.567345</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397759</th>\n",
-       "      <td>  839</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  1</td>\n",
-       "      <td> 1.444000</td>\n",
-       "      <td> 14.440000</td>\n",
-       "      <td> 32.990000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397760</th>\n",
-       "      <td>   43</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 1</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 1.399000</td>\n",
-       "      <td> 13.990000</td>\n",
-       "      <td> 29.590000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397761</th>\n",
-       "      <td>  638</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  4</td>\n",
-       "      <td> 0.695000</td>\n",
-       "      <td>  6.950000</td>\n",
-       "      <td> 20.080000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397762</th>\n",
-       "      <td>  760</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  6</td>\n",
-       "      <td> 0.388000</td>\n",
-       "      <td>  3.880000</td>\n",
-       "      <td> 12.790000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397763</th>\n",
-       "      <td>  797</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.366000</td>\n",
-       "      <td>  3.660000</td>\n",
-       "      <td> 11.680000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397764</th>\n",
-       "      <td> 1078</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  5</td>\n",
-       "      <td> 0.562000</td>\n",
-       "      <td>  5.620000</td>\n",
-       "      <td> 14.940000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397765</th>\n",
-       "      <td> 1152</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 2</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.562000</td>\n",
-       "      <td>  5.620000</td>\n",
-       "      <td> 14.940000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397766</th>\n",
-       "      <td>  799</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 3</td>\n",
-       "      <td>  3</td>\n",
-       "      <td> 1.070000</td>\n",
-       "      <td> 10.700000</td>\n",
-       "      <td> 25.150000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397767</th>\n",
-       "      <td>  837</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 5</td>\n",
-       "      <td> 3</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 1.070000</td>\n",
-       "      <td> 10.700000</td>\n",
-       "      <td> 25.150000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397768</th>\n",
-       "      <td>  726</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 4</td>\n",
-       "      <td>  6</td>\n",
-       "      <td> 1.357000</td>\n",
-       "      <td> 13.570000</td>\n",
-       "      <td> 25.410000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397769</th>\n",
-       "      <td>  955</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 4</td>\n",
-       "      <td> 4</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 1.357000</td>\n",
-       "      <td> 13.570000</td>\n",
-       "      <td> 27.550000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397770</th>\n",
-       "      <td>  720</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 6</td>\n",
-       "      <td>  1</td>\n",
-       "      <td> 1.347000</td>\n",
-       "      <td> 13.470000</td>\n",
-       "      <td> 33.970000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397771</th>\n",
-       "      <td> 1327</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 6</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 1.347000</td>\n",
-       "      <td> 13.470000</td>\n",
-       "      <td> 32.040000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397772</th>\n",
-       "      <td>  258</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 7</td>\n",
-       "      <td> 10</td>\n",
-       "      <td> 0.997000</td>\n",
-       "      <td>  9.970000</td>\n",
-       "      <td> 20.250000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397773</th>\n",
-       "      <td>  281</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 6</td>\n",
-       "      <td> 7</td>\n",
-       "      <td>  1</td>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td> 18.040000</td>\n",
-       "      <td> 46.130000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397774</th>\n",
-       "      <td>  953</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 6</td>\n",
-       "      <td> 7</td>\n",
-       "      <td> 10</td>\n",
-       "      <td> 0.000000</td>\n",
-       "      <td> 20.320000</td>\n",
-       "      <td> 46.130000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15397775</th>\n",
-       "      <td> 1002</td>\n",
-       "      <td> 1</td>\n",
-       "      <td> 1597077</td>\n",
-       "      <td> 3</td>\n",
-       "      <td> 7</td>\n",
-       "      <td>  0</td>\n",
-       "      <td> 0.997000</td>\n",
-       "      <td>  9.970000</td>\n",
-       "      <td> 26.560000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>15397776 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Departure Time  Expansion Factor  Household ID  Mode  Person Number  \\\n",
-       "0                    916                 1       1415445     3              1   \n",
-       "1                   1060                 1       1415445     4              1   \n",
-       "2                    814                 1        424160     4              1   \n",
-       "3                    944                 1        424160     4              1   \n",
-       "4                    954                 1        483970     3              1   \n",
-       "5                    108                 1        483970     3              1   \n",
-       "6                    679                 1        483970     3              2   \n",
-       "7                   1243                 1        483970     3              2   \n",
-       "8                   1272                 1        483970     3              2   \n",
-       "9                   1035                 1        365349     3              1   \n",
-       "10                  1071                 1        365349     3              1   \n",
-       "11                  1374                 1        365349     3              1   \n",
-       "12                   122                 1        365349     3              1   \n",
-       "13                   919                 1        682139     3              1   \n",
-       "14                   959                 1        682139     3              1   \n",
-       "15                  1028                 1        682139     3              1   \n",
-       "16                   644                 1        682139     3              2   \n",
-       "17                  1116                 1        682139     3              2   \n",
-       "18                   861                 1             1     1              1   \n",
-       "19                   871                 1             1     1              1   \n",
-       "20                   815                 1       1415446     3              1   \n",
-       "21                   829                 1       1415446     3              1   \n",
-       "22                   927                 1       1415446     3              1   \n",
-       "23                   578                 1       1415446     3              2   \n",
-       "24                   639                 1       1415446     3              2   \n",
-       "25                   675                 1       1415446     1              2   \n",
-       "26                   726                 1       1415446     1              2   \n",
-       "27                   621                 1        950917     3              1   \n",
-       "28                   644                 1        950917     3              1   \n",
-       "29                   970                 1        950917     4              1   \n",
-       "...                  ...               ...           ...   ...            ...   \n",
-       "15397746             548                 1       1597076     4              5   \n",
-       "15397747             912                 1       1597076     4              5   \n",
-       "15397748             937                 1       1597076     4              5   \n",
-       "15397749             945                 1       1597076     4              5   \n",
-       "15397750             992                 1       1597076     4              5   \n",
-       "15397751             588                 1       1597076     5              6   \n",
-       "15397752             608                 1       1597076     5              6   \n",
-       "15397753             927                 1       1597076     5              6   \n",
-       "15397754             962                 1       1597076     5              6   \n",
-       "15397755             271                 1       1597076     4              7   \n",
-       "15397756             387                 1       1597076     4              7   \n",
-       "15397757             669                 1       1597076     5              7   \n",
-       "15397758             888                 1       1597076     5              7   \n",
-       "15397759             839                 1       1597077     5              1   \n",
-       "15397760              43                 1       1597077     3              1   \n",
-       "15397761             638                 1       1597077     5              2   \n",
-       "15397762             760                 1       1597077     5              2   \n",
-       "15397763             797                 1       1597077     5              2   \n",
-       "15397764            1078                 1       1597077     3              2   \n",
-       "15397765            1152                 1       1597077     3              2   \n",
-       "15397766             799                 1       1597077     5              3   \n",
-       "15397767             837                 1       1597077     5              3   \n",
-       "15397768             726                 1       1597077     4              4   \n",
-       "15397769             955                 1       1597077     4              4   \n",
-       "15397770             720                 1       1597077     3              6   \n",
-       "15397771            1327                 1       1597077     3              6   \n",
-       "15397772             258                 1       1597077     3              7   \n",
-       "15397773             281                 1       1597077     6              7   \n",
-       "15397774             953                 1       1597077     6              7   \n",
-       "15397775            1002                 1       1597077     3              7   \n",
-       "\n",
-       "          Purpose  Travel Cost  Travel Distance  Travel Time  \n",
-       "0               4     1.708000        17.080000    31.080000  \n",
-       "1               0     1.708000        17.080000    31.340000  \n",
-       "2               6     0.191220         1.912200     7.154266  \n",
-       "3               0     0.191220         1.912200     7.483955  \n",
-       "4               5     0.293725         2.937249     8.912339  \n",
-       "5               0     0.293725         2.937249     8.811748  \n",
-       "6               1     0.717000         7.170000    16.960000  \n",
-       "7               5     0.160625         1.606255     8.077966  \n",
-       "8               0     0.723000         7.230000    16.600000  \n",
-       "9               4     0.973000         9.730000    23.180000  \n",
-       "10              0     0.973000         9.730000    23.180000  \n",
-       "11              1     0.852000         8.520000    19.760000  \n",
-       "12              0     0.852000         8.520000    19.760000  \n",
-       "13              5     0.410000         4.100000    11.950000  \n",
-       "14              5     0.046090         0.460904     7.262732  \n",
-       "15              0     0.427000         4.270000    14.200000  \n",
-       "16              6     0.166098         1.660985     6.965755  \n",
-       "17              0     0.166098         1.660985     6.976136  \n",
-       "18              3     0.000000         0.455372     9.107435  \n",
-       "19              0     0.000000         0.455372     9.107435  \n",
-       "20              5     0.170152         1.701519    10.232425  \n",
-       "21              4     0.078203         0.782027     7.072747  \n",
-       "22              0     0.106518         1.065183     7.478706  \n",
-       "23              4     0.947000         9.470000    22.500000  \n",
-       "24              0     0.947000         9.470000    22.500000  \n",
-       "25              6     0.000000         0.840820    16.816405  \n",
-       "26              0     0.000000         0.840820    16.816405  \n",
-       "27              4     0.649000         6.490000    18.130000  \n",
-       "28              0     0.649000         6.490000    18.130000  \n",
-       "29              7     0.121928         1.219276     7.315655  \n",
-       "...           ...          ...              ...          ...  \n",
-       "15397746        2     0.020549         0.205492     1.426534  \n",
-       "15397747        3     0.878000         8.780000    24.370000  \n",
-       "15397748        3     0.114255         1.142546     5.467134  \n",
-       "15397749        3     0.103504         1.035038     5.529464  \n",
-       "15397750        0     0.054941         0.549412     2.541671  \n",
-       "15397751        2     0.054941         0.549412     2.572479  \n",
-       "15397752        2     0.132879         1.328788     7.098759  \n",
-       "15397753        2     0.151344         1.513437     7.001414  \n",
-       "15397754        0     0.044886         0.448864     2.551042  \n",
-       "15397755        7     1.255000        12.550000    22.470000  \n",
-       "15397756        0     1.255000        12.550000    22.430000  \n",
-       "15397757        2     0.054941         0.549412     2.557075  \n",
-       "15397758        0     0.054941         0.549412     2.567345  \n",
-       "15397759        1     1.444000        14.440000    32.990000  \n",
-       "15397760        0     1.399000        13.990000    29.590000  \n",
-       "15397761        4     0.695000         6.950000    20.080000  \n",
-       "15397762        6     0.388000         3.880000    12.790000  \n",
-       "15397763        0     0.366000         3.660000    11.680000  \n",
-       "15397764        5     0.562000         5.620000    14.940000  \n",
-       "15397765        0     0.562000         5.620000    14.940000  \n",
-       "15397766        3     1.070000        10.700000    25.150000  \n",
-       "15397767        0     1.070000        10.700000    25.150000  \n",
-       "15397768        6     1.357000        13.570000    25.410000  \n",
-       "15397769        0     1.357000        13.570000    27.550000  \n",
-       "15397770        1     1.347000        13.470000    33.970000  \n",
-       "15397771        0     1.347000        13.470000    32.040000  \n",
-       "15397772       10     0.997000         9.970000    20.250000  \n",
-       "15397773        1     0.000000        18.040000    46.130000  \n",
-       "15397774       10     0.000000        20.320000    46.130000  \n",
-       "15397775        0     0.997000         9.970000    26.560000  \n",
-       "\n",
-       "[15397776 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trip_scen"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tours Taken"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/calibration/3-tours.ipynb b/scripts/summarize/notebooks/calibration/3-tours.ipynb
deleted file mode 100644
index daca2b5a..00000000
--- a/scripts/summarize/notebooks/calibration/3-tours.ipynb
+++ /dev/null
@@ -1,433 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These notebooks are used to compare a base and scenario, from expanded surveys or model outputs, in H5 format. To run: from the menu bar above, choose **Cell -> Run All ** or run lines individually. Use the toggle button below to hide/show the raw Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## Tour Behaviors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Summaries:*\n",
-    "    - Tour Primary Destination (3.1)\n",
-    "    - Work-Based Subtour Generation (3.2)\n",
-    "    - Tour Main Mode (3.3)\n",
-    "    - Tour Time of Day (3.4)\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>\n",
-       "code_show=true; \n",
-       "function code_toggle() {\n",
-       " if (code_show){\n",
-       " $('div.input').hide();\n",
-       " } else {\n",
-       " $('div.input').show();\n",
-       " }\n",
-       " code_show = !code_show\n",
-       "} \n",
-       "$( document ).ready(code_toggle);\n",
-       "</script>\n",
-       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "HTML('''<script>\n",
-    "code_show=true; \n",
-    "function code_toggle() {\n",
-    " if (code_show){\n",
-    " $('div.input').hide();\n",
-    " } else {\n",
-    " $('div.input').show();\n",
-    " }\n",
-    " code_show = !code_show\n",
-    "} \n",
-    "$( document ).ready(code_toggle);\n",
-    "</script>\n",
-    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<script src=\"//code.highcharts.com/stock/highstock.js\"></script>\n",
-       "<script src=\"//code.highcharts.com/highcharts-more.js\"></script>\n",
-       "<script src=\"//code.highcharts.com/modules/exporting.js\"></script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "from IPython.display import display, display_pretty, Javascript, HTML\n",
-    "from pandas_highcharts.core import serialize\n",
-    "from pandas_highcharts.display import display_charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Load h5 or daysim outputs records\n",
-    "# Refering to 2 datasets as BASE and SCEN (scenario)\n",
-    "\n",
-    "# Note that both 2006 and 2014 are un-expanded survey sampels\n",
-    "# Weights must be added to compare the two files\n",
-    "base = h5py.File(r'R:\\SoundCast\\estimation\\2014\\P5\\survey14.h5','r+')\n",
-    "base_name = '2014 Survey'\n",
-    "\n",
-    "scen = h5py.File(r'R:\\SoundCast\\releases\\TransportationFutures2010\\scripts\\summarize\\survey.h5','r+')\n",
-    "scen_name = '2006 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def build_df(h5file, h5table, var_dict, nested):\n",
-    "    ''' Convert H5 into dataframe '''\n",
-    "    data = {}\n",
-    "    if nested:\n",
-    "        # survey h5 have nested data structure, different than daysim_outputs\n",
-    "        for col_name, var in var_dict.iteritems():\n",
-    "            data[col_name] = [i[0] for i in h5file[h5table][var][:]]\n",
-    "    else:\n",
-    "        for col_name, var in var_dict.iteritems():\n",
-    "            data[col_name] = [i for i in h5file[h5table][var][:]]\n",
-    "\n",
-    "    return pd.DataFrame(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tripdict={'Household ID': 'hhno',\n",
-    "            'Person Number': 'pno',\n",
-    "            'Travel Time':'travtime',\n",
-    "            'Travel Cost': 'travcost',\n",
-    "            'Travel Distance': 'travdist',\n",
-    "            'Mode': 'mode',\n",
-    "            'Purpose':'dpurp',\n",
-    "            'Departure Time': 'deptm',\n",
-    "            'Expansion Factor': 'trexpfac'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "trip_base = build_df(h5file=base, h5table='Trip', var_dict=tripdict, nested=False)\n",
-    "trip_scen = build_df(h5file=scen, h5table='Trip', var_dict=tripdict, nested=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "persondict={'Household ID': 'hhno',\n",
-    "            'Person Number': 'pno',\n",
-    "            'Transit Pass': 'ptpass',\n",
-    "            'Auto Time to Work': 'pwautime',\n",
-    "            'Auto Distance to Work': 'pwaudist',\n",
-    "            'Worker Type': 'pwtyp',\n",
-    "            'Student Type': 'pstyp',\n",
-    "            'Usual Commute Mode': 'pwtaz',\n",
-    "            'Workplace TAZ': 'pwtaz',\n",
-    "            'Age': 'pagey',\n",
-    "            'Person Type': 'pptyp',\n",
-    "            'Expansion Factor': 'psexpfac'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "person_scen = build_df(h5file=scen, h5table='Person', var_dict=persondict, nested=True)\n",
-    "person_base = build_df(h5file=base, h5table='Person', var_dict=persondict, nested=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create unique ID for person by concatenating household ID and person number \n",
-    "person_scen['personID'] = (person_scen['Household ID'].astype('str')+person_scen['Person Number'].astype('str')).astype('int')\n",
-    "person_base['personID'] = (person_base['Household ID'].astype('str')+person_base['Person Number'].astype('str')).astype('int')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hhdict={'Household ID': 'hhno',\n",
-    "        'Household Size': 'hhsize',\n",
-    "        'Household Vehicles': 'hhvehs',\n",
-    "        'Household Workers': 'hhwkrs',\n",
-    "        'Household Income': 'hhincome',\n",
-    "        'Household TAZ': 'hhtaz',\n",
-    "        'Expansion Factor': 'hhexpfac'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "hh_scen = build_df(h5file=scen, h5table='Household', var_dict=hhdict, nested=True)\n",
-    "hh_base = build_df(h5file=base, h5table='Household', var_dict=hhdict, nested=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# person-day records\n",
-    "tourdict={}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# persday_scen = build_df(h5file=scen, h5table='PersonDay', var_dict=persondaydict, nested=True)\n",
-    "# persday_base = build_df(h5file=base, h5table='PersonDay', var_dict=persondaydict, nested=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create unique ID for person by concatenating household ID and person number\n",
-    "persday_scen['personID'] = (persday_scen['Household ID'].astype('str')+persday_scen['Person Number'].astype('str')).astype('int')\n",
-    "persday_base['personID'] = (persday_base['Household ID'].astype('str')+persday_base['Person Number'].astype('str')).astype('int')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# # Join household records to person records\n",
-    "# hh_per_scen = pd.merge(left=person_scen, right=hh_scen,on='Household ID',suffixes=('_p','_h'))\n",
-    "# hh_per_base = pd.merge(left=person_base, right=hh_base,on='Household ID',suffixes=('_p','_h'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# # Join household geography\n",
-    "# taz_geog = pd.read_csv(r'utils/taz_lookup.csv')\n",
-    "# taz_geog.reindex\n",
-    "# hh_per_scen_home_geog = pd.merge(hh_per_scen, taz_geog, left_on='Household TAZ', right_on='TAZ')\n",
-    "# hh_per_base_home_geog = pd.merge(hh_per_base, taz_geog, left_on='Household TAZ', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# # Join workplace geography\n",
-    "# hh_per_scen_work_geog = pd.merge(hh_per_scen, taz_geog, left_on='Workplace TAZ', right_on='TAZ')\n",
-    "# hh_per_base_work_geog = pd.merge(hh_per_base, taz_geog, left_on='Workplace TAZ', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tours Destinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/calibration/4-stops-trips.ipynb b/scripts/summarize/notebooks/calibration/4-stops-trips.ipynb
deleted file mode 100644
index 0bbbdac8..00000000
--- a/scripts/summarize/notebooks/calibration/4-stops-trips.ipynb
+++ /dev/null
@@ -1,460 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These notebooks are used to compare a base and scenario, from expanded surveys or model outputs, in H5 format. To run: from the menu bar above, choose **Cell -> Run All ** or run lines individually. Use the toggle button below to hide/show the raw Python code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## Tour Behaviors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Summaries:*\n",
-    "    - Intermediate Stop Generation (4.1)\n",
-    "    - Intermediate Stop Location (4.2)\n",
-    "    - Trip Mode (4.3)\n",
-    "    - Trip Departure Time (4.4)\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>\n",
-       "code_show=true; \n",
-       "function code_toggle() {\n",
-       " if (code_show){\n",
-       " $('div.input').hide();\n",
-       " } else {\n",
-       " $('div.input').show();\n",
-       " }\n",
-       " code_show = !code_show\n",
-       "} \n",
-       "$( document ).ready(code_toggle);\n",
-       "</script>\n",
-       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "HTML('''<script>\n",
-    "code_show=true; \n",
-    "function code_toggle() {\n",
-    " if (code_show){\n",
-    " $('div.input').hide();\n",
-    " } else {\n",
-    " $('div.input').show();\n",
-    " }\n",
-    " code_show = !code_show\n",
-    "} \n",
-    "$( document ).ready(code_toggle);\n",
-    "</script>\n",
-    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<script src=\"//code.highcharts.com/stock/highstock.js\"></script>\n",
-       "<script src=\"//code.highcharts.com/highcharts-more.js\"></script>\n",
-       "<script src=\"//code.highcharts.com/modules/exporting.js\"></script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "from IPython.display import display, display_pretty, Javascript, HTML\n",
-    "from pandas_highcharts.core import serialize\n",
-    "from pandas_highcharts.display import display_charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Define data sources\n",
-    "\n",
-    "# 2006 survey\n",
-    "survey06_dir = r'R:\\SoundCast\\releases\\TransportationFutures2010\\scripts\\summarize'\n",
-    "\n",
-    "\n",
-    "# 2014 survey\n",
-    "survey14_dir = r'D:\\travel-studies\\2014\\estimation'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Model Scenario Results\n",
-    "scen = h5py.File(survey06_dir + r'/survey.h5','r+')\n",
-    "scen_name = '2006 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Base Data\n",
-    "base_file = r'/survey14.h5'\n",
-    "\n",
-    "base = h5py.File(survey14_dir + base_file ,'r+')\n",
-    "base_name = '2014 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def build_df(h5file, h5table, var_dict, survey_file=False):\n",
-    "    ''' Convert H5 into dataframe '''\n",
-    "    data = {}\n",
-    "    if survey_file:\n",
-    "        # survey h5 have nested data structure, different than daysim_outputs\n",
-    "        for col_name, var in var_dict.iteritems():\n",
-    "            data[col_name] = [i[0] for i in h5file[h5table][var][:]]\n",
-    "    else:\n",
-    "        for col_name, var in var_dict.iteritems():\n",
-    "            data[col_name] = [i for i in h5file[h5table][var][:]]\n",
-    "\n",
-    "    return pd.DataFrame(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tripdict={'Household ID': 'hhno',\n",
-    "            'Person Number': 'pno',\n",
-    "            'Travel Time':'travtime',\n",
-    "            'Travel Cost': 'travcost',\n",
-    "            'Travel Distance': 'travdist',\n",
-    "            'Mode': 'mode',\n",
-    "            'Purpose':'dpurp',\n",
-    "            'Departure Time': 'deptm',\n",
-    "            'Expansion Factor': 'trexpfac'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "trip_scen = build_df(h5file=scen, h5table='Trip', var_dict=tripdict, survey_file=True)\n",
-    "trip_base = build_df(h5file=base, h5table='Trip', var_dict=tripdict, survey_file=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "persondict={'Household ID': 'hhno',\n",
-    "            'Person Number': 'pno',\n",
-    "            'Transit Pass': 'ptpass',\n",
-    "            'Auto Time to Work': 'pwautime',\n",
-    "            'Auto Distance to Work': 'pwaudist',\n",
-    "            'Worker Type': 'pwtyp',\n",
-    "            'Student Type': 'pstyp',\n",
-    "            'Usual Commute Mode': 'pwtaz',\n",
-    "            'Workplace TAZ': 'pwtaz',\n",
-    "            'Age': 'pagey',\n",
-    "            'Person Type': 'pptyp',\n",
-    "            'Expansion Factor': 'psexpfac'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "person_scen = build_df(h5file=scen, h5table='Person', var_dict=persondict, survey_file=True)\n",
-    "person_base = build_df(h5file=base, h5table='Person', var_dict=persondict, survey_file=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create unique ID for person by concatenating household ID and person number \n",
-    "person_scen['personID'] = (person_scen['Household ID'].astype('str')+person_scen['Person Number'].astype('str')).astype('int')\n",
-    "person_base['personID'] = (person_base['Household ID'].astype('str')+person_base['Person Number'].astype('str')).astype('int')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "hhdict={'Household ID': 'hhno',\n",
-    "        'Household Size': 'hhsize',\n",
-    "        'Household Vehicles': 'hhvehs',\n",
-    "        'Household Workers': 'hhwkrs',\n",
-    "        'Household Income': 'hhincome',\n",
-    "        'Household TAZ': 'hhtaz',\n",
-    "        'Expansion Factor': 'hhexpfac'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "hh_scen = build_df(h5file=scen, h5table='Household', var_dict=hhdict, survey_file=False)\n",
-    "hh_base = build_df(h5file=base, h5table='Household', var_dict=hhdict, survey_file=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# person-day records\n",
-    "tourdict={}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "persday_scen = build_df(h5file=scen, h5table='PersonDay', var_dict=persondaydict, survey_file=True)\n",
-    "persday_base = build_df(h5file=base, h5table='PersonDay', var_dict=persondaydict, survey_file=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Create unique ID for person by concatenating household ID and person number\n",
-    "persday_scen['personID'] = (persday_scen['Household ID'].astype('str')+persday_scen['Person Number'].astype('str')).astype('int')\n",
-    "persday_base['personID'] = (persday_base['Household ID'].astype('str')+persday_base['Person Number'].astype('str')).astype('int')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# # Join household records to person records\n",
-    "# hh_per_scen = pd.merge(left=person_scen, right=hh_scen,on='Household ID',suffixes=('_p','_h'))\n",
-    "# hh_per_base = pd.merge(left=person_base, right=hh_base,on='Household ID',suffixes=('_p','_h'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# # Join household geography\n",
-    "# taz_geog = pd.read_csv(r'utils/taz_lookup.csv')\n",
-    "# taz_geog.reindex\n",
-    "# hh_per_scen_home_geog = pd.merge(hh_per_scen, taz_geog, left_on='Household TAZ', right_on='TAZ')\n",
-    "# hh_per_base_home_geog = pd.merge(hh_per_base, taz_geog, left_on='Household TAZ', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# # Join workplace geography\n",
-    "# hh_per_scen_work_geog = pd.merge(hh_per_scen, taz_geog, left_on='Workplace TAZ', right_on='TAZ')\n",
-    "# hh_per_base_work_geog = pd.merge(hh_per_base, taz_geog, left_on='Workplace TAZ', right_on='TAZ')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tours Destinations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/centers.ipynb b/scripts/summarize/notebooks/centers.ipynb
new file mode 100644
index 00000000..87a50eb1
--- /dev/null
+++ b/scripts/summarize/notebooks/centers.ipynb
@@ -0,0 +1,1083 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Daysim data\n",
+    "trip = pd.read_csv(r'../../../outputs/daysim/_trip.tsv', sep='\\t')\n",
+    "person = pd.read_csv(r'../../../outputs/daysim/_person.tsv', sep='\\t')\n",
+    "hh = pd.read_csv(r'../../../outputs/daysim/_household.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def apply_labels(df,table_type):\n",
+    "    '''\n",
+    "    Replace values with human readable lablels.\n",
+    "    '''\n",
+    "    df_label = labels[labels['table'] == table_type]\n",
+    "    for field in df_label['field'].unique():\n",
+    "        newdf = df_label[df_label['field'] == field]\n",
+    "        local_series = pd.Series(newdf['text'].values, index=newdf['value'])\n",
+    "        df[field] = df[field].map(local_series)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Add labels\n",
+    "labels = pd.read_csv(r'../../../scripts/summarize/inputs/calibration/variable_labels.csv')\n",
+    "\n",
+    "trip = apply_labels(trip,'Trip')\n",
+    "person = apply_labels(person,'Person')\n",
+    "hh = apply_labels(hh,'Household')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "special_taz = pd.read_csv(r'../../../scripts/summarize/inputs/special_needs_taz.csv')\n",
+    "rgc_taz = pd.read_csv(r'../../../scripts/summarize/inputs/rgc_taz.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filename = r'../../../outputs/daysim/centers.xlsx'\n",
+    "if os.path.exists(filename):\n",
+    "    os.remove(filename)\n",
+    "writer = pd.ExcelWriter(filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mode Share to Centers by HH Location"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## All Trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>mode</th>\n",
+       "      <th>Bike</th>\n",
+       "      <th>HOV2</th>\n",
+       "      <th>HOV3+</th>\n",
+       "      <th>SOV</th>\n",
+       "      <th>Transit</th>\n",
+       "      <th>Walk</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geog_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Auburn</th>\n",
+       "      <td>0.028282</td>\n",
+       "      <td>0.213714</td>\n",
+       "      <td>0.119797</td>\n",
+       "      <td>0.311099</td>\n",
+       "      <td>0.068837</td>\n",
+       "      <td>0.258271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue</th>\n",
+       "      <td>0.030374</td>\n",
+       "      <td>0.151025</td>\n",
+       "      <td>0.054560</td>\n",
+       "      <td>0.239365</td>\n",
+       "      <td>0.094506</td>\n",
+       "      <td>0.430169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bremerton</th>\n",
+       "      <td>0.030780</td>\n",
+       "      <td>0.158886</td>\n",
+       "      <td>0.099620</td>\n",
+       "      <td>0.252888</td>\n",
+       "      <td>0.045339</td>\n",
+       "      <td>0.412486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett</th>\n",
+       "      <td>0.034945</td>\n",
+       "      <td>0.147653</td>\n",
+       "      <td>0.076774</td>\n",
+       "      <td>0.253053</td>\n",
+       "      <td>0.029858</td>\n",
+       "      <td>0.457717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah</th>\n",
+       "      <td>0.024430</td>\n",
+       "      <td>0.157980</td>\n",
+       "      <td>0.092834</td>\n",
+       "      <td>0.495114</td>\n",
+       "      <td>0.066775</td>\n",
+       "      <td>0.162866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lynnwood</th>\n",
+       "      <td>0.029911</td>\n",
+       "      <td>0.205891</td>\n",
+       "      <td>0.126836</td>\n",
+       "      <td>0.294715</td>\n",
+       "      <td>0.054596</td>\n",
+       "      <td>0.288051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond Downtown</th>\n",
+       "      <td>0.030135</td>\n",
+       "      <td>0.157212</td>\n",
+       "      <td>0.065385</td>\n",
+       "      <td>0.320105</td>\n",
+       "      <td>0.093029</td>\n",
+       "      <td>0.334135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renton</th>\n",
+       "      <td>0.029879</td>\n",
+       "      <td>0.165890</td>\n",
+       "      <td>0.107278</td>\n",
+       "      <td>0.294209</td>\n",
+       "      <td>0.073848</td>\n",
+       "      <td>0.328896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Downtown</th>\n",
+       "      <td>0.050147</td>\n",
+       "      <td>0.103374</td>\n",
+       "      <td>0.055766</td>\n",
+       "      <td>0.165351</td>\n",
+       "      <td>0.137082</td>\n",
+       "      <td>0.488275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Northgate</th>\n",
+       "      <td>0.039360</td>\n",
+       "      <td>0.152816</td>\n",
+       "      <td>0.065949</td>\n",
+       "      <td>0.262627</td>\n",
+       "      <td>0.131921</td>\n",
+       "      <td>0.347327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle University Community</th>\n",
+       "      <td>0.043007</td>\n",
+       "      <td>0.121707</td>\n",
+       "      <td>0.061432</td>\n",
+       "      <td>0.221332</td>\n",
+       "      <td>0.142713</td>\n",
+       "      <td>0.409807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Uptown</th>\n",
+       "      <td>0.046591</td>\n",
+       "      <td>0.118995</td>\n",
+       "      <td>0.042370</td>\n",
+       "      <td>0.245544</td>\n",
+       "      <td>0.144265</td>\n",
+       "      <td>0.402235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacoma Downtown</th>\n",
+       "      <td>0.045434</td>\n",
+       "      <td>0.140754</td>\n",
+       "      <td>0.045096</td>\n",
+       "      <td>0.288609</td>\n",
+       "      <td>0.065930</td>\n",
+       "      <td>0.414178</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "mode                              Bike      HOV2     HOV3+       SOV  \\\n",
+       "geog_name                                                              \n",
+       "Auburn                        0.028282  0.213714  0.119797  0.311099   \n",
+       "Bellevue                      0.030374  0.151025  0.054560  0.239365   \n",
+       "Bremerton                     0.030780  0.158886  0.099620  0.252888   \n",
+       "Everett                       0.034945  0.147653  0.076774  0.253053   \n",
+       "Issaquah                      0.024430  0.157980  0.092834  0.495114   \n",
+       "Lynnwood                      0.029911  0.205891  0.126836  0.294715   \n",
+       "Redmond Downtown              0.030135  0.157212  0.065385  0.320105   \n",
+       "Renton                        0.029879  0.165890  0.107278  0.294209   \n",
+       "Seattle Downtown              0.050147  0.103374  0.055766  0.165351   \n",
+       "Seattle Northgate             0.039360  0.152816  0.065949  0.262627   \n",
+       "Seattle University Community  0.043007  0.121707  0.061432  0.221332   \n",
+       "Seattle Uptown                0.046591  0.118995  0.042370  0.245544   \n",
+       "Tacoma Downtown               0.045434  0.140754  0.045096  0.288609   \n",
+       "\n",
+       "mode                           Transit      Walk  \n",
+       "geog_name                                         \n",
+       "Auburn                        0.068837  0.258271  \n",
+       "Bellevue                      0.094506  0.430169  \n",
+       "Bremerton                     0.045339  0.412486  \n",
+       "Everett                       0.029858  0.457717  \n",
+       "Issaquah                      0.066775  0.162866  \n",
+       "Lynnwood                      0.054596  0.288051  \n",
+       "Redmond Downtown              0.093029  0.334135  \n",
+       "Renton                        0.073848  0.328896  \n",
+       "Seattle Downtown              0.137082  0.488275  \n",
+       "Seattle Northgate             0.131921  0.347327  \n",
+       "Seattle University Community  0.142713  0.409807  \n",
+       "Seattle Uptown                0.144265  0.402235  \n",
+       "Tacoma Downtown               0.065930  0.414178  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trip_hh = pd.merge(trip,hh,on='hhno',how='left')\n",
+    "trip_hh = pd.merge(trip_hh, rgc_taz, left_on='hhtaz', right_on='taz', how='left')\n",
+    "trip_hh = trip_hh[trip_hh['mode']!='School Bus']\n",
+    "\n",
+    "df = trip_hh[['trexpfac','geog_name','mode']].groupby(['geog_name','mode']).sum().reset_index()\n",
+    "tot_rgc = trip_hh[['trexpfac','geog_name']].groupby('geog_name').sum().reset_index()\n",
+    "df = pd.merge(df,tot_rgc,on='geog_name', suffixes=['_mode','_total'])\n",
+    "df['mode_share'] = (df['trexpfac_mode']/df['trexpfac_total'])\n",
+    "\n",
+    "df = df.pivot(index='geog_name', columns='mode', values='mode_share')\n",
+    "# Trim the number of RGCs\n",
+    "rgc_list = ['Auburn','Bellevue','Bremerton','Everett','Issaquah','Lynnwood','Redmond Downtown',\n",
+    "       'Renton','Seattle Downtown','Seattle Northgate','Seattle University Community',\n",
+    "        'Seattle Uptown', 'Tacoma Downtown']\n",
+    "\n",
+    "\n",
+    "df.to_excel(writer,'HH in RGC - All Trips')\n",
+    "df.loc[rgc_list]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Commute Trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>mode</th>\n",
+       "      <th>Bike</th>\n",
+       "      <th>HOV2</th>\n",
+       "      <th>HOV3+</th>\n",
+       "      <th>SOV</th>\n",
+       "      <th>Transit</th>\n",
+       "      <th>Walk</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geog_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Auburn</th>\n",
+       "      <td>0.051044</td>\n",
+       "      <td>0.074246</td>\n",
+       "      <td>0.053364</td>\n",
+       "      <td>0.533643</td>\n",
+       "      <td>0.097448</td>\n",
+       "      <td>0.190255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue</th>\n",
+       "      <td>0.037370</td>\n",
+       "      <td>0.066570</td>\n",
+       "      <td>0.022802</td>\n",
+       "      <td>0.321890</td>\n",
+       "      <td>0.119775</td>\n",
+       "      <td>0.431594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bremerton</th>\n",
+       "      <td>0.048739</td>\n",
+       "      <td>0.069954</td>\n",
+       "      <td>0.028096</td>\n",
+       "      <td>0.398509</td>\n",
+       "      <td>0.061353</td>\n",
+       "      <td>0.393349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett</th>\n",
+       "      <td>0.037074</td>\n",
+       "      <td>0.069904</td>\n",
+       "      <td>0.027712</td>\n",
+       "      <td>0.356010</td>\n",
+       "      <td>0.065785</td>\n",
+       "      <td>0.443515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah</th>\n",
+       "      <td>0.017391</td>\n",
+       "      <td>0.078261</td>\n",
+       "      <td>0.017391</td>\n",
+       "      <td>0.634783</td>\n",
+       "      <td>0.113043</td>\n",
+       "      <td>0.139130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lynnwood</th>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.091667</td>\n",
+       "      <td>0.040625</td>\n",
+       "      <td>0.440625</td>\n",
+       "      <td>0.119792</td>\n",
+       "      <td>0.257292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond Downtown</th>\n",
+       "      <td>0.043171</td>\n",
+       "      <td>0.076622</td>\n",
+       "      <td>0.026635</td>\n",
+       "      <td>0.439914</td>\n",
+       "      <td>0.149583</td>\n",
+       "      <td>0.264075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renton</th>\n",
+       "      <td>0.040093</td>\n",
+       "      <td>0.091142</td>\n",
+       "      <td>0.034732</td>\n",
+       "      <td>0.453846</td>\n",
+       "      <td>0.101865</td>\n",
+       "      <td>0.278322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Downtown</th>\n",
+       "      <td>0.050401</td>\n",
+       "      <td>0.055842</td>\n",
+       "      <td>0.023932</td>\n",
+       "      <td>0.233699</td>\n",
+       "      <td>0.150080</td>\n",
+       "      <td>0.486020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Northgate</th>\n",
+       "      <td>0.046598</td>\n",
+       "      <td>0.074945</td>\n",
+       "      <td>0.026272</td>\n",
+       "      <td>0.375415</td>\n",
+       "      <td>0.186256</td>\n",
+       "      <td>0.290514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle University Community</th>\n",
+       "      <td>0.053873</td>\n",
+       "      <td>0.065917</td>\n",
+       "      <td>0.032670</td>\n",
+       "      <td>0.338093</td>\n",
+       "      <td>0.199553</td>\n",
+       "      <td>0.309895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Uptown</th>\n",
+       "      <td>0.050401</td>\n",
+       "      <td>0.056803</td>\n",
+       "      <td>0.021279</td>\n",
+       "      <td>0.291678</td>\n",
+       "      <td>0.169507</td>\n",
+       "      <td>0.410333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacoma Downtown</th>\n",
+       "      <td>0.053289</td>\n",
+       "      <td>0.071755</td>\n",
+       "      <td>0.023429</td>\n",
+       "      <td>0.400240</td>\n",
+       "      <td>0.070047</td>\n",
+       "      <td>0.381241</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "mode                              Bike      HOV2     HOV3+       SOV  \\\n",
+       "geog_name                                                              \n",
+       "Auburn                        0.051044  0.074246  0.053364  0.533643   \n",
+       "Bellevue                      0.037370  0.066570  0.022802  0.321890   \n",
+       "Bremerton                     0.048739  0.069954  0.028096  0.398509   \n",
+       "Everett                       0.037074  0.069904  0.027712  0.356010   \n",
+       "Issaquah                      0.017391  0.078261  0.017391  0.634783   \n",
+       "Lynnwood                      0.050000  0.091667  0.040625  0.440625   \n",
+       "Redmond Downtown              0.043171  0.076622  0.026635  0.439914   \n",
+       "Renton                        0.040093  0.091142  0.034732  0.453846   \n",
+       "Seattle Downtown              0.050401  0.055842  0.023932  0.233699   \n",
+       "Seattle Northgate             0.046598  0.074945  0.026272  0.375415   \n",
+       "Seattle University Community  0.053873  0.065917  0.032670  0.338093   \n",
+       "Seattle Uptown                0.050401  0.056803  0.021279  0.291678   \n",
+       "Tacoma Downtown               0.053289  0.071755  0.023429  0.400240   \n",
+       "\n",
+       "mode                           Transit      Walk  \n",
+       "geog_name                                         \n",
+       "Auburn                        0.097448  0.190255  \n",
+       "Bellevue                      0.119775  0.431594  \n",
+       "Bremerton                     0.061353  0.393349  \n",
+       "Everett                       0.065785  0.443515  \n",
+       "Issaquah                      0.113043  0.139130  \n",
+       "Lynnwood                      0.119792  0.257292  \n",
+       "Redmond Downtown              0.149583  0.264075  \n",
+       "Renton                        0.101865  0.278322  \n",
+       "Seattle Downtown              0.150080  0.486020  \n",
+       "Seattle Northgate             0.186256  0.290514  \n",
+       "Seattle University Community  0.199553  0.309895  \n",
+       "Seattle Uptown                0.169507  0.410333  \n",
+       "Tacoma Downtown               0.070047  0.381241  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "commute_trips = trip_hh[trip_hh['dpurp'] == 'Work']\n",
+    "\n",
+    "df = commute_trips[['trexpfac','geog_name','mode']].groupby(['geog_name','mode']).sum().reset_index()\n",
+    "tot_rgc = commute_trips[['trexpfac','geog_name']].groupby('geog_name').sum().reset_index()\n",
+    "df = pd.merge(df,tot_rgc,on='geog_name', suffixes=['_mode','_total'])\n",
+    "df['mode_share'] = (df['trexpfac_mode']/df['trexpfac_total'])\n",
+    "\n",
+    "df = df.pivot(index='geog_name', columns='mode', values='mode_share')\n",
+    "# Trim the number of RGCs\n",
+    "rgc_list = ['Auburn','Bellevue','Bremerton','Everett','Issaquah','Lynnwood','Redmond Downtown',\n",
+    "       'Renton','Seattle Downtown','Seattle Northgate','Seattle University Community',\n",
+    "        'Seattle Uptown', 'Tacoma Downtown']\n",
+    "df = df.fillna(0)\n",
+    "\n",
+    "df.to_excel(writer,'HH in RGC - Commute Trips')\n",
+    "df.loc[rgc_list]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trips to Centers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## All Trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>mode</th>\n",
+       "      <th>Bike</th>\n",
+       "      <th>HOV2</th>\n",
+       "      <th>HOV3+</th>\n",
+       "      <th>SOV</th>\n",
+       "      <th>Transit</th>\n",
+       "      <th>Walk</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geog_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Auburn</th>\n",
+       "      <td>0.014651</td>\n",
+       "      <td>0.217302</td>\n",
+       "      <td>0.132509</td>\n",
+       "      <td>0.460345</td>\n",
+       "      <td>0.035972</td>\n",
+       "      <td>0.139221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue</th>\n",
+       "      <td>0.020710</td>\n",
+       "      <td>0.138184</td>\n",
+       "      <td>0.072815</td>\n",
+       "      <td>0.279780</td>\n",
+       "      <td>0.130609</td>\n",
+       "      <td>0.357902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bremerton</th>\n",
+       "      <td>0.025652</td>\n",
+       "      <td>0.171939</td>\n",
+       "      <td>0.106825</td>\n",
+       "      <td>0.339382</td>\n",
+       "      <td>0.033400</td>\n",
+       "      <td>0.322802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett</th>\n",
+       "      <td>0.021917</td>\n",
+       "      <td>0.175576</td>\n",
+       "      <td>0.111464</td>\n",
+       "      <td>0.323696</td>\n",
+       "      <td>0.028213</td>\n",
+       "      <td>0.339134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah</th>\n",
+       "      <td>0.009260</td>\n",
+       "      <td>0.195197</td>\n",
+       "      <td>0.106362</td>\n",
+       "      <td>0.553346</td>\n",
+       "      <td>0.040759</td>\n",
+       "      <td>0.095077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lynnwood</th>\n",
+       "      <td>0.019428</td>\n",
+       "      <td>0.214920</td>\n",
+       "      <td>0.135169</td>\n",
+       "      <td>0.405961</td>\n",
+       "      <td>0.034556</td>\n",
+       "      <td>0.189965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond Downtown</th>\n",
+       "      <td>0.022419</td>\n",
+       "      <td>0.190049</td>\n",
+       "      <td>0.111052</td>\n",
+       "      <td>0.337265</td>\n",
+       "      <td>0.065434</td>\n",
+       "      <td>0.273781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renton</th>\n",
+       "      <td>0.020141</td>\n",
+       "      <td>0.192503</td>\n",
+       "      <td>0.124697</td>\n",
+       "      <td>0.396669</td>\n",
+       "      <td>0.053118</td>\n",
+       "      <td>0.212873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Downtown</th>\n",
+       "      <td>0.030081</td>\n",
+       "      <td>0.113147</td>\n",
+       "      <td>0.065098</td>\n",
+       "      <td>0.212002</td>\n",
+       "      <td>0.212006</td>\n",
+       "      <td>0.367664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Northgate</th>\n",
+       "      <td>0.036175</td>\n",
+       "      <td>0.161804</td>\n",
+       "      <td>0.088354</td>\n",
+       "      <td>0.308772</td>\n",
+       "      <td>0.099940</td>\n",
+       "      <td>0.304954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle University Community</th>\n",
+       "      <td>0.028940</td>\n",
+       "      <td>0.136040</td>\n",
+       "      <td>0.074546</td>\n",
+       "      <td>0.302975</td>\n",
+       "      <td>0.163247</td>\n",
+       "      <td>0.294252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Uptown</th>\n",
+       "      <td>0.042153</td>\n",
+       "      <td>0.134233</td>\n",
+       "      <td>0.069183</td>\n",
+       "      <td>0.288935</td>\n",
+       "      <td>0.120850</td>\n",
+       "      <td>0.344647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacoma Downtown</th>\n",
+       "      <td>0.036985</td>\n",
+       "      <td>0.147672</td>\n",
+       "      <td>0.071069</td>\n",
+       "      <td>0.309510</td>\n",
+       "      <td>0.061471</td>\n",
+       "      <td>0.373294</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "mode                              Bike      HOV2     HOV3+       SOV  \\\n",
+       "geog_name                                                              \n",
+       "Auburn                        0.014651  0.217302  0.132509  0.460345   \n",
+       "Bellevue                      0.020710  0.138184  0.072815  0.279780   \n",
+       "Bremerton                     0.025652  0.171939  0.106825  0.339382   \n",
+       "Everett                       0.021917  0.175576  0.111464  0.323696   \n",
+       "Issaquah                      0.009260  0.195197  0.106362  0.553346   \n",
+       "Lynnwood                      0.019428  0.214920  0.135169  0.405961   \n",
+       "Redmond Downtown              0.022419  0.190049  0.111052  0.337265   \n",
+       "Renton                        0.020141  0.192503  0.124697  0.396669   \n",
+       "Seattle Downtown              0.030081  0.113147  0.065098  0.212002   \n",
+       "Seattle Northgate             0.036175  0.161804  0.088354  0.308772   \n",
+       "Seattle University Community  0.028940  0.136040  0.074546  0.302975   \n",
+       "Seattle Uptown                0.042153  0.134233  0.069183  0.288935   \n",
+       "Tacoma Downtown               0.036985  0.147672  0.071069  0.309510   \n",
+       "\n",
+       "mode                           Transit      Walk  \n",
+       "geog_name                                         \n",
+       "Auburn                        0.035972  0.139221  \n",
+       "Bellevue                      0.130609  0.357902  \n",
+       "Bremerton                     0.033400  0.322802  \n",
+       "Everett                       0.028213  0.339134  \n",
+       "Issaquah                      0.040759  0.095077  \n",
+       "Lynnwood                      0.034556  0.189965  \n",
+       "Redmond Downtown              0.065434  0.273781  \n",
+       "Renton                        0.053118  0.212873  \n",
+       "Seattle Downtown              0.212006  0.367664  \n",
+       "Seattle Northgate             0.099940  0.304954  \n",
+       "Seattle University Community  0.163247  0.294252  \n",
+       "Seattle Uptown                0.120850  0.344647  \n",
+       "Tacoma Downtown               0.061471  0.373294  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trip_df = pd.merge(trip, rgc_taz, left_on='dtaz', right_on='taz', how='left')\n",
+    "trip_df = trip_df[trip_df['mode']!='School Bus']\n",
+    "\n",
+    "df = trip_df[['trexpfac','geog_name','mode']].groupby(['geog_name','mode']).sum().reset_index()\n",
+    "tot_rgc = trip_df[['trexpfac','geog_name']].groupby('geog_name').sum().reset_index()\n",
+    "df = pd.merge(df,tot_rgc,on='geog_name', suffixes=['_mode','_total'])\n",
+    "df['mode_share'] = (df['trexpfac_mode']/df['trexpfac_total'])\n",
+    "\n",
+    "df = df.pivot(index='geog_name', columns='mode', values='mode_share')\n",
+    "# Trim the number of RGCs\n",
+    "rgc_list = ['Auburn','Bellevue','Bremerton','Everett','Issaquah','Lynnwood','Redmond Downtown',\n",
+    "       'Renton','Seattle Downtown','Seattle Northgate','Seattle University Community',\n",
+    "        'Seattle Uptown', 'Tacoma Downtown']\n",
+    "\n",
+    "\n",
+    "df.to_excel(writer,'Trip to RGC - All Trips')\n",
+    "df.loc[rgc_list]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Commute Trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>mode</th>\n",
+       "      <th>Bike</th>\n",
+       "      <th>HOV2</th>\n",
+       "      <th>HOV3+</th>\n",
+       "      <th>SOV</th>\n",
+       "      <th>Transit</th>\n",
+       "      <th>Walk</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>geog_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Auburn</th>\n",
+       "      <td>0.026354</td>\n",
+       "      <td>0.087096</td>\n",
+       "      <td>0.043870</td>\n",
+       "      <td>0.662864</td>\n",
+       "      <td>0.058814</td>\n",
+       "      <td>0.121003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue</th>\n",
+       "      <td>0.020326</td>\n",
+       "      <td>0.076787</td>\n",
+       "      <td>0.037290</td>\n",
+       "      <td>0.381899</td>\n",
+       "      <td>0.219608</td>\n",
+       "      <td>0.264090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bremerton</th>\n",
+       "      <td>0.047028</td>\n",
+       "      <td>0.076664</td>\n",
+       "      <td>0.034250</td>\n",
+       "      <td>0.523514</td>\n",
+       "      <td>0.043656</td>\n",
+       "      <td>0.274889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett</th>\n",
+       "      <td>0.024320</td>\n",
+       "      <td>0.081898</td>\n",
+       "      <td>0.039411</td>\n",
+       "      <td>0.485886</td>\n",
+       "      <td>0.058953</td>\n",
+       "      <td>0.309532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah</th>\n",
+       "      <td>0.012775</td>\n",
+       "      <td>0.085346</td>\n",
+       "      <td>0.041224</td>\n",
+       "      <td>0.699839</td>\n",
+       "      <td>0.071927</td>\n",
+       "      <td>0.088889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lynnwood</th>\n",
+       "      <td>0.029098</td>\n",
+       "      <td>0.103227</td>\n",
+       "      <td>0.047462</td>\n",
+       "      <td>0.599986</td>\n",
+       "      <td>0.056441</td>\n",
+       "      <td>0.163786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond Downtown</th>\n",
+       "      <td>0.026955</td>\n",
+       "      <td>0.087766</td>\n",
+       "      <td>0.040037</td>\n",
+       "      <td>0.508051</td>\n",
+       "      <td>0.117165</td>\n",
+       "      <td>0.220026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renton</th>\n",
+       "      <td>0.023113</td>\n",
+       "      <td>0.101332</td>\n",
+       "      <td>0.046153</td>\n",
+       "      <td>0.596600</td>\n",
+       "      <td>0.075380</td>\n",
+       "      <td>0.157422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Downtown</th>\n",
+       "      <td>0.032103</td>\n",
+       "      <td>0.065885</td>\n",
+       "      <td>0.031165</td>\n",
+       "      <td>0.293904</td>\n",
+       "      <td>0.282165</td>\n",
+       "      <td>0.294773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Northgate</th>\n",
+       "      <td>0.042423</td>\n",
+       "      <td>0.097589</td>\n",
+       "      <td>0.045809</td>\n",
+       "      <td>0.478932</td>\n",
+       "      <td>0.108439</td>\n",
+       "      <td>0.226808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle University Community</th>\n",
+       "      <td>0.027714</td>\n",
+       "      <td>0.081649</td>\n",
+       "      <td>0.041109</td>\n",
+       "      <td>0.389233</td>\n",
+       "      <td>0.257838</td>\n",
+       "      <td>0.202458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle Uptown</th>\n",
+       "      <td>0.046729</td>\n",
+       "      <td>0.077743</td>\n",
+       "      <td>0.033038</td>\n",
+       "      <td>0.412227</td>\n",
+       "      <td>0.116733</td>\n",
+       "      <td>0.313531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tacoma Downtown</th>\n",
+       "      <td>0.039492</td>\n",
+       "      <td>0.075903</td>\n",
+       "      <td>0.034054</td>\n",
+       "      <td>0.478798</td>\n",
+       "      <td>0.056746</td>\n",
+       "      <td>0.315007</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "mode                              Bike      HOV2     HOV3+       SOV  \\\n",
+       "geog_name                                                              \n",
+       "Auburn                        0.026354  0.087096  0.043870  0.662864   \n",
+       "Bellevue                      0.020326  0.076787  0.037290  0.381899   \n",
+       "Bremerton                     0.047028  0.076664  0.034250  0.523514   \n",
+       "Everett                       0.024320  0.081898  0.039411  0.485886   \n",
+       "Issaquah                      0.012775  0.085346  0.041224  0.699839   \n",
+       "Lynnwood                      0.029098  0.103227  0.047462  0.599986   \n",
+       "Redmond Downtown              0.026955  0.087766  0.040037  0.508051   \n",
+       "Renton                        0.023113  0.101332  0.046153  0.596600   \n",
+       "Seattle Downtown              0.032103  0.065885  0.031165  0.293904   \n",
+       "Seattle Northgate             0.042423  0.097589  0.045809  0.478932   \n",
+       "Seattle University Community  0.027714  0.081649  0.041109  0.389233   \n",
+       "Seattle Uptown                0.046729  0.077743  0.033038  0.412227   \n",
+       "Tacoma Downtown               0.039492  0.075903  0.034054  0.478798   \n",
+       "\n",
+       "mode                           Transit      Walk  \n",
+       "geog_name                                         \n",
+       "Auburn                        0.058814  0.121003  \n",
+       "Bellevue                      0.219608  0.264090  \n",
+       "Bremerton                     0.043656  0.274889  \n",
+       "Everett                       0.058953  0.309532  \n",
+       "Issaquah                      0.071927  0.088889  \n",
+       "Lynnwood                      0.056441  0.163786  \n",
+       "Redmond Downtown              0.117165  0.220026  \n",
+       "Renton                        0.075380  0.157422  \n",
+       "Seattle Downtown              0.282165  0.294773  \n",
+       "Seattle Northgate             0.108439  0.226808  \n",
+       "Seattle University Community  0.257838  0.202458  \n",
+       "Seattle Uptown                0.116733  0.313531  \n",
+       "Tacoma Downtown               0.056746  0.315007  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "commute_trips = trip[trip['dpurp'] == 'Work']\n",
+    "trip_df = pd.merge(commute_trips, rgc_taz, left_on='dtaz', right_on='taz', how='left')\n",
+    "trip_df = trip_df[trip_df['mode']!='School Bus']\n",
+    "\n",
+    "df = trip_df[['trexpfac','geog_name','mode']].groupby(['geog_name','mode']).sum().reset_index()\n",
+    "tot_rgc = trip_df[['trexpfac','geog_name']].groupby('geog_name').sum().reset_index()\n",
+    "df = pd.merge(df,tot_rgc,on='geog_name', suffixes=['_mode','_total'])\n",
+    "df['mode_share'] = (df['trexpfac_mode']/df['trexpfac_total'])\n",
+    "\n",
+    "df = df.pivot(index='geog_name', columns='mode', values='mode_share')\n",
+    "# Trim the number of RGCs\n",
+    "rgc_list = ['Auburn','Bellevue','Bremerton','Everett','Issaquah','Lynnwood','Redmond Downtown',\n",
+    "       'Renton','Seattle Downtown','Seattle Northgate','Seattle University Community',\n",
+    "        'Seattle Uptown', 'Tacoma Downtown']\n",
+    "\n",
+    "\n",
+    "df.to_excel(writer,'Trip to RGC - Commute Trips')\n",
+    "df.loc[rgc_list]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "writer.save()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/scripts/summarize/notebooks/corridors.ipynb b/scripts/summarize/notebooks/corridors.ipynb
new file mode 100644
index 00000000..3e52e5bc
--- /dev/null
+++ b/scripts/summarize/notebooks/corridors.ipynb
@@ -0,0 +1,3770 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Corridor Travel Times"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define 2 comparison scenario locations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "scen1_loc = r'U:\\brice\\sc_2040demand_2014net'\n",
+    "scen2_loc = r'S:\\Stefan\\soundcast_10peak_5offpeak'\n",
+    "\n",
+    "current_run_name = 'soundcast_2014'\n",
+    "scen1_name = '2040 No Build'\n",
+    "scen2_name = '2040 10/5 Toll'\n",
+    "\n",
+    "# Set comparison scenario:\n",
+    "compare_scen = scen2_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Link Travel Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# corridors using .in files\n",
+    "\n",
+    "run_df = pd.read_excel(r'..\\..\\..\\outputs\\network_summary_detailed.xlsx', sheetname='Corridors').drop('length', axis=1)\n",
+    "scen1_df = pd.read_excel(os.path.join(scen1_loc,r'outputs\\network_summary_detailed.xlsx'), sheetname='Corridors').drop('length', axis=1)\n",
+    "scen2_df = pd.read_excel(os.path.join(scen2_loc,r'outputs\\network_summary_detailed.xlsx'), sheetname='Corridors').drop('length', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# merge scenarios \n",
+    "df = pd.merge(run_df, scen1_df, on=['Corridor Input File','Local ID','full_id','tod'], suffixes=['_'+current_run_name, '_'+scen1_name])\n",
+    "df.rename(columns={'auto_time_'+current_run_name:current_run_name,'auto_time_'+scen1_name:scen1_name}, inplace=True)\n",
+    "df = pd.merge(df, scen2_df, on=['Corridor Input File','Local ID','full_id','tod'])\n",
+    "df.rename(columns={'auto_time':scen2_name}, inplace=True)\n",
+    "\n",
+    "df[['Local ID','full_id']] = df[['Local ID','full_id']].astype('int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Corridor Input File'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-103-ba4629848fa5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Merge with observed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'..\\..\\..\\scripts\\summarize\\inputs\\network_summary\\corridor_travel_times.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mobs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mleft_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Corridor Input File'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Local ID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'tod'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'corridor'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'direction_id'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'tod'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[0;32m     59\u001b[0m                          \u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m                          \u001b[0mright_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                          copy=copy, indicator=indicator)\n\u001b[0m\u001b[0;32m     62\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[0;32m    541\u001b[0m         (self.left_join_keys,\n\u001b[0;32m    542\u001b[0m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 543\u001b[1;33m          self.join_names) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m    544\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\tools\\merge.pyc\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    821\u001b[0m                         \u001b[0mright_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    822\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mlk\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 823\u001b[1;33m                         \u001b[0mleft_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    824\u001b[0m                         \u001b[0mjoin_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    825\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2057\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2058\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2059\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2061\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2064\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2065\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2066\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2067\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2068\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1384\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1385\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1386\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1387\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1388\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3541\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3542\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3543\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3544\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3545\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\indexes\\base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2134\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2135\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2136\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2138\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:4433)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas\\index.c:4279)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\src\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:13742)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mpandas\\src\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas\\hashtable.c:13696)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Corridor Input File'"
+     ]
+    }
+   ],
+   "source": [
+    "# Merge with observed\n",
+    "obs = pd.read_csv(r'..\\..\\..\\scripts\\summarize\\inputs\\network_summary\\corridor_travel_times.csv')\n",
+    "df = pd.merge(df,obs,left_on=['Corridor Input File','Local ID','tod'], right_on=['corridor','direction_id','tod'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model vs observed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xaa82358>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "uabAky4WLI9oWdoIoq72AT4q6VmKX/Xvo+gV86Xyl/L1wP8uP/sh4DxJjwEr2PCrecol+myvlHQR\n",
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+       "fNL25ygW+LlA0k+AdRSLxUycN9GwvaWke4H/anv55Nub/H0RzWQ9goiIEZeqoYiIEZeqoYgKSfo+\n",
+       "sOmkw39s+7Z+lCdiKqkaiogYcakaiogYcQkEEREjLoEgImLEJRBERIy4BIKIiBH3/wHURO6/x+Ft\n",
+       "1gAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xac6b438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_run = current_run_name\n",
+    "\n",
+    "# For validation\n",
+    "import matplotlib.pyplot as plt\n",
+    "maxtime = 60\n",
+    "ax = df.plot(kind='scatter', x=model_run, y='observed_time_2010', xlim=[0,maxtime], ylim=[0,maxtime])\n",
+    "\n",
+    "# reference line \n",
+    "ref = pd.DataFrame([0,maxtime],[0,maxtime])\n",
+    "ref.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc1428d0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "nOnfnsm9JEmS1BMuy5EkSZJ6wuRekiRJ6gmTe0mSJKknTO4lSZKknjC5lyRJknrC5F6SJEnqCZN7\n",
+       "SZIkqSdM7iVJkqSe+P8BSorPU/t6+A0AAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb3c8b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "validation_df = df[['description','observed_time_2010',model_run]]\n",
+    "validation_df.index = validation_df.description\n",
+    "validation_df['Change (minutes)'] = validation_df[model_run] - validation_df['observed_time_2010']\n",
+    "validation_df['% Change'] = (validation_df[model_run] - validation_df['observed_time_2010'])/validation_df['observed_time_2010']\n",
+    "validation_df[['observed_time_2010',model_run]].plot(kind='barh', figsize=(10,15), alpha=0.6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run vs Scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>10peak_5offpeak</th>\n",
+       "      <th>Change (minutes)</th>\n",
+       "      <th>% Change</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Everett via I-5</th>\n",
+       "      <td>42.034000</td>\n",
+       "      <td>35.608445</td>\n",
+       "      <td>-6.425555</td>\n",
+       "      <td>-0.152866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett to Seattle via I-5</th>\n",
+       "      <td>63.659705</td>\n",
+       "      <td>48.649711</td>\n",
+       "      <td>-15.009994</td>\n",
+       "      <td>-0.235785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way to Seattle via I-5</th>\n",
+       "      <td>47.464196</td>\n",
+       "      <td>40.950104</td>\n",
+       "      <td>-6.514092</td>\n",
+       "      <td>-0.137242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Federal Way via I-5</th>\n",
+       "      <td>27.769661</td>\n",
+       "      <td>25.240065</td>\n",
+       "      <td>-2.529596</td>\n",
+       "      <td>-0.091092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Lynnwood via I-405</th>\n",
+       "      <td>21.670139</td>\n",
+       "      <td>20.770385</td>\n",
+       "      <td>-0.899754</td>\n",
+       "      <td>-0.041520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lynnwood to Bellevue via I-405</th>\n",
+       "      <td>40.883362</td>\n",
+       "      <td>35.903141</td>\n",
+       "      <td>-4.980221</td>\n",
+       "      <td>-0.121815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tukwila to Bellevue via I-405</th>\n",
+       "      <td>34.288670</td>\n",
+       "      <td>22.837504</td>\n",
+       "      <td>-11.451166</td>\n",
+       "      <td>-0.333964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Tukwila via I-405</th>\n",
+       "      <td>26.206000</td>\n",
+       "      <td>18.456346</td>\n",
+       "      <td>-7.749654</td>\n",
+       "      <td>-0.295721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Auburn to Renton via SR 167</th>\n",
+       "      <td>23.763551</td>\n",
+       "      <td>25.502637</td>\n",
+       "      <td>1.739086</td>\n",
+       "      <td>0.073183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renton to Auburn via SR 167</th>\n",
+       "      <td>13.250458</td>\n",
+       "      <td>13.286403</td>\n",
+       "      <td>0.035945</td>\n",
+       "      <td>0.002713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Redmond via SR 520</th>\n",
+       "      <td>20.650735</td>\n",
+       "      <td>17.448349</td>\n",
+       "      <td>-3.202386</td>\n",
+       "      <td>-0.155074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond to Seattle via SR 520</th>\n",
+       "      <td>27.610609</td>\n",
+       "      <td>21.341294</td>\n",
+       "      <td>-6.269315</td>\n",
+       "      <td>-0.227062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Redmond via SR 520</th>\n",
+       "      <td>10.045383</td>\n",
+       "      <td>9.390810</td>\n",
+       "      <td>-0.654572</td>\n",
+       "      <td>-0.065162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond to Bellevue via SR 520</th>\n",
+       "      <td>13.727440</td>\n",
+       "      <td>10.881764</td>\n",
+       "      <td>-2.845676</td>\n",
+       "      <td>-0.207298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Issaquah via I-90</th>\n",
+       "      <td>16.766342</td>\n",
+       "      <td>15.320846</td>\n",
+       "      <td>-1.445495</td>\n",
+       "      <td>-0.086214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah to Bellevue via I-90</th>\n",
+       "      <td>10.501553</td>\n",
+       "      <td>10.567556</td>\n",
+       "      <td>0.066003</td>\n",
+       "      <td>0.006285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Seattle via SR 520</th>\n",
+       "      <td>0.548259</td>\n",
+       "      <td>16.044593</td>\n",
+       "      <td>15.496334</td>\n",
+       "      <td>28.264600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Bellevue via I-90</th>\n",
+       "      <td>18.583682</td>\n",
+       "      <td>17.572769</td>\n",
+       "      <td>-1.010913</td>\n",
+       "      <td>-0.054398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Seattle via I-90</th>\n",
+       "      <td>18.497729</td>\n",
+       "      <td>20.633234</td>\n",
+       "      <td>2.135505</td>\n",
+       "      <td>0.115447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Issaquah via I-90</th>\n",
+       "      <td>17.125386</td>\n",
+       "      <td>17.207966</td>\n",
+       "      <td>0.082580</td>\n",
+       "      <td>0.004822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah to Seattle via I-90</th>\n",
+       "      <td>16.201631</td>\n",
+       "      <td>25.375385</td>\n",
+       "      <td>9.173755</td>\n",
+       "      <td>0.566224</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                soundcast_2014  10peak_5offpeak  \\\n",
+       "description                                                       \n",
+       "Seattle to Everett via I-5           42.034000        35.608445   \n",
+       "Everett to Seattle via I-5           63.659705        48.649711   \n",
+       "Federal Way to Seattle via I-5       47.464196        40.950104   \n",
+       "Seattle to Federal Way via I-5       27.769661        25.240065   \n",
+       "Bellevue to Lynnwood via I-405       21.670139        20.770385   \n",
+       "Lynnwood to Bellevue via I-405       40.883362        35.903141   \n",
+       "Tukwila to Bellevue via I-405        34.288670        22.837504   \n",
+       "Bellevue to Tukwila via I-405        26.206000        18.456346   \n",
+       "Auburn to Renton via SR 167          23.763551        25.502637   \n",
+       "Renton to Auburn via SR 167          13.250458        13.286403   \n",
+       "Seattle to Redmond via SR 520        20.650735        17.448349   \n",
+       "Redmond to Seattle via SR 520        27.610609        21.341294   \n",
+       "Bellevue to Redmond via SR 520       10.045383         9.390810   \n",
+       "Redmond to Bellevue via SR 520       13.727440        10.881764   \n",
+       "Bellevue to Issaquah via I-90        16.766342        15.320846   \n",
+       "Issaquah to Bellevue via I-90        10.501553        10.567556   \n",
+       "Bellevue to Seattle via SR 520        0.548259        16.044593   \n",
+       "Seattle to Bellevue via I-90         18.583682        17.572769   \n",
+       "Bellevue to Seattle via I-90         18.497729        20.633234   \n",
+       "Seattle to Issaquah via I-90         17.125386        17.207966   \n",
+       "Issaquah to Seattle via I-90         16.201631        25.375385   \n",
+       "\n",
+       "                                Change (minutes)   % Change  \n",
+       "description                                                  \n",
+       "Seattle to Everett via I-5             -6.425555  -0.152866  \n",
+       "Everett to Seattle via I-5            -15.009994  -0.235785  \n",
+       "Federal Way to Seattle via I-5         -6.514092  -0.137242  \n",
+       "Seattle to Federal Way via I-5         -2.529596  -0.091092  \n",
+       "Bellevue to Lynnwood via I-405         -0.899754  -0.041520  \n",
+       "Lynnwood to Bellevue via I-405         -4.980221  -0.121815  \n",
+       "Tukwila to Bellevue via I-405         -11.451166  -0.333964  \n",
+       "Bellevue to Tukwila via I-405          -7.749654  -0.295721  \n",
+       "Auburn to Renton via SR 167             1.739086   0.073183  \n",
+       "Renton to Auburn via SR 167             0.035945   0.002713  \n",
+       "Seattle to Redmond via SR 520          -3.202386  -0.155074  \n",
+       "Redmond to Seattle via SR 520          -6.269315  -0.227062  \n",
+       "Bellevue to Redmond via SR 520         -0.654572  -0.065162  \n",
+       "Redmond to Bellevue via SR 520         -2.845676  -0.207298  \n",
+       "Bellevue to Issaquah via I-90          -1.445495  -0.086214  \n",
+       "Issaquah to Bellevue via I-90           0.066003   0.006285  \n",
+       "Bellevue to Seattle via SR 520         15.496334  28.264600  \n",
+       "Seattle to Bellevue via I-90           -1.010913  -0.054398  \n",
+       "Bellevue to Seattle via I-90            2.135505   0.115447  \n",
+       "Seattle to Issaquah via I-90            0.082580   0.004822  \n",
+       "Issaquah to Seattle via I-90            9.173755   0.566224  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_df = df[[current_run_name,compare_scen]]\n",
+    "compare_df.index = df.description\n",
+    "compare_df['Change (minutes)'] = compare_df[compare_scen] - compare_df[current_run_name]\n",
+    "compare_df['% Change'] = (compare_df[compare_scen] - compare_df[current_run_name])/compare_df[current_run_name]\n",
+    "compare_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xa96c320>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "S3WAP+aYRQDYgKraT7sR9GjsvqyiqrbTHrO4O3Zf4L9TQQeADWia5j7JTq3xo6KRzdOejAOMTAUd\n",
+       "AAAmRAUdAAAmRIIOAAATIkEHAIAJkaADAMCESNABAGBCJOgAADAhX7WLW5EEx+OaAAAAAElFTkSu\n",
+       "QmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb5dd9b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.index = df.description\n",
+    "ax = df[[compare_scen,current_run_name]].plot(kind='barh', figsize=(10,15), alpha=0.6)\n",
+    "\n",
+    "# Change order of labels\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "ax.legend(reversed(handles), reversed(labels), loc='upper right')  # reverse both handles and labels\n",
+    "ax.yaxis.label.set_visible(False)    # hide y axis labels\n",
+    "ax.set_xlabel('SOV Travel Time (minutes)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xa6c1550>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "Fisfwa1lfweclJwJkTSn5r6PrdwvT483Ar1eKQbcC+ySqggBfBC4Oe+tBkE+MaAH0wYzW4mHRVYD\n",
+       "vwHuxAfQ44CPSFoFrAOOTE2+mhYe1wK3mtlqvE7ng8Ca9Pr31dz9HEmr8Tqjn0rbrgXOTAuccyvH\n",
+       "+TTwYTwMtAZ4Bg8Fweaz+zBTClojzLmCYAiS1gMLzeyxUR9LEAwiZuhBMJyY9QTTgpihB0EQjAkx\n",
+       "Qw+CIBgTYkAPgiAYE2JAD4IgGBNiQA+CIBgTYkAPgiAYE/4PxJS9gfDCXF4AAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xa48bb70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compare_df['Change (minutes)'].plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scenario vs Scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2040_no_tolls</th>\n",
+       "      <th>10peak_5offpeak</th>\n",
+       "      <th>Change (minutes)</th>\n",
+       "      <th>% Change</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Everett via I-5</th>\n",
+       "      <td>38.480593</td>\n",
+       "      <td>35.608445</td>\n",
+       "      <td>-2.872148</td>\n",
+       "      <td>-0.074639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett to Seattle via I-5</th>\n",
+       "      <td>52.183314</td>\n",
+       "      <td>48.649711</td>\n",
+       "      <td>-3.533602</td>\n",
+       "      <td>-0.067715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way to Seattle via I-5</th>\n",
+       "      <td>44.785897</td>\n",
+       "      <td>40.950104</td>\n",
+       "      <td>-3.835793</td>\n",
+       "      <td>-0.085647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Federal Way via I-5</th>\n",
+       "      <td>26.723916</td>\n",
+       "      <td>25.240065</td>\n",
+       "      <td>-1.483851</td>\n",
+       "      <td>-0.055525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Lynnwood via I-405</th>\n",
+       "      <td>22.490823</td>\n",
+       "      <td>20.770385</td>\n",
+       "      <td>-1.720438</td>\n",
+       "      <td>-0.076495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lynnwood to Bellevue via I-405</th>\n",
+       "      <td>38.709404</td>\n",
+       "      <td>35.903141</td>\n",
+       "      <td>-2.806263</td>\n",
+       "      <td>-0.072496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tukwila to Bellevue via I-405</th>\n",
+       "      <td>25.175834</td>\n",
+       "      <td>22.837504</td>\n",
+       "      <td>-2.338331</td>\n",
+       "      <td>-0.092880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Tukwila via I-405</th>\n",
+       "      <td>19.773659</td>\n",
+       "      <td>18.456346</td>\n",
+       "      <td>-1.317313</td>\n",
+       "      <td>-0.066620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Auburn to Renton via SR 167</th>\n",
+       "      <td>27.468212</td>\n",
+       "      <td>25.502637</td>\n",
+       "      <td>-1.965575</td>\n",
+       "      <td>-0.071558</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Renton to Auburn via SR 167</th>\n",
+       "      <td>13.990789</td>\n",
+       "      <td>13.286403</td>\n",
+       "      <td>-0.704386</td>\n",
+       "      <td>-0.050346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Redmond via SR 520</th>\n",
+       "      <td>17.868600</td>\n",
+       "      <td>17.448349</td>\n",
+       "      <td>-0.420251</td>\n",
+       "      <td>-0.023519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond to Seattle via SR 520</th>\n",
+       "      <td>22.668447</td>\n",
+       "      <td>21.341294</td>\n",
+       "      <td>-1.327153</td>\n",
+       "      <td>-0.058546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Redmond via SR 520</th>\n",
+       "      <td>9.787733</td>\n",
+       "      <td>9.390810</td>\n",
+       "      <td>-0.396922</td>\n",
+       "      <td>-0.040553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Redmond to Bellevue via SR 520</th>\n",
+       "      <td>11.898775</td>\n",
+       "      <td>10.881764</td>\n",
+       "      <td>-1.017011</td>\n",
+       "      <td>-0.085472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Issaquah via I-90</th>\n",
+       "      <td>16.638201</td>\n",
+       "      <td>15.320846</td>\n",
+       "      <td>-1.317355</td>\n",
+       "      <td>-0.079177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah to Bellevue via I-90</th>\n",
+       "      <td>10.767683</td>\n",
+       "      <td>10.567556</td>\n",
+       "      <td>-0.200127</td>\n",
+       "      <td>-0.018586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Seattle via SR 520</th>\n",
+       "      <td>16.798327</td>\n",
+       "      <td>16.044593</td>\n",
+       "      <td>-0.753734</td>\n",
+       "      <td>-0.044870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Bellevue via I-90</th>\n",
+       "      <td>19.252398</td>\n",
+       "      <td>17.572769</td>\n",
+       "      <td>-1.679629</td>\n",
+       "      <td>-0.087243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue to Seattle via I-90</th>\n",
+       "      <td>22.447702</td>\n",
+       "      <td>20.633234</td>\n",
+       "      <td>-1.814468</td>\n",
+       "      <td>-0.080831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle to Issaquah via I-90</th>\n",
+       "      <td>18.265970</td>\n",
+       "      <td>17.207966</td>\n",
+       "      <td>-1.058004</td>\n",
+       "      <td>-0.057922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Issaquah to Seattle via I-90</th>\n",
+       "      <td>27.695159</td>\n",
+       "      <td>25.375385</td>\n",
+       "      <td>-2.319774</td>\n",
+       "      <td>-0.083761</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                2040_no_tolls  10peak_5offpeak  \\\n",
+       "description                                                      \n",
+       "Seattle to Everett via I-5          38.480593        35.608445   \n",
+       "Everett to Seattle via I-5          52.183314        48.649711   \n",
+       "Federal Way to Seattle via I-5      44.785897        40.950104   \n",
+       "Seattle to Federal Way via I-5      26.723916        25.240065   \n",
+       "Bellevue to Lynnwood via I-405      22.490823        20.770385   \n",
+       "Lynnwood to Bellevue via I-405      38.709404        35.903141   \n",
+       "Tukwila to Bellevue via I-405       25.175834        22.837504   \n",
+       "Bellevue to Tukwila via I-405       19.773659        18.456346   \n",
+       "Auburn to Renton via SR 167         27.468212        25.502637   \n",
+       "Renton to Auburn via SR 167         13.990789        13.286403   \n",
+       "Seattle to Redmond via SR 520       17.868600        17.448349   \n",
+       "Redmond to Seattle via SR 520       22.668447        21.341294   \n",
+       "Bellevue to Redmond via SR 520       9.787733         9.390810   \n",
+       "Redmond to Bellevue via SR 520      11.898775        10.881764   \n",
+       "Bellevue to Issaquah via I-90       16.638201        15.320846   \n",
+       "Issaquah to Bellevue via I-90       10.767683        10.567556   \n",
+       "Bellevue to Seattle via SR 520      16.798327        16.044593   \n",
+       "Seattle to Bellevue via I-90        19.252398        17.572769   \n",
+       "Bellevue to Seattle via I-90        22.447702        20.633234   \n",
+       "Seattle to Issaquah via I-90        18.265970        17.207966   \n",
+       "Issaquah to Seattle via I-90        27.695159        25.375385   \n",
+       "\n",
+       "                                Change (minutes)  % Change  \n",
+       "description                                                 \n",
+       "Seattle to Everett via I-5             -2.872148 -0.074639  \n",
+       "Everett to Seattle via I-5             -3.533602 -0.067715  \n",
+       "Federal Way to Seattle via I-5         -3.835793 -0.085647  \n",
+       "Seattle to Federal Way via I-5         -1.483851 -0.055525  \n",
+       "Bellevue to Lynnwood via I-405         -1.720438 -0.076495  \n",
+       "Lynnwood to Bellevue via I-405         -2.806263 -0.072496  \n",
+       "Tukwila to Bellevue via I-405          -2.338331 -0.092880  \n",
+       "Bellevue to Tukwila via I-405          -1.317313 -0.066620  \n",
+       "Auburn to Renton via SR 167            -1.965575 -0.071558  \n",
+       "Renton to Auburn via SR 167            -0.704386 -0.050346  \n",
+       "Seattle to Redmond via SR 520          -0.420251 -0.023519  \n",
+       "Redmond to Seattle via SR 520          -1.327153 -0.058546  \n",
+       "Bellevue to Redmond via SR 520         -0.396922 -0.040553  \n",
+       "Redmond to Bellevue via SR 520         -1.017011 -0.085472  \n",
+       "Bellevue to Issaquah via I-90          -1.317355 -0.079177  \n",
+       "Issaquah to Bellevue via I-90          -0.200127 -0.018586  \n",
+       "Bellevue to Seattle via SR 520         -0.753734 -0.044870  \n",
+       "Seattle to Bellevue via I-90           -1.679629 -0.087243  \n",
+       "Bellevue to Seattle via I-90           -1.814468 -0.080831  \n",
+       "Seattle to Issaquah via I-90           -1.058004 -0.057922  \n",
+       "Issaquah to Seattle via I-90           -2.319774 -0.083761  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_df = df[[scen1_name,scen2_name]]\n",
+    "compare_df.index = df.description\n",
+    "compare_df['Change (minutes)'] = compare_df[scen2_name] - compare_df[scen1_name]\n",
+    "compare_df['% Change'] = (compare_df[scen2_name] - compare_df[scen1_name])/compare_df[scen1_name]\n",
+    "compare_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xbd28550>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "bNA3iz4HGFlmcQLdDPxZwKVtdnu87ujZ/hTw0ao6LcmuwGGt/FxgizYj/BLgva08wPZV9b+9DSZ5\n",
+       "oDdX3d1+3wf0tn0fS/+/eXhVfW4l2+1vu7fdfr39j3wSuYdlvwEf66bQM6tqv/7CdDcA70b3zcMb\n",
+       "2/ZIH96cJk0xl7hIkkjylCRb9hTNBhZX1Z3AccDH2w2gJPlbYL2q+j5AVf0MuJluCcZ4l7fc33XP\n",
+       "9oZ0M7jQrXuntV/A14EjgCur6nft0BnAwT3XsPWANh+IQe0U8B3gNT3ruR+X5JED6v46yZ+uolh6\n",
+       "/RzYKt3TdR4OPL/n2G104whwHrBTkie3ONdPsmWL++FV9W3gLcCzes5/DLB4EmKWNAEm6JIkgA2A\n",
+       "eSM3DtKtkz6sHfsn4C7gv5P8N7A3bU14j+Pp1lWfwsT0ztYeBpyU5ALgpr5j84FXsOySmIOBbduN\n",
+       "jlcAr+tpc6xZ4PHOEPe3M3KT7Jl0H0R+lORS4ES68ev3Q2DbMfpdru3R+uzr/5etz8vpxqN3GdLn\n",
+       "gNOTfK99k3EAcHz7my6k+xs9DDi1lZ0NvLnn/O1YfqmOpAeZj1mUJGkSJJlLdyPo66c6lvFIsiHd\n",
+       "YxafPdWxSGs6Z9AlSZoEVbUA2DITeFHRFDuA7sk4kqaYM+iSJEnSEHEGXZIkSRoiJuiSJEnSEDFB\n",
+       "lyRJkoaICbokSZI0REzQJUmSpCFigi5JkiQNkf8PLrPQXIPYwMIAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xbbb86d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.index = df.description\n",
+    "ax = df[[scen1_name,scen2_name]].plot(kind='barh', figsize=(10,15), alpha=0.6)\n",
+    "\n",
+    "# Change order of labels\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "ax.legend(reversed(handles), reversed(labels), loc='upper right')  # reverse both handles and labels\n",
+    "ax.yaxis.label.set_visible(False)    # hide y axis labels\n",
+    "ax.set_xlabel('SOV Travel Time (minutes)')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/scripts/summarize/notebooks/daysim_summary.ipynb b/scripts/summarize/notebooks/daysim_summary.ipynb
deleted file mode 100644
index 1b094bce..00000000
--- a/scripts/summarize/notebooks/daysim_summary.ipynb
+++ /dev/null
@@ -1,275 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set main model directory to parent directory\n",
-    "model_dir = os.path.dirname(os.getcwd()) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read DaySim Results\n",
-    "ds = h5py.File(model_dir + r'/outputs/daysim_outputs.h5','r+')\n",
-    "ds_name = 'Model: 2040'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Survey Results\n",
-    "survey_file = r'/inputs/hh_and_persons.h5'\n",
-    "\n",
-    "sv = h5py.File(model_dir + survey_file ,'r+')\n",
-    "sv_name = '2006 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Summarize Person-level results\n",
-    "\n",
-    "# Age distribution\n",
-    "ages_ds = np.asarray(ds['Person']['pagey'])\n",
-    "ages_sv = np.asarray(sv['Person']['pagey'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x11888e80>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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pJ510Ei1btqRdu3a8+OKLFBQUMHXqVNasWRP3sxXd9ip5uwsiM0z+8Y9/5KOPPsLdycvL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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd3f0080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Age Distribution Histogram\n",
-    "bins = 30\n",
-    "\n",
-    "# Set model results to boldest (alpha default to 1)\n",
-    "P.hist(ages_ds, bins=bins, normed=True, histtype='step', color='b', label=ds_name)\n",
-    "\n",
-    "# Compare with survey estimates (alpha < 0.5); Currently using fake data...\n",
-    "P.hist(ages_sv, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=sv_name)\n",
-    "P.xlabel('Age')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gender split (1 = female)\n",
-      "\n",
-      "Model results: 0.504\n",
-      "2006 Survey results: 0.504\n"
-     ]
-    }
-   ],
-   "source": [
-    "print \"Gender split (1 = female)\"\n",
-    "print \"\"\n",
-    "print \"Model results: \" + str((np.asarray(ds['Person']['pgend']).mean()-1).round(3))\n",
-    "print sv_name + \" results: \" + str((np.asarray(sv['Person']['pgend']).mean()-1).round(3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Worker type\n",
-    "worker_type_ds = np.asarray(ds['Person']['pptyp'])\n",
-    "worker_type_sv = np.asarray(sv['Person']['pptyp'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 8)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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mlFN+MuxS5m3vvTcddgkaMkNAmqddd92YCy9cOuwypAVxTECSOswQkKQOMwQkqcMMAUnq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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xc9f1898>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Worker Type Histogram\n",
-    "numbins = 8\n",
-    "bins = np.arange(numbins)-0.5    # This center the historgram columns and splits by numbins\n",
-    "\n",
-    "# Set model results to boldest (alpha default to 1)\n",
-    "P.hist(worker_type_ds, bins=bins, normed=True, histtype='step', color='b', label=ds_name)\n",
-    "\n",
-    "# Compare with survey estimates (alpha < 0.5); Currently using fake data...\n",
-    "P.hist(worker_type_sv, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=sv_name)\n",
-    "\n",
-    "#Labels\n",
-    "labels = {\n",
-    "  1: \"Full time worker\",\n",
-    "  2: \"Part time worker\",\n",
-    "  3: \"Non working adult age 65+\",\n",
-    "  4: \"Non working adult age<65\",\n",
-    "  5: \"University student\",\n",
-    "  6: \"High school student age 16+\",\n",
-    "  7: \"Child age 5-15\",\n",
-    "  8: \"Child age 0-4\"\n",
-    "}\n",
-    "\n",
-    "P.xlabel('Worker Type')\n",
-    "P.ylabel('Distribution')\n",
-    "P.xlim(1, numbins)    # Set min and max for x axis\n",
-    "#P.xticks(xrange(len(labels)), [labels[i] for i in xrange(labels.keys()[0], len(labels)+1)], rotation=90)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Student type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Transit Pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Paid parking"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/equity.ipynb b/scripts/summarize/notebooks/equity.ipynb
new file mode 100644
index 00000000..61c2257a
--- /dev/null
+++ b/scripts/summarize/notebooks/equity.ipynb
@@ -0,0 +1,1288 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def apply_labels(df,table_type):\n",
+    "    '''\n",
+    "    Replace values with human readable lablels.\n",
+    "    '''\n",
+    "    df_label = labels[labels['table'] == table_type]\n",
+    "    for field in df_label['field'].unique():\n",
+    "        newdf = df_label[df_label['field'] == field]\n",
+    "        local_series = pd.Series(newdf['text'].values, index=newdf['value'])\n",
+    "        df[field] = df[field].map(local_series)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load data from daysim_outputs.h5, apply labels\n",
+    "daysim = h5py.File(r'../../../outputs/daysim/daysim_outputs.h5', 'r')\n",
+    "\n",
+    "trip = pd.DataFrame()\n",
+    "for col in daysim['Trip'].keys():\n",
+    "    trip[col] = daysim['Trip'][col][:]\n",
+    "    \n",
+    "person = pd.DataFrame()\n",
+    "for col in daysim['Person'].keys():\n",
+    "    person[col] = daysim['Person'][col][:]\n",
+    "    \n",
+    "hh = pd.DataFrame()\n",
+    "for col in daysim['Household'].keys():\n",
+    "    hh[col] = daysim['Household'][col][:]\n",
+    "    \n",
+    "# Add labels\n",
+    "labels = pd.read_csv(r'../../../scripts/summarize/inputs/calibration/variable_labels.csv')\n",
+    "\n",
+    "trip = apply_labels(trip,'Trip')\n",
+    "person = apply_labels(person,'Person')\n",
+    "hh = apply_labels(hh,'Household')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "special_taz = pd.read_csv(r'../../../scripts/summarize/inputs/special_needs_taz.csv')\n",
+    "rgc_taz = pd.read_csv(r'../../../scripts/summarize/inputs/rgc_taz.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Join RGC, low income, and minority data, based on home location\n",
+    "\n",
+    "trip_hh = pd.merge(trip,hh,on='hhno',how='left')\n",
+    "\n",
+    "trip_hh = pd.merge(trip_hh, special_taz, left_on='hhtaz', right_on='TAZ', how='left')\n",
+    "trip_hh = pd.merge(trip_hh, rgc_taz, left_on='hhtaz', right_on='taz', how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trip_hh = trip_hh[trip_hh['mode']!='School Bus']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mode Share\n",
+    "Based on home location"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mode_share(trip_hh):\n",
+    "    \"\"\"\n",
+    "    Produce trip mode share for low income, minority, and regional populations\n",
+    "    \"\"\"\n",
+    "    # Regional total\n",
+    "    df_reg = pd.DataFrame(\n",
+    "        trip_hh.groupby('mode').sum()['trexpfac']/trip_hh['trexpfac'].sum())\n",
+    "    df_reg.columns = ['Region']\n",
+    "    df_reg = df_reg.reset_index()\n",
+    "    \n",
+    "    # Low Income\n",
+    "    df = trip_hh[['trexpfac','Low Income','mode']].groupby(['Low Income','mode']).sum().reset_index()\n",
+    "    tot_trips = pd.DataFrame(df.groupby('Low Income').sum()['trexpfac']).reset_index()\n",
+    "    df = pd.merge(df,tot_trips,on='Low Income', suffixes=['_mode','_total'])\n",
+    "    df['mode_share'] = (df['trexpfac_mode']/df['trexpfac_total'])\n",
+    "\n",
+    "    df_inc = df.pivot(index='Low Income', columns='mode', values='mode_share')\n",
+    "    df_inc.index = ['Other','People of Lower Income']\n",
+    "    \n",
+    "    # Minority\n",
+    "    df = trip_hh[['trexpfac','Minority','mode']].groupby(['Minority','mode']).sum().reset_index()\n",
+    "    tot_trips = pd.DataFrame(df.groupby('Minority').sum()['trexpfac']).reset_index()\n",
+    "    df = pd.merge(df,tot_trips,on='Minority', suffixes=['_mode','_total'])\n",
+    "    df['mode_share'] = (df['trexpfac_mode']/df['trexpfac_total'])\n",
+    "\n",
+    "    df_race = df.pivot(index='Minority', columns='mode', values='mode_share')\n",
+    "    df_race.index = ['Other','People of Color']\n",
+    "    \n",
+    "    # Merge all dataframes\n",
+    "    df = pd.merge(pd.DataFrame(df_race.loc['People of Color']).reset_index(),\n",
+    "         pd.DataFrame(df_inc.loc['People of Lower Income']).reset_index())\n",
+    "    df = pd.merge(df_reg, df)\n",
+    "    df.index = df['mode']\n",
+    "    df = df.drop('mode', axis=1)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### All Trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Region</th>\n",
+       "      <th>People of Color</th>\n",
+       "      <th>People of Lower Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bike</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV2</th>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV3+</th>\n",
+       "      <td>0.16</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>0.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOV</th>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Walk</th>\n",
+       "      <td>0.16</td>\n",
+       "      <td>0.16</td>\n",
+       "      <td>0.17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Region  People of Color  People of Lower Income\n",
+       "mode                                                    \n",
+       "Bike       0.02             0.02                    0.02\n",
+       "HOV2       0.23             0.23                    0.23\n",
+       "HOV3+      0.16             0.17                    0.17\n",
+       "SOV        0.40             0.38                    0.38\n",
+       "Transit    0.03             0.04                    0.03\n",
+       "Walk       0.16             0.16                    0.17"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pd.options.display.float_format = '{:.2f}'.format\n",
+    "df = mode_share(trip_hh)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Commute Trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Region</th>\n",
+       "      <th>People of Color</th>\n",
+       "      <th>People of Lower Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mode</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bike</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV2</th>\n",
+       "      <td>0.10</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV3+</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOV</th>\n",
+       "      <td>0.66</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>0.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit</th>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Walk</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Region  People of Color  People of Lower Income\n",
+       "mode                                                    \n",
+       "Bike       0.02             0.02                    0.03\n",
+       "HOV2       0.10             0.10                    0.10\n",
+       "HOV3+      0.04             0.05                    0.04\n",
+       "SOV        0.66             0.64                    0.64\n",
+       "Transit    0.06             0.07                    0.06\n",
+       "Walk       0.11             0.12                    0.13"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "commute_trips = trip_hh[trip_hh['dpurp'] == 'Work']\n",
+    "commute_trips = commute_trips[commute_trips['mode'] != 'School Bus']\n",
+    "df = mode_share(commute_trips)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Access to Frequent Transit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(r'../../../outputs/transit/freq_transit_access.csv')\n",
+    "# Join parcel data (transit access) to household recods\n",
+    "df = pd.merge(hh[['hhparcel','hhsize','hhtaz']],df[['PARCELID','dist_frequent']],how='left',left_on='hhparcel',right_on='PARCELID')\n",
+    "\n",
+    "# join to geography fiels\n",
+    "df = pd.merge(df, special_taz, left_on='hhtaz', right_on='TAZ', how='left')\n",
+    "df = pd.merge(df, rgc_taz, left_on='hhtaz', right_on='taz', how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Only include househodls on parcels within 1/2 mile of frequent transit\n",
+    "max_dist = 0.5\n",
+    "df_freq = df[df['dist_frequent'] <= max_dist]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate percent of people within that range"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df_lowinc = pd.DataFrame(df_freq.groupby('Low Income').sum()['hhsize']/df.groupby('Low Income').sum()['hhsize'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df_minority = pd.DataFrame(df_freq.groupby('Minority').sum()['hhsize']/df.groupby('Minority').sum()['hhsize'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "reg_share = df_freq.sum()['hhsize']/df.sum()['hhsize']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:.2f}'.format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Percent with Access to Frequent Transit Service</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>30.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Color</th>\n",
+       "      <td>40.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Low Income</th>\n",
+       "      <td>36.37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Percent with Access to Frequent Transit Service\n",
+       "Region                                                          30.74\n",
+       "People of Color                                                 40.38\n",
+       "People of Low Income                                            36.37"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_df = pd.DataFrame([reg_share,df_minority.loc[1].values[0],df_lowinc.loc[1].values[0]])\n",
+    "_df.index = ['Region','People of Color','People of Low Income']\n",
+    "_df.columns = ['Percent with Access to Frequent Transit Service']\n",
+    "_df['Percent with Access to Frequent Transit Service'] = _df['Percent with Access to Frequent Transit Service']*100\n",
+    "_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Percent of People Walking and Biking for Transport"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')\n",
+    "person_hh = pd.merge(person,hh[['hhno','hhtaz']],on='hhno',how='left')\n",
+    "# bike_walk_trips = trip_person[trip_person['mode'].isin(['Bike','Walk'])]\n",
+    "# Walk & bike modes, plus transit trips with walk access\n",
+    "bike_walk_trips = trip_person[trip_person['mode'].isin(['Bike','Walk']) | ((trip_person['mode'] == 'Transit') & (trip_person['dorp'] > 0))]\n",
+    "\n",
+    "df = bike_walk_trips.groupby(['hhno','pno']).count()\n",
+    "df = df.reset_index()\n",
+    "df = df[['hhno','pno']]\n",
+    "df['bike_walk'] = True\n",
+    "\n",
+    "df = pd.merge(person_hh,df,on=['hhno','pno'], how='left')\n",
+    "df['bike_walk'] = df['bike_walk'].fillna(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:,.1%}'.format\n",
+    "reg_active = pd.DataFrame(df.groupby('bike_walk').sum()['psexpfac']/df['psexpfac'].sum()).loc[True]['psexpfac']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df, special_taz, left_on='hhtaz', right_on='TAZ', how='left')\n",
+    "df = pd.merge(df, rgc_taz, left_on='hhtaz', right_on='taz', how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "_df = pd.DataFrame(df[df['Low Income'] == 1].groupby(['bike_walk']).count()).reset_index()\n",
+    "low_inc_active = _df[_df['bike_walk'] == True]['psexpfac'].tolist()[0]/_df.sum()['psexpfac']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "_df = pd.DataFrame(df[df['Minority'] == 1].groupby(['bike_walk']).count()).reset_index()\n",
+    "minority_active = _df[_df['bike_walk'] == True]['psexpfac'].tolist()[0]/_df.sum()['psexpfac']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>% Walks or Bikes for Transportation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>30.8%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Color</th>\n",
+       "      <td>32.2%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Low Income</th>\n",
+       "      <td>32.9%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      % Walks or Bikes for Transportation\n",
+       "Region                                              30.8%\n",
+       "People of Color                                     32.2%\n",
+       "People of Low Income                                32.9%"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_df = pd.DataFrame([reg_active,minority_active,low_inc_active])\n",
+    "_df.index = ['Region','People of Color','People of Low Income']\n",
+    "_df.columns = ['% Walks or Bikes for Transportation']\n",
+    "_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Average Time Spent Walking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = \"{0:.2f}\".format\n",
+    "# walk_trips = trip_person[trip_person['mode'] == 'Walk']\n",
+    "# daily_walk_times = walk_trips.groupby(['hhno','pno']).sum()['travtime']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "daily_tot_time = pd.DataFrame(trip_person.groupby(['pno','hhno','mode']).sum()['travtime'])\n",
+    "daily_tot_time = daily_tot_time.reset_index()\n",
+    "daily_walk_time = daily_tot_time[daily_tot_time['mode'] == 'Walk']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Total walk time across region divided by total number of people\n",
+    "reg_walk_time = daily_walk_time['travtime'].sum()/person['psexpfac'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Total for low inc and for rgc\n",
+    "df = pd.merge(daily_tot_time,hh[['hhno','hhtaz']],on='hhno', how='left')\n",
+    "df = pd.merge(df,rgc_taz,left_on='hhtaz',right_on='taz',how='left')\n",
+    "df = pd.merge(df,special_taz,left_on='hhtaz',right_on='TAZ',how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_walk_time = df[df['Low Income'] == 1]['travtime'].sum()/person['psexpfac'].sum()\n",
+    "minority_walk_time = df[df['Minority'] == 1]['travtime'].sum()/person['psexpfac'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Average Minutes Walking per Day</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>15.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Color</th>\n",
+       "      <td>30.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Low Income</th>\n",
+       "      <td>28.37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Average Minutes Walking per Day\n",
+       "Region                                          15.34\n",
+       "People of Color                                 30.82\n",
+       "People of Low Income                            28.37"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame([reg_walk_time,minority_walk_time,low_inc_walk_time])\n",
+    "df.columns = ['Average Minutes Walking per Day']\n",
+    "df.index = ['Region','People of Color','People of Low Income']\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Annual Out-of-Pocket Costs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "annual_factor = 300\n",
+    "reg_costs = trip_hh[['hhno','travcost']].groupby('hhno').sum()['travcost'].mean()*annual_factor\n",
+    "low_inc_costs = trip_hh[trip_hh['Low Income'] == 1][['hhno','travcost']].groupby('hhno').sum()['travcost'].mean()*annual_factor\n",
+    "minority_costs = trip_hh[trip_hh['Minority'] == 1][['hhno','travcost']].groupby('hhno').sum()['travcost'].mean()*annual_factor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Annual Out-of-Pocket Costs</th>\n",
+       "      <th>Median Income</th>\n",
+       "      <th>% of Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>3392.83</td>\n",
+       "      <td>68610.00</td>\n",
+       "      <td>4.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Color</th>\n",
+       "      <td>3114.21</td>\n",
+       "      <td>59295.00</td>\n",
+       "      <td>5.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Low Income</th>\n",
+       "      <td>2972.20</td>\n",
+       "      <td>51041.00</td>\n",
+       "      <td>5.82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Annual Out-of-Pocket Costs  Median Income  % of Income\n",
+       "Region                                   3392.83       68610.00         4.95\n",
+       "People of Color                          3114.21       59295.00         5.25\n",
+       "People of Low Income                     2972.20       51041.00         5.82"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame([reg_costs,minority_costs,low_inc_costs])\n",
+    "df.columns = ['Annual Out-of-Pocket Costs']\n",
+    "df.index = ['Region','People of Color','People of Low Income']\n",
+    "\n",
+    "# Out of pocket costs as percentage of household income\n",
+    "hh = pd.merge(hh, special_taz, left_on='hhtaz', right_on='TAZ', how='left')\n",
+    "hh = pd.merge(hh, rgc_taz, left_on='hhtaz', right_on='taz', how='left')\n",
+    "\n",
+    "regional_avg_inc = hh.groupby('hhno').sum()['hhincome'].median()\n",
+    "low_inc_avg_inc = hh[hh['Low Income'] == 1].groupby('hhno').sum()['hhincome'].median()\n",
+    "minority_avg_inc = hh[hh['Minority'] == 1].groupby('hhno').sum()['hhincome'].median()\n",
+    "\n",
+    "df_inc = pd.DataFrame([regional_avg_inc,low_inc_avg_inc,minority_avg_inc])\n",
+    "df_inc.columns = ['Median Income']\n",
+    "df_inc.index = ['Region','People of Low Income','People of Color']\n",
+    "\n",
+    "df = pd.merge(df,df_inc,left_index=True, right_index=True)\n",
+    "df['% of Income'] = (df['Annual Out-of-Pocket Costs']/df['Median Income'])*100\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Annual Out-of-Pocket Costs</th>\n",
+       "      <th>Median Income</th>\n",
+       "      <th>% of Income</th>\n",
+       "      <th>% Income for $30000 income household</th>\n",
+       "      <th>% Income for $20000 income household</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>3392.83</td>\n",
+       "      <td>68610.00</td>\n",
+       "      <td>4.95</td>\n",
+       "      <td>11.31</td>\n",
+       "      <td>16.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Color</th>\n",
+       "      <td>3114.21</td>\n",
+       "      <td>59295.00</td>\n",
+       "      <td>5.25</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>15.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Low Income</th>\n",
+       "      <td>2972.20</td>\n",
+       "      <td>51041.00</td>\n",
+       "      <td>5.82</td>\n",
+       "      <td>9.91</td>\n",
+       "      <td>14.86</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Annual Out-of-Pocket Costs  Median Income  % of Income  \\\n",
+       "Region                                   3392.83       68610.00         4.95   \n",
+       "People of Color                          3114.21       59295.00         5.25   \n",
+       "People of Low Income                     2972.20       51041.00         5.82   \n",
+       "\n",
+       "                      % Income for $30000 income household  \\\n",
+       "Region                                               11.31   \n",
+       "People of Color                                      10.38   \n",
+       "People of Low Income                                  9.91   \n",
+       "\n",
+       "                      % Income for $20000 income household  \n",
+       "Region                                               16.96  \n",
+       "People of Color                                      15.57  \n",
+       "People of Low Income                                 14.86  "
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Percent of income for households making 30,000\n",
+    "df['% Income for $30000 income household'] = (df[['Annual Out-of-Pocket Costs']]/30000)*100\n",
+    "df['% Income for $20000 income household'] = (df[['Annual Out-of-Pocket Costs']]/20000)*100\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Daily VMT per capita"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "driver_trips = trip[(trip['dorp'] == 1) & (trip['mode'].isin(['SOV','HOV2','HOV3+']))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "person_hh = pd.merge(person,hh[['hhno','hhtaz']],on='hhno',how='left')\n",
+    "person_hh = pd.merge(person_hh, rgc_taz, left_on='hhtaz', right_on='taz', how='left')\n",
+    "person_hh = pd.merge(person_hh, special_taz, left_on='hhtaz', right_on='TAZ', how='left')\n",
+    "df = pd.merge(driver_trips,person_hh,on=['hhno','pno'], how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "reg_vmt = df['travdist'].sum()/person_hh['psexpfac'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vmt = pd.DataFrame(df[['Minority','travdist']].groupby('Minority').sum()['travdist'])\n",
+    "# Population totals\n",
+    "pop = person_hh[['Minority','psexpfac']].groupby(['Minority']).sum()\n",
+    "minority_vmt = pd.DataFrame(vmt['travdist']/pop['psexpfac']).loc[1].values[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vmt = pd.DataFrame(df[['Low Income','travdist']].groupby('Low Income').sum()['travdist'])\n",
+    "# Population totals\n",
+    "pop = person_hh[['Low Income','psexpfac']].groupby(['Low Income']).sum()\n",
+    "lowinc_vmt = pd.DataFrame(vmt['travdist']/pop['psexpfac']).loc[1].values[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Average VMT per Capita</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>16.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Color</th>\n",
+       "      <td>14.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>People of Lower Income</th>\n",
+       "      <td>14.38</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Average VMT per Capita\n",
+       "Region                                   16.49\n",
+       "People of Color                          14.68\n",
+       "People of Lower Income                   14.38"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_df = pd.DataFrame([reg_vmt,minority_vmt,lowinc_vmt])\n",
+    "_df.columns = ['Average VMT per Capita']\n",
+    "_df.index = ['Region','People of Color','People of Lower Income']\n",
+    "_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Average Auto Delay per Capita"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "trip_auto = trip_hh[trip_hh['mode'].isin(['SOV','HOV2','HOV3+']) & (trip['dorp'] == 1)]\n",
+    "trip_auto['delay'] = trip_auto['travtime'] - trip_auto['sov_ff_time']/100.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Regional average\n",
+    "reg_delay = (trip_auto['delay'].sum()/person['psexpfac'].sum())*300/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "low_inc_delay = trip_auto[trip_auto['Low Income'] == 1]['delay'].sum()/person_hh[person_hh['Low Income'] == 1][['psexpfac']].sum()\n",
+    "low_inc_delay = low_inc_delay.values[0]*300/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "minority_delay = trip_auto[trip_auto['Minority'] == 1]['delay'].sum()/person_hh[person_hh['Minority'] == 1][['psexpfac']].sum()\n",
+    "minority_delay = minority_delay.values[0]*300/60"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame([reg_delay,low_inc_delay,minority_delay])\n",
+    "df.columns = ['Average Annual Vehicle Delay (hours)']\n",
+    "df.index = ['Region','People of Low Income','People of Color']\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/summarize/notebooks/household.ipynb b/scripts/summarize/notebooks/household.ipynb
deleted file mode 100644
index 99e2f548..00000000
--- a/scripts/summarize/notebooks/household.ipynb
+++ /dev/null
@@ -1,410 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set main model directory to parent directory\n",
-    "model_dir = os.path.dirname(os.getcwd()) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Model Scenario Results\n",
-    "scen = h5py.File(model_dir + r'/outputs/daysim_outputs.h5','r+')\n",
-    "scen_name = 'Model: 2040'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Base Data\n",
-    "base_file = r'/inputs/hh_and_persons.h5'\n",
-    "\n",
-    "base = h5py.File(model_dir + base_file ,'r+')\n",
-    "base_name = '2006 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Household Size\n",
-    "####################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Summarize Household Size\n",
-    "\n",
-    "# Age distribution\n",
-    "hhsize_scen = np.asarray(scen['Household']['hhsize'])\n",
-    "hhsize_base = np.asarray(base['Household']['hhsize'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xf761e80>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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PPAPAM888w9VXX51QDHWhRCEiacvduf7669m0aRPPP/98uU7gnj17kpeXF1uvOD1o2dSj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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xf370240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Household Size Distribution\n",
-    "bins = ages_scen.max()\n",
-    "\n",
-    "# Set model results to boldest (alpha default to 1)\n",
-    "P.hist(ages_scen, bins=bins, normed=True, histtype='step', color='b', label=scen_name)\n",
-    "\n",
-    "# Compare with survey estimates (alpha < 0.5); Currently using fake data...\n",
-    "P.hist(ages_base, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=base_name)\n",
-    "P.xlabel('Household Members')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average household size for Scenario: 2.399\n",
-      "Max household size for Scenario: 17\n"
-     ]
-    }
-   ],
-   "source": [
-    "print \"Average household size for Scenario: \" + str(hhsize_scen.mean().round(3))\n",
-    "print \"Max household size for Scenario: \" + str(bins)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Household Income\n",
-    "####################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Summarize Household Income Distribution\n",
-    "\n",
-    "hhinc_scen = np.asarray(scen['Household']['hhincome'])\n",
-    "hhinc_base = np.asarray(base['Household']['hhincome'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xfb5cb00>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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bb7+dOXPmsHr16ibbWn+W/qVLlzJkyBA2b97ML3/5S/7t3/6NefPm1WxfsGABubm59OjR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\nku8ZIcoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xf6e15f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Household Income Distribution\n",
-    "bins = hhinc_scen.max()/10000\n",
-    "\n",
-    "# Set model results to boldest (alpha default to 1)\n",
-    "P.hist(hhinc_scen, bins=bins, normed=True, histtype='step', color='b', label=scen_name)\n",
-    "\n",
-    "# Compare with survey estimates (alpha < 0.5); Currently using fake data...\n",
-    "P.hist(hhinc_base, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=base_name)\n",
-    "P.xlabel('Household Income')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average household income for SCENARIO: 148741\n",
-      "Average household income for BASE: 64344\n",
-      "\n",
-      "Median household income for SCNEARIO: 112921\n",
-      "Median household income for BASE: 48400\n"
-     ]
-    }
-   ],
-   "source": [
-    "print \"Average household income for SCENARIO: \" + str(int(hhinc_scen.mean()))\n",
-    "print \"Average household income for BASE: \" + str(int(hhinc_base.mean()))\n",
-    "print \"\"\n",
-    "print \"Median household income for SCNEARIO: \" + str(int(np.median(hhinc_scen)))\n",
-    "print \"Median household income for BASE: \" + str(int(np.median(hhinc_base)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Residence Type\n",
-    "####################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Worker type\n",
-    "type_var = 'hrestype'    # Assuming this is same as base as in scenario\n",
-    "\n",
-    "hrestype_scen = np.asarray(scen['Household'][type_var])\n",
-    "hrestype_base = np.asarray(base['Household'][type_var])\n",
-    "\n",
-    "restype_labels = {\n",
-    "  0: \"Error\",  \n",
-    "  1: \"Detached single house\",\n",
-    "  2: \"Duplex/triplex/rowhouse\",\n",
-    "  3: \"Apartment/condo\",\n",
-    "  4: \"Mobile home/trailer\",\n",
-    "  5: \"Dorm room/rented room\",\n",
-    "  6: \"Other\",\n",
-    "  9: \"Missing\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Sum by residence type for scenario results\n",
-    "df = pd.DataFrame(hrestype_scen, columns=[type_var])\n",
-    "df['Residence Type'] = [restype_labels[x] for x in df[type_var].as_matrix()]\n",
-    "\n",
-    "# Ignore \"0\" (missing) response\n",
-    "df = df.query(\"hrestype <> 0\")\n",
-    "restypes_scen = df.groupby('Residence Type').count()[type_var]     # Sum by category\n",
-    "restypes_scen = restypes_scen/restypes_scen.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Sum by worker type for base\n",
-    "df = pd.DataFrame(hrestype_base, columns=[type_var])\n",
-    "df['Residence Type'] = [restype_labels[x] for x in df[type_var].as_matrix()]\n",
-    "\n",
-    "# Ignore \"0\" (missing) response\n",
-    "df = df.query(\"hrestype <> 0\")\n",
-    "restypes_base = df.groupby('Residence Type').count()[type_var]    # Sum by category\n",
-    "restypes_base = restypes_base/restypes_base.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x22d3f2b0>"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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mZlZLU2re+wIvRMTciFgG3AwML70gIt6MiCpgWSvEaGZmdTRa8wb6Aa+WHM8D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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x22d3f1d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set bar chart params\n",
-    "ind = np.arange(len(restypes_scen))    # index array to match dataset size\n",
-    "width = 0.3    # width of bars\n",
-    "\n",
-    "fig, ax = P.subplots()    # Initialize bar chart object\n",
-    "\n",
-    "res_scen = ax.bar(ind, restypes_base.values, width=width, color='b')    # plot scenario data\n",
-    "res_base = ax.bar(ind+width, restypes_base.values, width=width, color='grey', alpha=0.3)    # plot base data (note the index offset by the bar width)\n",
-    "ax.set_xticks(ind+width)   \n",
-    "ax.set_xticklabels(restypes_scen.index, rotation=90)\n",
-    "ax.set_title('Residence Types')\n",
-    "\n",
-    "ax.legend((res_scen, res_base), (scen_name, base_name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Own vs. Rent\n",
-    "####################"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/metrics.ipynb b/scripts/summarize/notebooks/metrics.ipynb
new file mode 100644
index 00000000..c954b85f
--- /dev/null
+++ b/scripts/summarize/notebooks/metrics.ipynb
@@ -0,0 +1,1117 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Daysim data\n",
+    "trip = pd.read_csv(r'../../../outputs/daysim/_trip.tsv', sep='\\t')\n",
+    "person = pd.read_csv(r'../../../outputs/daysim/_person.tsv', sep='\\t')\n",
+    "hh = pd.read_csv(r'../../../outputs/daysim/_household.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:0,.2f}'.format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Weekdays in year\n",
+    "annual_factor = 300"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Soundcast Metrics\n",
+    "----"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Average Daily Miles Driven per Person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_excel(r'../../../outputs/network/network_summary_detailed.xlsx', sheetname='UC VMT')\n",
+    "tot_vmt = df.drop(['@lttrk','@mveh','@hveh','@bveh'], axis=1).sum().sum()\n",
+    "tot_pop = person['psexpfac'].sum()\n",
+    "x = tot_vmt/tot_pop\n",
+    "print '{:0,.1f}'.format(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hours of Congestion per Person per Year\n",
+    "For average Puget Sound resident:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "33.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "net_sum = pd.read_excel(r'../../../outputs/network/network_summary_detailed.xlsx', sheetname='UC Delay')\n",
+    "veh_delay = net_sum.drop(['@lttrk','@mveh','@hveh','@bveh'], axis=1).sum().sum()\n",
+    "tot_pop = person['psexpfac'].sum()\n",
+    "x = (veh_delay/tot_pop)*annual_factor\n",
+    "print '{:0,.1f}'.format(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Annual Hours of Delay by Average Truck\n",
+    "Average annual delay (hours) per truck trip in and through the region:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "71.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load truck trips\n",
+    "df = pd.read_csv(r'../../../outputs/network/trucks.csv')\n",
+    "\n",
+    "# Truck delay\n",
+    "net_sum = pd.read_excel(r'../../../outputs/network/network_summary_detailed.xlsx', sheetname='UC Delay')\n",
+    "\n",
+    "# Annual delay hours\n",
+    "daily_delay = net_sum[['@mveh','@hveh']].sum().sum()\n",
+    "\n",
+    "\n",
+    "# total truck trips\n",
+    "trips = df['prod'].sum()\n",
+    "\n",
+    "# average annual delay hours per truck\n",
+    "x = (daily_delay*annual_factor)/trips\n",
+    "print'{:0,.1f}'.format(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Medium trucks only:*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "61.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = (net_sum['@mveh'].sum()*annual_factor)/df[df['Unnamed: 0'] == 'mt']['prod'].values[0]\n",
+    "print '{:0,.1f}'.format(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Heavy trucks only:*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "111.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = (net_sum['@hveh'].sum()*annual_factor)/df[df['Unnamed: 0'] == 'ht']['prod'].values[0]\n",
+    "print '{:0,.1f}'.format(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### % Population Walking or Biking for Transportation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')\n",
+    "bike_walk_trips = trip_person[trip_person['mode'].isin([1,2])]\n",
+    "\n",
+    "df = bike_walk_trips.groupby(['hhno','pno']).count()\n",
+    "df = df.reset_index()\n",
+    "df = df[['hhno','pno']]\n",
+    "df['bike_walk'] = True\n",
+    "\n",
+    "df = pd.merge(person,df,on=['hhno','pno'], how='left')\n",
+    "df['bike_walk'] = df['bike_walk'].fillna(False)\n",
+    "\n",
+    "pd.options.display.float_format = '{:,.1%}'.format\n",
+    "df = pd.DataFrame(df.groupby('bike_walk').sum()['psexpfac']/df['psexpfac'].sum())\n",
+    "print '{:,.1%}'.format(df.loc[True]['psexpfac'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Household and Jobs within 1/4 mile transit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Network data\n",
+    "df = pd.read_excel(r'../../../outputs/network/network_summary_detailed.xlsx', sheetname='Transit Job Access')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Households**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "871,384 households within 1/4 mile of transit\n",
+      "47.7% of total households\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = df.loc['hh_p']['quarter_mile_transit']\n",
+    "print '{:,.0f}'.format(x) + \" households within 1/4 mile of transit\"\n",
+    "x = (df.loc['hh_p']['quarter_mile_transit']*1.0)/df.loc['hh_p']['total']\n",
+    "print '{:,.1%}'.format(x) + \" of total households\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Jobs**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1,606,232 jobs within 1/4 mile of transit\n",
+      "68.6% of total jobs\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = df.loc['emptot_p']['quarter_mile_transit']\n",
+    "print '{:,.0f}'.format(x) + \" jobs within 1/4 mile of transit\"\n",
+    "x = (df.loc['emptot_p']['quarter_mile_transit']*1.0)/df.loc['emptot_p']['total']\n",
+    "print '{:,.1%}'.format(x) + \" of total jobs\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Percent of Population within 1/2 mile of Frequent Transit\n",
+    "15 minute frequency during AM/PM peak"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:0,.2f}'.format\n",
+    "df = pd.read_csv(r'..\\..\\..\\outputs\\transit\\freq_transit_access.csv')\n",
+    "hh = pd.read_csv(r'..\\..\\..\\outputs\\daysim\\_household.tsv', sep='\\t')\n",
+    "df = pd.merge(hh, df, left_on='hhparcel', right_on='PARCELID', how='left')\n",
+    "max_dist = 0.5\n",
+    "\n",
+    "tot_within = df[df['dist_frequent'] <= max_dist]['hhsize'].sum()\n",
+    "tot_pop = df['hhsize'].sum()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1,343,767 people within 1/2 mile of frequent transit\n",
+      "31.4% of regional population\n"
+     ]
+    }
+   ],
+   "source": [
+    "print str('{:,.0f}'.format(tot_within)) + \" people within 1/2 mile of frequent transit\"\n",
+    "print str('{:,.1%}'.format(tot_within*1.0/tot_pop)) + \" of regional population\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Highway Peak Period Travel Times\n",
+    "(Zone to Zone travel times)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Load observed data (2013 currently)\n",
+    "od_obs = pd.read_csv(r'..\\..\\..\\scripts\\summarize\\inputs\\network_summary\\od_travel_times.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**AM Travel Times**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "user_class = 'svtl1t'\n",
+    "max_internal_zone = 3700\n",
+    "zone_list = [532,2286,1552,1125,3108]\n",
+    "\n",
+    "# Need zone terminal times to compare to observed\n",
+    "o_tt = pd.read_csv(r'..\\..\\..\\inputs\\IntraZonals\\origin_tt.in',\n",
+    "                   sep=' ',header=4)\n",
+    "\n",
+    "\n",
+    "o_tt.index = o_tt.index.get_level_values(1)\n",
+    "o_tt['taz'] = o_tt.index\n",
+    "o_tt['terminal_time'] = o_tt['matrix=mo\"prodtt\"']\n",
+    "o_tt = pd.DataFrame(o_tt[['terminal_time','taz']])\n",
+    "\n",
+    "results_dict = {}\n",
+    "\n",
+    "for skim_name in ['7to8','10to14','17to18']:\n",
+    "    user_class_dict = {}\n",
+    "    for user_class in ['svtl2t','h3tl2t']:\n",
+    "        results = pd.DataFrame()\n",
+    "        h5_contents = h5py.File(r'..\\..\\..\\inputs' + r'\\\\' + skim_name + '.h5')\n",
+    "        for zone in zone_list:\n",
+    "            df = pd.DataFrame()\n",
+    "            df[user_class] = h5_contents['Skims'][user_class][:][zone-1,:max_internal_zone]\n",
+    "            df['otaz'] = zone\n",
+    "            df['dtaz'] = df.index+1\n",
+    "            df['travel_time'] = df[user_class]/100 # skim data is stored in hundreds\n",
+    "\n",
+    "            df = df[df['dtaz'].isin(zone_list)]\n",
+    "\n",
+    "    #      Subtract the terminal times\n",
+    "            df = pd.merge(df,o_tt,left_on='dtaz',right_on='taz')\n",
+    "            # Subtract destination terminal time\n",
+    "            df['travel_time'] = df['travel_time']-df['terminal_time']\n",
+    "            # Subtract origin terminal time\n",
+    "            df['travel_time'] = df['travel_time']-(o_tt[o_tt['taz'] == zone]['terminal_time'].values[0])\n",
+    "\n",
+    "            results = results.append(df)\n",
+    "\n",
+    "        user_class_dict[user_class] = results\n",
+    "    results_dict[skim_name] = user_class_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>From</th>\n",
+       "      <th>To</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Observed (Low)</th>\n",
+       "      <th>Observed (Mid)</th>\n",
+       "      <th>Observed (High)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>21</td>\n",
+       "      <td>17</td>\n",
+       "      <td>22</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>43</td>\n",
+       "      <td>31</td>\n",
+       "      <td>34</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>49</td>\n",
+       "      <td>32</td>\n",
+       "      <td>35</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Everett</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>64</td>\n",
+       "      <td>44</td>\n",
+       "      <td>50</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Everett</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>57</td>\n",
+       "      <td>37</td>\n",
+       "      <td>45</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Everett</td>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>111</td>\n",
+       "      <td>70</td>\n",
+       "      <td>80</td>\n",
+       "      <td>106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>26</td>\n",
+       "      <td>19</td>\n",
+       "      <td>26</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>40</td>\n",
+       "      <td>30</td>\n",
+       "      <td>33</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>58</td>\n",
+       "      <td>37</td>\n",
+       "      <td>40</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>70</td>\n",
+       "      <td>47</td>\n",
+       "      <td>56</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>79</td>\n",
+       "      <td>49</td>\n",
+       "      <td>62</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>111</td>\n",
+       "      <td>75</td>\n",
+       "      <td>87</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        From        To  Model  Observed (Low)  Observed (Mid)  Observed (High)\n",
+       "0    Seattle  Bellevue     21              17              22               29\n",
+       "1    Seattle   Everett     43              31              34               41\n",
+       "2    Seattle    Tacoma     49              32              35               41\n",
+       "3    Everett   Seattle     64              44              50               76\n",
+       "4    Everett  Bellevue     57              37              45               68\n",
+       "5    Everett    Tacoma    111              70              80              106\n",
+       "6   Bellevue   Seattle     26              19              26               41\n",
+       "7   Bellevue   Everett     40              30              33               37\n",
+       "8   Bellevue    Tacoma     58              37              40               48\n",
+       "9     Tacoma   Seattle     70              47              56               89\n",
+       "10    Tacoma  Bellevue     79              49              62              101\n",
+       "11    Tacoma   Everett    111              75              87              125"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = results_dict['7to8']['svtl2t']\n",
+    "# df = results_dict['10to14']['svtl2t']\n",
+    "df = pd.merge(df,od_obs,on=['otaz','dtaz'])\n",
+    "df = df[['o_city','d_city','travel_time','optimistic','best_guess','pessimistic','time']]\n",
+    "df['corridor']=df['o_city']+'-'+df['d_city']\n",
+    "df = df[df['time'] == 'am']\n",
+    "\n",
+    "df = df.rename(columns={'travel_time':'travel_time_model',\n",
+    "                   'optimistic':'observed_low',\n",
+    "                  'best_guess':'observed_mid',\n",
+    "                   'pessimistic':'observed_high'})\n",
+    "df.index = df.corridor\n",
+    "pd.options.display.float_format = '{:0,.0f}'.format\n",
+    "df = df.drop(['corridor','time'], axis=1)\n",
+    "df = df.rename(columns={'o_city':'From','d_city':'To','travel_time_model':'Model', \n",
+    "                   'observed_low': 'Observed (Low)', 'observed_mid': 'Observed (Mid)', 'observed_high': 'Observed (High)'})\n",
+    "df.reset_index().drop('corridor', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xacef3f98>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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XV3EzjDHGVCbvVjfqkvw/eD5CVb1fVbuqatemTZvmuxnGGGPKUdWK/gsRaQ4Q/f4yWv8/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\nxllFb4wxGWcVvTHGZJxV9MYYk3H/DwW1NGjDyKY4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb1c92ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**PM Travel Times**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>From</th>\n",
+       "      <th>To</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Observed (Low)</th>\n",
+       "      <th>Observed (Mid)</th>\n",
+       "      <th>Observed (High)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>22</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>65</td>\n",
+       "      <td>42</td>\n",
+       "      <td>52</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50</td>\n",
+       "      <td>59</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Everett</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>48</td>\n",
+       "      <td>44</td>\n",
+       "      <td>53</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Everett</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>42</td>\n",
+       "      <td>30</td>\n",
+       "      <td>34</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Everett</td>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>116</td>\n",
+       "      <td>81</td>\n",
+       "      <td>91</td>\n",
+       "      <td>109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>22</td>\n",
+       "      <td>24</td>\n",
+       "      <td>34</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>58</td>\n",
+       "      <td>38</td>\n",
+       "      <td>49</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>79</td>\n",
+       "      <td>61</td>\n",
+       "      <td>74</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>51</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>Bellevue</td>\n",
+       "      <td>62</td>\n",
+       "      <td>39</td>\n",
+       "      <td>46</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Tacoma</td>\n",
+       "      <td>Everett</td>\n",
+       "      <td>113</td>\n",
+       "      <td>68</td>\n",
+       "      <td>78</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        From        To  Model  Observed (Low)  Observed (Mid)  Observed (High)\n",
+       "0    Seattle  Bellevue     26              16              22               31\n",
+       "1    Seattle   Everett     65              42              52               68\n",
+       "2    Seattle    Tacoma     71              50              59               76\n",
+       "3    Everett   Seattle     48              44              53               70\n",
+       "4    Everett  Bellevue     42              30              34               41\n",
+       "5    Everett    Tacoma    116              81              91              109\n",
+       "6   Bellevue   Seattle     22              24              34               51\n",
+       "7   Bellevue   Everett     58              38              49               64\n",
+       "8   Bellevue    Tacoma     79              61              74               98\n",
+       "9     Tacoma   Seattle     51              35              40               52\n",
+       "10    Tacoma  Bellevue     62              39              46               58\n",
+       "11    Tacoma   Everett    113              68              78               94"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = results_dict['17to18']['svtl2t']\n",
+    "# df = results_dict['10to14']['svtl2t']\n",
+    "df = pd.merge(df,od_obs,on=['otaz','dtaz'])\n",
+    "df = df[['o_city','d_city','travel_time','optimistic','best_guess','pessimistic','time']]\n",
+    "df['corridor']=df['o_city']+'-'+df['d_city']\n",
+    "df = df[df['time'] == 'pm']\n",
+    "\n",
+    "df = df.rename(columns={'travel_time':'travel_time_model',\n",
+    "                   'optimistic':'observed_low',\n",
+    "                  'best_guess':'observed_mid',\n",
+    "                   'pessimistic':'observed_high'})\n",
+    "df.drop(['corridor','time'], axis=1)\n",
+    "df = df.rename(columns={'o_city':'From','d_city':'To','travel_time_model':'Model', \n",
+    "                   'observed_low': 'Observed (Low)', 'observed_mid': 'Observed (Mid)', 'observed_high': 'Observed (High)'})\n",
+    "df.reset_index().drop(['corridor','index','time'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x86ed3668>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6Grg20fGAGyppy/5EBnL3h83sH8AFwG/cfVN4fTHQK5GxomwExrj7v8tuMLNk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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xaa50dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Annual Travel Cost per Household \n",
+    "Out of pocket costs (fuel, tolls, transit fares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$4,446\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = trip.groupby('hhno').sum()['travcost']\n",
+    "df = df.reset_index()\n",
+    "df = pd.merge(hh,df,on='hhno',how='left')\n",
+    "df['travcost'] = df['travcost'].fillna(0)\n",
+    "x = df['travcost'].mean()*annual_factor\n",
+    "print '${:0,.0f}'.format(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "max_income = 200000 # in \n",
+    "income_bins = [i for i in xrange(0,max_income,10000)]\n",
+    "income_bins.append(1000000000)\n",
+    "income_labels = [str(i) for i in xrange(10000,max_income,10000)]\n",
+    "income_labels.append(str(max_income)+'+')\n",
+    "df['income'] = pd.cut(df['hhincome'],bins=income_bins,labels=income_labels)\n",
+    "df = pd.DataFrame(df.groupby('income').mean()['travcost'])\n",
+    "df['annual_cost'] = df['travcost']*annual_factor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Annual costs by income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xc33544a8>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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c+8YOfvzK+1wyu4b/u3J2xg05Z8Rv09jRizGaI6DSk9NhJxA0HOr2DfnaWG4Z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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x364a3828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.options.display.float_format = '{:0,.2f}'.format\n",
+    "df['annual_cost'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transit Boardings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>line_total</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>agency</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>King County Metro</th>\n",
+       "      <td>974,963.11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   line_total\n",
+       "agency                       \n",
+       "King County Metro  974,963.11"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_excel(r'../../../outputs/network/network_summary_detailed.xlsx', sheetname='Transit Summaries')\n",
+    "tod_list = ['5to6','6to7','7to8','8to9','9to10','10to14','14to15','15to16','16to17','17to18','18to20']\n",
+    "df = df[[tod+'_board' for tod in tod_list]+['route_code']]\n",
+    "df = df.fillna(0)\n",
+    "df['line_total'] = df[[tod+'_board' for tod in tod_list]].sum(axis=1)\n",
+    "\n",
+    "#Boardings by transit agency\n",
+    "agency_lookup = {\n",
+    "    '1': 'King County Metro',\n",
+    "    '2': 'Pierce Transit',\n",
+    "    '3': 'Community Transit',\n",
+    "    '4': 'Kitsap Transit',\n",
+    "    '5': 'Washington Ferries',\n",
+    "    '6': 'Sound Transit',\n",
+    "    '7': 'Everett Transit'\n",
+    "}\n",
+    "df['agency'] = df['route_code'].astype('str').iloc[0][0]\n",
+    "df['agency'] = df['agency'].map(agency_lookup)\n",
+    "df = pd.DataFrame(df.groupby('agency').sum()['line_total'])\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/summarize/notebooks/od_corridors.ipynb b/scripts/summarize/notebooks/od_corridors.ipynb
new file mode 100644
index 00000000..2fa1b145
--- /dev/null
+++ b/scripts/summarize/notebooks/od_corridors.ipynb
@@ -0,0 +1,2847 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Corridor Travel Times"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define 2 comparison scenario locations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "scen1_loc = r'U:\\brice\\sc_2040demand_2014net'\n",
+    "scen2_loc = r'S:\\Stefan\\soundcast_10peak_5offpeak'\n",
+    "\n",
+    "current_run_name = 'soundcast_2014'\n",
+    "scen1_name = '2040 No Build'\n",
+    "scen2_name = '2040 10/5 Toll'\n",
+    "\n",
+    "# Set comparison scenario:\n",
+    "compare_scen = scen2_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import h5py\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# O-D SOV Travel Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Load observed data (2013 currently)\n",
+    "od_obs = pd.read_csv(r'..\\..\\..\\scripts\\summarize\\inputs\\network_summary\\od_travel_times.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "user_class = 'svtl1t'\n",
+    "max_internal_zone = 3700\n",
+    "zone_list = [532,2286,1552,1125]\n",
+    "\n",
+    "run_dict = {'run': current_run_name,\n",
+    "            scen1_loc: scen1_name,\n",
+    "           scen2_loc: scen2_name}\n",
+    "\n",
+    "# Need zone terminal times to compare to observed\n",
+    "o_tt = pd.read_csv(r'..\\..\\..\\inputs\\IntraZonals\\origin_tt.in',\n",
+    "                   sep=' ',header=4)\n",
+    "\n",
+    "\n",
+    "o_tt.index = o_tt.index.get_level_values(1)\n",
+    "o_tt['taz'] = o_tt.index\n",
+    "o_tt['terminal_time'] = o_tt['matrix=mo\"prodtt\"']\n",
+    "o_tt = pd.DataFrame(o_tt[['terminal_time','taz']])\n",
+    "\n",
+    "results_dict = {}\n",
+    "\n",
+    "for skim_name in ['7to8','17to18']:\n",
+    "    user_class_dict = {}\n",
+    "    for user_class in ['svtl2t','h3tl2t']:\n",
+    "        results = pd.DataFrame()\n",
+    "\n",
+    "        for run_dir, current_run_name in run_dict.iteritems():\n",
+    "            skims_dir = run_dir + r'\\inputs'\n",
+    "\n",
+    "            if run_dir == 'run':\n",
+    "                h5_contents = h5py.File(r'..\\..\\..\\inputs' + r'\\\\' + skim_name + '.h5')\n",
+    "            else:\n",
+    "                h5_contents = h5py.File(skims_dir + r'\\\\' + skim_name + '.h5')\n",
+    "            for zone in zone_list:\n",
+    "                df = pd.DataFrame()\n",
+    "                df[user_class] = h5_contents['Skims'][user_class][:][zone-1,:max_internal_zone]\n",
+    "                df['otaz'] = zone\n",
+    "                df['dtaz'] = df.index+1\n",
+    "                df['travel_time'] = df[user_class]/100 # skim data is stored in hundreds\n",
+    "\n",
+    "                df = df[df['dtaz'].isin(zone_list)]\n",
+    "\n",
+    "        #      Subtract the terminal times\n",
+    "                df = pd.merge(df,o_tt,left_on='dtaz',right_on='taz')\n",
+    "                # Subtract destination terminal time\n",
+    "                df['travel_time'] = df['travel_time']-df['terminal_time']\n",
+    "                # Subtract origin terminal time\n",
+    "                df['travel_time'] = df['travel_time']-(o_tt[o_tt['taz'] == zone]['terminal_time'].values[0])\n",
+    "\n",
+    "                df['source'] = current_run_name\n",
+    "                results = results.append(df)\n",
+    "\n",
+    "        user_class_dict[user_class] = results\n",
+    "    results_dict[skim_name] = user_class_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Auto Travel Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "results_df = {}\n",
+    "for time, results in results_dict['7to8'].iteritems():\n",
+    "    scen1 = results[results['source'] == current_run_name][['travel_time','otaz','dtaz']]\n",
+    "    scen2 = results[results['source'] == scen1_name][['travel_time','otaz','dtaz']]\n",
+    "    scen3 = results[results['source'] == scen2_name][['travel_time','otaz','dtaz']]\n",
+    "\n",
+    "    df = pd.merge(scen1, scen2, on=['otaz','dtaz'], suffixes=[\"_\"+current_run_name,\"_\"+scen1_name])\n",
+    "\n",
+    "    df = pd.merge(df, scen3, on=['otaz','dtaz'], suffixes=[\"_\"+current_run_name,\"_\"+scen2_name])\n",
+    "    df.rename(columns={'travel_time':'travel_time_'+scen2_name}, inplace=True)\n",
+    "\n",
+    "    df.rename(columns={'travel_time_'+scen1_name:scen1_name,\n",
+    "                      'travel_time_'+scen2_name:scen2_name,\n",
+    "                      'travel_time_'+current_run_name:current_run_name}, inplace=True)\n",
+    "\n",
+    "    df = pd.merge(df, od_obs[['otaz','dtaz','description','time_cong','time']], on=['otaz','dtaz'])\n",
+    "    df.index = df['description']\n",
+    "    df.rename(columns={'time_cong':'observed'},inplace=True)\n",
+    "    results_df[time] = df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AM SOV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>2040 No Build</th>\n",
+       "      <th>2040 10/5 Toll</th>\n",
+       "      <th>observed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Federal Way</th>\n",
+       "      <td>Seattle-Federal Way</td>\n",
+       "      <td>31.59</td>\n",
+       "      <td>34.25</td>\n",
+       "      <td>30.89</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Bellevue</th>\n",
+       "      <td>Seattle-Bellevue</td>\n",
+       "      <td>23.10</td>\n",
+       "      <td>25.87</td>\n",
+       "      <td>22.10</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Everett</th>\n",
+       "      <td>Seattle-Everett</td>\n",
+       "      <td>45.56</td>\n",
+       "      <td>51.55</td>\n",
+       "      <td>41.28</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett-Seattle</th>\n",
+       "      <td>Everett-Seattle</td>\n",
+       "      <td>67.25</td>\n",
+       "      <td>85.17</td>\n",
+       "      <td>59.17</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue-Seattle</th>\n",
+       "      <td>Bellevue-Seattle</td>\n",
+       "      <td>27.10</td>\n",
+       "      <td>30.74</td>\n",
+       "      <td>24.64</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way-Seattle</th>\n",
+       "      <td>Federal Way-Seattle</td>\n",
+       "      <td>49.72</td>\n",
+       "      <td>70.27</td>\n",
+       "      <td>48.61</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             description  soundcast_2014  2040 No Build  \\\n",
+       "description                                                               \n",
+       "Seattle-Federal Way  Seattle-Federal Way           31.59          34.25   \n",
+       "Seattle-Bellevue        Seattle-Bellevue           23.10          25.87   \n",
+       "Seattle-Everett          Seattle-Everett           45.56          51.55   \n",
+       "Everett-Seattle          Everett-Seattle           67.25          85.17   \n",
+       "Bellevue-Seattle        Bellevue-Seattle           27.10          30.74   \n",
+       "Federal Way-Seattle  Federal Way-Seattle           49.72          70.27   \n",
+       "\n",
+       "                     2040 10/5 Toll  observed  \n",
+       "description                                    \n",
+       "Seattle-Federal Way           30.89      32.0  \n",
+       "Seattle-Bellevue              22.10      28.0  \n",
+       "Seattle-Everett               41.28      39.0  \n",
+       "Everett-Seattle               59.17      50.0  \n",
+       "Bellevue-Seattle              24.64      26.0  \n",
+       "Federal Way-Seattle           48.61      49.0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = results_df['svtl2t']\n",
+    "df[['description',current_run_name,scen1_name,scen2_name,'observed']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x239a908>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "oueSgERERETPJQGJiIiInksCEhERET33/wFh+zz2aIWlfwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xacc6518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[current_run_name,scen1_name,scen2_name]].plot(kind='bar', alpha=0.7)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))\n",
+    "ax.set_xlabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AM HOV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>2040 No Build</th>\n",
+       "      <th>2040 10/5 Toll</th>\n",
+       "      <th>observed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Federal Way</th>\n",
+       "      <td>Seattle-Federal Way</td>\n",
+       "      <td>28.31</td>\n",
+       "      <td>29.11</td>\n",
+       "      <td>30.75</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Bellevue</th>\n",
+       "      <td>Seattle-Bellevue</td>\n",
+       "      <td>21.74</td>\n",
+       "      <td>24.65</td>\n",
+       "      <td>20.01</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Everett</th>\n",
+       "      <td>Seattle-Everett</td>\n",
+       "      <td>38.52</td>\n",
+       "      <td>41.59</td>\n",
+       "      <td>41.28</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett-Seattle</th>\n",
+       "      <td>Everett-Seattle</td>\n",
+       "      <td>50.44</td>\n",
+       "      <td>60.61</td>\n",
+       "      <td>55.33</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue-Seattle</th>\n",
+       "      <td>Bellevue-Seattle</td>\n",
+       "      <td>20.19</td>\n",
+       "      <td>21.87</td>\n",
+       "      <td>17.46</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way-Seattle</th>\n",
+       "      <td>Federal Way-Seattle</td>\n",
+       "      <td>42.85</td>\n",
+       "      <td>59.08</td>\n",
+       "      <td>48.17</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             description  soundcast_2014  2040 No Build  \\\n",
+       "description                                                               \n",
+       "Seattle-Federal Way  Seattle-Federal Way           28.31          29.11   \n",
+       "Seattle-Bellevue        Seattle-Bellevue           21.74          24.65   \n",
+       "Seattle-Everett          Seattle-Everett           38.52          41.59   \n",
+       "Everett-Seattle          Everett-Seattle           50.44          60.61   \n",
+       "Bellevue-Seattle        Bellevue-Seattle           20.19          21.87   \n",
+       "Federal Way-Seattle  Federal Way-Seattle           42.85          59.08   \n",
+       "\n",
+       "                     2040 10/5 Toll  observed  \n",
+       "description                                    \n",
+       "Seattle-Federal Way           30.75      32.0  \n",
+       "Seattle-Bellevue              20.01      28.0  \n",
+       "Seattle-Everett               41.28      39.0  \n",
+       "Everett-Seattle               55.33      50.0  \n",
+       "Bellevue-Seattle              17.46      26.0  \n",
+       "Federal Way-Seattle           48.17      49.0  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = results_df['h3tl2t']\n",
+    "df[['description',current_run_name,scen1_name,scen2_name,'observed']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xacb8710>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "FdFFrRyECiBpL2Bz4CLb91bH5gIzsl5DRETE1NbaBCQiIiLaq61jQCIiIqLFkoBERERE7ZKARERE\n",
+       "RO2SgERERETtkoBERERE7f4/zoai6ISQHOEAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x4175320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[current_run_name,scen1_name,scen2_name]].plot(kind='bar', alpha=0.7)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))\n",
+    "ax.set_xlabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "results_df = {}\n",
+    "for time, results in results_dict['17to18'].iteritems():\n",
+    "    scen1 = results[results['source'] == current_run_name][['travel_time','otaz','dtaz']]\n",
+    "    scen2 = results[results['source'] == scen1_name][['travel_time','otaz','dtaz']]\n",
+    "    scen3 = results[results['source'] == scen2_name][['travel_time','otaz','dtaz']]\n",
+    "\n",
+    "    df = pd.merge(scen1, scen2, on=['otaz','dtaz'], suffixes=[\"_\"+current_run_name,\"_\"+scen1_name])\n",
+    "\n",
+    "    df = pd.merge(df, scen3, on=['otaz','dtaz'], suffixes=[\"_\"+current_run_name,\"_\"+scen2_name])\n",
+    "    df.rename(columns={'travel_time':'travel_time_'+scen2_name}, inplace=True)\n",
+    "\n",
+    "    df.rename(columns={'travel_time_'+scen1_name:scen1_name,\n",
+    "                      'travel_time_'+scen2_name:scen2_name,\n",
+    "                      'travel_time_'+current_run_name:current_run_name}, inplace=True)\n",
+    "\n",
+    "    df = pd.merge(df, od_obs[['otaz','dtaz','description','time_cong','time']], on=['otaz','dtaz'])\n",
+    "    df.index = df['description']\n",
+    "    df.rename(columns={'time_cong':'observed'},inplace=True)\n",
+    "    results_df[time] = df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PM SOV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>2040 No Build</th>\n",
+       "      <th>2040 10/5 Toll</th>\n",
+       "      <th>observed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Federal Way</th>\n",
+       "      <td>Seattle-Federal Way</td>\n",
+       "      <td>47.01</td>\n",
+       "      <td>64.91</td>\n",
+       "      <td>46.24</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Bellevue</th>\n",
+       "      <td>Seattle-Bellevue</td>\n",
+       "      <td>27.57</td>\n",
+       "      <td>31.20</td>\n",
+       "      <td>26.42</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Everett</th>\n",
+       "      <td>Seattle-Everett</td>\n",
+       "      <td>67.21</td>\n",
+       "      <td>86.08</td>\n",
+       "      <td>61.08</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett-Seattle</th>\n",
+       "      <td>Everett-Seattle</td>\n",
+       "      <td>50.16</td>\n",
+       "      <td>58.04</td>\n",
+       "      <td>45.23</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue-Seattle</th>\n",
+       "      <td>Bellevue-Seattle</td>\n",
+       "      <td>24.90</td>\n",
+       "      <td>29.73</td>\n",
+       "      <td>23.45</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way-Seattle</th>\n",
+       "      <td>Federal Way-Seattle</td>\n",
+       "      <td>33.80</td>\n",
+       "      <td>36.93</td>\n",
+       "      <td>32.11</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             description  soundcast_2014  2040 No Build  \\\n",
+       "description                                                               \n",
+       "Seattle-Federal Way  Seattle-Federal Way           47.01          64.91   \n",
+       "Seattle-Bellevue        Seattle-Bellevue           27.57          31.20   \n",
+       "Seattle-Everett          Seattle-Everett           67.21          86.08   \n",
+       "Everett-Seattle          Everett-Seattle           50.16          58.04   \n",
+       "Bellevue-Seattle        Bellevue-Seattle           24.90          29.73   \n",
+       "Federal Way-Seattle  Federal Way-Seattle           33.80          36.93   \n",
+       "\n",
+       "                     2040 10/5 Toll  observed  \n",
+       "description                                    \n",
+       "Seattle-Federal Way           46.24      32.0  \n",
+       "Seattle-Bellevue              26.42      28.0  \n",
+       "Seattle-Everett               61.08      39.0  \n",
+       "Everett-Seattle               45.23      50.0  \n",
+       "Bellevue-Seattle              23.45      26.0  \n",
+       "Federal Way-Seattle           32.11      49.0  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = results_df['svtl2t']\n",
+    "df[['description',current_run_name,scen1_name,scen2_name,'observed']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xb79ae48>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "A5e1natrUhUREbHKqOub9dnA5ZLuAx4GfgIgaQuKkskREStjO0lLyu212rYBbHtKFUFFNFEtB6EC\n",
+       "SNoN2BCYa/uh8thMYHLWa4iIiBjfapuARERERH3VdQxIRERE1FgSkIiIiOi7JCARERHRd0lAIiIi\n",
+       "ou+SgERERETf/X/HTEJd7WglPAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xb743710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[current_run_name,scen1_name,scen2_name]].plot(kind='bar', alpha=0.7)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))\n",
+    "ax.set_xlabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PM HOV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>description</th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>2040 No Build</th>\n",
+       "      <th>2040 10/5 Toll</th>\n",
+       "      <th>observed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Federal Way</th>\n",
+       "      <td>Seattle-Federal Way</td>\n",
+       "      <td>42.62</td>\n",
+       "      <td>58.40</td>\n",
+       "      <td>46.01</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Bellevue</th>\n",
+       "      <td>Seattle-Bellevue</td>\n",
+       "      <td>20.44</td>\n",
+       "      <td>22.65</td>\n",
+       "      <td>18.29</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Everett</th>\n",
+       "      <td>Seattle-Everett</td>\n",
+       "      <td>56.17</td>\n",
+       "      <td>69.82</td>\n",
+       "      <td>57.19</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett-Seattle</th>\n",
+       "      <td>Everett-Seattle</td>\n",
+       "      <td>44.36</td>\n",
+       "      <td>48.83</td>\n",
+       "      <td>45.23</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue-Seattle</th>\n",
+       "      <td>Bellevue-Seattle</td>\n",
+       "      <td>23.66</td>\n",
+       "      <td>27.91</td>\n",
+       "      <td>21.58</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way-Seattle</th>\n",
+       "      <td>Federal Way-Seattle</td>\n",
+       "      <td>31.82</td>\n",
+       "      <td>33.08</td>\n",
+       "      <td>31.79</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             description  soundcast_2014  2040 No Build  \\\n",
+       "description                                                               \n",
+       "Seattle-Federal Way  Seattle-Federal Way           42.62          58.40   \n",
+       "Seattle-Bellevue        Seattle-Bellevue           20.44          22.65   \n",
+       "Seattle-Everett          Seattle-Everett           56.17          69.82   \n",
+       "Everett-Seattle          Everett-Seattle           44.36          48.83   \n",
+       "Bellevue-Seattle        Bellevue-Seattle           23.66          27.91   \n",
+       "Federal Way-Seattle  Federal Way-Seattle           31.82          33.08   \n",
+       "\n",
+       "                     2040 10/5 Toll  observed  \n",
+       "description                                    \n",
+       "Seattle-Federal Way           46.01      32.0  \n",
+       "Seattle-Bellevue              18.29      28.0  \n",
+       "Seattle-Everett               57.19      39.0  \n",
+       "Everett-Seattle               45.23      50.0  \n",
+       "Bellevue-Seattle              21.58      26.0  \n",
+       "Federal Way-Seattle           31.79      49.0  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = results_df['h3tl2t']\n",
+    "df[['description',current_run_name,scen1_name,scen2_name,'observed']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xba60be0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "koBERERE7ZKARERERO3+P2RbiOy8wNr6AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xba6e6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[current_run_name,scen1_name,scen2_name]].plot(kind='bar', alpha=0.7)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))\n",
+    "ax.set_xlabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Transit Travel Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "max_internal_zone = 3700\n",
+    "zone_list = [532,2286,1552,1125]\n",
+    "\n",
+    "run_dict = {'run': current_run_name,\n",
+    "            scen1_loc: scen1_name,\n",
+    "           scen2_loc: scen2_name}\n",
+    "\n",
+    "results_dict = {}\n",
+    "\n",
+    "for skim_name in ['7to8','17to18']:\n",
+    "    user_class_dict = {}\n",
+    "    for user_class in ['ivtwa','ivtwr']:\n",
+    "        results = pd.DataFrame()\n",
+    "\n",
+    "        for run_dir, current_run_name in run_dict.iteritems():\n",
+    "            skims_dir = run_dir + r'\\inputs'\n",
+    "\n",
+    "            if run_dir == 'run':\n",
+    "                h5_contents = h5py.File(r'..\\..\\..\\inputs' + r'\\\\' + skim_name + '.h5')\n",
+    "            else:\n",
+    "                h5_contents = h5py.File(skims_dir + r'\\\\' + skim_name + '.h5')\n",
+    "            for zone in zone_list:\n",
+    "                df = pd.DataFrame()\n",
+    "                \n",
+    "                df[user_class] = h5_contents['Skims'][user_class][:][zone-1,:max_internal_zone]\n",
+    "                df['otaz'] = zone\n",
+    "                df['dtaz'] = df.index+1\n",
+    "                df['travel_time'] = df[user_class]/100 # skim data is stored in hundreds\n",
+    "\n",
+    "                df = df[df['dtaz'].isin(zone_list)]\n",
+    "\n",
+    "                df['source'] = current_run_name\n",
+    "                results = results.append(df)\n",
+    "            \n",
+    "        user_class_dict[user_class] = results\n",
+    "    results_dict[skim_name] = user_class_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AM Transit Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "skim_name = '7to8'\n",
+    "\n",
+    "df_all = results_dict[skim_name]['ivtwa']\n",
+    "df_all['mode'] = 'ivtwa'\n",
+    "df_rail = results_dict[skim_name]['ivtwr']\n",
+    "df_rail['mode'] = 'ivtwr'\n",
+    "df = pd.merge(df_all,df_rail,on=['otaz','dtaz','source'], suffixes=['_all','_rail'])\n",
+    "\n",
+    "df_all = results_dict[skim_name]['ivtwa']\n",
+    "df_all['mode'] = 'ivtwa'\n",
+    "df_rail = results_dict[skim_name]['ivtwr']\n",
+    "df_rail['mode'] = 'ivtwr'\n",
+    "df = pd.merge(df_all,df_rail,on=['otaz','dtaz','source'], suffixes=['_all','_rail'])\n",
+    "\n",
+    "# Take minimum travel time \n",
+    "df['travel_time_min'] = df[['travel_time_all','travel_time_rail']].min(axis=1)\n",
+    "\n",
+    "# Separate results by scenario\n",
+    "run_df = df[df['source'] == current_run_name]\n",
+    "scen1 = df[df['source'] == scen1_name]\n",
+    "scen2 = df[df['source'] == scen2_name]\n",
+    "\n",
+    "# Reformat output for scenarios 1 and 2 stacked\n",
+    "df = pd.merge(scen1,scen2,on=['otaz','dtaz'],suffixes=['_'+scen1_name,'_'+scen2_name])\n",
+    "df.rename(columns={'travel_time_min_'+scen1_name:scen1_name,'travel_time_min_'+scen2_name:scen2_name},inplace=True)\n",
+    "df = df[['otaz','dtaz',scen1_name,scen2_name]]\n",
+    "\n",
+    "# add base scenario column\n",
+    "df = pd.merge(df,run_df,on=['otaz','dtaz'])\n",
+    "df.rename(columns={'travel_time_min':current_run_name},inplace=True)\n",
+    "df = df[['otaz','dtaz',current_run_name, scen1_name, scen2_name]]\n",
+    "\n",
+    "# join observed data to get description\n",
+    "df = pd.merge(df, od_obs[['otaz','dtaz','description']], on=['otaz','dtaz'])\n",
+    "df.index = df.description\n",
+    "df= df.drop('description', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AM Travel Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>otaz</th>\n",
+       "      <th>dtaz</th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>2040 No Build</th>\n",
+       "      <th>2040 10/5 Toll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Federal Way</th>\n",
+       "      <td>532</td>\n",
+       "      <td>1125</td>\n",
+       "      <td>28.60</td>\n",
+       "      <td>29.20</td>\n",
+       "      <td>35.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Bellevue</th>\n",
+       "      <td>532</td>\n",
+       "      <td>1552</td>\n",
+       "      <td>33.77</td>\n",
+       "      <td>36.02</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Everett</th>\n",
+       "      <td>532</td>\n",
+       "      <td>2286</td>\n",
+       "      <td>49.85</td>\n",
+       "      <td>52.69</td>\n",
+       "      <td>61.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett-Seattle</th>\n",
+       "      <td>2286</td>\n",
+       "      <td>532</td>\n",
+       "      <td>60.40</td>\n",
+       "      <td>70.65</td>\n",
+       "      <td>61.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue-Seattle</th>\n",
+       "      <td>1552</td>\n",
+       "      <td>532</td>\n",
+       "      <td>25.97</td>\n",
+       "      <td>26.55</td>\n",
+       "      <td>22.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way-Seattle</th>\n",
+       "      <td>1125</td>\n",
+       "      <td>532</td>\n",
+       "      <td>41.80</td>\n",
+       "      <td>49.84</td>\n",
+       "      <td>49.82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     otaz  dtaz  soundcast_2014  2040 No Build  2040 10/5 Toll\n",
+       "description                                                                   \n",
+       "Seattle-Federal Way   532  1125           28.60          29.20           35.12\n",
+       "Seattle-Bellevue      532  1552           33.77          36.02           24.00\n",
+       "Seattle-Everett       532  2286           49.85          52.69           61.00\n",
+       "Everett-Seattle      2286   532           60.40          70.65           61.00\n",
+       "Bellevue-Seattle     1552   532           25.97          26.55           22.00\n",
+       "Federal Way-Seattle  1125   532           41.80          49.84           49.82"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xcfef4e0>"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "s0TWrzO2Hmvl76/DVayYgfYY8FXbP6k3pImT9CngJ8B3bS8CLgeeWW9UEe3U6ASEdjdzz6WddRZe\n",
+       "QXEfW7JiNhMUsylaU8+F9v7+Bi0Fvt2iFsdBtwAHAieX6y9dQZGQ/AS41nbTl3qImDSaPgj1KtvP\n",
+       "66yTIela27vUHVsvtLzOwgdsnzxk3yzbt9YVU69IWgv4B+AyVvz+fm773vqi6i1JZ1EsBHk5cB5F\n",
+       "jZPHaw2qxyRtCexB8QHn1cAmtrMeTESPND0BuQLYG7jC9q5lM/c5tnevObRxW4Omqa5SXK1NU6jb\n",
+       "dC8jkbQu8ErgYOBvgfm2j6g3qokrWz52pkg8XkRRC+Q+ir8zJ9QZW0SbNL0LZi7ta+Y+hVGmqQKN\n",
+       "nqYqaQeKP+gbSTqIlceAtGkWzHxJ/4eidWD5OjfluIJWsP1XSf9NsQL1+hRdF41OQCTNp/i3+Cvg\n",
+       "58BJFIPbm/tJLWKSamQLiKR1bD9WPn4KRTMptKibYqRpqkP3NY2kV1NUsN2fYnbBoKXAuUPW3Wgs\n",
+       "SQsYJpG03YoBjZL2o2j52ItizZTzgEub3g0j6fPALsBfKBKQK4CftuXvSsRk0tQE5Grbu5WPT7X9\n",
+       "rrpj6rXOexxtX1NJelFbko01kaRzKJKO/27hQFQkbUQxfmeP8mtj4Ebbb641sIgWaWoXTGdBhT1r\n",
+       "i6IP1qBpqtcMWWtjcIxLK9baaHslVNuHStqaYuzH9yWtD0yxvaTWwHrnEYpWkIcppk8/HXhSrRFF\n",
+       "tExTE5A2W1OmqZ5NsdbGvnSstVFrRL3V5iniSDoSeCswE9gGeBrwOYpB4Y0l6RMUv7PZwDUUXTCf\n",
+       "A95se3GdsUW0TVO7YB6mmK8PxR+/33cctu2dq4+qt9o8TRXav9bGGjBF/FqKBeh+1nF/19t+Tr2R\n",
+       "TYykdwM/pqj50ejxLBGTXVNbQNq05sRIDgVOHrLvAqAtUzvbvtZG2yuhPmr70cHy8pKmMPrsrUaw\n",
+       "/am6Y4hYUzQyAbG9YOg+Sa9qQ//6GjRNte1rbcylfVPEO10u6ViKsUovB94OfKvmmCKiQRrZBTOc\n",
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+       "RwPYfkxSa2Yd2B4ABsrBmXtRTFn9IsVYnibbv+4A+sn21nXHELGmaHQCQrtrLTzL9uskvdr2vHIg\n",
+       "44/rDqqHHizL6AMg6YXAAzXG03NlK9YBFCXLdwPm1RvRxA03ALytJD0Z2JaOwd+2/6e+iCLapekJ\n",
+       "yDa2D5Z0CIDthwanBbZA26ep/gvFrIlZ5arGG1MsYd8Kks4HXkAxE+YzwOW2l9Ub1cRJepCRuyjc\n",
+       "luXqJb0VOIqiAuo1FIOJfwq8rM64Itqk6QlIm2sttHKaqqTdgT/avkrSS4EjgdcC84E/1hpcb30R\n",
+       "ONT2E3UH0ku2p9UdQ0XeDfwNxUJ0e0naHvj3mmOKaJW16g5gguaycq2FHwAfrDWiHrF9mu1Fti+3\n",
+       "/Uzbm9j+f3XH1QOfZ0WSuAdFgvVZ4H7gC3UF1SuSPgBg+xLgoCHHTqolqD6R9LeS3lI+3kRSmwZo\n",
+       "PmL7YVi+CvVvgO1qjimiVRo/C2ZIrYWftWXZ7LZOU+0sRy7ps8C9tucOPdZUQ0qvrzQ1vE1TxSXN\n",
+       "BZ4PzLY9W9KWwAW2XzT6M5tB0sXAWyhaQvamSJCn2N6v1sAiWqSRXTCSnsfK/dB3ld+3krSV7atr\n",
+       "CKvXzqKcplpu/w44H2h0AgKsLWkd248B+1B0wQxq5L/HNdRrgF0pBoFj+0+SWtM9Y/vA8uFcSQMU\n",
+       "s5cuqS+iiPZp6h/8Uxh93Ym9qgqkj9o6TfUcijLe91Esd/4jgHIKdVYbbY5HbS/rWAtmg5rj6Zly\n",
+       "6vQNtreH5VOqI6LHGpmA2J4Dy/tmVyrsJKkt66W0cpqq7Y9K+gGwGXBpx8wQAe+qL7Ke2VnS0vLx\n",
+       "eh2PoSgo1xYXSPo8MEPSkcA/AqfXHFNP2H5c0m8lPcP2bXXHE9FWjR4DIulq27uNta+Jym6mU4Gd\n",
+       "gBspp6navrbWwCJKkl5Bx1owtufXGU8vSfoRRRfTlcBgKX3bPqC+qCLapZEtIJI2B7agWIlzN1Ze\n",
+       "MXb9OmObqDVommo0nO1LgUslbQK0YvB3h+GmvDf301rEJNTIFhBJh1Esbf584Jcdh5YCZ9n+Wh1x\n",
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+       "fUnrU8yCWVJvVBHt0cgEZJCkD9g+eci+WbZvrSumiWr7NNVoNklXAccAGwGnAfva/llZqOtc28+t\n",
+       "NcAeKce1vBWYaXsbSbOBz9neu+bQIlqj6YXIDh1m3wWVR9Fba5crw0IxTfWHHcca2WUWrbK27Utt\n",
+       "XwDcZftnAGWhruZ+mlnVO4A9gSUAtm8GnlprRBEt08g3NEk7UCxTv5Gkg1h5DEjTZ8FkmmpMZp1J\n",
+       "xiMjntV8j9p+tGOa8RTalWBF1K6RCQgwm2JZ8I1YeXnwpRTNpo21BkxTjWZbU6YZXy7pWIqB7i8H\n",
+       "3k6xeGJE9EjTx4C8yPYVdccREe0iaW3gCDqmGQOnu8l/MCMmmaYnIOtR/JHYkeLTlwFs/2OdcUVE\n",
+       "RMTomtoFM+hs4CZgX+AE4I3ldkTEapN0/SiHbXvnyoKJaLmmt4D8yvZzJV1ne+dy9siPbb+g7tgi\n",
+       "onnK2h9QjPmA4kOOgDcA2P5g9VFFtFPTW0D+Wn5/QNJzgLuBTWqMJyIazPYCKMrMD6lpcl1ZJDAJ\n",
+       "SESPNL0OyGmSZgLHAd8Efg2cPPpTIiLGJEl7dmy8mKIlJCJ6pNFdMBER/VAuBnkmxVR/KGrwvMX2\n",
+       "1fVFFdEujU5AJG0GfBTY0va+knYE9rB9Rs2hRUQLSNqI4u9kigBG9FjTu2DOAi6lWBkX4HfAe2uL\n",
+       "JiJaQdJmks4AzrO9WNKOko6oO66INml6ArKx7fOAJwBsPwY8Xm9IEdECZ5EPNxF91fQE5EFJTxnc\n",
+       "kPRC4IEa44mIdsiHm4g+a/o03H+hWJ9hlqQrKKbgvq7ekCKiBfLhJqLPGj0IFaAsPrZdufnb8pNK\n",
+       "RMS4lbNgTgV2Am6k/HBj+9paA4tokUYmIJJ2B/5o+65y+zDgtcACYK7tRTWGFxENJWkr27eXj6cA\n",
+       "21PU//it7b+O+uSIWC1NHQPyeeBRAEkvAT4GzAOWAF+oMa6IaLZvdDw+z/YNtq9P8hHRe00dA7JW\n",
+       "RyvH64HP274IuEhSmkgjohdm1R1ARJs1tQVk7XLsB8A+wA87jjU1qYqIiFhjNPXN+hzgckn3AX8B\n",
+       "fgQgaVuKkskREeOxs6Sl5eP1Oh4D2Pb0OoKKaKNGDkIFkLQHsBlwqe2Hyn2zgWlZryEiImJya2wC\n",
+       "EhEREc3V1DEgERER0WBJQCIiIqJySUAiIiKicklAIiIionJJQCIiIqJy/x8MtPhNVcVRrgAAAABJ\n",
+       "RU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xd01cdd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[current_run_name,scen1_name,scen2_name]].plot(kind='bar', alpha=0.7)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))\n",
+    "ax.set_xlabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PM Transit Travel Times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "skim_name = '17to18'\n",
+    "\n",
+    "df_all = results_dict[skim_name]['ivtwa']\n",
+    "df_all['mode'] = 'ivtwa'\n",
+    "df_rail = results_dict[skim_name]['ivtwr']\n",
+    "df_rail['mode'] = 'ivtwr'\n",
+    "df = pd.merge(df_all,df_rail,on=['otaz','dtaz','source'], suffixes=['_all','_rail'])\n",
+    "\n",
+    "df_all = results_dict[skim_name]['ivtwa']\n",
+    "df_all['mode'] = 'ivtwa'\n",
+    "df_rail = results_dict[skim_name]['ivtwr']\n",
+    "df_rail['mode'] = 'ivtwr'\n",
+    "df = pd.merge(df_all,df_rail,on=['otaz','dtaz','source'], suffixes=['_all','_rail'])\n",
+    "\n",
+    "# Take minimum travel time \n",
+    "df['travel_time_min'] = df[['travel_time_all','travel_time_rail']].min(axis=1)\n",
+    "\n",
+    "# Separate results by scenario\n",
+    "run_df = df[df['source'] == current_run_name]\n",
+    "scen1 = df[df['source'] == scen1_name]\n",
+    "scen2 = df[df['source'] == scen2_name]\n",
+    "\n",
+    "# Reformat output for scenarios 1 and 2 stacked\n",
+    "df = pd.merge(scen1,scen2,on=['otaz','dtaz'],suffixes=['_'+scen1_name,'_'+scen2_name])\n",
+    "df.rename(columns={'travel_time_min_'+scen1_name:scen1_name,'travel_time_min_'+scen2_name:scen2_name},inplace=True)\n",
+    "df = df[['otaz','dtaz',scen1_name,scen2_name]]\n",
+    "\n",
+    "# add base scenario column\n",
+    "df = pd.merge(df,run_df,on=['otaz','dtaz'])\n",
+    "df.rename(columns={'travel_time_min':current_run_name},inplace=True)\n",
+    "df = df[['otaz','dtaz',current_run_name, scen1_name, scen2_name]]\n",
+    "\n",
+    "# join observed data to get description\n",
+    "df = pd.merge(df, od_obs[['otaz','dtaz','description']], on=['otaz','dtaz'])\n",
+    "df.index = df.description\n",
+    "df= df.drop('description', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>otaz</th>\n",
+       "      <th>dtaz</th>\n",
+       "      <th>soundcast_2014</th>\n",
+       "      <th>2040 No Build</th>\n",
+       "      <th>2040 10/5 Toll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>description</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Federal Way</th>\n",
+       "      <td>532</td>\n",
+       "      <td>1125</td>\n",
+       "      <td>45.98</td>\n",
+       "      <td>52.80</td>\n",
+       "      <td>51.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Bellevue</th>\n",
+       "      <td>532</td>\n",
+       "      <td>1552</td>\n",
+       "      <td>28.29</td>\n",
+       "      <td>29.62</td>\n",
+       "      <td>24.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Seattle-Everett</th>\n",
+       "      <td>532</td>\n",
+       "      <td>2286</td>\n",
+       "      <td>61.34</td>\n",
+       "      <td>72.56</td>\n",
+       "      <td>61.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Everett-Seattle</th>\n",
+       "      <td>2286</td>\n",
+       "      <td>532</td>\n",
+       "      <td>55.26</td>\n",
+       "      <td>59.35</td>\n",
+       "      <td>61.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bellevue-Seattle</th>\n",
+       "      <td>1552</td>\n",
+       "      <td>532</td>\n",
+       "      <td>33.32</td>\n",
+       "      <td>37.24</td>\n",
+       "      <td>22.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Federal Way-Seattle</th>\n",
+       "      <td>1125</td>\n",
+       "      <td>532</td>\n",
+       "      <td>31.01</td>\n",
+       "      <td>32.21</td>\n",
+       "      <td>33.35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     otaz  dtaz  soundcast_2014  2040 No Build  2040 10/5 Toll\n",
+       "description                                                                   \n",
+       "Seattle-Federal Way   532  1125           45.98          52.80           51.89\n",
+       "Seattle-Bellevue      532  1552           28.29          29.62           24.00\n",
+       "Seattle-Everett       532  2286           61.34          72.56           61.00\n",
+       "Everett-Seattle      2286   532           55.26          59.35           61.00\n",
+       "Bellevue-Seattle     1552   532           33.32          37.24           22.00\n",
+       "Federal Way-Seattle  1125   532           31.01          32.21           33.35"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0xf33d710>"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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+       "nundwO7AYgDbtwDPrTWiiJZp5BuapK0py9SvK+kAlh8D0vRZMJmmGhNZZ5Lx6JBnNd9jth/rmGY8\n",
+       "mXYlWBG1a2QCAsyiLAu+LssvD76E0mzaWKvANNVotlVlmvHlko6lDHR/DfAuyuKJEdElTR8D8grb\n",
+       "V9YdR0S0i6RJwOF0TDMGznCTf2FGTDBNT0DWpPyS2Iby6csAtv+hzrgiIiJieE3tgul3NnAzsDdw\n",
+       "AvDWajsiYoVJumGYw7a9Xc+CiWi5preA/Mr2SyVdb3u7avbIFbZ3qTu2iGieqvYHlDEfUD7kCHgL\n",
+       "gO2jeh9VRDs1vQXk8erPByW9BLgHWL/GeCKiwWzPh1JmfkBNk+urIoFJQCK6pOl1QE6XNAM4Dvgm\n",
+       "8Gvg5OEviYgYkSTt3rGxG6UlJCK6pNFdMBER46FaDPJMylR/KDV4DrN9TX1RRbRLoxMQSRsCHwM2\n",
+       "sb23pG2AXW1/oebQIqIFJK1L+T2ZIoARXdb0LpizgEspK+MC/A74QG3RREQrSNpQ0heA82wvkrSN\n",
+       "pMPrjiuiTZqegKxn+zzgKQDbTwBP1htSRLTAWeTDTcS4anoC8pCk5/RvSHo58GCN8UREO+TDTcQ4\n",
+       "a/o03H+mrM8wU9KVlCm4b6o3pIhogXy4iRhnjR6EClAVH9uy2vxt9UklImKlVbNgTgW2BW6i+nBj\n",
+       "+7paA4tokUYmIJJ2Bu60fXe1fQjwRmA+MMf2whrDi4iGkrSp7Tuq15OBrSj1P35r+/FhL46IFdLU\n",
+       "MSCnAY8BSHol8HFgLrAY+HyNcUVEs32j4/V5tm+0fUOSj4jua+oYkNU6WjneDJxm+yLgIklpIo2I\n",
+       "bphZdwARbdbUFpBJ1dgPgL2AH3Uca2pSFRERscpo6pv1OcDlku4H/gL8GEDSFpSSyRERK2M7SUuq\n",
+       "12t2vAaw7Wl1BBXRRo0chAogaVdgQ+BS2w9X+2YBU7NeQ0RExMTW2AQkIiIimqupY0AiIiKiwZKA\n",
+       "RERERM8lAYmIiIieSwISERERPZcEJCIiInru/wPWOAwOXxqI3AAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xf0231d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df[[current_run_name,scen1_name,scen2_name]].plot(kind='bar', alpha=0.7)\n",
+    "ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))\n",
+    "ax.set_xlabel(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/scripts/summarize/notebooks/person.ipynb b/scripts/summarize/notebooks/person.ipynb
deleted file mode 100644
index 0dbd2e85..00000000
--- a/scripts/summarize/notebooks/person.ipynb
+++ /dev/null
@@ -1,406 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set main model directory to parent directory\n",
-    "model_dir = os.path.dirname(os.getcwd()) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Model Scenario Results\n",
-    "scen = h5py.File(model_dir + r'/outputs/daysim_outputs.h5','r+')\n",
-    "scen_name = 'Model: 2040'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Base Data\n",
-    "base_file = r'/inputs/hh_and_persons.h5'\n",
-    "\n",
-    "base = h5py.File(model_dir + base_file ,'r+')\n",
-    "base_name = '2006 Survey'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Age Distribution\n",
-    "####################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Summarize Person-level results\n",
-    "\n",
-    "# Age distribution\n",
-    "ages_scen = np.asarray(scen['Person']['pagey'])\n",
-    "ages_base = np.asarray(base['Person']['pagey'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x123fdba8>"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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PH+bZZ58lJyeH008/nXbt2tGlSxdefPFFiouLmTJlCmvXro372Uqbvco3d0Fkhsk//vGP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\nvgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x123bbe10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Age Distribution Histogram\n",
-    "bins = 30\n",
-    "\n",
-    "# Set model results to boldest (alpha default to 1)\n",
-    "P.hist(ages_scen, bins=bins, normed=True, histtype='step', color='b', label=scen_name)\n",
-    "\n",
-    "# Compare with survey estimates (alpha < 0.5); Currently using fake data...\n",
-    "P.hist(ages_base, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=base_name)\n",
-    "P.xlabel('Age')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gender split (1 = female)\n",
-      "\n",
-      "Model results: 0.504\n",
-      "2006 Survey results: 0.508\n"
-     ]
-    }
-   ],
-   "source": [
-    "print \"Gender split (1 = female)\"\n",
-    "print \"\"\n",
-    "print \"Model results: \" + str((np.asarray(scen['Person']['pgend']).mean()-1).round(3))\n",
-    "print base_name + \" results: \" + str((np.asarray(base['Person']['pgend']).mean()-1).round(3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Worker Type\n",
-    "####################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Worker type\n",
-    "worker_type_var = 'pptyp'    # Assuming this is same as base as in scenario\n",
-    "\n",
-    "worker_type_scen = np.asarray(scen['Person'][worker_type_var])\n",
-    "worker_type_base = np.asarray(base['Person'][worker_type_var])\n",
-    "\n",
-    "worker_labels = {\n",
-    "  1: \"Full time worker\",\n",
-    "  2: \"Part time worker\",\n",
-    "  3: \"Non working adult age 65+\",\n",
-    "  4: \"Non working adult age<65\",\n",
-    "  5: \"University student\",\n",
-    "  6: \"High school student age 16+\",\n",
-    "  7: \"Child age 5-15\",\n",
-    "  8: \"Child age 0-4\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Sum by worker type for scenario results\n",
-    "df = pd.DataFrame(worker_type_scen, columns=[worker_type_var])\n",
-    "df['Worker Type'] = [worker_labels[x] for x in df[worker_type_var].as_matrix()]\n",
-    "worker_types_scen = df.groupby('Worker Type').count()['pptyp']     # Sum by category\n",
-    "worker_types_scen = worker_types_scen/worker_types_scen.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Sum by worker type for base\n",
-    "df = pd.DataFrame(worker_type_base, columns=[worker_type_var])\n",
-    "df['Worker Type'] = [worker_labels[x] for x in df[worker_type_var].as_matrix()]\n",
-    "worker_types_base = df.groupby('Worker Type').count()['pptyp']    # Sum by category\n",
-    "worker_types_base = worker_types_base/worker_types_base.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x39e40630>"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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XEuqXpFu2bMVNN91Ur3N8vYDi4YndufVQq1at6rVWAPh6AcXEa+zOOVdiPLE7\n51yJ8cTunHMlxhO7c86VGE/szjlXYjyxO+dcifHE7pxzJcYTu3POlRhP7M45V2I8sTvnXInxxO6c\ncyXGE7tzzpUYT+zOOVdiPLE751yJKZjYJQ2SNFPS25LOzfN8X0nPSPpG0tn1Odc551zDqzWxS2oO\nXAUMAvoBx0raPuewz4Azgb+sxbnOOecaWKEW+wDgHTObY2YrgJuBIdkHmNkCM6sAVtT3XOeccw2v\nUGLvDnyQtT032VcX63Kuc865tVRoabx1We22zueOHj268nFZWRllZWXr8GWdc6Xi9ttvZ9myZXU+\nvpTXZS0vL6e8vLxOxxZK7POAHlnbPQgt77qo87nZid25mEiq9zlm69IectmWLVtWr7VZS3ld1txG\n75gxY2o8tlAppgLYRlIvSS2BY4B7ajg29z+gPuc6FzGrx4dz6au1xW5mKyWNAh4EmgPjzWyGpJHJ\n82MlbQ5MA9oDqyX9FOhnZovznduY34xzzrnCpRjMbDIwOWff2KzHH1O15FLruc455xpXwcTunKu7\nli1bctNNN9XrnFK+4Zdtbe5XtGvXjmnTpjVCNKXNE7tzDahVq1b1utkHpX3Dr7r63odo3yhRlDqf\nK8Y550qMJ3bnnCsxntidc67EeGJ3zrkS44ndOedKjCd255wrMZ7YnXOuxHhid865EuOJ3TnnSown\nduecKzFFOaWAT77vnHM1K8rE7pPvO+dczbwU45xzJSaKFnt9p/P0qTydc65mkbTY67P0mC8/5pxz\ntYkksTvnnGsontidc67EFKyxSxoEXE5YkPpaM7sozzF/A74HfA2caGYvJfvnAF8Bq4AVZjag4UJ3\n65P6dnEF7+bq1l+1JnZJzYGrgO8A84Bpku4xsxlZx3wf2NrMtpG0F3A1sHfytAFlZvZ5o0Tv1hv1\n7eIK3s3Vrb8KtdgHAO+Y2RwASTcDQ4AZWcccBtwAYGbPSeooaTMzm588X/8VbF3J855QzjWeQjX2\n7sAHWdtzk311PcaARyRVSDptXQJ1pch7QjnXGAq12Ov6H1VT82tfM/tQUhfgYUkzzeyJ6oeNznpc\nlnw455zLKC8vp7y8vE7HFkrs84AeWds9CC3y2o7ZItmHmX2YfF4g6S5CaadAYnfOOZerrKyMsrKy\nyu0xY8bUeGyhUkwFsI2kXpJaAscA9+Qccw8wAkDS3sCXZjZf0kaS2iX72wAHA6/W71txzjlXX7W2\n2M1spaTMZA8TAAAgAElEQVRRwIOE7o7jzWyGpJHJ82PN7H5J35f0DrAEOCk5fXPgzuQmWQvgJjN7\nqLG+Eeecc0HBfuxmNhmYnLNvbM72qDznvQvsuq4BOuecqx8feeqccyXGE7tzzpWYKKbtdc65Yhbb\nqm6e2J1zbh3FtqqbJ3bnnMtS3+kuIL4pL7zG7pxz1RT34j+e2J1zrsR4YnfOuRLjid0550qMJ3bn\nnCsxntidc67EeGJ3zrkS44ndOedKjCd255wrMZ7YnXOuxHhid865EuOJ3TnnSownduecKzEFE7uk\nQZJmSnpb0rk1HPO35Pnpknarz7nOOecaVq2JXVJz4CpgENAPOFbS9jnHfB/Y2sy2AU4Hrq7ruWtr\n5cqVDfEyja4Y4iyGGMHjbGgeZ8OKLc5CLfYBwDtmNsfMVgA3A0NyjjkMuAHAzJ4DOkravI7nrpVV\nq1Y1xMs0umKIsxhiBI+zoXmcDSu2OAsl9u7AB1nbc5N9dTmmWx3Odc4518AKJfa6ziBf/yVHnHPO\nNQqZ1Zy7Je0NjDazQcn2ecBqM7so65h/AuVmdnOyPRP4NrBVoXOT/fEtP+Kcc0XAzPI2qguteVoB\nbCOpF/AhcAxwbM4x9wCjgJuTN4IvzWy+pM/qcG6NgTnnnFs7tSZ2M1spaRTwINAcGG9mMySNTJ4f\na2b3S/q+pHeAJcBJtZ3bmN+Mc865AqUY55xzxcdHnjrnXIkpusQu6a20Y6gLSRunHUMuSS0kvZl2\nHPWR3HSPnqTvph1DXUhqJ2l3SR3TjiWXpGaS9kk7jrqQdGNd9qUl6sQuaZGkr5LPiyQtAvpk9qcd\nX4ak32U97pe8+bwgaU5yQzkKZrYSmClpy7RjqYej0w6gji5OO4B8JP0j6/G+wOvApcBrkn6QWmB5\nmNlq4B8FD4zDjtkbkloAe6QUSzVRJ3ZgAvAfYBszawe0B943s3Zm1j7d0KoYmvX4L8BPzWwrQlK6\nLJ2QarQx8LqkRyXdm3zck3ZQrtF8K+vxBcDhZnYAsD/wx3RCqtUjko6UFGVvOUm/SRqYO+U0OD8h\n9BCMQvQ3TyX1J7SG7ibMPfNOkjSjIeklM9steTzdzHbJeu5lM9s1veiqklSWZ7eZ2dSmjqUmkuaw\nZnBcN0J3WQhx9k4lqDwkTcjaPIw1/9hmZienEFI1OX+blY/zbcdA0mJgI2AV8E2y2yJryCHpz2b2\n67TjqEn0iR0qJxQbBRxJmHCsa8ohVSFpITCVMAJ3H6CHmX2dtDpeNbMda32BJpaMLdjazB6RtBHQ\nwsyiKW1lizH5ZCRvkkb4vY8DTk0eR/NGKWkp8E6yuRXhb/OL5H9qemx/m8VEUndgS7K6jZvZ4+lF\ntEahAUpRMLNVwBWSbgOiaf1myZ7c7C+EfvsAmwL/bPpwaibpdOA0QkmmD7AFYUbOg9KMqxiZWXnm\nsaTFsSTzHLkzqi5JPncC/tDEsRQkqRkwDNjKzP4oqSewuZk9n3JoVUi6iDDo8g3C1UVGFIm9KFrs\n2SSNM7PT0o6jWEmaTph589msS/RXzWyndCPLT9LfzeyMtOMoRNKzZhbNjfLaSNrEzD5LO458kilK\nVgMHmlnfpHfZQ2bWP+XQqkg6SOxkZsvSjiWf2G+e5hPVL7gQSdekHUOOZdl/jMnd/Gjf3YshqQPE\nmtQlXSSpS/K4v6R3geckvV/D/Za07WVmPwGWApjZ58AG6YaU1yygZdpB1KQoSjE5Pkk7gFy19FkX\nEFWXMmCqpN8CGyV9r38C3JtyTJWSrpifmNnS5LL8RGB3Qje9cUmXzShJ2jhJRDH5gZllVi/7C3CM\nmU2TtC0wiYi66CWWJ/V/AJI3pdUpxlOTpcDLkqYAmYaSmdlZKcZUqehKMTGStBp4r4anu5tZNO/s\nSbI8FTg42fUgcK1F8ocg6XVgz+Tm88VAb0KX14OIq7fJ78zsguRxP0KMGxDezH9oZs+mGV+GpBnA\nzma2IrdcFGMJTtLxhG7CexAW8DkS+J2Z3ZpqYDkknZg8zPzfZG6a35BORFVFndglbQCcAhzOmkU6\n5hK6Po5PVmZKXTIB2kFmVi25S/rAzHqkEFZekv5oZn/I2m4O3Ghmx6UYViVJb5hZv+Txi4QkvyrZ\nfsXMdk41wERON8L7gSvNbLKkAcDlZhbFCEpJZxK6Yv6J0He9E3AncCDQ28yGpxheNZJaE3rvZG7m\nTyFcwUV3TyDpUdbTzGamHUuu2GvsNwK7AKOB7ycffyT0jPlXemFVcznhHyafS5oykDromRmmL6kV\n4Z88pmka5krK/FPPBnoASOpMvPcCupvZZICk98ZGKcdTycyuBP4P+BGh99aBwK8JYwOiuPrJcSdh\nrMpVZnYV8CXwcMoxVSPpMOAl4IFke7eYBvrF3mJ/O1kku17PuZolpZibgFeBA4D7zeyv6Ua1RtK9\nbSKhy+iXwH7Ay0BH4BwzeyTF8CoV29iFYiHpNEID7kjCm/o9hN/7Q6kGliO5mjwQeCzryu21WH7v\nsd88/VzS0cDtyTwSmcR0FBDbTaoqJF1jZqenHUeGpD1Y0+K9HBgLPE24mbq7mb2YWnBZzOx9oCyp\nW29LqLN+AFRkSjKRyB67cClVxy5c3fTh1ExSe2Bg5qoi2bcbsDS2MoKZjUuuJO8mDP75kZk9lXJY\n+awwsy9zZj6I5iZv7C32rYCLCC3LL5PdHYHHgHPNbHZasRUS24hJSeVULWUoezuZP8SVoKQxNBPY\nO9NrR9IzwEmxJHZJZ+fsGkG4qnyJcFMyqjmXJF1HqP//Gvgf4CxgAzP7UaqBJaJusSeJ++jk8naT\nZPdnsfTgKCCqbplmVpbcKD3SzG5JO561EVMvjuSm5M1mtkDS1sB1wM7Am8CpZvZqqgFmMbPVkv4N\nHA/8TVJfwvrDUST1RDuqNjzuSrbbphNOQWcCvyV0dZxE6F32v6lGlCXqFrtreJJeMLPY+i5XkjQ0\nz+7MfCxjzaxzE4eUV07vnfsJc8X8h7CQ+4VmNjDN+HIl4wPuMrPdk+Hwb5rZdWnHlS0ZLHeRmeW2\n3l09Rd1iLxaSdjazV5LHLYFzCcP2XwUuMLOv04wvx8OSzgFuYc28IZkRfjG4Gfg31euVAlo3fTg1\nap71uIuZ3ZU8LpfULo2AamNm70n6JOmOeQQQTZkww8I6yQMlKdarcknZg/kyDY7KbTM7rIlDystb\n7A0gp0/zZYQJtq4n9L/f2MxGpBheFao6JW6GWSTT4Sa9DU7IV8qIaUyApAsJYyv+CPyQMBIx0z98\nqJkdmmJ4eUk6itB1+BkzOzXlcPJK5orpBtwGZBpEZmZ3phfVGlnTMBwBbE7odi3gWGC+mf0spdCq\nKIrEHvuMbzmJfTphUM3y5N7AK7HUhYuBpP2B92oY7LWnmU1LIay8JJ1E6B/eB2hFGDz3H+DPZrYw\nzdjySa4mKwj3AKL438kl6frkYZXEZGYnNX00NctX0oypzFksiT3qGd+SiZXOIbxz/8nMts16LprR\nklD5z/1jwihEI/TF/mcso3idKwbJVA2HmtmsZLs3cJ+Z5U6TnIpiqbHvZWa7SXoJQj04mW4gFo8D\ng5PHT0na3Mw+ltQVWJBiXPlcTfi9/53wRjQ82RflpblrGLFf9WZI6gH8Ddg32fU4YanJuelFldfP\ngcckZbpc9wLiGbdSJC325wij+yqSBN+F0GKP7gZQ7PJdQcR2VeEaXuxXvRmSHiGMjM5MGTIMGGZm\n300vqvySeW36Eq58Z1pEc7MXS4v9SkK/1k0l/R/JjG/phlS0Vkra2szeAZDUB4h2KlzXYGK/6s3o\nYmbZa8leL+nnqUVTA0knULVXzC6SMLOJKYZVqSgSu5n9S9ILrJnxbYiZzUgzpiL2S+DRnEvIqG5M\nAUjaHLiQMMHWoGSKgW+Z2fiUQ6uiWEocFM88559JGk7o8ipCj6NP0w0prz1Zc4O3NSE3vUiY5yh1\nxVKKyV7IIjMUfpHf8Fs7yVwcfZPNN83sm9qOT4OkB4AJwG/NbOekdflSLJMsZRRRiaNY5jnvRbhC\nz8wb/zRwZjKHULQkdQRuMbND0o4FiqTFTngn7Al8kWx3Aj6W9DFwmpm9kFpkWYqh9SbpSUJPmCeA\np2JM6onOZnaLpF8DWFgoIsaSUVGUOIroqvdjMxtc+LDofE2YRz4KxZLYHybM8PgggKSDCS2OCYQe\nHQNSjC3bP0hab4SBK4uTfTG13kYQpsIdCvxF0jfAk7EMrMiyWFJmfiAk7Q1E1zecIilxJFcS81lT\n4jBJG0R41fu6pPmE3jBPEP42o/u954xAbQb0A6K5+imWxP4tMzsts2FmD0m61MxOT/plxyL61puZ\nvZsk82XACsLMmVH0vc1xNmEt1t6Snga6EN7MY1MsN/aL4qrXzPok89rsCxwK/EPSF2a2a8qh5foL\na26criQMqvsgxXiqKJbE/pGkcwnziIhQK5yftJRiah1F33qTNItwM+rfwHhglCVz3cfEzF6Q9G1g\nu2TXmxG2LoupxFEUV72StgAGEq4qdyUsYv5EqkHl9wMz+1X2DkkX2ZqFw1NVLDdPuwDnE37hAE8B\nYwiX5j0zXffSVgw3qCT9lPBPswVhitmpwOOx/AwzFGZ5zP3jXEhYnSiaKZGL5ca+8qzuo2QaZEkv\nx9IiVlgYfhphjda7I54MrNp6C4ppWulIf25FS9L2ZC3EG2nrDUltCd0cf0noUti8wClNStJ9wLcI\ni6oAlBHKCVsBf4ylv3AyqVq1EkfyEU2JQ9LDwCNUveo9GDgEmGZmu6cYXiVJuxAaHvsRfq5vExoe\n16YaWELSj4GfEOYHmpX1VDtCZ4RhqQSWoygSu6RNgV8RblBsmOw2MzswvaiqK4bWm6RLCf80bQld\nyTI3qGbVemITk/QQMNzM5ifbmxEWNz+W8I++Q5rxZUgaR80ljivMLJYSR1Fc9QIoTHs8kDCf0fEA\nZtYz1aASkjoQ3rz/TJieO1Nn/8rimfq6aBL7w4T5w88BRgInAgtya1xpK4bWm8LUrY9nEmasJM3I\nnlBJkoA3zGz7fJfBaSmWEkexkFRBGPDzNEnPmHwzfaZNYdWsuWb2jaQDgJ2AiWb2ZYFTm0Sx3Dzd\nxMyulXSWmU0lLMBckXZQeUR/g8rMbks7hjp6LCnH3EpoFQ0lLGLRhjXr38agKG7sF8tVL/D9mO6h\n1OJ2oH+S4McSFt/+N/D9VKNKNEs7gDpannz+WNKhknYntIZj861MUofQLTPZ9wwQU7fMYjCK8Ia4\nG7AL4Wb0T8xsicW18PZxQA/CPOx3Ea7YjiWssHR0inHluomwoHVvwmIbcwhzs0elSJI6hDfFlYSF\nrK80s18CXVOOqVKxlGIGE2rBPQj9htsDo83snlQDy1EsN6jc+kfSixbWO62cyVNSRWxTHxQLhRln\nrwB+Aww2s9n5ynJpKYpSjJllRnl9SegdEavjCDeo/pNsP0WErTdJ+wFbm9mE5KZaWzObXei8piTp\nW4R5ubcnrE7UHFhsZu1TDSxHEZU4qlz1Ah8S51VvsTiZcL/vwiSpb0W4uR+Fomixu4YjaTShn/12\nZratpO7ArWY2sPYzm1Yy6OeHhBp7f8JUCNuZ2a9TDSxHEd3YL5ar3jbALwg9dU6TtA3h9/7flEMr\nKp7YG1AxtN4U1mTdDXjB1qzTGt1CG0rWj8wpHUTXy8RLHA1L0q3AC8AIM9shSfRPm9kuKYdWVIrl\n5mmxKIYbVMuypxBI/nFitERheuHpki6W9AvW9BmOSbHc2C8WfczsIpKfq5ktSTmeohR1jV3S2Vmb\n2auVGICZXdbkQdWuGLpl3iZpLNBR0umEWmEUo/pyjCA0PEYR1pfcgtDlMTYXKszFfTZrShzRrfhT\nRJZJylztZlb4imbJuQxJO5nZq2nHUZOoEzthmK4RJoLaE7iHkNwPBaKZ4zxL9DeozOySpH/9ImBb\n4Pdm9nDKYVVjZnOSh0sJVz9RKpYb+5J6m9m7hfZFYDTwALCFpH8TRqCemGZANbg6uaKcANxkkU0t\nXBQ1dklPEAYuLEq22wH3m9l+6UZWVbHcoILKodEtWHP1E81waNfwapi06gUz2yOtmGoiqTNrVlB6\n1sxiXBoPSdsSrniPIjQ0JyRjV1IXe4s9Y1PC3OEZK5J9USmG1pukkYQ5QpaxZmSkEe4LuBKTTErX\nD+gg6X9YM4dRe8LQ/Rh1J3RvbQHsr7BI9J0px1SNmb0l6XeE+2h/A3ZVWEXtN2Z2R5qxFUtinwg8\nL+lOwh/m4YSRiK7+fgnsGGsrKEPSUbnTH+Tbl7YiKHFsBwwGOiSfMxYBp+U9I0WSJhDmXXmdqlMy\nRJXYk1koTySUhR8GDjWzFyV1A54FUk3sRVGKAZC0B2FWQiNMYvVSyiEVpWTWxCNi721QQ+kgmsm/\nMoqlxCEpM7VF1CS9AexgkScmSVMJC9XcbmZf5zw3Iu1ppaNusedMgzub0H0QwnqNG3tdeK38GnhG\n0jOsudlrZnZWijFVkvQ9wkRK3SX9jTU9odpRtRyXqmIpcUi6MuvxcTlPR/N7zzKN8HN9Pe1ACrgr\nN3lL+qmZXZF2UofIEzthYYXa3rmjWBW8yLplXkOYz+ZVwqVuJiHF4kPCAJUhyefK+a6JqxthsZQ4\nXqDq32S2mH7vGRMIDY+PWdPN0WIbQAecAFyes+8kwvwxqSuaUkzMkmH6NXbLNLPj04uuqhjLGflI\n2sAiWqCkJsVS4igWCmvy/hx4jawae1b311RJOpYwJ9R+VF2LtR2wyswOyntiE4s6sUvqa2Yzk9F8\n1ZjZi00dU22KoVumpP8D3iO8+VQO/IitrCVpX8KEar1Yc2VpZhZF753sEkce0ZU4JD2WZ3dU010A\nSHrGzL6Vdhw1kbQloVKQu4LSImB6MpVv6mJP7OOSiYDKyXPZGNm83Eh6E9jFzL5JtlsTftnbpRvZ\nGgqrPOX7WUZR1spIfpY/I5TjVmX2x9KbR9KJ1FLiMLOoem1Jyp67pjVhFO/KZB7xaEj6B9ARuJeq\n94Ci6hUTu6gTe7GR9FvgGELXrEy3zFvM7P9SDawISXrOzPZKO45SJmmame2ZdhzZJF2fPKySmMzs\npKaPpjpJT5nZQEmLqd5AslimlS6axC5pH6pelhPD3edcsXbLlHSQmU2RNJT8LfaoWkSS/kwYpHIn\nVUtGsZXfiqXEkd3DrBlhKuQrYrqadA0n9l4xAEj6F2Fk5MtkXZYTBi6lrki6Ze4PTCH04Mj3bh5V\nYicMKTdCAsoWVfmNMOAro7LEkVIstcnuYbaS8Dd6SmrR5JB0rpldVMO9ixjvWfQB5lmki1kXRYtd\n0gygX6yDFmqqW2fEVL8ugpGSRS/GEkfsJA02s3uz7l1UPkWc9yymExas6QXcT1jMegczi2Ix66Jo\nsRO6PnUl9HGOjpn1SjuGergdyO1ldBvhjzQakjYHLgS6m9kgSf0IC4OPTzm0KmoocURRZwWoqfSW\nEUsJLmuepa/N7Nbs5yRFs6xkltVmtjIZnHalmV0pKYqyK0Se2CVlftltgTckPU/VQQuHpRNZVcXQ\nLTNrpGTHmEdKZrmeMFjlt8n224Rl8qJK7ERe4mBN6W1TYB/g0WT/AcDTxFeCO4/wey60L20rkpG8\nI1gzQG2DFOOpIurEDlzKmn+a7G5lsZVkziaMNryM/LHFUBfeluIYKZnR2cxukfRrADNbISm62nXs\nV2tmdiKQWZu1n5l9lGx3JaKJ9IplKoksJxHxYtaxJ/Z5wGZm9mT2zmTwykfphFSdmZ2WfC5LOZQa\nmdndwN2S9jGzp9OOpw4WS9oksyFpbyCaxQyKpcSRpQfwcdb2fKBnSrHkk28qCSM0PGKaSgJJLQhT\n8w7L7DOz2cBF6UVVVeyJ/XLCZViur5LnBud5LlWxd8sskqQO4SroXqC3pKeBLsCR6YZURbGVOB4B\nHkxWJRJhvEU0K2eZ2XTC+rb/NrPlBU9IUVJb31JSKzOLbtk+iLxXjGpZ7V3Sa2a2Y1PHVJuaumWa\n2ZmpBVXEJG1AmH8H4M0Y545JShwjckscZnZwupFVJUnAEYRur5kxFnelG1XxknQj0JcwNUdm2l6L\nZcK/2FvsHWt5LsYbfnsQcbfMYpBV4siddXJbxbmSTuwlDiBkHMJVRGw/v2I1K/loRujcEZXYE3uF\npNPN7JrsnZJOI9ThYhNtt8ycqYVzRdPSwEscjULStwjLt20PtCKM6l0cyxD4DBXJyllmNhpAUhuL\ncNGa2EsxmwN3ESYDyiTyPQh/mEdkLn/TltMtczfCwrZRdcvMmlq42lOEGMc0bUS18xJHw5L0AvBD\nQrfB/oRuetuZ2a9TDSxHvmmlY5xqOrmXdi3Qzsx6KCyVN9LMfpJyaEDkiR0q/3EOAHYk/OO8bmaP\n1n5W05JURi3dMs1salPHVOwkzQS2z5S1FBYJfsPM+qYbWXFSslyfpFcsWbRC0stmtmvasUGV7o7H\nADdTtbtjPzMbkFZs+SRjao4E7s686Uh63cx2SDeyIPZSTKY2+ChrLsljFH23zGKbPxwvcTS0JZJa\nEXqeXEy4L5BvyuG0ZLo7Hkbk3R0zzOz90O6sFM04i+gTe5Eohm6ZxbZE2plULXGMjbHEAVxFnhJH\nqhHlN4Jwo28UIVFuQZiwLApmNl3S68DBsc0LU4P3JQ0EkNQSOAuYkW5Ia0RfiikGxdYt0zWc2Esc\nxUbSk8BBsfYPz5DUhbC+6XcIjaWHgLPM7LNUA0t4i71hFE23zCKaPzx7IYOWhHk4vMRR+mYDT0qK\nsn94NjM7Lu0YauKJvWEUU7fMopg/3Mwq+wYnN04PI8zRHpuoSxxFKLd/eO54hlg8LWk2cAtwp5l9\nkXZA2bwU0wCKpVtmTYpl/nAvcbiYSNqLcH9lCPAGYRnMKCYC88TeQIqhWyYUzxJpyQjUjGaEN8pv\nW8Qr2McsGWuRffPcCDf3pxFuTH+TVmzZJG0K/IowxfSGye7oSoXZJHUG/goMM7NmaccDXoppMEXS\nLRPinz88I3sJv0ycQ1KLpvjNBjoDk1jTfXQRYTrnccDw9EKr4iZCeeNQwrS4JwIL0gwoH0kdCL22\njgG2JlyxR3PV6y329YSknmb2ftpx1JWkffOMCxhoZk+lFVMxy9dzK7MvpoE1kl40s91zehnV2Oss\nLUl9/W7Cm9Czsc0P5S329cfdhOkOkHSHmcV+g+9vVF/C7yqS7yEWxVLiANpI2tLM3gOQtCXQJnku\npmlyM7F8LOlQwsClTinGU5PesSXzbJ7Y10+90w6gJslIzn2ATSX9gqpDy6OoX+YolhLH2cATkjKL\nlvcGfiKpDRGtpARcIKkjId4rCUs3RjPyVNIVZvZT4J6cUacQybxQ4IndxaclIYk3Tz5nfEVcC21k\n7JNTJrgnu8SRWlQ5zOx+SdsS5hA3wvz2mauJy9OLLJC0IfAjQr26OzDe4lyRLLNozqWpRlGA19jX\nE5JWsWbAx4bA0qynLbaBPzllg+ZAWzOLZmm8DEkzgEE5JY4HzGz72GYlTGYk3IrQoMtMUBfF6l6S\nbiWUYZ4gTAY2J2kZu7XgLfb1hJk1TzuGevqTpB8RVqKaBnRILoMvTjmuXEVR4qhpdS/WtEDTtr2Z\n7QQgaTzhdx6tZIK/86m6DKaZWRRlTm+xuyhJmm5mu0gaRriJ+mvgxcw/f0wktSZ/iSMayZVFtKt7\n5V7dxHa1k0vSm8DPCN2Hs5fB/DS1oLJ4i93FqkWy5unhwN/NbIWkKJMS4Y0nU+LYJVnCL5aWcEa0\nq3sldpa0KGt7w6zt6EqFwJdmNjntIGriid3FaixhUNIrwOOSegEx1thjL3FkdAHeSBaIiGp1LyjK\nUuFjki4hLNVYOROlmb2YXkhreCnGFYVkyobmZhbVhGWxlzgyklW+qjGz8qaNpDRIKifP5GRmdkDT\nR1OdJ3bn1oGk24CfmlmsJQ63HvJSjHPrJuoSh6SnzGxgzvz2GTHWrqMm6ezkoWV9/hR40sxmpxNV\ndd5id24deIlj/SJpNNXfIDcBDgFGm9mkJg8qD0/sLirJdL01rs1qZnc2cUglQdIpZjY+Z9+fzezX\nacVUSpLpsKfE0kXTSzEuNtnT9eYTRWIvwhLHkZKWmdm/ACT9nTXznbt1ZGaf55k7JjWe2F1UzOzE\ntGOoCzMbmHxuW+jYSPwPYR6bVcD3gC/M7OSUYyoZkg4Aolkez0sxLkrJDH/nA/snu8qBP8Y2X0zs\nJY6cFbPaEaZvfhL4A4SWZhpxFStJr+bZ3Qn4CBhhZjOaOKS8PLG7KEm6E3iVMN+KCNPf7mxm/5Nq\nYDkkTQZuyi1xxNIaljSHqqWi7MWho5nbpFgkA+WyGfCZmS1u+mhq5ondRSkzV0yhfWlLppu9B7iO\nNSWOqGYlTGbH3NtXn1p/xLhwgXMASyXtl9lIZtP7upbjm5SkjZMyx4bAqcC5hDnjx+SUP1JnZquA\nv6cdh2s63mJ3UZK0K2G+lQ7Jri+AE8xsenpRrVFsJQ5JfwGeBe6IffoDt+48sbuoSWoPYGZfpR1L\nrmIqcSTdMjciTFSWmVY4xm6ZrgF4YndRKqJeMS+b2a5px+FcNq+xu1hdR6hZHwUcTVggekKqEeX3\niKQjFdPolBpIGiLpUkl/kTQ47Xhc4/EWu4tSEfWKKYoSh6Q/A3sCNxHuB/wQqDCz81INzDUKH3nq\nYrVU0n5m9gTE1ysmo4hGnv4A2DXpIYOk6wmLg3hiL0Ge2F2sfgRMlFSlV0yK8dRI0hDCvQADpprZ\nvSmHlI8BHYHPku2O1D4njytinthdlMzsZcI6mNH2ioG8JY6zJO0TYYnjT8CLyco/AN8mLBDuSpDX\n2F2UJLUGhgK9gOYk/cTN7I9pxpUrmTsku8TRHHjZzHZKN7LqJHUjvAkZMM3MPko5JNdIvMXuYnU3\n8CXwAmtuSsaoKEocyaLbU4EnzGxm2vG4xuWJ3cWqu5kdknYQdVAsJY7rgP2AKyVtDbxISPKXpxuW\na/E3QLgAAAS6SURBVAxeinFRknQNcJWZvZJ2LIUUS4lDUgugP3Ag4eb0UjPbLt2oXGPwxO6ikjXf\ndXNgG2A2VReJ3jmVwGpQLCUOSVOANsAzhPnYnzCzT9KNyjUWL8W42BTbiMhiKXG8Qmit70gY0fuF\npGfMbGm6YbnG4C12FyVJewNvZLo5Jt0etzez59KNrLpiKnFIagecCJwDbG5mrdKNyDUGT+wuSpJe\nBnY3s9XJdnPCEPgoVoHPKJYSh6QzCVcWexDKW08QYn001cBco/BSjItWJqknj1clyT02xVLiaA1c\nCrxoZivSDsY1Lm+xuyhJugt4DLiaMDjpx8ABZnZ4qoHVwEscLiae2F2UJG0G/A04INk1BfhpbGUO\nL3G4GHlid24dSPol8Dhe4nAR8YU2XJQkXSKpvaQNJE2R9Kmk4WnHlcvMLjGz5zypu5h4YnexOjjp\n6ngoMAfoA/wy1YiKmKShkt6W9JWkRclHlDNmunXnvWJcrDJ/m4cCt5vZQkleN1x7FwOHmtmMtANx\njc8Tu4vVvZJmEmZ2/LGkTYl7lsfYfexJff3hN09dtCRtAnyZ9GFvA7Qzs4/TjiubpKHAn4HNCN0y\nIc41T68ANgf+AyxPdpuZ3ZleVK6xeGJ3bh1ImkURlDiSNU4hZ654Mzup6aNxjc0Tu3PrQNJTZjYw\n7Ticy+aJ3bl1UCwlDkk9CAO+9k12PU4Y8DU3vahcY/Gbpy5akrpTfc3Tx1MNqroOwFLg4Jz9USV2\nYAJhwe2jk+1hyb7vphaRazTeYndRknQRcAzwBrAqs9/Mim2+9ihImm5muxTa50qDt9hdrI4AtjOz\nZQWPTFERlTg+S0bu/ptw9fND4NN0Q3KNxUeeuljNAlqmHUQdTADuAbolH/cm+2JzMqEM8zHwEXAU\n4D1iSpSXYlxUJF2ZPOwG7EqY1TF7zdOzUgmsBl7icDHyUoyLzQuEvtYitH6zxdgKibrEIen8Gp4y\nADP7YxOG45qIt9idWweSegFXAnsnu54GzjSz99OKKZukc6j+htgGOAXobGZtmj4q19g8sbsoSXqV\nNS33jIXANOACM/sslcCKWLIg+FmEpH4rcGlsC5f8f3t3jNJAFEVh+JzSoLgTJY0rcBtWwQWIvWBl\nI5YWWii6CbdgFcgurESinXgtZkImGsQi5t15/l8TprvV4XHy5g5WgyoGWT1IetdixTGQ9CTpVlLR\na499qjjanTtHau6u36n5SPhz2anwlwh2ZLUfEcPO88T2OCKG7Wm+tDf9UHFIShHsts/VXB29krQT\nEdPCI2ENqGKQku2JpMOIeGyf9yRdR8TuLODLTjiXueKw/aFm1cGyLzyl20KJ1eDEjqxGkm5sb7bP\nU0mjdn3vWbmx5vpQcUQE76r8Q5zYkZrtbUmKiJfSs3R9qTguqTiQCcGOVGwfRMS97WMtdtizJWAX\nhUZbQMWBzKhikM2g/d3SkmBf/zjLUXEgM07sAFAZTuxIpbMrRvr+glK6XTFARgQ7sunuijmVdKLO\nR6JLDQX0CVUM0sp2Xx3oC/4AAoDKEOwAUBmqGKRi+1XzLn1DzYeiZ7gfDvwCwQ4AlaGKAYDKEOwA\nUBmCHQAqQ7ADQGUIdgCoDMEOAJX5BM6L7O36miTZAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x39ce72b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set bar chart params\n",
-    "ind = np.arange(len(worker_types_scen))    # index array to match dataset size\n",
-    "width = 0.3    # width of bars\n",
-    "\n",
-    "fig, ax = P.subplots()    # Initialize bar chart object\n",
-    "\n",
-    "workers_scen = ax.bar(ind, worker_types_scen.values, width=width, color='b')    # plot scenario data\n",
-    "workers_base = ax.bar(ind+width, worker_types_base.values, width=width, color='grey', alpha=0.3)    # plot base data (note the index offset by the bar width)\n",
-    "ax.set_xticks(ind+width)   \n",
-    "ax.set_xticklabels(worker_types_scen.index, rotation=90)\n",
-    "ax.set_title('Worker Types')\n",
-    "\n",
-    "ax.legend((workers_scen, workers_base), (scen_name, base_name))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "####################\n",
-    "# Student Status\n",
-    "####################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "student_type_var = 'pstyp'    # Assuming this is same as base as in scenario\n",
-    "\n",
-    "student_type_scen = np.asarray(scen['Person'][student_type_var])\n",
-    "student_type_base = np.asarray(base['Person'][student_type_var])\n",
-    "\n",
-    "student_labels = {\n",
-    "  0: \"Not a student\",\n",
-    "  1: \"Full-time student\",\n",
-    "  2: \"Part-time student\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Sum by student type for scenario results\n",
-    "df = pd.DataFrame(student_type_scen, columns=[student_type_var])\n",
-    "df['Student Status'] = [student_labels[x] for x in df[student_type_var].as_matrix()]\n",
-    "student_type_scen = df.groupby('Student Status').count()[student_type_var]     # Sum by category\n",
-    "student_type_scen = student_type_scen/student_type_scen.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Sum by student type for base\n",
-    "df = pd.DataFrame(student_type_base, columns=[student_type_var])\n",
-    "df['Student Status'] = [student_labels[x] for x in df[student_type_var].as_matrix()]\n",
-    "student_type_base = df.groupby('Student Status').count()[student_type_var]    # Sum by category\n",
-    "student_type_base = student_type_base/student_type_base.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x3a0736d8>"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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DDz/k4osvZtq0aaxYsYKuXbtyzjnnMGrUBreppJrHNjFLuc8++6xJx3wpZiyXNWvW0KNH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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x3a0731d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set bar chart params\n",
-    "ind = np.arange(len(student_type_scen))    # index array to match dataset size\n",
-    "width = 0.3    # width of bars\n",
-    "\n",
-    "fig, ax = P.subplots()    # Initialize bar chart object\n",
-    "\n",
-    "students_scen = ax.bar(ind, student_type_scen.values, width=width, color='b')    # plot scenario data\n",
-    "students_base = ax.bar(ind+width, student_type_base.values, width=width, color='grey', alpha=0.3)    # plot base data (note the index offset by the bar width)\n",
-    "ax.set_xticks(ind+width)   \n",
-    "ax.set_xticklabels(student_type_scen.index, rotation=90)\n",
-    "ax.set_title('Worker Types')\n",
-    "\n",
-    "ax.legend((students_scen, students_base), (scen_name, base_name))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/topsheet.ipynb b/scripts/summarize/notebooks/topsheet.ipynb
new file mode 100644
index 00000000..076be532
--- /dev/null
+++ b/scripts/summarize/notebooks/topsheet.ipynb
@@ -0,0 +1,873 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".output {\n",
+       "    flex-direction: row;\n",
+       "}\n",
+       "</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "CSS = \"\"\"\n",
+    ".output {\n",
+    "    flex-direction: row;\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "HTML('<style>{}</style>'.format(CSS))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:0,.0f}'.format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Relative path between notebooks and goruped output directories\n",
+    "relative_path = '../../../outputs/grouped'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tod_dict = {\n",
+    "    '5to6': 'AM',\n",
+    "    '6to7': 'AM',\n",
+    "    '7to8': 'AM',\n",
+    "    '8to9': 'AM',\n",
+    "    '9to10': 'Mid-Day',\n",
+    "    '10to14': 'Mid-Day',\n",
+    "    '14to15': 'Mid-Day',\n",
+    "    '15to16': 'PM',\n",
+    "    '16to17': 'PM',\n",
+    "    '17to18': 'PM',\n",
+    "    '18to20': 'Evening',\n",
+    "    '20to5': 'Night'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vehicle Miles Traveled (VMT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>facility_type</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>arterial</th>\n",
+       "      <td>37,857,548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>connectors</th>\n",
+       "      <td>5,445,342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>highway</th>\n",
+       "      <td>49,253,326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>92,556,215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               sc_2025_20_5\n",
+       "facility_type              \n",
+       "arterial         37,857,548\n",
+       "connectors        5,445,342\n",
+       "highway          49,253,326\n",
+       "Total            92,556,215"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time_period</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AM</th>\n",
+       "      <td>21,674,072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Evening</th>\n",
+       "      <td>11,582,191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mid-Day</th>\n",
+       "      <td>30,854,643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Night</th>\n",
+       "      <td>6,558,330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PM</th>\n",
+       "      <td>21,886,979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>92,556,215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             sc_2025_20_5\n",
+       "time_period              \n",
+       "AM             21,674,072\n",
+       "Evening        11,582,191\n",
+       "Mid-Day        30,854,643\n",
+       "Night           6,558,330\n",
+       "PM             21,886,979\n",
+       "Total          92,556,215"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'net_summary.csv'))\n",
+    "df['time_period'] = df['tod'].apply(lambda row: tod_dict[row])\n",
+    "\n",
+    "# VMT by Facility Type\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "df_fac = pd.DataFrame(df_dict[i][df_dict[i]['metric']=='vmt'].groupby('facility_type').sum()['value'] for i in df_dict.keys()).T\n",
+    "df_fac.columns=[i for i in df_dict.keys()]\n",
+    "df_fac.loc['Total'] = df_fac.sum()\n",
+    "\n",
+    "# VMT by Time of Day        \n",
+    "df_tod = pd.DataFrame(df_dict[i][df_dict[i]['metric']=='vmt'].groupby('time_period').sum()['value'] for i in df_dict.keys()).T\n",
+    "df_tod.columns=[i for i in df_dict.keys()]\n",
+    "df_tod.loc['Total'] = df_tod.sum()\n",
+    "display(df_fac)\n",
+    "display(df_tod)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vehicle Hours Traveled (VHT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>facility_type</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>arterial</th>\n",
+       "      <td>1,293,924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>connectors</th>\n",
+       "      <td>345,743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>highway</th>\n",
+       "      <td>1,133,746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>2,773,412</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               sc_2025_20_5\n",
+       "facility_type              \n",
+       "arterial          1,293,924\n",
+       "connectors          345,743\n",
+       "highway           1,133,746\n",
+       "Total             2,773,412"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time_period</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AM</th>\n",
+       "      <td>659,215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Evening</th>\n",
+       "      <td>364,953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mid-Day</th>\n",
+       "      <td>865,445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Night</th>\n",
+       "      <td>164,631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PM</th>\n",
+       "      <td>719,168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>2,773,412</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             sc_2025_20_5\n",
+       "time_period              \n",
+       "AM                659,215\n",
+       "Evening           364,953\n",
+       "Mid-Day           865,445\n",
+       "Night             164,631\n",
+       "PM                719,168\n",
+       "Total           2,773,412"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'net_summary.csv'))\n",
+    "df['time_period'] = df['tod'].apply(lambda row: tod_dict[row])\n",
+    "\n",
+    "# VMT by Facility Type\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "df_fac = pd.DataFrame(df_dict[i][df_dict[i]['metric']=='vht'].groupby('facility_type').sum()['value'] for i in df_dict.keys()).T\n",
+    "df_fac.columns=[i for i in df_dict.keys()]\n",
+    "df_fac.loc['Total'] = df_fac.sum()\n",
+    "\n",
+    "# VMT by Time of Day        \n",
+    "df_tod = pd.DataFrame(df_dict[i][df_dict[i]['metric']=='vht'].groupby('time_period').sum()['value'] for i in df_dict.keys()).T\n",
+    "df_tod.columns=[i for i in df_dict.keys()]\n",
+    "df_tod.loc['Total'] = df_tod.sum()\n",
+    "display(df_fac)\n",
+    "display(df_tod)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>facility_type</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>arterial</th>\n",
+       "      <td>257,691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>highway</th>\n",
+       "      <td>269,911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>527,602</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               sc_2025_20_5\n",
+       "facility_type              \n",
+       "arterial            257,691\n",
+       "highway             269,911\n",
+       "Total               527,602"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time_period</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>AM</th>\n",
+       "      <td>152,272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Evening</th>\n",
+       "      <td>68,842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mid-Day</th>\n",
+       "      <td>114,152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Night</th>\n",
+       "      <td>10,158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PM</th>\n",
+       "      <td>182,179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>527,602</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             sc_2025_20_5\n",
+       "time_period              \n",
+       "AM                152,272\n",
+       "Evening            68,842\n",
+       "Mid-Day           114,152\n",
+       "Night              10,158\n",
+       "PM                182,179\n",
+       "Total             527,602"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'net_summary.csv'))\n",
+    "df['time_period'] = df['tod'].apply(lambda row: tod_dict[row])\n",
+    "\n",
+    "# VMT by Facility Type\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "df_fac = pd.DataFrame(df_dict[i][df_dict[i]['metric']=='delay'].groupby('facility_type').sum()['value'] for i in df_dict.keys()).T\n",
+    "df_fac.columns=[i for i in df_dict.keys()]\n",
+    "df_fac.loc['Total'] = df_fac.sum()\n",
+    "df_fac = df_fac.drop('connectors', axis=0)\n",
+    "\n",
+    "# VMT by Time of Day        \n",
+    "df_tod = pd.DataFrame(df_dict[i][df_dict[i]['metric']=='delay'].groupby('time_period').sum()['value'] for i in df_dict.keys()).T\n",
+    "df_tod.columns=[i for i in df_dict.keys()]\n",
+    "df_tod.loc['Total'] = df_tod.sum()\n",
+    "display(df_fac)\n",
+    "display(df_tod)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VMT per Person\n",
+    "From Daysim records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>sc_2025_20_5</td>\n",
+       "      <td>16.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>survey</td>\n",
+       "      <td>17.44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        source  value\n",
+       "  sc_2025_20_5  16.51\n",
+       "        survey  17.44"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.options.display.float_format = '{:0,.2f}'.format\n",
+    "\n",
+    "df = pd.read_csv(os.path.join(relative_path,'agg_measures.csv'))\n",
+    "# Save results by source as seperate df\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    \n",
+    "_df = df[df['description'] == 'VMT per Person'][['source','value']]\n",
+    "_df.index =['','']    # hide index\n",
+    "display(_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trips per Person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>sc_2025_20_5</td>\n",
+       "      <td>3.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>survey</td>\n",
+       "      <td>4.54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        source  value\n",
+       "  sc_2025_20_5   3.89\n",
+       "        survey   4.54"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_df = df[df['description'] == 'Average Trips per Person'][['source','value']].reset_index(drop=True)\n",
+    "_df.index = [\"\" for i in xrange(len(_df))]    # hide index\n",
+    "display(_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Average Trip Length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'trips.csv'))\n",
+    "# Save results by source as seperate df\n",
+    "df_dict = {}\n",
+    "for source in df.groupby('source').count().index.tolist():\n",
+    "    df_dict[source] = df[df['source'] == source]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>survey</td>\n",
+       "      <td>5.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>sc_2025_20_5</td>\n",
+       "      <td>6.06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        source  value\n",
+       "        survey   5.87\n",
+       "  sc_2025_20_5   6.06"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfplot = pd.DataFrame([(df_dict[i]['travdist']*df_dict[i]['trexpfac']).sum()/df_dict[i]['trexpfac'].sum() for i in df_dict.keys()])\n",
+    "dfplot.columns = ['value']\n",
+    "dfplot['source'] = [i for i in df_dict.keys()]\n",
+    "dfplot.index = [\"\" for i in xrange(len(dfplot))]    # hide index\n",
+    "display(dfplot[['source','value']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trip Mode Share"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>survey</th>\n",
+       "      <th>sc_2025_20_5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bike</th>\n",
+       "      <td>1.4%</td>\n",
+       "      <td>1.9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV2</th>\n",
+       "      <td>22.2%</td>\n",
+       "      <td>21.6%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HOV3+</th>\n",
+       "      <td>16.0%</td>\n",
+       "      <td>14.8%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SOV</th>\n",
+       "      <td>40.6%</td>\n",
+       "      <td>38.3%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>School Bus</th>\n",
+       "      <td>2.2%</td>\n",
+       "      <td>2.1%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Transit</th>\n",
+       "      <td>4.4%</td>\n",
+       "      <td>4.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Walk</th>\n",
+       "      <td>12.7%</td>\n",
+       "      <td>17.3%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            survey  sc_2025_20_5\n",
+       "Bike          1.4%          1.9%\n",
+       "HOV2         22.2%         21.6%\n",
+       "HOV3+        16.0%         14.8%\n",
+       "SOV          40.6%         38.3%\n",
+       "School Bus    2.2%          2.1%\n",
+       "Transit       4.4%          4.0%\n",
+       "Walk         12.7%         17.3%"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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QcPY4BCxJkqROYwCUJEnKGKeBkSSpRER0aNRPamAAlCSpBHj/nzqTQ8CSJEkZ4xXAnDGn\njil0EzqsqqKK6lnVhW6GJEkqcgbAnKGnDy10EzqsK18JJEmSeg6HgCVJkjLGAChJkpQxBkBJkqSM\n6fYAGBG7RcTC3OfViHg5t/xkRHTqPYkRcWlEfCq3/PWIqOzM+iVJkkpRtz8EklJaCRwBEBEXA2tT\nSlfXfx8RvVJKWzrpWBfnrZ4HzAY2dEbdkiRJpaoYngKOiPgZ8L/A4cCDEfEL4BqggrrA9uWU0rKI\nmAicCFQC7wduSylNjYhewE+Bo4AE/DSldE2u3juAQbnPfRHxZkrp093ZQUmSpGJSDAEQ6kLbIOBj\nKaUUEQOAT6SUtkTEZ4DLgZNz+x5GXVB8F1gaEdcCewGDUkqHAkTEwLx6U0rp2og4HxiVUlrVfd2S\nJEkqPsUSAAF+lba956YK+HlE7E9diMtv570ppbUAEbEEGAIsAYZHxA+B3wDzu6/ZkiRJpaWYAuA7\necuXURf0/jEihgI1ed9tzFveAvROKb0dEYcBfw9MAb4EnN2eg8+eMKdhuayynLLKiva1vgiUbe7D\nmDGTt9tWVQXV1dcXqEWSJKmjampqqKmp6dQ6iykA5hsIvJJb/nIr+0ZE7AZsSinNjYhlwM+b2G9t\nrt4mh4DPOGXNjra1qNXWTm59J0mSVLRGjRrFqFGjGtYvvfTSDtdZTPMAprzlq4ArIuJJoFfed6nR\nfvXb9qHuAY+F1D3pe2ET9d8AzIuIezu11ZIkSSUmtt12l10RkSZN6pm/Q23tZObNcwhYkqSeIiJI\nKUVH6iimK4CSJEnqBgZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScqYYp0Iutv11AmTq6oK\n3QJJklRsnAeQunkA/R0kSVIpcB5ASZIktZsBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIy\nxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIyxgAoSZKU\nMQZASZKkjDEASpIkZUzvQjegWIw5dUyXH6OqoorqWdVdfhxJkqSWGABzhp4+tMuPUXtzbZcfQ5Ik\nqTUOAUuSJGWMAVCSJCljDICSJEkZYwCUJEnKmFYDYERMj4hnI+KpiFgYESNa2HdiRFzbGQ2LiBUR\nsWsz25/OteXpiDixM44nSZKUFS0+BRwRHwOOB45IKW3KBbLyFoqkTmxbc3UlYFRKaVVEHADMB37d\niceVJEnq0Vq7Avg+4G8ppU0AKaVVKaVXASLi7yLioYhYFBF/iIj+uTKDIuLuiFgWEVfWVxQR43JX\n7J6JiO+2tr0VkftzZ2BVrp5hEfFMXr0XRMTFueVzI2Jx7irmnDYeQ5IkqUdqbR7A+cC3ImIp8Dvg\nFyml+yOiD1ANfCml9EQu/G2gLpgdnvu8CyyNiB9Sd9Xuu8CRwNvA/Ij4PPDHpranlP6nhTYFcF9E\nBDAcOKWZ/RLbriJOBYblrmIObKXPkiRJPVqLATCltD4ijgI+AXwS+EVETAOeBF5NKT2R228dQEQk\n4N6U0trc+hJgGLA7UJNSWpnbfgtwLHUBrantLQXA/CHg4cC9EXFIM/vWXyl8Grg1Im4Hbm9qx9kT\ntl0YLKssp6yyooUm7JiyzX0YM2YyVVVQXX19p9cvSZJ6npqaGmpqajq1zlbfBJJS2gr8Hvh9boh1\nAvBEC0U25i1vyR2j8f18QdOiiX1battLEfE6cDDwGtsPaVfmLR9PXbD8HDA9Ig5NKW3Jr+uMU9a0\n9bAdVls7uduOJUmSStuoUaMYNWpUw/qll17a4TpbvAcwIg6IiA/kbToCWAEsBfaOiKNz+w2IiF40\nHewS8BjwfyJit9x+Y4GaZrb/vg3tjtxx9wT2A2qB14E9I2LXiCgHTgBSbqh4SEqpBphG3X2D/dpw\nDEmSpB6ptSuA/YFrI6IK2Ay8AEzK3Ut3au67SuAd4LNsf99dg5TSa7mh4/uoC293ppTuAGhue1P1\n5LkvIrYAZcDUlNKbubq+TV2o/CuwJLdvL2B2ROycO8Y1KaXuu9wnSZJUZCKlzpy5pTRFRJo0qft+\nh9raycyb5z2AkiSp/SKClFJzt9O1iW8CkSRJyhgDoCRJUsYYACVJkjLGAChJkpQxBkBJkqSMaXUi\n6KzozsmZq6q67VCSJEnv4TQw1E0D4+8gSZJKgdPASJIkqd0MgJIkSRljAJQkScoYA6AkSVLGGAAl\nSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLGGAAlSZIyxgAo\nSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRnTu9ANKBZjTh1T6CZIktSjVVVUUT2rutDNEAbA\nBkNPH1roJkiS1KPV3lxb6CYoxyFgSZKkjDEASpIkZYwBUJIkKWMMgJIkSRlTMgEwIqZHxLMR8VRE\nLIyIERHRJyJmRsQLEbEsIm6PiH1y+y+PiAMa1TEzIv6lMD2QJEkqDiURACPiY8DxwBEppcOATwN/\nAS4H+gEHpJQOAG4H5uaKVQNj8+rYCTgJmNONTZckSSo6JREAgfcBf0spbQJIKa0CVgMTgW+klFJu\n+8+AjRHxKeqC3ql5dRwL1KaU/tKN7ZYkSSo6pRIA5wODI2JpRPw4Io4F9gf+nFJa12jfx4FDUkrP\nAlsj4sO57WOBW7uvyZIkScWpJCaCTimtj4ijgE8AnwR+Qd3wb7NFcn/OAcZGxGLg88CM5grMnrBt\nZLisspyyyoqONluSJOUp29yHMWMmd9vxqqqguvr6bjteV6mpqaGmpqZT64zc6GlJiYiTgCnA4cB+\n+VcBI+L3wCUppfsiYjh1Vw/PAc5PKf19M/WlSZNK73eQJEnNq62dzLx5pR8AG4sIUkrRkTpKYgg4\nIg6IiA/kbToCeA74OXB17gEPIuJMoDKldB9ASukl4G/Ad3H4V5IkCSiRIWCgP3BtRFQBm4EXgEnA\nOuB7wLKI2EpdKPzHRmXnAFew7elgSZKkTCuJAJhSehIY2czX5+Y+zZW9BrimK9olSZJUikpiCFiS\nJEmdxwAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWNK4ing7lBb230zk0uSpK5XVVXoFhSvknwTSGeL\niOTvIEmSSkFm3gQiSZKkzmMAlCRJyhgDoCRJUsYYACVJkjLGAChJkpQxBkBJkqSMMQBKkiRljAFQ\nkiQpYwyAkiRJGWMAlCRJyhgDoCRJUsYYACVJkjLGAChJkpQxBkBJkqSMMQBKkiRljAFQkiQpYwyA\nkiRJGWMAlCRJypjehW5AsRhz6phCN0HqdFUVVVTPqi50MyRJRcYAmDP09KGFboLU6Wpvri10EyRJ\nRcghYEmSpIwxAEqSJGWMAVCSJCljDICSJEkZ060BMCLWNVqfGBHX5q1Piojncp9HI2JkbvtNETGp\nUdkvRMRdEVEeEY9FxKKIWBIRV3RPbyRJkkpTd18BTM2tR8QJwCRgZErpIGAKcGtE7AXcCoxtVHYs\ncGtKaSMwKqV0OPBh4JMRcUzjA0fEik7rhSRJUgkr9BBw5C1PBS5IKa0CSCktBGYB5wD3AgdGxPsA\nIqIf8Gng9ty+7+Tq6AP0AlY1cazG4VOSJCmTujsAVkbEwvoPcCnbgtnBwBON9n8cOCSltBX4b+BL\nue2fA+5LKa0DiIidImIR8Hpu+5Ku7ogkSVKp6u6JoDeklI6oX4mICcDRLeyff4VwDvA94IfUDf/O\nqv8iFxAPj4idgd9GxKiUUk1ETAdOzu02KBc6AR5MKX0t/0CzJ8xpWC6rLKessqLdnZOKTdnmPowZ\nM3mHylZVQXX19Z3cIklSe9XU1FBTU9OpdUZK3TcyGhFrU0oD8tYnAkellL4WEQ8A30op3Zf3/beB\nlFK6OCICeAn4AjAfGJxSereJY8ygLmh+r9H2P6WU9mumXWnSJEeIpXy1tZOZN88AKEnFJiJIKUXr\nezav0PcA5rsKuDIidgWIiMOBCcBPoC4FAr+g7srfXfXhLyJ2j4iq3HIl8Flg4XurlyRJEnT/EHBT\nTwEngJTSHRGxD/BwRCRgDTA+pfR63v5zgG8C/5K3bW9gVkTsRF2gnZ1SurcNx5YkScqkbg2AKaWB\njdZnsf29fNcB17VQ/inqnvLN3/YMcGQbjj28ve2VJEnqiYppCFiSJEndwAAoSZKUMQZASZKkjDEA\nSpIkZYwBUJIkKWO6exqYolVbu2NvS5B6qqqqQrdAktRVuvVNIMUqIpK/gyRJKgU97U0gkiRJ6gYG\nQEmSpIwxAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMsYAKEmSlDEGQEmSpIwx\nAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMsYAKEmSlDEGQEmSpIzpXegGFIsx\np44pdBMklYiqiiqqZ1UXuhmStMMMgDlDTx9a6CZIKhG1N9cWugmS1CEOAUuSJGWMAVCSJCljDICS\nJEkZU1QBMCLWNVqfGBHX5q1Piojncp9HI2JkbvtNETGpUdkvRMRdEbFvRNwXEYsj4tmIOLd7eiNJ\nklSciioAAqm59Yg4AZgEjEwpHQRMAW6NiL2AW4GxjcqOzW3fDHwjpXQI8FHgnIg4qIvaL0mSVPSK\nLQA2FnnLU4ELUkqrAFJKC4FZwDnAvcCBEfE+gIjoB3wauD2l9FpKaVGuzDrgOWBQ93VBkiSpuBRb\nAKyMiIX1H+BStl0FPBh4otH+jwOHpJS2Av8NfCm3/XPAfbnA1yAihgFHAI92TfMlSZKKX7EFwA0p\npSPqP8C32P4qYGP5381h2zDw2Nz6th0j+gP/BZzXOBhKkiRlSbFPBJ0f8JYARwP35W07Cng2t/wI\nsHdEHAZ8jG1XA4mIMuquEN6cUrq9qQPNnrAtL5ZVllNWWdEZ7ZfUA5Vt7sOYMZOb/K6qCqqrr+/m\nFknqyWpqaqipqenUOiOlxs9dFE5ErE0pDchbnwgclVL6WkR8DpgBjEkprYqIw4H/AUaklF7P7f9d\nYAywMKX05dy2oO5ewZUppW80c9w0aVLx/A6SSldt7WTmzTMASuo6EUFKqaUR0lYV2xXApp4CTgAp\npTsiYh/g4YhIwBpgfH34y5kDfBP4l7xtI4HTgadz9xUCXJhSmtcVHZAkSSp2RRUAU0oDG63Pou7q\nXf36dcB1LZR/CujVaNuDFN+9jpIkSQVjMJIkScoYA6AkSVLGGAAlSZIyxgAoSZKUMQZASZKkjDEA\nSpIkZUxRTQNTSLW1Tc/qL0ntUVVV6BZIUuuK6k0ghRIRyd9BkiSVgs54E4hDwJIkSRljAJQkScoY\nA6AkSVLGGAAlSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScoYA6AkSVLG\nGAAlSZIyxgAoSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgJIkSRljAJQkScqY3oVuQLEYc+qYLqu7\nqqKK6lnVXVa/JElSexgAc4aePrTL6q69ubbL6pYkSWovh4AlSZIyxgAoSZKUMQZASZKkjCmZABgR\nWyJiYUQsiognIuJjue2DIuJXueWJEXFtYVsqSZJU3ErpIZB3UkpHAETEaOAKYFRK6RXglNw+qVCN\nkyRJKhUlcwWwkZ2BVQARMSwinsltj/odIuL4iHg4InaLiNG55Sci4pcR0a8QjZYkSSoGpXQFsDIi\nFgIVwN7Ap5rbMSL+EfgGcBxQBkwHPp1S2hARU4Hzgcu6vsmSJEnFp5QC4Ia8IeCPAj8HPtTEfp8C\njgY+m1JaFxEnAAcDD0cEQB/g4e5psiRJUvEppQDYIKX0h4jYPSJ2b/wVsBzYD/gg8ERu+z0ppdNa\nqnP2hDkNy2WV5ZRVVrSrTWW94bDDDmnyu6qKqnbVJUmSVK+mpoaamppOrTNSKo3nJiJibUppQG75\nQOABYE9gKHBHSunQiJgIHAX8CJhL3cMhbwKPA59KKS3P3f83KKX0Ql7dadKkjv0OtbWTmTfv+g7V\nIUmS1JqIIKUUre/ZvFK6Alh/DyDUPexxZkop5YZ169NbAlJKaWlEjAd+BZwATATmRER5br/pQEMA\nlCRJypLU9AanAAAFDUlEQVSSCYAppSbbmlJaAXw4tzwLmJVbXgTUj8n+CRjR9a2UJEkqfqU6DYwk\nSZJ2kAFQkiQpYwyAkiRJGWMAlCRJyhgDoCRJUsYYACVJkjKmZKaB6Wq1tZM7VL7Kl31IkqQSUTJv\nAulKEZH8HSRJUinojDeBOAQsSZKUMQZASZKkjDEASpIkZYwBUJIkKWMMgBlWU1NT6CYUhP3OFvud\nLfY7W7La785gAMywrP7Fsd/ZYr+zxX5nS1b73RkMgJIkSRljAJQkScoYJ4KmbiLoQrdBkiSprTo6\nEbQBUJIkKWMcApYkScoYA6AkSVLG9PgAGBFjIuL5iHghIqY2s88Pc98/FRFHtKdssepgv1dExNMR\nsTAiHuu+Vndca/2OiAMj4pGI+N+I+H/tKVvMOtjvnny+x+f++X46Ih6KiA+3tWwx62C/e/L5/nyu\n3wsj4omI+FRbyxazDva7x57vvP3+LiI2R8RJ7S1bjDrY77af75RSj/0AvYAXgWFAGbAIOKjRPv8A\n3JVb/gjwh7aWLdZPR/qdW/8TsGuh+9FF/d4DOBr4DvD/2lO2WD8d6XcGzvfHgJ1zy2My9Pe7yX5n\n4Hz3y1s+FHgxI+e7yX739POdt98C4E7gpCyc7+b63d7z3dOvAI6g7i/CipTSJqAa+HyjfU4EZgGk\nlB4FqiLifW0sW6x2tN975X3foaeLCqTVfqeU3kwpPQ5sam/ZItaRftfrqef7kZTS6tzqo8C+bS1b\nxDrS73o99Xyvz1vtD/ytrWWLWEf6Xa9Hnu+crwH/Bby5A2WLUUf6Xa9N57unB8B9gL/krb+c29aW\nfQa1oWyx6ki/ARLwu4h4PCK+0mWt7Hxt6XdXlC20jrY9K+f7bOCuHSxbTDrSb+jh5zsivhARzwF3\nA+e2p2yR6ki/oQef74jYh7pw9O+5TfXTmvTo891Cv+uX23S+e3e8rUWtrXPclOL/HbWko/0+JqX0\nSkTsAdwTEc+nlB7opLZ1pY7MaVTK8yF1tO0jU0qv9uTzHRGfBM4CRra3bBHqSL+hh5/vlNLtwO0R\n8QlgdkQc2LXN6nI71G/gg7mvevL5nglMSymliAi2/Tetp//9bq7f0I7z3dMD4F+BwXnrg6lL0y3t\ns29un7I2lC1WO9rvvwKklF7J/flmRNxG3SXpUvgXRlv63RVlC61DbU8pvZr7s0ee79wDEP8BjEkp\nvdWeskWqI/3u8ee7XkrpgYjoDeya269Hn+969f2OiN1SSit7+Pk+Cqiuy0DsDhwXEZvaWLZY7XC/\nU0q/btf5LvQNj135oS7gLqfuZso+tP4wxEfZdpN4q2WL9dPBfvcFBuSW+wEPAaML3afO6nfevpew\n/UMgPfp8t9DvHn2+gSHU3VD90R39zYrt08F+9/Tz/X62veDgSGB5Rs53c/3u0ee70f43AV/Mwvlu\nod/tOt89+gpgSmlzRPxf4LfUPTHz05TScxExOff99SmluyLiHyLiRWA98OWWyhamJ+3TkX4D7wPm\n5v7PojdwS0ppfvf3ov3a0u/cAz5/BAYCWyPiPODglNK6nny+m+s3sCc9+HwD3wJ2Af4918dNKaUR\nPf3vN830mx7+9xs4CTgzdxVoHTC2pbKF6Ed7daTf9Pzz3a6y3dHujupIv2nn+fZVcJIkSRnT058C\nliRJUiMGQEmSpIwxAEqSJGWMAVCSJCljDICSJEkZYwCUJEnKGAOgJElSxhgAJUmSMub/A/iw2rpB\nDx5EAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x93dd470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.options.display.float_format = '{:,.1%}'.format\n",
+    "dfplot = pd.DataFrame([df_dict[i].groupby('mode').sum()['trexpfac']/df_dict[i].sum()['trexpfac'] for i in df_dict.keys()]).T\n",
+    "dfplot = dfplot.drop('Other', axis=0)\n",
+    "dfplot.columns = df_dict.keys()\n",
+    "dfplot.plot(kind='barh', alpha=0.6, figsize=(10,5))\n",
+    "display(dfplot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/scripts/summarize/notebooks/transit.ipynb b/scripts/summarize/notebooks/transit.ipynb
new file mode 100644
index 00000000..13ee1016
--- /dev/null
+++ b/scripts/summarize/notebooks/transit.ipynb
@@ -0,0 +1,2413 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Relative path between notebooks and goruped output directories\n",
+    "relative_path = '../../../outputs/grouped'\n",
+    "\n",
+    "# Load lookup tables\n",
+    "transit_route_lookup = pd.read_csv('../inputs/network_summary/transit_route_groups.csv')\n",
+    "transit_time_lookup = pd.read_csv('../inputs/network_summary/transit_time_groups.csv')\n",
+    "special_routes = pd.read_csv('../inputs/network_summary/transit_special_routes.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(relative_path,'transit_boardings.csv'))\n",
+    "df_dict = {}\n",
+    "source_list = df.groupby('source').count().index.tolist()\n",
+    "for source in source_list:\n",
+    "    df_dict[source] = df[df['source'] == source]\n",
+    "    df_dict[source]['route_id'] = df_dict[source]['route_id'].astype('int')\n",
+    "    \n",
+    "    # separate transit agency ID from the route code\n",
+    "    df_dict[source]['agency_id'] = df_dict[source]['route_id'].apply(lambda x: str(x)[0]).astype('int')\n",
+    "    \n",
+    "    df_dict[source] = pd.merge(df_dict[source], transit_route_lookup, left_on='agency_id', right_on='RouteGroupID')\n",
+    "    df_dict[source] = pd.merge(df_dict[source], transit_time_lookup, left_on='tod', right_on='tod')\n",
+    "    df_dict[source] = pd.merge(df_dict[source], special_routes, left_on='route_id', right_on='route_code', how='left',\n",
+    "                              suffixes=['_standard','_special'])\n",
+    "    \n",
+    "tod_lookup = {  '20to5':'ni',\n",
+    "                '5to6':'ni',\n",
+    "                '6to7':'am',\n",
+    "                '7to8':'am',\n",
+    "                '8to9':'am',\n",
+    "                '9to10':'md',\n",
+    "                '10to14':'md',\n",
+    "                '14to15':'md',\n",
+    "                '15to16':'pm',\n",
+    "                '16to17':'pm',\n",
+    "                '17to18':'pm',\n",
+    "                '18to20':'ev'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pd.options.display.float_format = '{:0,.2f}'.format"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# All Routes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Axes(0.125,0.125;0.775x0.775)\n",
+      "Axes(0.125,0.125;0.775x0.775)\n"
+     ]
+    },
+    {
+     "data": {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1176be10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "ga9K+qmkYzLPvVnSiyVNlSdUuzWthHtqOr+XpG8lDd/F02u3XCQyCIqgdAtKBkHRpOGks/DFDCfg\n",
+       "y+7/B/B8PDHTTyVdAZwr6SvA6Wb2gnRtT7rNZ4FFZvZfko7Ac1PUjccLgJPMbLOk9wJvAC5Mi/sd\n",
+       "ZGY/l3QJcKOZvT2tL3arpBuAc4ANZnaMpPoyMdFTCUpD9FSCYCQnAd81s6fNbCO+ttOfAA+Z2U9T\n",
+       "na+nek8Cz0i6QtIZ+Lp04IsMfk7SHfgipvukRRINX3trc6p3Nb6gKbhxqS9PPgdYkK6/CV+r7gg8\n",
+       "c+fXAcxsFb7mWBCUhuipBMFIjJFDSs2+C+HpKAYlHQ+8CjcO70r7wjOLbsnexBd5ZdOzNzV7WNLj\n",
+       "qdfxBuCdmeqvN7P7W1wfw11BaYmeShCM5CfA6cl/MRVfHfsnwBGSTkx1/hL4STq/n5n9EM8B/wfp\n",
+       "/DKgr35DSX/A9vlXPFd4j5ndmcqWNl1/XNr9cXo2kl4I/P4uf8og6ABhVIKgCfMMiUuAFXhahC8B\n",
+       "/XjKgr+XdDe+1PnlQA9wraRf4IZnXrpNH/CS5Ly/i+E9kGYfyLfx3DpXZ8ouAiZL+qWkO4GPpPLL\n",
+       "gWlJw0fwHD1BUBpi6fsgCIIgN6KnEgRBEORGGJUgCIIgN8KoBEEQBLkRRiUIgiDIjTAqQRAEQW6E\n",
+       "UQmCIAhyI4xKEARBkBthVIIgCILc+P8OeIyG7SOu/AAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfb1bc88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    print df_dict[source].plot(kind='scatter', x='observed',y='model', title=source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Total Boardings by Agency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Axes(0.125,0.125;0.775x0.775)\n",
+      "Axes(0.125,0.125;0.775x0.775)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": [
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+      "text/plain": [
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+     "metadata": {},
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+       "qPkqcBtprOOBjqsWpj8DfCq3bftG27Fq8o0EzdaTpOOAPSLipGbLmvUXPrIwW38Nf3mb9Wc+sjDr\n",
+       "wyR9B3h/h+J/i4hZZbTH+i8HCzMza8ppKDMza8rBwszMmnKwMDOzphwszMysqf8Pna59D8JXjroA\n",
+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12954b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    print df_dict[source].groupby('RouteGroupName_standard').sum()[['observed','model']].plot(kind='bar', title=source)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                         observed        model  % difference\n",
+      "RouteGroupName_standard                                     \n",
+      "CT                         105558   180,321.81          0.71\n",
+      "Kitsap                       8178     8,193.67          0.00\n",
+      "Metro                     1445250 1,130,052.35         -0.22\n",
+      "Pierce                     168581   203,227.55          0.21\n",
+      "Sound Transit              334863   265,944.66         -0.21\n",
+      "--------------------------------------------------\n",
+      "                         observed        model  % difference\n",
+      "RouteGroupName_standard                                     \n",
+      "CT                         105558   138,765.45          0.31\n",
+      "Kitsap                       8178     9,371.71          0.15\n",
+      "Metro                     1445250 1,357,826.54         -0.06\n",
+      "Pierce                     168581   172,333.10          0.02\n",
+      "Sound Transit              334863   319,355.66         -0.05\n",
+      "--------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    df = df_dict[source].groupby('RouteGroupName_standard').sum()[['observed','model']]\n",
+    "    df['% difference'] = (df['model']-df['observed'])/df['observed']\n",
+    "    print df \n",
+    "    print \"-\"*50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boardings Peak/Off-Peak"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### By Agency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                          model  observed  % difference\n",
+      "RouteGroupName_standard BigTimeGroup                                   \n",
+      "CT                      Off-Peak     142,198.48     74462          0.91\n",
+      "                        Peak          38,123.34     31096          0.23\n",
+      "Kitsap                  Off-Peak       7,000.40      6843          0.02\n",
+      "                        Peak           1,193.27      1335         -0.11\n",
+      "Metro                   Off-Peak     948,725.73   1102534         -0.14\n",
+      "                        Peak         181,326.62    342716         -0.47\n",
+      "Pierce                  Off-Peak     166,628.67    140231          0.19\n",
+      "                        Peak          36,598.87     28350          0.29\n",
+      "Sound Transit           Off-Peak     187,383.23    224238         -0.16\n",
+      "                        Peak          78,561.43    110625         -0.29\n",
+      "---------------------------------------------------------------------------\n",
+      "                                          model  observed  % difference\n",
+      "RouteGroupName_standard BigTimeGroup                                   \n",
+      "CT                      Off-Peak      97,020.36     74462          0.30\n",
+      "                        Peak          41,745.09     31096          0.34\n",
+      "Kitsap                  Off-Peak       7,081.37      6843          0.03\n",
+      "                        Peak           2,290.34      1335          0.72\n",
+      "Metro                   Off-Peak     956,316.73   1102534         -0.13\n",
+      "                        Peak         401,509.81    342716          0.17\n",
+      "Pierce                  Off-Peak     127,294.68    140231         -0.09\n",
+      "                        Peak          45,038.42     28350          0.59\n",
+      "Sound Transit           Off-Peak     164,571.25    224238         -0.27\n",
+      "                        Peak         154,784.41    110625          0.40\n",
+      "---------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    df = df_dict[source].groupby(['RouteGroupName_standard','BigTimeGroup']).sum()[['model','observed']]\n",
+    "    df['% difference'] = (df['model']-df['observed'])/df['observed']\n",
+    "    print df\n",
+    "    print \"-\"*75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Axes(0.125,0.125;0.775x0.775)\n",
+      "Axes(0.125,0.125;0.775x0.775)\n"
+     ]
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xfb08b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "a8lq9bc2vjQ3/VHgwym29Zvtx8rJNw6zypM0Ddg+Ij4y1LpmVeEzd+sHTc9szarMZ+5mXSTpeOC1\n",
+       "DYu/ExGnFRGPVZeTu5lZBbksY2ZWQU7uZmYV5ORuZlZBTu5mZhX0/2JKqZR3wyifAAAAAElFTkSu\n",
+       "QmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11541160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    print df.unstack()['% difference'].plot(kind='bar', title=source)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### By Select Routes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                              model  observed  % difference\n",
+      "RouteGroupName_special      BigTimeGroup                                   \n",
+      "RapidRideA                  Off-Peak      74,458.01     94152         -0.21\n",
+      "                            Peak          14,942.52     17544         -0.15\n",
+      "RapidRideB                  Off-Peak      11,471.29     17114         -0.33\n",
+      "                            Peak           2,492.32      5192         -0.52\n",
+      "RapidRideC                  Off-Peak     107,473.36    122208         -0.12\n",
+      "                            Peak          15,194.58     38640         -0.61\n",
+      "RapidRideD                  Off-Peak      66,518.00    109304         -0.39\n",
+      "                            Peak           9,406.41     34392         -0.73\n",
+      "RapidRideE                  Off-Peak      35,793.44     36930         -0.03\n",
+      "                            Peak           6,223.60     10612         -0.41\n",
+      "ST Commuter Rail North      Off-Peak       1,178.57      6896         -0.83\n",
+      "                            Peak          10,046.06     36160         -0.72\n",
+      "ST Commuter Rail South      Off-Peak       3,535.71      1068          2.31\n",
+      "                            Peak          30,138.18     11796          1.55\n",
+      "ST Light Rail - Tacoma      Off-Peak      11,531.41     11620         -0.01\n",
+      "                            Peak           2,173.18      2406         -0.10\n",
+      "ST Light Rail -Central Link Off-Peak     111,182.33    170264         -0.35\n",
+      "                            Peak          24,414.12     44276         -0.45\n",
+      "---------------------------------------------------------------------------\n",
+      "                                              model  observed  % difference\n",
+      "RouteGroupName_special      BigTimeGroup                                   \n",
+      "RapidRideA                  Off-Peak      65,122.43     94152         -0.31\n",
+      "                            Peak          24,634.79     17544          0.40\n",
+      "RapidRideB                  Off-Peak      18,406.03     17114          0.08\n",
+      "                            Peak           8,800.25      5192          0.69\n",
+      "RapidRideC                  Off-Peak     111,468.41    122208         -0.09\n",
+      "                            Peak          34,732.14     38640         -0.10\n",
+      "RapidRideD                  Off-Peak      76,809.07    109304         -0.30\n",
+      "                            Peak          27,545.78     34392         -0.20\n",
+      "RapidRideE                  Off-Peak      38,543.45     36930          0.04\n",
+      "                            Peak          14,720.90     10612          0.39\n",
+      "ST Commuter Rail North      Off-Peak       2,454.18      6896         -0.64\n",
+      "                            Peak          25,495.73     36160         -0.29\n",
+      "ST Commuter Rail South      Off-Peak       6,906.53      1068          5.47\n",
+      "                            Peak          70,615.36     11796          4.99\n",
+      "ST Light Rail - Tacoma      Off-Peak      10,597.19     11620         -0.09\n",
+      "                            Peak           2,701.38      2406          0.12\n",
+      "ST Light Rail -Central Link Off-Peak     102,792.77    170264         -0.40\n",
+      "                            Peak          38,364.79     44276         -0.13\n",
+      "---------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    df = df_dict[source].groupby(['RouteGroupName_special','BigTimeGroup']).sum()[['model','observed']]\n",
+    "    df['% difference'] = (df['model']-df['observed'])/df['observed']\n",
+    "    print df\n",
+    "    print \"-\"*75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Axes(0.125,0.125;0.775x0.775)\n",
+      "Axes(0.125,0.125;0.775x0.775)\n"
+     ]
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11541160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "/gS1+9Z6+Hhj2pvYPrzxt030vnnAW/Lf9OG2vykMqWi0AwC27yftfR4C3APcKemp+el9gPn59gJg\n",
+       "u3x7r8Yk7ibtMbeckqcHgKTW0ExfAfZV6rehZVU6lDps/30ZcgA8W9JauYa7J/CbtuneCRxFarhb\n",
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+       "3/8YSesAvyKVftbOj6/VIfNqwC25fPOqxt/azd8UKhXlkbC4sbR9iaRrgZcCryGVSlYB/gjsl1/2\n",
+       "aeAoSW8ATmq8/3Tg4Pw1/mPAR4DPS7qMtHPwJ2AP27dKehnpIONjgNuAv5IOZC6VaRlyQGrsjgHW\n",
+       "A77b6Lyn+ZrPkA6KtswFjpZ0J6lB3KDxnub72jcutv1vSXsBX8wlneWBzwFXsbRZwHfz6wR8wfbf\n",
+       "JZlUNjkdeDjwv7ZvITW6mwJn59LLPcCrbF8l6RBST3MPkso//9WW8YPAuaTley6pEe/0N4UhEh1G\n",
+       "hRlF0r7AtrbfXjpLL5TOF7/X9mdKZwl1i/JImGmGeS9yWHOHAYo97RD6RNKXgZ3bHv687SNK5Akz\n",
+       "QzTaIYQwRKI8EkIIQyQa7RBCGCLRaIcQwhCJRjuEEIZINNohhDBE/j8QnckG9yYljwAAAABJRU5E\n",
+       "rkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x103f77f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for source in source_list:\n",
+    "    print df.unstack()['% difference'].plot(kind='bar', title=source)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/scripts/summarize/notebooks/trip.ipynb b/scripts/summarize/notebooks/trip.ipynb
deleted file mode 100644
index 6309d0dc..00000000
--- a/scripts/summarize/notebooks/trip.ipynb
+++ /dev/null
@@ -1,1388 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "## Results from Daysim Trip Demand"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<script src=\"//code.highcharts.com/stock/highstock.js\"></script>\n",
-       "<script src=\"//code.highcharts.com/highcharts-more.js\"></script>\n",
-       "<script src=\"//code.highcharts.com/modules/exporting.js\"></script>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import h5py\n",
-    "import pylab as P\n",
-    "from IPython.display import display, display_pretty, Javascript, HTML\n",
-    "from pandas_highcharts.core import serialize\n",
-    "from pandas_highcharts.display import display_charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Show charts in notebook\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>\n",
-       "code_show=true; \n",
-       "function code_toggle() {\n",
-       " if (code_show){\n",
-       " $('div.input').hide();\n",
-       " } else {\n",
-       " $('div.input').show();\n",
-       " }\n",
-       " code_show = !code_show\n",
-       "} \n",
-       "$( document ).ready(code_toggle);\n",
-       "</script>\n",
-       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "\n",
-    "HTML('''<script>\n",
-    "code_show=true; \n",
-    "function code_toggle() {\n",
-    " if (code_show){\n",
-    " $('div.input').hide();\n",
-    " } else {\n",
-    " $('div.input').show();\n",
-    " }\n",
-    " code_show = !code_show\n",
-    "} \n",
-    "$( document ).ready(code_toggle);\n",
-    "</script>\n",
-    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set main model directory to parent directory\n",
-    "model_dir = os.path.dirname(os.getcwd())\n",
-    "base_dir = r'R:\\SoundCast\\Inputs\\2010\\etc'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Model Scenario Results\n",
-    "scen = h5py.File(model_dir + r'/outputs/daysim_outputs.h5','r+')\n",
-    "scen_name = 'Model: 2040'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Read Base Data\n",
-    "base_file = '/daysim_outputs_seed_trips.h5'\n",
-    "\n",
-    "base = h5py.File(base_dir + base_file ,'r+')\n",
-    "base_name = '2010 Base'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "container = 'Trip'\n",
-    "fieldname = 'Trip Purpose'\n",
-    "fieldvar = 'dpurp'\n",
-    "labels = {\n",
-    "  0: \"None/Home\",  \n",
-    "  1: \"Work\",\n",
-    "  2: \"School\",\n",
-    "  3: \"Escort\",\n",
-    "  4: \"Personal Business\",\n",
-    "  5: \"Shopping\",\n",
-    "  6: \"Meal\",\n",
-    "  7: \"Social\",\n",
-    "  8: \"Recreational\",\n",
-    "  9: \"Medical\",\n",
-    "  10: \"Change mode\"\n",
-    "}\n",
-    "df = pd.DataFrame(np.asarray(base[container][fieldvar]), columns=[fieldvar])\n",
-    "df[fieldname] = [labels[x] for x in df[fieldvar].as_matrix()]\n",
-    "df_base = df.groupby(fieldname).count()[fieldvar]    # Sum by category\n",
-    "df_base = df_base/df_base.sum()     # Convert totals to shares\n",
-    "\n",
-    "# Sum by worker type for scenario\n",
-    "df = pd.DataFrame(np.asarray(scen[container][fieldvar]), columns=[fieldvar])\n",
-    "df[fieldname] = [labels[x] for x in df[fieldvar].as_matrix()]\n",
-    "df_scen = df.groupby(fieldname).count()[fieldvar]    # Sum by category\n",
-    "df_scen = df_scen/df_scen.sum()     # Convert totals to shares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "trip_p_df = pd.DataFrame([df_scen, df_base]).T\n",
-    "trip_p_df.columns = [ 'Model', 'Base 2010']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Trip Purpose"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div id=\"chart_idipvKTUQE\"</div>\n",
-       "    <script type=\"text/javascript\">new Highcharts.Chart({\"title\":{\"text\":\"Trip Rates by Purpose\"},\"series\":[{\"data\":[[\"Change mode\",0.0093718191],[\"Escort\",0.0992980817],[\"Meal\",0.0763836],[\"None\\/Home\",0.3413676986],[\"Personal Business\",0.144931157],[\"School\",0.0520938028],[\"Shopping\",0.0544915243],[\"Social\",0.086149598],[\"Work\",0.1359127185]],\"name\":\"Model\",\"yAxis\":0},{\"data\":[[\"Change mode\",0.0051642432],[\"Escort\",0.1162315815],[\"Meal\",0.0439198079],[\"None\\/Home\",0.3472099647],[\"Personal Business\",0.1426298976],[\"School\",0.0631386849],[\"Shopping\",0.0524979739],[\"Social\",0.0833326739],[\"Work\",0.1458751725]],\"name\":\"Base 2010\",\"yAxis\":0}],\"yAxis\":[{}],\"chart\":{\"renderTo\":\"chart_idipvKTUQE\",\"type\":\"column\"},\"xAxis\":{\"categories\":[\"Change mode\",\"Escort\",\"Meal\",\"None\\/Home\",\"Personal Business\",\"School\",\"Shopping\",\"Social\",\"Work\"],\"title\":{\"text\":\"Trip Purpose\"}},\"legend\":{\"enabled\":true}});</script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display_charts(trip_p_df, title='Trip Rates by Purpose', kind='bar')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "trip_p_df.to_clipboard()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Mode Choice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Travel Cost by Mode\n",
-    "trip_scen = pd.DataFrame(data={'Household ID': scen['Trip']['hhno'][:],\n",
-    "                                'Travel Time': scen['Trip']['travtime'][:],\n",
-    "                                'Travel Cost': scen['Trip']['travcost'][:],\n",
-    "                                'Travel Distance': scen['Trip']['travdist'][:],\n",
-    "                                'Mode': scen['Trip']['mode'][:],\n",
-    "                                'Purpose': scen['Trip']['dpurp'][:]})\n",
-    "\n",
-    "trip_base = pd.DataFrame(data={'Household ID': base['Trip']['hhno'][:],\n",
-    "                                'Travel Time': base['Trip']['travtime'][:],\n",
-    "                                'Travel Cost': base['Trip']['travcost'][:],\n",
-    "                                'Travel Distance': base['Trip']['travdist'][:],\n",
-    "                                'Mode': base['Trip']['mode'][:],\n",
-    "                                'Purpose': base['Trip']['dpurp'][:]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# pd.pivot_table(data=trip_scen, index='Mode', columns='Purpose', aggfunc='count')['Household ID']\n",
-    "trips_by_mode = trip_scen.groupby('Mode').count()[['Household ID']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "avg_dist_by_mode = trip_scen.groupby('Mode').mean()[['Travel Time']]\n",
-    "avg_dist_by_mode.index = ['Walk', 'Bike', 'SOV', 'HOV2', 'HOV3+', 'Transit', 'School Bus']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div id=\"chart_idWphFRwLu\"</div>\n",
-       "    <script type=\"text/javascript\">new Highcharts.Chart({\"title\":{\"text\":\"Average Travel Time by Mode\"},\"series\":[{\"data\":[[\"Walk\",22.9892346485],[\"Bike\",15.6657646471],[\"SOV\",17.5541061295],[\"HOV2\",14.5857743592],[\"HOV3+\",14.1486075941],[\"Transit\",37.1360340271],[\"School Bus\",12.394218989]],\"name\":\"Travel Time\",\"yAxis\":0}],\"yAxis\":[{\"max\":30,\"min\":0}],\"chart\":{\"renderTo\":\"chart_idWphFRwLu\",\"type\":\"column\"},\"xAxis\":{\"categories\":[\"Walk\",\"Bike\",\"SOV\",\"HOV2\",\"HOV3+\",\"Transit\",\"School Bus\"]},\"legend\":{\"enabled\":true}});</script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display_charts(avg_dist_by_mode, kind='bar', title='Average Travel Time by Mode', ylim=(0, 30))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26861540374013.0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trip_scen.sum()['Household ID']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Trips by Purpose"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(data=[trip_scen.groupby('Purpose').count()['Household ID']/trip_scen.count()['Household ID'], \n",
-    "                        trip_base.groupby('Purpose').count()['Household ID']/trip_base.count()['Household ID']]).T\n",
-    "df.columns = ['scen', 'base']\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div id=\"chart_idwHXGoBpn\"</div>\n",
-       "    <script type=\"text/javascript\">new Highcharts.Chart({\"title\":{\"text\":\"Travel Time by Mode\"},\"series\":[{\"data\":[[0,0.3413676986],[1,0.1359127185],[2,0.0520938028],[3,0.0992980817],[4,0.144931157],[5,0.0544915243],[6,0.0763836],[7,0.086149598],[10,0.0093718191]],\"name\":\"scen\",\"yAxis\":0},{\"data\":[[0,0.3472099647],[1,0.1458751725],[2,0.0631386849],[3,0.1162315815],[4,0.1426298976],[5,0.0524979739],[6,0.0439198079],[7,0.0833326739],[10,0.0051642432]],\"name\":\"base\",\"yAxis\":0}],\"yAxis\":[{\"tickPositions\":false}],\"chart\":{\"renderTo\":\"chart_idwHXGoBpn\",\"type\":\"column\"},\"xAxis\":{\"title\":{\"text\":\"Purpose\"}},\"legend\":{\"enabled\":true}});</script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display_charts(df, kind='bar', title='Travel Time by Mode', yticks=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Trip Length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "varname = 'travdist'\n",
-    "\n",
-    "triplen_base = np.asarray(base['Trip'][varname])\n",
-    "triplen_scen = np.asarray(scen['Trip'][varname])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xd043860>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "H/w7pgT4pCUJUy6sRmGMMWZY1pltjDFmWJYojDHGDMsShTHGmGFZojDGGDMsSxTGGGOGZYnCGGPM\n",
-       "sP4/Utfg5JI9Ar0AAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd043828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bins = 1000\n",
-    "\n",
-    "P.hist(triplen_scen, bins=bins, normed=True, histtype='step', color='b', label=scen_name)\n",
-    "P.hist(triplen_base, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=base_name)\n",
-    "P.xlim([0,40])    # Cutting the tail at 40 to focus on shorter trip distribution\n",
-    "P.xlabel('Trip Length')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Travel Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "varname = 'travtime'\n",
-    "\n",
-    "triptime_base = np.asarray(base['Trip'][varname])\n",
-    "triptime_scen = np.asarray(scen['Trip'][varname])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xe58ee10>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "5/FHFR0MHETMftzS+zTHiKvYvS99HiT1lDSiBfdwrs14zcK5hlnOY8t6PB94ntjBfYWZbaVxjd2n\n",
-       "oWO7Xmi2Mu0Uv0tS13T314GZ+bwA59qSD511rgV8OKzrqLwZyjnnXLO8ZuGcc65ZXrNwzjnXLC8s\n",
-       "nHPONcsLC+ecc83ywsI551yzvLBwzjnXLC8snHPONev/A9s3/z1uwCCzAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xd2291d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bins = 1000\n",
-    "\n",
-    "P.hist(triptime_scen, bins=bins, normed=True, histtype='step', color='b', label=scen_name)\n",
-    "P.hist(triptime_base, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=base_name)\n",
-    "P.xlim([0,40])    # Cutting the tail at 40 to focus on shorter trip distribution\n",
-    "P.xlabel('Trip Time')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Value of Time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "varname = 'vot'\n",
-    "\n",
-    "vot_base = np.asarray(base['Trip'][varname])\n",
-    "vot_scen = np.asarray(scen['Trip'][varname])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0xe5ddda0>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-       "OZfV/wcgMejapLGIPgAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0xe548dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bins = 1000\n",
-    "\n",
-    "P.hist(vot_scen, bins=bins, normed=True, histtype='step', color='b', label=scen_name)\n",
-    "P.hist(vot_base, bins=bins, normed=True, histtype='step', color='grey', alpha=0.3, label=base_name)\n",
-    "P.xlim([0,70])    # Cutting the tail at 70 to focus on shorter trip distribution\n",
-    "P.xlabel('Value of Time')\n",
-    "P.ylabel('Distribution')\n",
-    "P.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/notebooks/trucks.ipynb b/scripts/summarize/notebooks/trucks.ipynb
deleted file mode 100644
index 9e2a3e8a..00000000
--- a/scripts/summarize/notebooks/trucks.ipynb
+++ /dev/null
@@ -1,1006 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from os.path import basename\n",
-    "import pandas as pd\n",
-    "from pandas import *\n",
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "#  Read in the csv files for Parcel ID and Taz and Parcel ID and Use Type for join\n",
-    "parcels = pd.read_csv(r'D:\\soundcast\\soundcast\\inputs\\buffered_parcels.dat', sep=' ')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Ideally, we attach a land use code to the urbansim input and filter directly from there\n",
-    "# For now, we need to join in parcel land use info\n",
-    "parcel_lu = pd.read_csv(r'R:\\Craig\\Trucks\\inputs\\TripGen\\Base\\parcels\\parcels_allowable_lu.txt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "df = parcels.merge(parcel_lu, left_on='parcelid',right_on='parcel_id')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# List of allowable truck land uses\n",
-    "truck_uses = ['Agriculture','Fisheries','Forest, harvestable','Forest, protected','Industrial','Military','Mining','Warehousing']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# select truck rows only, using the allowable truck land uses\n",
-    "truck_df = df[df[\"generic_land_use_1\"].isin(truck_uses)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# For future reference, lookup all the use_codes for these generic_land_uses\n",
-    "# return list of tuples that includes the generic name and all the use codes\n",
-    "truck_use_codes = np.array(truck_df.groupby(['generic_land_use_1','use_code']).count().index)    # returns list of tuples\n",
-    "truck_use_codes = [i[1] for i in truck_use_codes]    # extract only the use_code field, drop the generic_land_use_1 data\n",
-    "\n",
-    "# in future, probably want to use the use_code field to select truck rows like this:\n",
-    "# df[df['use_code'].isin(truck_use_codes)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Brice\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  from ipykernel import kernelapp as app\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Add a flag for truck allowable field\n",
-    "truck_df['trucks_allowed_parcel'] = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# merge the truck_df back into the main df\n",
-    "df = df.merge(truck_df[['parcelid','trucks_allowed_parcel']], how='left')\n",
-    "df['trucks_allowed_parcel'].fillna(0,inplace=True)    # Truck restricted parcels get a 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Now, groupby TAZ and create new flag that allows trucks on TAZ with allowable land use\n",
-    "df_taz = pd.DataFrame(df.groupby('taz_p').sum()[['trucks_allowed_parcel']])\n",
-    "df_taz['trucks_allowed_taz'] = pd.cut(df_taz['trucks_allowed_parcel'], bins=[0,1,df_taz['trucks_allowed_parcel'].max()], labels=[0,1], include_lowest=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1860"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_taz['trucks_allowed_taz'].astype('int').sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import h5py\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# look at the output for midday\n",
-    "\n",
-    "newtruck = h5py.File(r'D:\\soundcast\\soundcast\\inputs\\4k\\auto.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "oldtruck = h5py.File(r'R:\\SoundCast\\releases\\TransportationFutures2010\\inputs\\4k\\auto.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Get results by regional growth center\n",
-    "taz_lookup = pd.read_csv(r'R:\\SoundCast\\releases\\TransportationFutures2010\\scripts\\summarize\\TAZ_TAD_County.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "h5file = newtruck"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def prod_results(h5file):    \n",
-    "    production_total = {}\n",
-    "    for trk in ['lttrk', 'mdtrk','hvtrk']:\n",
-    "        df = 0\n",
-    "        for tod in ['am','md','pm','ev','ni']:\n",
-    "            df += pd.DataFrame(h5file[tod][tod[0]+trk][:])\n",
-    "        df = pd.DataFrame([df.sum(axis=0),df.index]).T\n",
-    "        df.columns = [trk,'TAZ']\n",
-    "        df['TAZ'] = [i+1 for i in df['TAZ']]    # offset TAZ by 1 to get true value\n",
-    "\n",
-    "        # Join to geographic data\n",
-    "        df = df.merge(taz_lookup, on='TAZ', how='left')\n",
-    "\n",
-    "        production_total[trk] = df\n",
-    "    return production_total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "def attr_results(h5file):\n",
-    "    attraction_total = {}\n",
-    "    for trk in ['lttrk', 'mdtrk','hvtrk']:\n",
-    "        df = 0\n",
-    "        for tod in ['am','md','pm','ev','ni']:\n",
-    "            df += pd.DataFrame(h5file[tod][tod[0]+trk][:])\n",
-    "        df = pd.DataFrame([df.sum(axis=1),df.index]).T\n",
-    "        df.columns = [trk,'TAZ']\n",
-    "        df['TAZ'] = [i+1 for i in df['TAZ']]    # offset TAZ by 1 to get true value\n",
-    "\n",
-    "        # Join to geographic data\n",
-    "        df = df.merge(taz_lookup, on='TAZ', how='left')\n",
-    "\n",
-    "        attraction_total[trk] = df\n",
-    "    return attraction_total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Format output\n",
-    "pd.options.display.float_format = '{:,.0f}'.format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "truck_list = ['lttrk', 'mdtrk','hvtrk']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def dict_to_df(dict_in, geography):\n",
-    "    df = pd.DataFrame([dict_in[trk].groupby(geography).sum()[trk] for trk in truck_list])\n",
-    "    df.loc['Total'] = df.sum()\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def compare_df(df1,df1_name,df2,df2_name):\n",
-    "    '''Compare 2 identical dataframes'''\n",
-    "    df = pd.concat([df1,df2], axis=1)[df1.columns]\n",
-    "    df.columns = pd.MultiIndex.from_product([np.array(df1.columns),[df1_name,df2_name]])\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare results of the old with the new!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "old_prod = prod_results(oldtruck)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "old_attr = attr_results(oldtruck)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "new_prod = prod_results(newtruck)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "new_attr = attr_results(newtruck)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "old_pro_sum = dict_to_df(old_prod, 'County')\n",
-    "new_pro_sum = dict_to_df(new_prod, 'County')\n",
-    "compare_df(old_pro_sum,'Old 2010',new_pro_sum,'New 2010').to_clipboard()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "tod = 'am'\n",
-    "trk = 'hvtrk'\n",
-    "df = pd.DataFrame(h5file[tod][tod[0]+trk][:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21927.413814836123"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.sum().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>hvtrk</th>\n",
-       "      <th>TAZ</th>\n",
-       "      <th>TAD</th>\n",
-       "      <th>OldDistric</th>\n",
-       "      <th>County</th>\n",
-       "      <th>District</th>\n",
-       "      <th>New DistrictName</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <td>nan</td>\n",
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-       "    <tr>\n",
-       "      <th>3711</th>\n",
-       "      <td>5,657</td>\n",
-       "      <td>3,712</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
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-       "    <tr>\n",
-       "      <th>3712</th>\n",
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-       "      <td>3,713</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3713</th>\n",
-       "      <td>   64</td>\n",
-       "      <td>3,714</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3714</th>\n",
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-       "      <td>3,715</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3715</th>\n",
-       "      <td>   55</td>\n",
-       "      <td>3,716</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3716</th>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3717</th>\n",
-       "      <td>  201</td>\n",
-       "      <td>3,718</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3718</th>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3721</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,722</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3722</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,723</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3723</th>\n",
-       "      <td>    0</td>\n",
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-       "      <td>nan</td>\n",
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-       "      <th>3724</th>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3726</th>\n",
-       "      <td>    0</td>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3728</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,729</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3729</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,730</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3730</th>\n",
-       "      <td>    0</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3731</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,732</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3732</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,733</td>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3733</th>\n",
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-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3734</th>\n",
-       "      <td>    0</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "    <tr>\n",
-       "      <th>3735</th>\n",
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-       "      <td>nan</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
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-       "      <th>3736</th>\n",
-       "      <td>    0</td>\n",
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-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3737</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,738</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3738</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,739</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3739</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,740</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3740</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,741</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3741</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,742</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3742</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,743</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3743</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,744</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3744</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,745</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3745</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,746</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3746</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,747</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3747</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,748</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3748</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,749</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3749</th>\n",
-       "      <td>    0</td>\n",
-       "      <td>3,750</td>\n",
-       "      <td>nan</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "      <td>nan</td>\n",
-       "      <td> NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      hvtrk   TAZ  TAD  OldDistric County  District New DistrictName\n",
-       "3710      0 3,711  nan         nan    NaN       nan              NaN\n",
-       "3711  5,657 3,712  nan         nan    NaN       nan              NaN\n",
-       "3712      0 3,713  nan         nan    NaN       nan              NaN\n",
-       "3713     64 3,714  nan         nan    NaN       nan              NaN\n",
-       "3714      0 3,715  nan         nan    NaN       nan              NaN\n",
-       "3715     55 3,716  nan         nan    NaN       nan              NaN\n",
-       "3716      0 3,717  nan         nan    NaN       nan              NaN\n",
-       "3717    201 3,718  nan         nan    NaN       nan              NaN\n",
-       "3718      0 3,719  nan         nan    NaN       nan              NaN\n",
-       "3719      0 3,720  nan         nan    NaN       nan              NaN\n",
-       "3720      0 3,721  nan         nan    NaN       nan              NaN\n",
-       "3721      0 3,722  nan         nan    NaN       nan              NaN\n",
-       "3722      0 3,723  nan         nan    NaN       nan              NaN\n",
-       "3723      0 3,724  nan         nan    NaN       nan              NaN\n",
-       "3724      0 3,725  nan         nan    NaN       nan              NaN\n",
-       "3725      0 3,726  nan         nan    NaN       nan              NaN\n",
-       "3726      0 3,727  nan         nan    NaN       nan              NaN\n",
-       "3727      0 3,728  nan         nan    NaN       nan              NaN\n",
-       "3728      0 3,729  nan         nan    NaN       nan              NaN\n",
-       "3729      0 3,730  nan         nan    NaN       nan              NaN\n",
-       "3730      0 3,731  nan         nan    NaN       nan              NaN\n",
-       "3731      0 3,732  nan         nan    NaN       nan              NaN\n",
-       "3732      0 3,733  nan         nan    NaN       nan              NaN\n",
-       "3733      0 3,734  nan         nan    NaN       nan              NaN\n",
-       "3734      0 3,735  nan         nan    NaN       nan              NaN\n",
-       "3735      0 3,736  nan         nan    NaN       nan              NaN\n",
-       "3736      0 3,737  nan         nan    NaN       nan              NaN\n",
-       "3737      0 3,738  nan         nan    NaN       nan              NaN\n",
-       "3738      0 3,739  nan         nan    NaN       nan              NaN\n",
-       "3739      0 3,740  nan         nan    NaN       nan              NaN\n",
-       "3740      0 3,741  nan         nan    NaN       nan              NaN\n",
-       "3741      0 3,742  nan         nan    NaN       nan              NaN\n",
-       "3742      0 3,743  nan         nan    NaN       nan              NaN\n",
-       "3743      0 3,744  nan         nan    NaN       nan              NaN\n",
-       "3744      0 3,745  nan         nan    NaN       nan              NaN\n",
-       "3745      0 3,746  nan         nan    NaN       nan              NaN\n",
-       "3746      0 3,747  nan         nan    NaN       nan              NaN\n",
-       "3747      0 3,748  nan         nan    NaN       nan              NaN\n",
-       "3748      0 3,749  nan         nan    NaN       nan              NaN\n",
-       "3749      0 3,750  nan         nan    NaN       nan              NaN"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get external totals\n",
-    "low_stat = 3733\n",
-    "high_stat = 3750\n",
-    "\n",
-    "new_prod['hvtrk'].iloc[3710:high_stat]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/scripts/summarize/standard/RegionalCenterSummaries.py b/scripts/summarize/standard/RegionalCenterSummaries.py
index 2afc61a0..4a412d1c 100644
--- a/scripts/summarize/standard/RegionalCenterSummaries.py
+++ b/scripts/summarize/standard/RegionalCenterSummaries.py
@@ -5,8 +5,9 @@
 import xlautofit
 import math
 import xlrd
-import sys
+import os, sys
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 
 def get_total(exp_fac):
     total = exp_fac.sum()
diff --git a/scripts/summarize/standard/air_quality.py b/scripts/summarize/standard/air_quality.py
new file mode 100644
index 00000000..a2b729bf
--- /dev/null
+++ b/scripts/summarize/standard/air_quality.py
@@ -0,0 +1,237 @@
+#Copyright [2014] [Puget Sound Regional Council]
+
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+
+#    http://www.apache.org/licenses/LICENSE-2.0
+
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import os
+import sys
+CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+#CURRENT_DIR = r'D:\stefan\soundcast_2014_aq'
+sys.path.append(os.path.dirname(CURRENT_DIR))
+import array as _array
+import inro.emme.desktop.app as app
+import inro.modeller as _m
+import inro.emme.matrix as ematrix
+import inro.emme.database.matrix
+import inro.emme.database.emmebank as _eb
+import json
+import numpy as np
+import time
+import h5py
+import Tkinter, tkFileDialog
+import multiprocessing as mp
+import subprocess
+import csv
+import xlsxwriter
+#import xlautofit
+import sqlite3 as lite
+from datetime import datetime
+import os
+sys.path.append(CURRENT_DIR)
+sys.path.append(os.path.join(os.getcwd(),"inputs"))
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+from EmmeProject import *
+from multiprocessing import Pool
+import pandas as pd
+import collections
+from standard_summary_configuration import *
+from input_configuration import *
+from emme_configuration import *
+#os.chdir(r'D:\stefan\soundcast_2014_aq')
+
+
+#Intrazonals
+#Need to get intrazonal distances
+my_project = EmmeProject(project)
+my_project.change_active_database('7to8')
+np_iz_dist = my_project.bank.matrix('izdist').get_numpy_data().diagonal()
+
+
+writer = pd.ExcelWriter('outputs/aq_' + model_year + '.xlsx', engine='xlsxwriter')
+
+county_flag_df = pd.read_csv('scripts/summarize/inputs/network_summary/county_flags_' + model_year + '.csv', dtype = {'inode' : str, 'jnode' : str })
+county_flag_df['ij'] = county_flag_df.inode + '-' + county_flag_df.jnode
+county_taz_df = pd.read_csv('scripts/summarize/inputs/county_taz.csv')
+
+emission_rates_df = pd.read_csv(r'scripts\summarize\inputs\network_summary\emission_rates_' + str(model_year) + '.csv')
+
+
+zones = my_project.current_scenario.zone_numbers
+dictZoneLookup = dict((index,value) for index,value in enumerate(zones))
+
+tod_lookup = {'5to6' : 5, '6to7' : 6, '7to8' : 7, '8to9' : 8, '9to10' : 9, '10to14' : 10, 
+                     '14to15' : 14, '15to16' : 15, '16to17' : 16, '17to18' : 17, '18to20' : 18, '20to5' : 20}
+
+speed_bins = [-999999, 2.5, 7.5, 12.5, 17.5, 22.5, 27.5, 32.5, 37.5, 42.5, 47.5, 52.5, 57.5, 62.5, 67.5, 72.5, 999999] 
+speed_bins_labels =  range(1, len(speed_bins))
+
+fac_type_lookup = {1:4, 2:4, 3:5, 4:5, 5:5, 6:3, 7:5, 8:0}
+
+months_recode = {0:1, 1:7}
+
+pollutant_type_list = [1,2,3,5,6,79,87,90,91,98,100,106,107,110,112,115,116,117,118,119]
+pollutant_type_lookup = dict(zip(range(0, len(pollutant_type_list)), pollutant_type_list))
+
+
+
+def get_intrazonal_vol(emme_project, trip_table_name):
+    vot_list = [1,2,3]
+    matrix_dict = {}
+    np_iz_volume = emme_project.bank.matrix(trip_table_name + '1').get_numpy_data() + emme_project.bank.matrix(trip_table_name + '2').get_numpy_data() + emme_project.bank.matrix(trip_table_name + '3').get_numpy_data()
+    return np_iz_volume.diagonal()
+
+
+df_dict = {}
+for key, value in sound_cast_net_dict.iteritems():
+    my_project.change_active_database(key)
+    #Intrazonal volume and vmt:
+    taz_index = np.asarray(dictZoneLookup.values())
+    sov_iz_vol = get_intrazonal_vol(my_project, 'svtl')
+    sov_iz_vmt = sov_iz_vol * np_iz_dist
+    hov2_iz_vol = get_intrazonal_vol(my_project, 'h2tl')
+    hov2_iz_vmt = hov2_iz_vol * np_iz_dist
+    hov3_iz_vol = get_intrazonal_vol(my_project, 'h3tl')
+    hov3_iz_vmt = hov3_iz_vol * np_iz_dist
+    med_truck_vol = my_project.bank.matrix('metrk').get_numpy_data().diagonal()
+    med_truck_vmt = med_truck_vol* np_iz_dist
+    hvy_truck_vol = my_project.bank.matrix('hvtrk').get_numpy_data().diagonal()
+    hvy_truck_vmt = hvy_truck_vol * np_iz_dist
+
+    data = collections.OrderedDict()
+    data['taz']=taz_index
+    data['sov_iz_vol'] = sov_iz_vol
+    data['sov_iz_vmt'] = sov_iz_vmt
+    data['hov2_iz_vol'] = hov2_iz_vol
+    data['hov2_iz_vmt'] = hov2_iz_vmt
+    data['hov3_iz_vol'] = hov3_iz_vol
+    data['hov3_iz_vmt'] = hov3_iz_vmt
+    data['med_truck_vol'] = med_truck_vol
+    data['med_truck_iz_vmt'] = med_truck_vmt
+    data['hvy_truck_iz_vol'] = hvy_truck_vol
+    data['hvy_truck_iz_vmt'] = hvy_truck_vmt
+
+    iz_df = pd.DataFrame(data)
+    merged = county_taz_df.merge(iz_df, left_on = 'taz', right_on = 'taz', how = 'left')
+    sum_counties_df = pd.DataFrame(merged.groupby('geog_name').sum())
+    sum_counties_df.drop(['taz', 'lat_1', 'lon_1'], axis=1, inplace=True)
+
+    # write out intrazonal data 
+    sum_counties_df.to_excel(excel_writer=writer, sheet_name=my_project.tod + '_intrazonal')
+    
+    # now get each link, for each time period
+    network = my_project.current_scenario.get_network()
+    records = []
+    for link in network.links():
+        if link.data3 <> 0:
+            sov = link['@svtl1'] + link['@svtl2'] + link['@svtl3']
+            hov2 = link['@h2tl1'] + link['@h2tl2'] + link['@h2tl3']
+            hov3 = link['@h3tl1'] + link['@h3tl2'] + link['@h3tl3']
+            sov_vmt = sov * link.length
+            hov2_vmt = hov2 * link.length
+            hov3_vmt = hov3 * link.length
+            if my_project.tod in transit_tod.keys():
+                bus_vol = link['@bveh']
+                bus_vmt = link['@bveh'] * link.length
+            else:
+                bus_vol = 0
+                bus_vmt = 0
+
+            med_truck_vmt = link['@mveh'] * link.length
+            hev_truck_vmt = link['@hveh'] * link.length
+            row = collections.OrderedDict()
+            row['inode'] = link.i_node.id
+            row['jnode'] = link.j_node.id
+            row['ij'] = link.i_node.id + '-' + link.j_node.id
+            row['length'] = link.length
+            row['posted_speed'] = link.data2
+            row['congested_speed'] = (link.length/link.auto_time) * 60
+            row['facility_type'] = link.data3
+            row['total_volume'] = link['@tveh']
+            row['sov_vol'] = sov
+            row['hov2_vol'] = hov2
+            row['hov3_vol'] = hov3
+            row['bus_vol'] = bus_vol
+            row['med_truck_vol'] = link['@mveh']
+            row['hev_truck_vol'] = link['@hveh']
+            row['sov_vmt'] = sov_vmt
+            row['hov2_vmt'] = hov2_vmt
+            row['hov3_vmt'] = hov3_vmt
+            row['bus_vmt'] = bus_vmt
+            row['med_truck_vmt'] = med_truck_vmt
+            row['hev_truck_vmt'] = hev_truck_vmt
+
+            records.append(row)
+
+    df = pd.DataFrame(records, columns = row.keys())
+    df = df.merge(county_flag_df, how = 'left', on = 'ij')
+    df['countyId'] = df.flag 
+    df.drop(['inode_y', 'jnode_y'], axis=1, inplace=True)
+    df['sound_cast_tod'] = key
+    df['hourId'] = tod_lookup[key]
+    df_dict[key] = df
+
+# merge all the link data into one table
+df = pd.concat(df_dict.values(), axis = 0, ignore_index=True)
+
+
+#******Now create attributes to join emissions data******
+
+# get model year
+df['yearid'] = model_year
+
+# recode speed into moves bins
+df['avgspeedbinId'] = pd.cut(df['congested_speed'], speed_bins, labels=speed_bins_labels)
+df['roadtypeId'] = df["facility_type"].map(fac_type_lookup)
+
+# print out min and max values for each speed bin to show if recode is working correctly
+print df.groupby(df.avgspeedbinId)['congested_speed'].min()
+print df.groupby(df.avgspeedbinId)['congested_speed'].max()
+
+# replicate each row for each month (1 & 7)
+df = pd.concat([df]*2)
+df['monthId']= df.groupby(['ij', 'hourId']).cumcount()
+df['monthId'] = df['monthId'].map(months_recode)
+
+# replicate each link (row) for each pollutant type
+# dont want to overwrite df here since we need it later
+df1 = pd.concat([df]*len(pollutant_type_list))
+df1['pollutantId']= df1.groupby(['ij', 'monthId',  'hourId']).cumcount()
+df1['pollutantId'] = df1['pollutantId'].map(pollutant_type_lookup)
+
+# sort the df
+df1.sort_values(['ij', 'monthId',  'hourId'], inplace=True)
+
+# merge with emission rates table
+df1 = df1.merge(emission_rates_df, how = 'left', on = ['countyId','monthId', 'hourId', 'pollutantId', 'roadtypeId', 'avgspeedbinId'])
+df1 = df1[['ij', 'monthId',  'hourId', 'pollutantId', 'gramsPerMile']]
+df1.reset_index(level = None, inplace=True)
+
+# pivot the table so that emission factors are columns
+p = df1.pivot_table(index =['ij', 'monthId',  'hourId'], columns='pollutantId', values='gramsPerMile') 
+p = p.rename_axis(None, axis = 1)
+p.reset_index(inplace = True)
+
+# join back to df
+df = df.merge(p, how='left', on = ['ij', 'monthId',  'hourId'])
+
+# seperate by month
+df_jan = df[(df.monthId==1)]
+df_july = df[(df.monthId==7)]
+
+# wrute out to disk
+df_jan.to_csv('outputs/aq_' + model_year + '_jan.csv')
+df_july.to_csv('outputs/aq_' + model_year + '_july.csv')
+
+
+
+
+
diff --git a/scripts/summarize/standard/census_summary.py b/scripts/summarize/standard/census_summary.py
new file mode 100644
index 00000000..48919ae5
--- /dev/null
+++ b/scripts/summarize/standard/census_summary.py
@@ -0,0 +1,507 @@
+import os, sys, math, h5py
+import pandas as pd
+sys.path.append(os.getcwd())
+from standard_summary_configuration import *
+
+labels = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/variable_labels.csv'))
+districts = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/district_lookup.csv'))
+county_taz = pd.read_csv(r'scripts/summarize/inputs/county_taz.csv')
+rgc_taz = pd.read_csv(r'scripts/summarize/inputs/rgc_taz.csv')
+lu = pd.read_csv(r'inputs/accessibility/parcels_urbansim.txt', sep=' ')
+income_tiers = pd.read_csv(r'scripts/summarize/inputs/income_tiers.csv')
+
+# Census lookup
+census_parcel = pd.read_csv(r'R:\Brice\gis\parcels_urbansim_census.txt')
+
+table_list = ['Household','Trip','Tour','Person','HouseholdDay','PersonDay']
+
+overwrite = True
+
+output_csv_list = ['hh_rgc','trip_rgc_homeloc','trip_rgc_dest']
+
+def h5_to_df(h5file, table_list, name=False):
+    """
+    Load h5-formatted data based on a table list. Assumes heirarchy of a set of tables.
+    """
+    output_dict = {}
+    
+    for table in table_list:
+        df = pd.DataFrame()
+        for field in h5file[table].keys():
+            df[field] = h5file[table][field][:]
+            
+        output_dict[table] = df
+    
+    if name:
+        output_dict['name'] = name
+    
+    return output_dict
+
+def add_row(df, row_name, description, value):
+    df.ix[row_name,'description'] = description
+    df.ix[row_name,'value'] = value
+    
+    return df
+
+def apply_lables(h5data):
+    '''
+    Replace daysim formatted values with human readable lablels.
+    '''
+    for table in labels['table'].unique():
+        df = labels[labels['table'] == table]
+        for field in df['field'].unique():
+            newdf = df[df['field'] == field]
+            local_series = pd.Series(newdf['text'].values, index=newdf['value'])
+            h5data[table][field] = h5data[table][field].map(local_series)
+    
+    return h5data
+
+def hh(dataset, geog_file):
+	"""
+	geog_file: TAZ-based geography lookup, e.g., district, county, RGC
+	"""
+
+	hh = dataset['Household']
+
+	# Join RGC geography based on household TAZ location
+	hh = pd.merge(hh,geog_file,left_on='hhtaz', right_on='taz')
+
+	hh['income_bins'] = pd.cut(hh['hhincome'],bins=income_bins,labels=income_bin_labels)
+
+	agg_fields = ['geog_name','income_bins','hrestype']
+
+	# Sums
+	df = pd.DataFrame(hh.groupby(agg_fields).sum()['hhexpfac'])
+	# df.reset_index(inplace=True)
+
+	# average hhsize and hhvehs (weighted)
+	hh['hhsize_wt'] = hh['hhsize'].astype('float')*hh['hhexpfac']
+	hhsize_df = pd.DataFrame(hh.groupby(agg_fields).sum()['hhsize_wt']/hh.groupby(agg_fields).sum()['hhexpfac'])
+	hhsize_df.rename(columns={0:'avg_hhsize'},inplace=True)
+	df = df.join(hhsize_df)
+
+	hh['hhvehs_wt'] = hh['hhvehs'].astype('float')*hh['hhexpfac']
+	hhvehs_df = pd.DataFrame(hh.groupby(agg_fields).sum()['hhvehs_wt']/hh.groupby(agg_fields).sum()['hhexpfac'])
+	hhvehs_df.rename(columns={0:'avg_hhvehs'},inplace=True)
+	df = df.join(hhvehs_df)
+
+	df = df.fillna(0)
+	df = df.reset_index()
+
+	# Join with taz lookup to get lat and lon of desired geog_field
+	geog_lat_lon = geog_file.groupby('geog_name').min()[['lat_1','lon_1']]
+	geog_lat_lon.reset_index(inplace=True)
+	df = pd.merge(df, geog_lat_lon, on='geog_name', how='left')
+
+	df['source'] = dataset['name']
+
+	return df
+
+def trip(dataset, geog_file, geog_field, grouping, filter_parcels=None):
+	"""
+	geog_file: TAZ-based geography lookup, e.g., district, county, RGC
+	geog_field: join field, e.g., hhtaz to summarize trips for households in RGC or dtaz 
+	            for trips ending in RGC
+	"""
+
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+	    
+	# total trips
+	# join with person file and rgc based on destination
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	# Filter for special populations based on home parcel
+	if filter_parcels != None:
+		trip_person = trip_person[trip_person['hhparcel'].isin(filter_parcels)]
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Tours by person type, purpose, mode, and destination district
+	agg_fields = ['pptyp','dpurp','mode', 'geog_name']
+	trips_df = pd.DataFrame(trip_person.groupby(agg_fields)['trexpfac'].sum())
+
+	# average trip distance and time (weighted)
+	trip_person['travdist_wt'] = trip_person['travdist']*trip_person['trexpfac']
+	trip_person['travtime_wt'] = trip_person['travtime']*trip_person['trexpfac']
+	trip_person['travcost_wt'] = trip_person['travcost']*trip_person['trexpfac']
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay_wt'] = trip_person['delay']*trip_person['trexpfac']
+
+	travdist_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travdist_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	travdist_df.rename(columns={0:'travdist'},inplace=True)
+	trips_df = trips_df.join(travdist_df)
+
+	travtime_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travtime_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	travtime_df.rename(columns={0:'travtime'},inplace=True)
+	trips_df = trips_df.join(travtime_df)
+
+	travcost_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travcost_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	travcost_df.rename(columns={0:'travcost'},inplace=True)
+	trips_df = trips_df.join(travcost_df)	    
+
+	if 'sov_ff_time' in trip.columns:
+	    travtime_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['delay_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	    travtime_df.rename(columns={0:'delay'},inplace=True)
+	    trips_df = trips_df.join(travtime_df)
+	else:
+	    trips_df['delay'] = 0   
+
+	trips_df = trips_df.reset_index()
+
+	# add datasource and grouping fields
+	trips_df['source'] = dataset['name']
+	trips_df['grouping'] = grouping
+
+	# Attach geog lookup
+	geog_lat_lon = geog_file.groupby('geog_name').min()[['lat_1','lon_1']]
+	geog_lat_lon.reset_index(inplace=True)
+	df = pd.merge(trips_df, geog_lat_lon, on='geog_name', how='left')
+    
+	return trips_df
+
+def person(dataset, geog_file, geog_field):
+
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+
+	hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	hh['income_group'] = '0'
+	hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Don't double count household travcost - only sum travcost for drive trips
+	trip_person.ix[(trip_person.dorp != 1) & (trip_person['mode'].isin(['SOV','HOV2','HOV3+'])), 'travcost'] = 0
+
+	# Total travel time, cost, and delay per person
+	df = trip_person.groupby(['pno','hhno']).sum()[['travcost','delay','travtime']]
+	df = df.reset_index()
+
+	df = pd.merge(df, trip_person[['pno','hhno','geog_name','income_group','pptyp','hhincome']], on=['pno','hhno'], how='left')
+
+	# Results by geography
+	agg_fields = ['geog_name','income_group','pptyp']
+
+	# Sums
+	df = pd.DataFrame(df.groupby(agg_fields).mean()[['hhincome','travcost','travtime','delay']])
+
+	df['source'] = dataset['name']
+
+
+	return df
+
+def costs(dataset, geog_file, geog_field, grouping, filter_parcels=None):
+	"""
+	Calculate daily travel costs for each household
+	"""
+
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+	    
+	# total trips
+	# join with person file and rgc based on destination
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	# Filter for special populations based on home parcel
+	if filter_parcels != None:
+		trip_person = trip_person[trip_person['hhparcel'].isin(filter_parcels)]
+
+	# trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	# hh['income_group'] = pd.cut(hh['hhincome'],
+	#     bins=income_bins,
+	#     labels=income_bin_labels)
+
+	# Calculate low income as 200% of state povery level, which is based on hhsize
+	# hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	# hh['income_group'] = '0'
+	# hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	# hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Don't double count household travcost - only sum travcost for drive trips
+	trip_person.ix[(trip_person.dorp != 1) & (trip_person['mode'].isin(['SOV','HOV2','HOV3+'])), 'travcost'] = 0
+
+	# Total travcost for each household
+	tot_by_hh = pd.DataFrame(trip_person.groupby('hhno').sum()[['travcost','travtime','delay']])
+	tot_by_hh.reset_index(inplace=True)
+
+	df = pd.merge(tot_by_hh, hh[['hhno','hhincome','hhtaz']], on='hhno')
+
+	# calculate share of income spent on 
+	# total weekday household travel
+
+	# Replace income <= 1 with 1 to avoid negative and div/0
+	# df.ix[df['hhincome'] < 1, 'hhincome'] = 1
+
+	df['travcost_inc_share'] = (df['travcost']*262)/df['hhincome']
+
+	# Average of the daily total for each TAZ
+	df = df.groupby(['hhtaz']).mean()[['travcost_inc_share','hhincome','travcost','travtime','delay']]
+	df.reset_index(inplace=True)
+
+	# Attach geog lookup
+	# geog_lat_lon = geog_file.groupby('geog_name').min()
+	# geog_lat_lon.reset_index(inplace=True)
+	df = pd.merge(df, geog_file, left_on='hhtaz', right_on='taz', how='left')
+
+	df['source'] = dataset['name']
+	df['grouping'] = grouping
+
+	return df
+
+def income(dataset, geog_file, geog_field):
+	'''
+	income distribution by geography
+	'''
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+	    
+	# total trips
+	# join with person file and rgc based on destination
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	# Attach parking costs
+	# trip_person = pd.merge(trip_person, lu[['PARCEL_ID','PPRICHRP']], left_on='dpcl', right_on='PARCEL_ID', how='left')
+	# trip_person = pd.merge(trip_person, lu[['parcel_id','pprichrp']], left_on='dpcl', right_on='parcel_id', how='left')
+
+	# Calculate parking costs based on time at destination
+	# trip_person['dur'] = trip_person['endacttm']-trip_person['arrtm']
+	# trip_person['park_cost'] = trip_person['PPRICHRP']*(trip_person['dur']/60)    # for drive modes only!
+
+	income_bins = [-99999999]+[i*10000 for i in xrange(21)]+[999999999]
+	income_bin_labels = [str(i*10000) for i in xrange(21)]+['+']
+
+	hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	hh['income_group'] = '0'
+	hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Sum of travel costs by household
+	tot_by_hh = pd.DataFrame(trip_person.groupby('hhno').sum()[['travcost','travtime','delay']])
+	tot_by_hh.reset_index(inplace=True)
+
+	df = pd.merge(tot_by_hh, hh[['hhno','hhincome','income_group','hhtaz']], on='hhno')
+
+	# Replace income <= 1 with 1 to avoid negative and div/0
+	# df.ix[df['hhincome'] < 1, 'hhincome'] = 1
+
+	# df['travcost_inc_share'] = (df['travcost']*262)/df['hhincome']
+
+	# # Average 
+	# df = df.groupby(['hhtaz','income_group']).mean()[['travcost_inc_share','hhincome','travcost']]
+	# df.reset_index(inplace=True)
+
+	# # Attach geog lookup
+	# # geog_lat_lon = geog_file.groupby('geog_name').min()
+	# # geog_lat_lon.reset_index(inplace=True)
+	# df = pd.merge(df, geog_file, left_on='hhtaz', right_on='taz', how='left')
+
+	df['source'] = dataset['name']
+
+	return df
+
+def landuse(landuse_file, fname):
+
+	lu_df = pd.read_csv(landuse_file, sep=' ')
+
+	if 'taz_p' in lu_df.columns:
+
+		sums_df = lu_df.groupby('taz_p').sum()
+		sums_df = sums_df.drop(['ppricdyp','pprichrp','xcoord_p','ycoord_p'], axis=1)
+		sums_df.reset_index(inplace=True)
+
+		means_df = lu_df.groupby('taz_p').mean()[['ppricdyp','pprichrp']]
+		means_df.reset_index(inplace=True)
+
+	else:
+
+		sums_df = lu_df.groupby('TAZ_P').sum()
+		sums_df = sums_df.drop(['PPRICDYP','PPRICHRP','XCOORD_P','YCOORD_P'], axis=1)
+		sums_df.reset_index(inplace=True)
+
+		means_df = lu_df.groupby('TAZ_P').mean()[['PPRICDYP','PPRICHRP']]
+		means_df.reset_index(inplace=True)
+
+	df = pd.merge(sums_df, means_df)
+
+	df['source'] = fname
+
+	return df
+
+def grouping_parcels(group):
+
+	# Parcel to census lookup
+	df = pd.read_csv(r'R:\Brice\gis\parcels_urbansim_census.txt')
+
+	# add low income geography tag
+	low_inc = pd.read_excel(r'R:\Brice\gis\special-needs\ACS_15_5YR_Low-income.xlsx', sheetname='Map-income')
+	minority = pd.read_excel(r'R:\Brice\gis\special-needs\ACS_15_5YR_Minority.xlsx', sheetname='Mapping')
+
+	if group == 'low_income':
+		# Threshold for % of households as total for determining low income or not
+		income_threshold = 0.5
+
+		# Define low_inc tracts as those with more HH below 200% median income than those above it
+		low_inc['% low inc'] = low_inc['Below200']/low_inc['Total']
+
+		# Create flag for whether low income or not
+		low_inc.ix[low_inc['% low inc'] >= income_threshold,'low_inc_tract'] = 1
+		low_inc.ix[low_inc['% low inc'] < income_threshold,'low_inc_tract'] = 0
+
+		# Merge with parcel file
+		newdf = pd.merge(df, low_inc[['GEOID10','low_inc_tract']], on='GEOID10', how='left')
+		parcels_list = newdf[newdf['low_inc_tract'] == 1].parcelid.values
+
+	elif group == 'minority':
+		minority['% minority'] = minority['Minority']/minority['Total']
+		minority_threshold = 0.5
+		minority.ix[minority['% minority'] >= minority_threshold, 'minority_tract'] = 1
+		minority.ix[minority['% minority'] < minority_threshold, 'minority_tract'] = 0
+
+		# Merge with parcel file
+		newdf = pd.merge(df, minority[['GEOID10','minority_tract']], on='GEOID10', how='left')
+		parcels_list = newdf[newdf['minority_tract'] == 1].parcelid.values
+
+	return parcels_list
+
+def process_dataset(h5file, scenario_name):
+	# Load h5 data as dataframes
+    dataset = h5_to_df(h5file, table_list=['Household','Trip','Tour','Person','HouseholdDay','PersonDay'], name=scenario_name)
+
+    dataset = apply_lables(dataset)
+
+    # Compute outputs for region, low-income households, and minority zones
+
+    # get list of parcels in each grouping
+    low_income_parcels = grouping_parcels(group='low_income')
+    minority_parcels = grouping_parcels(group='minority')
+
+    # Trips by Mode and Purpose
+    trip_df = trip(dataset, geog_file=county_taz, geog_field='hhtaz', grouping='Low Income', filter_parcels=low_income_parcels)
+    write_csv(trip_df,fname='trip_county_homeloc.csv')
+
+    trip_df = trip(dataset, geog_file=county_taz, geog_field='hhtaz', grouping='Minority', filter_parcels=minority_parcels)
+    write_csv(trip_df,fname='trip_county_homeloc.csv')
+
+    trip_df = trip(dataset, geog_file=county_taz, geog_field='hhtaz', grouping='Region',)
+    write_csv(trip_df,fname='trip_county_homeloc.csv')
+    
+    # Costs
+    df = costs(dataset, geog_file=county_taz, geog_field='hhtaz', grouping='Low Income', filter_parcels=low_income_parcels)
+    write_csv(df, fname='costs.csv')
+
+    df = costs(dataset, geog_file=county_taz, geog_field='hhtaz', grouping='Minority', filter_parcels=minority_parcels)
+    write_csv(df, fname='costs.csv')
+
+    df = costs(dataset, geog_file=county_taz, geog_field='hhtaz', grouping='Region')
+    write_csv(df, fname='costs.csv')
+
+    # hh_county = hh(dataset, geog_file=county_taz)
+    # write_csv(hh_county,fname='hh_county.csv')
+
+    # hh_rgc = hh(dataset, geog_file=rgc_taz)
+    # write_csv(hh_rgc,fname='hh_rgc.csv')
+
+    # # Trips based on location in regional center
+    # trip_df = trip(dataset, geog_file=rgc_taz, geog_field='hhtaz')
+    # write_csv(trip_df,fname='trip_rgc_homeloc.csv')
+
+    # trip_df = trip(dataset, geog_file=county_taz, geog_field='hhtaz')
+    # write_csv(trip_df,fname='trip_county_homeloc.csv')
+
+    # # Trips based on destination location in regional center
+    # trip_df = trip(dataset, geog_file=rgc_taz, geog_field='dtaz')
+    # write_csv(trip_df,fname='trip_rgc_dest.csv')
+
+    # trip_df = trip(dataset, geog_file=county_taz, geog_field='dtaz')
+    # write_csv(trip_df,fname='trip_county_dest.csv')
+
+    # TAZ Costs
+    # df = costs(dataset, geog_file=county_taz, geog_field='hhtaz')
+    # write_csv(df, fname='costs.csv')
+
+    # df = income(dataset, geog_file=county_taz, geog_field='hhtaz')
+    # write_csv(df, fname='income.csv')
+
+    # Person totals
+    # df = person(dataset, geog_file=county_taz, geog_field='hhtaz')
+    # write_csv(df, fname='person_mean.csv')
+
+def write_csv(df,fname):
+    '''
+    Write dataframe to file; append existing file
+    '''
+#     df.to_csv(os.path.join(output_dir,fname),mode='a')
+    if not os.path.isfile(os.path.join(output_dir,fname)):
+
+        df.to_csv(os.path.join(output_dir,fname),index=False)
+    else: # append without writing the header
+        df.to_csv(os.path.join(output_dir,fname), mode ='a', header=False, index=False)
+
+if __name__ == '__main__':
+
+    # Use root directory name as run name
+    run_name = os.getcwd().split('\\')[-1]
+
+    output_dir = r'outputs/geog'
+
+    comparison_run_dir = r'p:\stefan\soundcast_new_lu_5_10_tolls'
+
+    # file_dict = {'2014': os.getcwd()}
+
+    file_dict = {
+        '2014': os.getcwd(), 
+        '2040_plan_10_5': os.path.join(comparison_run_dir),
+                    }
+
+    # Create output directory if it doesn't exist
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    if overwrite:
+	    for fname in os.listdir(output_dir):
+	        if os.path.isfile(os.path.join(output_dir,fname)):
+	            os.remove(os.path.join(output_dir,fname))
+
+	# Process all h5 files
+    for name, file_dir in file_dict.iteritems():
+
+		daysim_h5 = h5py.File(os.path.join(file_dir,r'outputs/daysim_outputs.h5'))
+
+		print 'processing h5: ' + name
+
+		process_dataset(h5file=daysim_h5, scenario_name=name)
+		del daysim_h5 # drop from memory to save space for next comparison
+
+		# df = landuse(landuse_file=os.path.join(file_dir,r'inputs/accessibility/parcels_urbansim.txt'), fname=name)
+		# write_csv(df, fname='landuse.csv')
\ No newline at end of file
diff --git a/scripts/summarize/standard/daily_bank.py b/scripts/summarize/standard/daily_bank.py
index bd6cb934..1e5a3e75 100644
--- a/scripts/summarize/standard/daily_bank.py
+++ b/scripts/summarize/standard/daily_bank.py
@@ -1,14 +1,15 @@
 import inro.emme.database.emmebank as _emmebank
-import os
+import os, sys
 import numpy as np
-from input_configuration import *
-from emme_configuration import *
 import json
 import shutil
 from distutils import dir_util
+sys.path.append(os.getcwd())
+from input_configuration import *
+from emme_configuration import *
 from scripts.EmmeProject import *
 
-daily_network_fname = 'outputs/daily_network_results.csv'
+daily_network_fname = 'outputs/network/daily_network_results.csv'
 keep_atts = ['@type']
 def json_to_dictionary(dict_name):
 
@@ -130,14 +131,12 @@ def main():
     for matrix in daily_emmebank.matrices():
         daily_arr = matrix.get_numpy_data()
         daily_matrix_dict[matrix.name] = daily_arr
-        print matrix.name
 
     time_period_list = []
 
 
     for tod, time_period in sound_cast_net_dict.iteritems():
         path = os.path.join('Banks', tod, 'emmebank')
-        print path
         bank = _emmebank.Emmebank(path)
         scenario = bank.scenario(1002)
         network = scenario.get_network()
@@ -164,7 +163,6 @@ def main():
 
 
     for extra_attribute in daily_scenario.extra_attributes():
-        print extra_attribute
         if extra_attribute not in keep_atts:
             daily_scenario.delete_extra_attribute(extra_attribute)
     daily_volume_attr = daily_scenario.create_extra_attribute('LINK', '@tveh')
@@ -172,7 +170,6 @@ def main():
 
     for tod, time_period in sound_cast_net_dict.iteritems():
         path = os.path.join('Banks', tod, 'emmebank')
-        print path
         bank = _emmebank.Emmebank(path)
         scenario = bank.scenario(1002)
         network = scenario.get_network()
@@ -197,11 +194,8 @@ def main():
     zone1 = 100
     zone2 = 100
 
-    print 'from zone', zone1, 'to zone', zone2
     for matrix1 in daily_emmebank.matrices():
         NAME = matrix1.name
-        print NAME
-        print 'daily:' , matrix1.get_numpy_data()[zone1][zone2]
         a = 0
         for tod, time_period in sound_cast_net_dict.iteritems():
             path = os.path.join('banks', tod, 'emmebank')
@@ -210,8 +204,6 @@ def main():
                 if matrix2.name == NAME:
                     my_arr = matrix2.get_numpy_data()
                     a += my_arr[zone1][zone2]
-        print 'hourly total:', a
-
 
     print 'daily bank created'
 
diff --git a/scripts/summarize/standard/geog_summary.py b/scripts/summarize/standard/geog_summary.py
new file mode 100644
index 00000000..07174178
--- /dev/null
+++ b/scripts/summarize/standard/geog_summary.py
@@ -0,0 +1,478 @@
+import os, sys, math, h5py
+import pandas as pd
+sys.path.append(os.getcwd())
+from standard_summary_configuration import *
+
+labels = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/variable_labels.csv'))
+districts = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/district_lookup.csv'))
+county_taz = pd.read_csv(r'scripts/summarize/inputs/county_taz.csv')
+rgc_taz = pd.read_csv(r'scripts/summarize/inputs/rgc_taz.csv')
+lu = pd.read_csv(r'inputs/accessibility/parcels_urbansim.txt', sep=' ')
+income_tiers = pd.read_csv(r'scripts/summarize/inputs/income_tiers.csv')
+
+table_list = ['Household','Trip','Tour','Person','HouseholdDay','PersonDay']
+
+overwrite = True
+
+output_csv_list = ['hh_rgc','trip_rgc_homeloc','trip_rgc_dest']
+
+def h5_to_df(h5file, table_list, name=False):
+    """
+    Load h5-formatted data based on a table list. Assumes heirarchy of a set of tables.
+    """
+    output_dict = {}
+    
+    for table in table_list:
+        df = pd.DataFrame()
+        for field in h5file[table].keys():
+            df[field] = h5file[table][field][:]
+            
+        output_dict[table] = df
+    
+    if name:
+        output_dict['name'] = name
+    
+    return output_dict
+
+def add_row(df, row_name, description, value):
+    df.ix[row_name,'description'] = description
+    df.ix[row_name,'value'] = value
+    
+    return df
+
+def apply_lables(h5data):
+    '''
+    Replace daysim formatted values with human readable lablels.
+    '''
+    for table in labels['table'].unique():
+        df = labels[labels['table'] == table]
+        for field in df['field'].unique():
+            newdf = df[df['field'] == field]
+            local_series = pd.Series(newdf['text'].values, index=newdf['value'])
+            h5data[table][field] = h5data[table][field].map(local_series)
+    
+    return h5data
+
+def hh(dataset, geog_file):
+	"""
+	geog_file: TAZ-based geography lookup, e.g., district, county, RGC
+	"""
+
+	hh = dataset['Household']
+
+	# Join RGC geography based on household TAZ location
+	hh = pd.merge(hh,geog_file,left_on='hhtaz', right_on='taz')
+
+	hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	hh['income_group'] = '0'
+	hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+
+	agg_fields = ['geog_name','income_group','hrestype']
+
+	# Sums
+	df = pd.DataFrame(hh.groupby(agg_fields).sum()['hhexpfac'])
+	# df.reset_index(inplace=True)
+
+	# average hhsize and hhvehs (weighted)
+	hh['hhsize_wt'] = hh['hhsize'].astype('float')*hh['hhexpfac']
+	hhsize_df = pd.DataFrame(hh.groupby(agg_fields).sum()['hhsize_wt']/hh.groupby(agg_fields).sum()['hhexpfac'])
+	hhsize_df.rename(columns={0:'avg_hhsize'},inplace=True)
+	df = df.join(hhsize_df)
+
+	hh['hhvehs_wt'] = hh['hhvehs'].astype('float')*hh['hhexpfac']
+	hhvehs_df = pd.DataFrame(hh.groupby(agg_fields).sum()['hhvehs_wt']/hh.groupby(agg_fields).sum()['hhexpfac'])
+	hhvehs_df.rename(columns={0:'avg_hhvehs'},inplace=True)
+	df = df.join(hhvehs_df)
+
+	df = df.fillna(0)
+	df = df.reset_index()
+
+	# Join with taz lookup to get lat and lon of desired geog_field
+	geog_lat_lon = geog_file.groupby('geog_name').min()[['lat_1','lon_1']]
+	geog_lat_lon.reset_index(inplace=True)
+	df = pd.merge(df, geog_lat_lon, on='geog_name', how='left')
+
+	df['source'] = dataset['name']
+
+	return df
+
+def trip(dataset, geog_file, geog_field):
+	"""
+	geog_file: TAZ-based geography lookup, e.g., district, county, RGC
+	geog_field: join field, e.g., hhtaz to summarize trips for households in RGC or dtaz 
+	            for trips ending in RGC
+	"""
+
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+	    
+	hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	hh['income_group'] = '0'
+	hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+	# total trips
+	# join with person file and rgc based on destination
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	# # Attach parking costs
+	# if 'PARCEL_ID' in trip_person.columns:
+	# 	trip_person = pd.merge(trip_person, lu[['PARCEL_ID','PPRICHRP']], left_on='dpcl', right_on='PARCEL_ID', how='left')
+	# else:
+	# 	trip_person = pd.merge(trip_person, lu[['parcel_id','pprichrp']], left_on='dpcl', right_on='parcel_id', how='left')
+
+	# Calculate parking costs based on time at destination
+	# trip_person['dur']	 = trip_person['endacttm']-trip_person['arrtm']
+	# trip_person['park_cost'] = trip_person['PPRICHRP']*(trip_person['dur']/60)    # for drive modes only!
+
+	# trip_person['income_group'] = pd.cut(trip_person['hhincome'],
+	#     bins=income_bins,
+	#     labels=income_bin_labels)
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Tours by person type, purpose, mode, and destination district
+	agg_fields = ['pptyp','dpurp','mode', 'geog_name','income_group']
+	trips_df = pd.DataFrame(trip_person.groupby(agg_fields)['trexpfac'].sum())
+
+	# average trip distance and time (weighted)
+	trip_person['travdist_wt'] = trip_person['travdist']*trip_person['trexpfac']
+	trip_person['travtime_wt'] = trip_person['travtime']*trip_person['trexpfac']
+	trip_person['travcost_wt'] = trip_person['travcost']*trip_person['trexpfac']
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay_wt'] = trip_person['delay']*trip_person['trexpfac']
+
+	travdist_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travdist_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	travdist_df.rename(columns={0:'travdist'},inplace=True)
+	trips_df = trips_df.join(travdist_df)
+
+	travtime_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travtime_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	travtime_df.rename(columns={0:'travtime'},inplace=True)
+	trips_df = trips_df.join(travtime_df)
+
+	travcost_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travcost_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	travcost_df.rename(columns={0:'travcost'},inplace=True)
+	trips_df = trips_df.join(travcost_df)	    
+
+	if 'sov_ff_time' in trip.columns:
+	    travtime_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['delay_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+	    travtime_df.rename(columns={0:'delay'},inplace=True)
+	    trips_df = trips_df.join(travtime_df)
+	else:
+	    trips_df['delay'] = 0   
+
+	trips_df = trips_df.reset_index()
+
+	# add datasource field
+	trips_df['source'] = dataset['name']
+
+	# Attach geog lookup
+	geog_lat_lon = geog_file.groupby('geog_name').min()[['lat_1','lon_1']]
+	geog_lat_lon.reset_index(inplace=True)
+	df = pd.merge(trips_df, geog_lat_lon, on='geog_name', how='left')
+    
+	return trips_df
+
+def person(dataset, geog_file, geog_field):
+
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+
+	hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	hh['income_group'] = '0'
+	hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Don't double count household travcost - only sum travcost for drive trips
+	trip_person.ix[(trip_person.dorp != 1) & (trip_person['mode'].isin(['SOV','HOV2','HOV3+'])), 'travcost'] = 0
+
+	# Total travel time, cost, and delay per person
+	df = trip_person.groupby(['pno','hhno']).sum()[['travcost','delay','travtime']]
+	df = df.reset_index()
+
+	df_out = pd.merge(df, trip_person[['pno','hhno','geog_name','income_group','pptyp','hhincome']], on=['pno','hhno'], how='left')
+
+	# Get total time walked and biked, and indicator of whether walked/biked during day
+	df = trip.groupby(['pno','hhno','mode','travdist','travtime']).sum()['trexpfac']
+	df = pd.DataFrame(df).reset_index()
+
+	bike_trips = df[df['mode'] == 'Bike'].groupby(['hhno','pno']).sum().reset_index()[['hhno','pno','travtime','travdist']]
+	walk_trips = df[df['mode'] == 'Walk'].groupby(['hhno','pno']).sum().reset_index()[['hhno','pno','travtime','travdist']]
+	bike_trips['bike_trip_taken'] = 1
+	walk_trips['walk_trip_taken'] = 1
+
+	# Join bike and walk data at person-level to person files
+	df_out = pd.merge(df_out, bike_trips,on=['hhno','pno'], how='left')
+	df_out[['bike_trip_taken','travtime','travdist']] = df_out[['bike_trip_taken','travtime','travdist']].fillna(0)
+	df_out.rename(columns={'travtime':'total_bike_travtime','travdist':'total_bike_travdist'}, inplace=True)
+
+	df_out = pd.merge(df_out,walk_trips[['hhno','pno','travtime','travdist','walk_trip_taken']],on=['hhno','pno'], how='left')
+	df_out[['walk_trip_taken','travtime','travdist']] = df_out[['walk_trip_taken','travtime','travdist']].fillna(0)
+	df_out.rename(columns={'travtime':'total_walk_travtime','travdist':'total_walk_travdist'}, inplace=True)
+
+	# Group 
+	# total persons
+	df_tot = pd.DataFrame(df_out.groupby(['pptyp','district_name']).sum()['psexpfac'])
+	df_tot = df_tot.reset_index()
+	df_mean = pd.DataFrame(df_out.groupby(['pptyp','district_name']).mean()[['total_walk_travdist','total_walk_travtime','walk_trip_taken',
+	    'total_bike_travdist','total_bike_travtime','bike_trip_taken']])
+	df_mean = df_mean.reset_index()
+
+	df = pd.merge(df_tot,df_mean,on=['pptyp','district_name'])
+
+	# Results by geography
+	agg_fields = ['geog_name','income_group','pptyp']
+
+	# Sums
+	df = pd.DataFrame(df.groupby(agg_fields).mean()[['hhincome','travcost','travtime','delay']])
+
+	df['source'] = dataset['name']
+
+	return df
+
+def costs(dataset, geog_file, geog_field):
+	"""
+	Calculate daily travel costs for each household
+	"""
+
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+
+	# Calculate low income as 200% of state povery level, which is based on hhsize
+	hh = pd.merge(hh, income_tiers, on='hhsize', how='left')
+	hh['income_group'] = '0'
+	hh.ix[hh['hhincome'] <= hh['income_threshold'], 'income_group'] = 'lower'
+	hh.ix[hh['hhincome'] > hh['income_threshold'], 'income_group'] = 'higher'
+	    
+	# total trips
+	# join with person file and rgc based on destination
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	# trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	# hh['income_group'] = pd.cut(hh['hhincome'],
+	#     bins=income_bins,
+	#     labels=income_bin_labels)
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Don't double count household travcost - only sum travcost for drive trips
+	trip_person.ix[(trip_person.dorp != 1) & (trip_person['mode'].isin(['SOV','HOV2','HOV3+'])), 'travcost'] = 0
+
+	# Total travcost for each household
+	tot_by_hh = pd.DataFrame(trip_person.groupby('hhno').sum()[['travcost','travtime','delay']])
+	tot_by_hh.reset_index(inplace=True)
+
+	df = pd.merge(tot_by_hh, hh[['hhno','hhincome','income_group','hhtaz']], on='hhno')
+
+	# calculate share of income spent on 
+	# total weekday household travel
+
+	# Replace income <= 1 with 1 to avoid negative and div/0
+	df.ix[df['hhincome'] < 1, 'hhincome'] = 1
+
+	df['travcost_inc_share'] = (df['travcost']*262)/df['hhincome']
+
+	# Average of the daily total for each TAZ
+	df = df.groupby(['hhtaz','income_group']).mean()[['travcost_inc_share','hhincome','travcost','travtime','delay']]
+	df.reset_index(inplace=True)
+
+	# Attach geog lookup
+	# geog_lat_lon = geog_file.groupby('geog_name').min()
+	# geog_lat_lon.reset_index(inplace=True)
+	df = pd.merge(df, geog_file, left_on='hhtaz', right_on='taz', how='left')
+
+	df['source'] = dataset['name']
+
+	return df
+
+def income(dataset, geog_file, geog_field):
+	'''
+	income distribution by geography
+	'''
+	trip = dataset['Trip']
+	trip = trip[trip['travdist'] >= 0]
+	person = dataset['Person']
+	hh = dataset['Household']
+	    
+	# total trips
+	# join with person file and rgc based on destination
+	trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+	trip_person = pd.merge(trip_person,hh,on='hhno',how='left')
+
+	trip_person = pd.merge(trip_person,geog_file,left_on=geog_field, right_on='taz')
+
+	# Attach parking costs
+	# trip_person = pd.merge(trip_person, lu[['PARCEL_ID','PPRICHRP']], left_on='dpcl', right_on='PARCEL_ID', how='left')
+	# trip_person = pd.merge(trip_person, lu[['parcel_id','pprichrp']], left_on='dpcl', right_on='parcel_id', how='left')
+
+	# Calculate parking costs based on time at destination
+	# trip_person['dur'] = trip_person['endacttm']-trip_person['arrtm']
+	# trip_person['park_cost'] = trip_person['PPRICHRP']*(trip_person['dur']/60)    # for drive modes only!
+
+	# Note that we're binning by 10k interval here to create histograms
+	income_bins = [-99999999]+[i*10000 for i in xrange(21)]+[999999999]
+	income_bin_labels = [str(i*10000) for i in xrange(21)]+['+']
+
+	hh['income_group'] = pd.cut(hh['hhincome'],
+		bins=income_bins,
+		labels=income_bin_labels)
+
+	if 'sov_ff_time' in trip.columns:
+		trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+	# Sum of travel costs by household
+	tot_by_hh = pd.DataFrame(trip_person.groupby('hhno').sum()[['travcost','travtime','delay']])
+	tot_by_hh.reset_index(inplace=True)
+
+	df = pd.merge(tot_by_hh, hh[['hhno','hhincome','income_group','hhtaz']], on='hhno')
+
+	# Replace income <= 1 with 1 to avoid negative and div/0
+	# df.ix[df['hhincome'] < 1, 'hhincome'] = 1
+
+	# df['travcost_inc_share'] = (df['travcost']*262)/df['hhincome']
+
+	# # Average 
+	# df = df.groupby(['hhtaz','income_group']).mean()[['travcost_inc_share','hhincome','travcost']]
+	# df.reset_index(inplace=True)
+
+	# # Attach geog lookup
+	# # geog_lat_lon = geog_file.groupby('geog_name').min()
+	# # geog_lat_lon.reset_index(inplace=True)
+	# df = pd.merge(df, geog_file, left_on='hhtaz', right_on='taz', how='left')
+
+	df['source'] = dataset['name']
+
+	return df
+
+def landuse(landuse_file, fname):
+
+	lu_df = pd.read_csv(landuse_file, sep=' ')
+
+	if 'taz_p' in lu_df.columns:
+
+		sums_df = lu_df.groupby('taz_p').sum()
+		sums_df = sums_df.drop(['ppricdyp','pprichrp','xcoord_p','ycoord_p'], axis=1)
+		sums_df.reset_index(inplace=True)
+
+		means_df = lu_df.groupby('taz_p').mean()[['ppricdyp','pprichrp']]
+		means_df.reset_index(inplace=True)
+
+	else:
+
+		sums_df = lu_df.groupby('TAZ_P').sum()
+		sums_df = sums_df.drop(['PPRICDYP','PPRICHRP','XCOORD_P','YCOORD_P'], axis=1)
+		sums_df.reset_index(inplace=True)
+
+		means_df = lu_df.groupby('TAZ_P').mean()[['PPRICDYP','PPRICHRP']]
+		means_df.reset_index(inplace=True)
+
+	df = pd.merge(sums_df, means_df)
+
+	df['source'] = fname
+
+	return df
+
+def process_dataset(h5file, scenario_name):
+	# Load h5 data as dataframes
+    dataset = h5_to_df(h5file, table_list=['Household','Trip','Tour','Person','HouseholdDay','PersonDay'], name=scenario_name)
+
+    dataset = apply_lables(dataset)
+    
+    # hh_county = hh(dataset, geog_file=county_taz)
+    # write_csv(hh_county,fname='hh_county.csv')
+
+    # hh_rgc = hh(dataset, geog_file=rgc_taz)
+    # write_csv(hh_rgc,fname='hh_rgc.csv')
+
+    # Trips based on location in regional center
+    trip_df = trip(dataset, geog_file=rgc_taz, geog_field='hhtaz')
+    write_csv(trip_df,fname='trip_rgc_homeloc.csv')
+
+    trip_df = trip(dataset, geog_file=county_taz, geog_field='hhtaz')
+    write_csv(trip_df,fname='trip_county_homeloc.csv')
+
+    # Trips based on destination location in regional center
+    # trip_df = trip(dataset, geog_file=rgc_taz, geog_field='dtaz')
+    # write_csv(trip_df,fname='trip_rgc_dest.csv')
+
+    # trip_df = trip(dataset, geog_file=county_taz, geog_field='dtaz')
+    # write_csv(trip_df,fname='trip_county_dest.csv')
+
+    # TAZ Costs
+    df = costs(dataset, geog_file=county_taz, geog_field='hhtaz')
+    write_csv(df, fname='costs.csv')
+
+    df = income(dataset, geog_file=county_taz, geog_field='hhtaz')
+    write_csv(df, fname='income.csv')
+
+    # # Person totals
+    # df = person(dataset, geog_file=county_taz, geog_field='hhtaz')
+    # write_csv(df, fname='person_mean.csv')
+
+def write_csv(df,fname):
+    '''
+    Write dataframe to file; append existing file
+    '''
+#     df.to_csv(os.path.join(output_dir,fname),mode='a')
+    if not os.path.isfile(os.path.join(output_dir,fname)):
+
+        df.to_csv(os.path.join(output_dir,fname),index=False)
+    else: # append without writing the header
+        df.to_csv(os.path.join(output_dir,fname), mode ='a', header=False, index=False)
+
+if __name__ == '__main__':
+
+    # Use root directory name as run name
+    run_name = os.getcwd().split('\\')[-1]
+
+    output_dir = r'outputs/geog'
+
+    comparison_run_dir = r'U:\stefan\soundcast_2040_no_toll'
+
+    file_dict = {
+        '50 cent toll': os.getcwd(), 
+        'No toll': os.path.join(comparison_run_dir),
+                    }
+
+    # Create output directory if it doesn't exist
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    if overwrite:
+	    for fname in os.listdir(output_dir):
+	        if os.path.isfile(os.path.join(output_dir,fname)):
+	            os.remove(os.path.join(output_dir,fname))
+
+	# Process all h5 files
+    for name, file_dir in file_dict.iteritems():
+
+		daysim_h5 = h5py.File(os.path.join(file_dir,r'outputs/daysim/daysim_outputs.h5'))
+
+		print 'processing h5: ' + name
+
+		process_dataset(h5file=daysim_h5, scenario_name=name)
+		del daysim_h5 # drop from memory to save space for next comparison
+
+		df = landuse(landuse_file=os.path.join(file_dir,r'inputs/accessibility/parcels_urbansim.txt'), fname=name)
+		write_csv(df, fname='landuse.csv')
\ No newline at end of file
diff --git a/scripts/summarize/standard/group.py b/scripts/summarize/standard/group.py
new file mode 100644
index 00000000..ee787cd3
--- /dev/null
+++ b/scripts/summarize/standard/group.py
@@ -0,0 +1,751 @@
+import os, sys, shutil, math, h5py
+import pandas as pd
+sys.path.append(os.getcwd())
+from standard_summary_configuration import *
+
+labels = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/variable_labels.csv'))
+districts = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/district_lookup.csv'))
+
+table_list = ['Household','Trip','Tour','Person','HouseholdDay','PersonDay']
+
+# look up psrc time of day 
+tod_list = ['5to6','6to7','7to8','8to9','9to10','10to14','14to15','15to16','16to17','17to18','18to20']
+
+tod_lookup = {  0:'20to5',
+                1:'20to5',
+                2:'20to5',
+                3:'20to5',
+                4:'20to5',
+                5:'5to6',
+                6:'6to7',
+                7:'7to8',
+                8:'8to9',
+                9:'9to10',
+                10:'10to14',
+                11:'10to14',
+                12:'10to14',
+                13:'10to14',
+                14:'14to15',
+                15:'15to16',
+                16:'16to17',
+                17:'17to18',
+                18:'18to20',
+                19:'18to20',
+                20:'18to20',
+                21:'20to5',
+                22:'20to5',
+                23:'20to5',
+                24:'20to5'}
+
+def h5_to_df(h5file, table_list, name=False):
+    """
+    Load h5-formatted data based on a table list. Assumes heirarchy of a set of tables.
+    """
+    output_dict = {}
+    
+    for table in table_list:
+        df = pd.DataFrame()
+        for field in h5file[table].keys():
+            df[field] = h5file[table][field][:]
+            
+        output_dict[table] = df
+    
+    if name:
+        output_dict['name'] = name
+    
+    return output_dict
+
+def add_row(df, row_name, description, value):
+    df.ix[row_name,'description'] = description
+    df.ix[row_name,'value'] = value
+    
+    return df
+
+def apply_lables(h5data):
+    '''
+    Replace daysim formatted values with human readable lablels.
+    '''
+    for table in labels['table'].unique():
+        df = labels[labels['table'] == table]
+        for field in df['field'].unique():
+            newdf = df[df['field'] == field]
+            local_series = pd.Series(newdf['text'].values, index=newdf['value'])
+            h5data[table][field] = h5data[table][field].map(local_series)
+    
+    return h5data
+
+def agg_measures(dataset):
+    df = pd.DataFrame()
+
+    # VMT per capita
+    driver_trips = dataset['Trip'][dataset['Trip']['dorp'] == 1]
+    vmt_per_cap = (driver_trips['travdist']*driver_trips['trexpfac']).sum()/dataset['Person']['psexpfac'].sum()
+    df = add_row(df, row_name='vmt_per_cap', description='VMT per Person', value=vmt_per_cap)
+    
+    # Average trips per person
+    trips_per_person = dataset['Trip']['trexpfac'].sum()/dataset['Person']['psexpfac'].sum()
+    df = add_row(df, row_name='trips_per_person', description='Average Trips per Person', value=trips_per_person)
+
+    # add datasource field
+    df['source'] = dataset['name']
+    
+    return df
+
+def household(dataset):
+    
+    hh = dataset['Household']
+    agg_fields = ['hhsize','hhvehs','hhftw']
+    hh_df = pd.DataFrame(hh.groupby(agg_fields)['hhexpfac'].sum())
+    
+    hh_df['source'] = dataset['name']
+    
+    return hh_df
+
+def person(dataset):
+    
+    hh = dataset['Household']
+    person = dataset['Person']
+
+    person_hh = pd.merge(person, hh, on='hhno')
+
+    # district_df = pd.DataFrame(district.groupby('district_name').min())
+    person_hh = pd.merge(person_hh,districts[['taz','district_name']],left_on='hhtaz',right_on='taz', how='left')
+
+    df = pd.DataFrame(person_hh.groupby(['pptyp','district_name']).sum()['psexpfac'])
+    
+    df['pptyp'] = df.index.get_level_values(0)
+    df['district_name'] = df.index.get_level_values(1)
+    
+    districts_df = districts.groupby('district_name').min()[['lat_district','lon_district']]
+    df.index = df['district_name']
+    
+    df = df.join(districts_df,how='left')
+    df.reset_index(inplace=True,drop=True)
+    
+    df['source'] = dataset['name']
+    
+    return df
+
+def person_day(dataset):
+    
+    # total number of persons by purpose and person type
+    person = dataset['Person']
+    personday = dataset['PersonDay']
+
+    # join with person records to get person type
+    df = pd.merge(person,personday,on=['hhno','pno'])
+
+    # calculate weighted tours for each group
+    purp_fields = ['wk','sc','es','pb','sh','ml','so']
+    purp_dict = {'wk':'work', 'sc':'school', 'es':'escort','pb':'personal business','sh':'shop','ml':'meal','so':'social'}
+
+    for field in purp_fields:
+        df['wt_'+field] = df[field+'tours']*df['pdexpfac']
+
+    df = pd.DataFrame(df.groupby('pptyp').sum()[['psexpfac','pdexpfac']+['wt_'+field for field in purp_fields]])
+    for field in purp_fields:
+        df[purp_dict[field]] = df['wt_'+field]/df['psexpfac']
+        df.drop('wt_'+field,axis=1,inplace=True)
+
+    df.drop(['psexpfac','pdexpfac'],axis=1,inplace=True)
+    
+    df = pd.DataFrame(df.stack())
+    df.columns = ['values']
+    df['pptyp'] = df.index.get_level_values(0)
+    df['measure'] = df.index.get_level_values(1)
+    df.reset_index(inplace=True,drop=True)
+    
+    df['source'] = dataset['name']
+    
+    return df
+
+def tours(dataset, time_field):
+    """
+    Specify a time field to segment the data (e.g., 'tlvorig' for time left the origin)
+    """
+    
+    tour = dataset['Tour']
+    person = dataset['Person']
+        
+    # total tours
+    # join with person file and district names based on destination
+    tour_person = pd.merge(tour,person,on=['hhno','pno'])
+    tour_person = pd.merge(tour_person,districts[['taz','district_name']],left_on='tdtaz',right_on='taz',how='left')
+    
+    # Convert time field to hour
+    tour_person[time_field+'_hr'] = tour_person[time_field].apply(lambda row: int(math.floor(row/60)))
+
+    # Calculate tour duration
+    tour_person.ix[tour_person.tlvorig > tour_person.tarorig, 'tarorig'] = tour_person.ix[tour_person.tlvorig > tour_person.tarorig, 'tarorig']+1440
+    tour_person['duration'] = tour_person.tarorig - tour_person.tlvorig
+    
+    # Tours by person type, purpose, mode, destination district, and time of day
+    agg_fields = ['pptyp','pdpurp','tmodetp',time_field+'_hr','district_name']
+    tours_df = pd.DataFrame(tour_person.groupby(agg_fields)['toexpfac'].sum())
+    
+    # average trip distance and time
+    tours_df = tours_df.join(pd.DataFrame(tour_person.groupby(agg_fields)['tautodist'].mean()))
+    tours_df = tours_df.join(pd.DataFrame(tour_person.groupby(agg_fields)['tautotime'].mean()))
+    tours_df = tours_df.join(pd.DataFrame(tour_person.groupby(agg_fields)['duration'].mean()))
+    # average trip 
+    
+    tours_df = tours_df.join(pd.DataFrame(person.groupby('pptyp').sum()['psexpfac']))
+    
+    # Add the district lat and lon values
+    tours_df['pptyp'] = tours_df.index.get_level_values(0)
+    tours_df['pdpurp'] = tours_df.index.get_level_values(1)
+    tours_df['tmodetp'] = tours_df.index.get_level_values(2)
+    tours_df[time_field+'_hr'] = tours_df.index.get_level_values(3)
+    tours_df['district_name'] = tours_df.index.get_level_values(4)
+    tours_df.reset_index(inplace=True, drop=True)
+
+    district_df = districts.groupby('district_name').min()[['lat_district','lon_district']]
+    district_df['district_name'] = district_df.index
+
+    tours_df = pd.merge(tours_df,district_df)
+    
+    # add datasource field
+    tours_df['source'] = dataset['name']
+    
+    return tours_df
+
+def trips(dataset):
+    
+    trip = dataset['Trip']
+    trip = trip[trip['travdist'] >= 0]
+    person = dataset['Person']
+    hh = dataset['Household']
+        
+    # total trips
+    # join with person file and district names based on destination
+    trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+
+    # join with household to add income
+    trip_person = pd.merge(trip_person, hh, on='hhno', how='left')
+    
+    trip_person['deptm_hr'] = trip_person['deptm'].apply(lambda row: int(math.floor(row/60)))
+
+    trip_person['income_group'] = pd.cut(trip_person['hhincome'],
+        bins=income_bins,
+        labels=income_bin_labels)
+    
+    # Calcualte delay field
+    if 'sov_ff_time' in trip.columns:
+        trip_person['delay'] = trip['travtime']-(trip['sov_ff_time']/100.0)
+
+    # Tours by person type, purpose, mode, and destination district
+    agg_fields = ['pptyp','dpurp','mode','deptm_hr']
+    trips_df = pd.DataFrame(trip_person.groupby(agg_fields)['trexpfac'].sum())
+    
+    # average trip distance, time, and delay (weighted)
+    trip_person['travdist_wt'] = trip_person['travdist']*trip_person['trexpfac']
+    trip_person['travtime_wt'] = trip_person['travtime']*trip_person['trexpfac']
+    if 'sov_ff_time' in trip.columns:
+        trip_person['delay_wt'] = trip_person['delay']*trip_person['trexpfac']
+
+    travdist_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travdist_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+    travdist_df.rename(columns={0:'travdist'},inplace=True)
+    trips_df = trips_df.join(travdist_df)
+
+    travtime_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['travtime_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+    travtime_df.rename(columns={0:'travtime'},inplace=True)
+    trips_df = trips_df.join(travtime_df)
+
+    if 'sov_ff_time' in trip.columns:
+        travtime_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['delay_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+        travtime_df.rename(columns={0:'delay'},inplace=True)
+        trips_df = trips_df.join(travtime_df)
+    else:
+        trips_df['delay'] = 0
+    
+    # add datasource field
+    trips_df['source'] = dataset['name']
+    
+    return trips_df
+
+def taz_trips(dataset):
+    """
+    Trips based on otaz
+    """
+    trip = dataset['Trip']
+    trip = trip[trip['travdist'] >= 0]
+    person = dataset['Person']
+    hh = dataset['Household']
+        
+    # total trips
+    # join with person file and district names based on destination
+    trip_person = pd.merge(trip,person,on=['hhno','pno'], how='left')
+
+    # join with household to add income
+    trip_person = pd.merge(trip_person, hh, on='hhno', how='left')
+
+    # Tours by person type, purpose, mode, and destination district
+    agg_fields = ['pathtype','pptyp','dpurp','mode','otaz']
+    trips_df = pd.DataFrame(trip_person.groupby(agg_fields)['trexpfac'].sum())
+
+    # Median income
+    trip_person['inc_wt'] = trip_person['hhincome']*trip_person['trexpfac']
+    inc_df = pd.DataFrame(trip_person.groupby(agg_fields).sum()['inc_wt']/trip_person.groupby(agg_fields).sum()['trexpfac'])
+    inc_df.rename(columns={0:'inc_wt'},inplace=True)
+    trips_df = trips_df.join(inc_df)
+
+    # add datasource field
+    trips_df['source'] = dataset['name']
+    
+    return trips_df
+
+def taz_tours(dataset):
+    
+    tour = dataset['Tour']
+    
+#   tour_dest = pd.merge(tour,districts[['taz','district_name','lat','lon']],left_on='tdtaz',right_on='taz',how='left')
+    tour_dest = pd.DataFrame(tour.groupby('tdtaz').sum()['toexpfac'])
+    tour_dest['taz'] = tour_dest.index
+    tour_dest.reset_index(inplace=True, drop=True)
+    
+    
+#     tour_origin = pd.merge(tour,districts[['taz','district_name','lat','lon']],left_on='totaz',right_on='taz',how='left')
+    tour_origin = pd.DataFrame(tour.groupby('totaz').sum()['toexpfac'])
+    tour_origin['taz'] = tour_origin.index
+    tour_origin.reset_index(inplace=True, drop=True)
+    
+    df = pd.merge(tour_dest,tour_origin,on='taz', suffixes=['_dest','_origin'])
+    df = pd.merge(df,districts, on='taz',how='left' )
+    
+    df['source'] = dataset['name']
+    
+    return df
+
+def taz_avg(dataset):
+
+	trip = dataset['Trip']
+	hh = dataset['Household']
+	person = dataset['Person']
+
+	person_hh = pd.merge(person, hh, on='hhno', how='left')
+	trip = pd.merge(trip, person_hh, on=['pno','hhno'], how='left')
+
+	# total VMT by home TAZ
+	taz_vmt = trip[['hhtaz','travdist']].groupby('hhtaz').sum()
+	taz_pop = person_hh[['hhtaz','psexpfac']].groupby('hhtaz').sum()
+	taz_df = pd.DataFrame(taz_vmt['travdist']/taz_pop['psexpfac'])
+	taz_df.columns = ['Average VMT per Capita']
+	taz_df = taz_df.reset_index()
+
+	# Non-SOV Mode Share
+	trip.ix[trip['mode'] != 'SOV', 'Non-SOV'] = 'Non-SOV'
+	trip.ix[trip['mode'] == 'SOV', 'Non-SOV'] = 'SOV'
+	df = pd.DataFrame(trip[trip['Non-SOV'] == 'Non-SOV'][['hhtaz','Non-SOV','trexpfac']].groupby(['hhtaz']).sum()['trexpfac'])
+	df = df.reset_index()
+	df.columns = ['hhtaz','non-sov trips']
+	df_trip = trip[['hhtaz','trexpfac']].groupby('hhtaz').sum()['trexpfac']
+	df_trip = df_trip.reset_index()
+
+	df = pd.merge(df_trip, df, on='hhtaz') 
+	df['Percent Non-SOV'] = df['non-sov trips']/df['trexpfac']
+	df = df[['hhtaz','Percent Non-SOV']]
+
+    # Non-Auto Mode Share
+	trip.ix[trip['mode'].isin(['Bike', 'Walk', 'Transit']), 'Non-Auto'] = 'Non-Auto'
+	trip.ix[~trip['mode'].isin(['Bike', 'Walk', 'Transit']), 'Non-Auto'] = 'Auto'
+	df = pd.DataFrame(trip[trip['Non-Auto'] == 'Non-SOV'][['hhtaz','Non-Auto','trexpfac']].groupby(['hhtaz']).sum()['trexpfac'])
+	df = df.reset_index()
+	df.columns = ['hhtaz','non-auto trips']
+	df_trip = trip[['hhtaz','trexpfac']].groupby('hhtaz').sum()['trexpfac']
+	df_trip = df_trip.reset_index()
+
+	df = pd.merge(df_trip, df, on='hhtaz') 
+	df['Percent Non-Auto'] = df['non-auto trips']/df['trexpfac']
+	df = df[['hhtaz','Percent Non-Auto']]
+
+	# Join mode share and VMT per capita
+	taz_df = pd.merge(taz_df, df, on='hhtaz')
+
+	# Calculate percent walking or biking for transportation
+	bike_walk_trips = trip[trip['mode'].isin(['Bike','Walk']) | ((trip['mode'] == 'Transit') & (trip['dorp'] > 0))]
+	df = bike_walk_trips.groupby(['hhno','pno']).count()
+	df = df.reset_index()
+	df = df[['hhno','pno']]
+	df['bike_walk'] = 1
+	df = pd.merge(person_hh,df,on=['hhno','pno'], how='left')
+	df['bike_walk'] = df['bike_walk'].fillna(0)
+	df['indicator'] = 1
+	df = df[['hhtaz','bike_walk','indicator']].groupby(['hhtaz']).sum()
+	df = df.reset_index()
+	df['Percent Biking or Walking'] = df['bike_walk']/df['indicator']
+
+	# Merge with taz_df
+	taz_df = pd.merge(taz_df, df[['Percent Biking or Walking','hhtaz']], on='hhtaz')
+
+	# Delay
+	trip_auto = trip[trip['mode'].isin(['SOV','HOV2','HOV3+']) & (trip['dorp'] == 1)]
+	trip_auto['delay'] = trip_auto['travtime'] - trip_auto['sov_ff_time']/100.0
+	df = trip_auto[['hhtaz','delay']].groupby('hhtaz').sum()[['delay']].reset_index()
+	df = pd.merge(df, hh[['hhtaz','hhsize']].groupby('hhtaz').sum()[['hhsize']].reset_index(),on='hhtaz')
+	df['Delay per Capita per Day'] = df['delay']/df['hhsize']
+
+	taz_df = pd.merge(taz_df, df[['hhtaz','Delay per Capita per Day']], on='hhtaz')
+
+	taz_df['source'] = dataset['name']
+
+	return taz_df
+
+def time_of_day(dataset):
+    """
+    tours and trips by time of day hour
+    """
+    trip = dataset['Trip']
+    tour = dataset['Tour']
+    
+    # Trip start hour
+    trip['deptm_hr'] = trip['deptm'].apply(lambda row: int(math.floor(row/60)))
+    trip['arrtm_hr'] = trip['arrtm'].apply(lambda row: int(math.floor(row/60)))
+    
+    # tour start hour
+    tour['tlvorg_hr'] = tour['tlvorig'].apply(lambda row: int(math.floor(row/60)))
+    tour['tardest_hr'] = tour['tardest'].apply(lambda row: int(math.floor(row/60)))
+    tour['tlvdest_hr'] = tour['tlvdest'].apply(lambda row: int(math.floor(row/60)))
+    tour['tarorig_hr'] = tour['tarorig'].apply(lambda row: int(math.floor(row/60)))
+    
+   
+    trip_dep = pd.DataFrame(trip.groupby('deptm_hr').sum()['trexpfac'])
+    trip_dep['tod'] = trip_dep.index
+    trip_dep.reset_index(inplace=True)
+    trip_dep.rename(columns={'trexpfac':'trip_deptm'},inplace=True)
+        
+    trip_arr = pd.DataFrame(trip.groupby('arrtm_hr').sum()['trexpfac'])
+    trip_arr['tod'] = trip_arr.index
+    trip_arr.reset_index(inplace=True)
+    trip_arr.rename(columns={'trexpfac':'trip_arrtm'},inplace=True)
+    
+    results_df = pd.merge(trip_dep, trip_arr, on='tod')
+    
+    results_df['source'] = dataset['name']
+    
+    return results_df 
+
+def net_summary(net_file, fname):
+    
+    net_summary_df = pd.read_excel(net_file, sheetname='Network Summary')
+    net_summary_df.index = net_summary_df['tod']
+    df = pd.DataFrame(net_summary_df.stack())
+    df.reset_index(inplace=True)
+    df.rename(columns={0:'value','level_1':'fieldname'}, inplace=True)
+
+    # Drop the rows with TP_4k column headers
+    df.drop(df[df['fieldname'] == 'TP_4k'].index, inplace=True)
+    df.drop(df[df['fieldname'] == 'tod'].index, inplace=True)
+
+    # Split the fields by vmt, vht, delay
+    df['facility_type'] = df.fieldname.apply(lambda row: row.split('_')[0])
+    df['metric'] = df.fieldname.apply(lambda row: row.split('_')[-1])
+
+    df['source'] = fname.split('.xlsx')[0]
+    
+    write_csv(df=df, fname='net_summary.csv')
+
+def daily_counts(net_file, fname):
+    
+    sheetname = 'Daily Counts'
+    if sheetname not in pd.ExcelFile(net_file).sheet_names:
+        return
+    else:
+        df = pd.read_excel(net_file, sheetname=sheetname)
+            
+        # Get total of model @tveh
+        # take min of count value because it represents potentially multiple linkes
+        df = pd.DataFrame([df.groupby('@scrn').sum()['@tveh'].values,
+                           df.groupby('@scrn').min()['count'].values,
+                           df.groupby('@scrn').min()['ul3'].values,
+                           df.groupby('@scrn').first()['i'].values,
+                           df.groupby('@scrn').first()['j'].values,]).T
+        df.columns = ['model','observed','facility','i','j']
+        df['source'] = fname.split('.xlsx')[0]
+                
+        write_csv(df=df, fname='traffic_counts.csv')
+
+def hourly_counts(net_file, fname):
+
+	sheetname='Counts Output'
+	if sheetname not in pd.ExcelFile(net_file).sheet_names:
+		return
+	else:
+	    df = pd.read_excel(net_file, sheetname=sheetname)
+
+		# separate observed counts
+	    df_observed = df[df.columns[['obs' in i for i in df.columns]]]
+	    df_obs = pd.DataFrame(df_observed.stack()).reset_index()
+	    df_obs.columns=['count_id','tod','observed']
+		# Trim time period from tod field
+	    df_obs['tod'] = df_obs['tod'].apply(lambda row: str(row.split('_')[-1]))
+		# Drop total by time of day rows
+	    df_obs = df_obs[df_obs['tod'] != 'obs_total']
+
+		# separate model volumes
+	    df_model = df[df.columns[['vol' in i for i in df.columns]]]
+	    df_model = pd.DataFrame(df_model.stack()).reset_index()
+	    df_model.columns=['count_id','tod','model']
+	    df_model['tod'] = df_model['tod'].apply(lambda row: str(row.split('vol')[-1]))
+
+		# Join model and observed data
+	    df = pd.merge(df_obs, df_model, on =['count_id','tod'])
+
+		# Link type (hov or general purpose)
+	    df['link_type'] = df['count_id'].apply(lambda row: str(row)[-1])	
+
+	    df['source'] = fname.split('.xlsx')[0]
+
+	    write_csv(df=df, fname='hourly_counts.csv')
+
+def transit_summary(net_file, fname):
+
+    sheetname = 'Transit Summaries'
+    if sheetname not in pd.ExcelFile(net_file).sheet_names:
+        return
+    else:
+        transit_df = pd.read_excel(net_file, sheetname=sheetname)
+        transit_df.index = transit_df['route_code']
+
+        # Add model results
+        dict_result = {}
+        for field in ['board','time']:
+            df = pd.DataFrame(transit_df[[tod+'_'+ field for tod in tod_list]].stack())
+            df.rename(columns={0:field}, inplace=True)
+            df['tod'] = [i.split('_')[0] for i in df.index.get_level_values(1)]
+            df['route_id'] = df.index.get_level_values(0)
+            df.reset_index(inplace=True, drop=True)
+
+            dict_result[field] = df
+
+        # Only keep the boardings for now - observed time data is not available at the route level
+        df = dict_result['board'].groupby(['route_id','tod']).sum()
+        df.reset_index(inplace=True)
+        df['source'] = fname.split('.xlsx')[0]
+
+        model = df
+
+        # Join observed data
+
+        df = pd.read_csv(r'scripts\summarize\inputs\network_summary\transit_boardings_2014.csv')
+        df.index = df['PSRC_Rte_ID']
+       # df.drop([u'Unnamed: 0','PSRC_Rte_ID','SignRt'],axis=1,inplace=True)
+
+        df = pd.DataFrame(df.stack())
+        df.reset_index(inplace=True)
+        df.rename(columns={0:'board', 'level_1':'hour','PSRC_Rte_ID':'route_id'}, inplace=True)
+
+        # Convert hour to time of day definition
+        df['hour'] = df['hour'].apply(lambda row: row.split('_')[-1])
+        tod_df = pd.DataFrame(data=tod_lookup.values(),index=tod_lookup.keys(), columns=['tod'])
+        tod_df['hour'] = tod_df.index.astype('str')
+
+        df = pd.merge(df,tod_df,on='hour')
+        df.drop('hour', axis=1,inplace=True)
+
+        # Group by tod
+        df = df.groupby(['tod','route_id']).sum()
+        df['tod'] = df.index.get_level_values(0)
+        df['route_id'] = df.index.get_level_values(1)
+        df.reset_index(inplace=True, drop=True)
+
+        df = pd.merge(model, df, on=['route_id','tod'], suffixes=['_model','_observed'])
+        df.rename(columns={'board_model':'model','board_observed':'observed'}, inplace=True)
+        df['source'] = fname.split('.xlsx')[0]
+        fname_out = 'transit_boardings.csv'
+        
+        # Add the route description
+        df = pd.merge(df,transit_df[['route_code','description']],left_on='route_id',right_on='route_code',how='left')
+        df.drop_duplicates(inplace=True)
+        
+        write_csv(df=df, fname=fname_out)
+
+def truck_summary(net_file, fname):
+    """Process medium and heavy truck counts where observed data is provided"""
+
+    sheetname = 'Truck Counts'
+    if sheetname not in pd.ExcelFile(net_file).sheet_names:
+        return
+    else:
+        df = pd.read_excel(net_file, sheetname='Truck Counts')
+        df['source'] = fname.split('.xlsx')[0]
+
+        # stack by medium, heavy, and total counts
+        med_df = df.drop(['observedHvy','modeledHvy','observedTot','modeledTot'], axis=1)
+        med_df.rename(columns={'modeledMed':'model','observedMed':'observed'},inplace=True)
+        med_df['truck_type'] = 'medium'
+
+        hvy_df = df.drop(['observedMed','modeledMed','observedTot','modeledTot'], axis=1)
+        hvy_df.rename(columns={'modeledHvy':'model','observedHvy':'observed'},inplace=True)
+        hvy_df['truck_type'] = 'heavy'
+
+        tot_df = df.drop(['observedMed','modeledMed','observedHvy','modeledHvy'], axis=1)
+        tot_df.rename(columns={'modeledTot':'model','observedTot':'observed'},inplace=True)
+        tot_df['truck_type'] = 'all'
+
+        df = med_df.append(hvy_df).append(tot_df)
+        write_csv(df=df, fname='trucks.csv')
+
+def screenlines(net_file, fname):
+    """Process screenline results from model output"""
+
+    sheetname = 'Screenline Volumes'
+    if sheetname not in pd.ExcelFile(net_file).sheet_names:
+        return
+    else:
+        df = pd.read_excel(net_file, sheetname=sheetname)
+        df['source'] = fname.split('.xlsx')[0]
+
+    # Load observed data and join
+    obs = pd.read_csv(r'scripts\summarize\inputs\screenlines.csv')
+
+    obs_col = '2010'
+    df = pd.merge(df,obs,left_on='Screenline', right_on='id', how='inner')
+
+    df.rename(columns={'Volumes':'model',obs_col:'observed'}, inplace=True)
+
+    write_csv(df=df, fname='screenlines.csv')
+
+def logsums(name, dir_name):
+    # 
+
+    logsum = 'CFULL/SHO'
+    logsum_output = 'outputs/grouped/logsums.csv'
+
+    df = pd.read_csv(os.path.join(dir_name, 'aggregate_logsums.1.dat'), delim_whitespace=True, skipinitialspace=True)
+    df = df.reset_index()
+    df = pd.DataFrame(df[['level_0',logsum]])
+    df['source'] = name
+
+    # Separate into accessibility bins
+    df['accessibility'] = pd.qcut(df[logsum],5,labels=['lowest','low','moderate','high','highest'])
+    bins = pd.qcut(df[logsum],5,retbins=True)[1]
+
+    df.columns = ['taz','logsum','source','accessibility']
+
+    # Attach population
+    hh = pd.read_csv(os.path.join(dir_name,'_household.tsv'), sep='\t')
+    df_pop = pd.DataFrame(hh.groupby('hhtaz').sum()['hhsize'])
+    df_pop['taz'] = df_pop.index
+    df = pd.merge(df,df_pop,on='taz',how='left')
+    df.columns = [['taz','logsum','source','accessibility','population']]
+
+    # Write to file
+    if os.path.exists(logsum_output):
+        df_current = pd.read_csv(logsum_output)
+        df_current.append(df).to_csv(logsum_output, index=False)
+    else:
+        df.to_csv(logsum_output, index=False)
+
+def process_dataset(h5file, scenario_name):
+    
+    # Process all daysim results
+    
+    # Load h5 data as dataframes
+    dataset = h5_to_df(h5file, table_list=['Household','Trip','Tour','Person','HouseholdDay','PersonDay'], name=scenario_name)
+
+    dataset = apply_lables(dataset)
+    
+    # Calculate aggregate measures csv
+    #agg_df = agg_measures(dataset)
+    #write_csv(agg_df,fname='agg_measures.csv')
+
+    #hh_df = household(dataset)
+    #write_csv(hh_df, fname='household.csv')
+
+    ## Tours based on time left origin
+    #tours_df = tours(dataset,'tlvorig')
+    #write_csv(tours_df,fname='tours_tlvorig.csv')
+
+    #tours_df = tours(dataset, 'tardest')
+    #write_csv(tours_df,fname='tours_tardest.csv')
+
+    #tours_df = tours(dataset, 'tlvdest')
+    #write_csv(tours_df,fname='tours_tlvdest.csv')
+
+    #taz_df = taz_tours(dataset)
+    #write_csv(taz_df,fname='taz_tours.csv')
+    
+    #trips_df = trips(dataset)
+    #write_csv(trips_df, fname='trips.csv')
+
+    #trip_taz_df = taz_trips(dataset)
+    #write_csv(trip_taz_df, fname='trips_taz.csv')
+    
+    #person_day_df = person_day(dataset)
+    #write_csv(person_day_df, fname='person_day.csv')
+    
+    #person_df = person(dataset)
+    #write_csv(person_df, 'person.csv')
+    
+    #tod_df = time_of_day(dataset)
+    #write_csv(tod_df, fname='time_of_day.csv')
+
+    taz_avg_df = taz_avg(dataset)
+    write_csv(taz_avg_df, fname='taz_avg.csv')
+
+def write_csv(df,fname):
+    '''
+    Write dataframe to file; append existing file
+    '''
+    if not os.path.isfile(os.path.join(output_dir,fname)):
+        df.to_csv(os.path.join(output_dir,fname))
+    else: # append without writing the header
+        df.to_csv(os.path.join(output_dir,fname), mode ='a', header=False)
+
+if __name__ == '__main__':
+
+    # Use root directory name as run name
+    run_name = os.getcwd().split('\\')[-1]
+
+    # Create output directory, overwrite existing results
+    output_dir = r'outputs/grouped'
+    if os.path.exists(output_dir):
+        shutil.rmtree(output_dir)
+    os.makedirs(output_dir)
+
+    # Create list of runs to compare, starting with current run
+    run_dir_dict = {
+        run_name: os.getcwd(), 
+    }
+
+    # Add runs, if set in standard_summary_configuration.py
+    if len(comparison_runs.keys()) > 0:
+        for comparison_name, comparison_dir in comparison_runs.iteritems():
+            run_dir_dict[comparison_name] = comparison_dir
+
+	# Create daysim summaries
+    for name, run_dir in run_dir_dict.iteritems():
+
+		daysim_h5 = h5py.File(os.path.join(run_dir,r'outputs/daysim/daysim_outputs.h5'))
+		print 'processing h5: ' + name
+		process_dataset(h5file=daysim_h5, scenario_name=name)
+		del daysim_h5 # drop from memory to save space for next comparison
+
+    # Compare daysim to survey if set in standard_summary_configuration.py
+    #if compare_survey:
+    #    process_dataset(h5file=h5py.File(r'scripts\summarize\inputs\calibration\survey.h5'), scenario_name='survey')
+
+    # Create network and accessibility summaries
+    #for name, run_dir in run_dir_dict.iteritems():
+    #    file_dir = os.path.join(run_dir,r'outputs/network/network_summary_detailed.xlsx')
+    #    print 'processing excel: ' + name
+    #    transit_summary(file_dir, name)
+    #    daily_counts(file_dir, name)
+    #    # hourly_counts(file_dir, name)
+    #    net_summary(file_dir, name)
+    #    truck_summary(file_dir, name)
+    #    screenlines(file_dir, name)
+    #    file_dir = os.path.join(run_dir,r'outputs/daysim')
+    #    logsums(name, file_dir)
+
+    ## Write notebooks based on these outputs to HTML
+    #for nb in ['topsheet','metrics']:
+    #    try:
+    #        os.system("jupyter nbconvert --ExecutePreprocessor.timeout=600 --to=html --execute scripts/summarize/notebooks/"+nb+".ipynb")
+    #    except:
+    #        print 'Unable to produce topsheet, see: scripts/summarize/standard/group.py'
+
+    #    # Move these files to output
+    #    if os.path.exists(r"outputs/"+nb+".html"):
+    #        os.remove(r"outputs/"+nb+".html")
+    #    os.rename(r"scripts/summarize/notebooks/"+nb+".html", r"outputs/"+nb+".html")
\ No newline at end of file
diff --git a/scripts/summarize/standard/mic_summary.py b/scripts/summarize/standard/mic_summary.py
new file mode 100644
index 00000000..1da5b1fc
--- /dev/null
+++ b/scripts/summarize/standard/mic_summary.py
@@ -0,0 +1,143 @@
+import os, sys, math, h5py
+import pandas as pd
+sys.path.append(os.getcwd())
+from standard_summary_configuration import *
+
+labels = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/variable_labels.csv'))
+districts = pd.read_csv(os.path.join(os.getcwd(), r'scripts/summarize/inputs/calibration/district_lookup.csv'))
+mic_taz = pd.read_csv(r'scripts/summarize/inputs/mic_taz.csv')
+# Get MIC data from land use file
+
+table_list = ['Household','Trip','Tour','Person','HouseholdDay','PersonDay']
+
+overwrite = True
+
+output_csv_list = ['mic']
+
+def h5_to_df(h5file, table_list, name=False):
+    """
+    Load h5-formatted data based on a table list. Assumes heirarchy of a set of tables.
+    """
+    output_dict = {}
+    
+    for table in table_list:
+        df = pd.DataFrame()
+        for field in h5file[table].keys():
+            df[field] = h5file[table][field][:]
+            
+        output_dict[table] = df
+    
+    if name:
+        output_dict['name'] = name
+    
+    return output_dict
+
+def add_row(df, row_name, description, value):
+    df.ix[row_name,'description'] = description
+    df.ix[row_name,'value'] = value
+    
+    return df
+
+def apply_lables(h5data):
+    '''
+    Replace daysim formatted values with human readable lablels.
+    '''
+    for table in labels['table'].unique():
+        df = labels[labels['table'] == table]
+        for field in df['field'].unique():
+            newdf = df[df['field'] == field]
+            local_series = pd.Series(newdf['text'].values, index=newdf['value'])
+            h5data[table][field] = h5data[table][field].map(local_series)
+    
+    return h5data
+
+def mic(scenario_name, working_dir):
+	'''
+	Calculate truck trips and job totals by MIC 
+	'''
+	# Truck trips to/from this zone
+	lu_file = pd.read_csv(os.path.join(working_dir,r'inputs\accessibility\parcels_urbansim.txt'), sep=' ')
+
+	metrk = pd.read_csv(os.path.join(working_dir,r'outputs\mfmetrk.csv'))
+	metrk.index = metrk['Unnamed: 0']
+	metrk = metrk.drop('Unnamed: 0', axis=1)
+	metrk.columns = metrk.columns.astype('int')
+
+	hvtrk = pd.read_csv(os.path.join(working_dir,r'outputs\mfhvtrk.csv'))
+	hvtrk.index = hvtrk['Unnamed: 0']
+	hvtrk = hvtrk.drop('Unnamed: 0', axis=1)
+	hvtrk.columns = hvtrk.columns.astype('int')
+
+	tazlist = mic_taz.groupby('TAZ').count().index.values
+
+	df = pd.DataFrame([metrk.ix[:,tazlist].sum(),hvtrk.ix[:,tazlist].sum()]).T
+	df.columns = ['medium_trucks','heavy_trucks']
+	df['TAZ'] = df.index
+	df = pd.merge(df, mic_taz, on='TAZ', how='left')
+	df = pd.merge(df.groupby('MIC').sum()[['medium_trucks','heavy_trucks']].reset_index(),
+                 df.groupby('MIC').min()[['lat','lon']].reset_index())
+
+	# Attach employment by RGC
+	if 'TAZ_P' in lu_file.columns:
+		taz_col = 'TAZ_P'
+		emp_col = 'EMPTOT_P'
+	else:
+		taz_col = 'taz_p'
+		emp_col = 'emptot_p'
+	lu = pd.merge(lu_file, mic_taz, left_on=taz_col, right_on='TAZ')
+	lu_mic = lu.groupby('MIC').sum()
+	lu_mic['MIC'] = lu_mic.index
+
+	df_out = pd.merge(df,lu_mic[[emp_col,'MIC']],on='MIC')
+
+	df_out['source'] = scenario_name
+
+	return df_out
+
+def process_dataset(scenario_name, working_dir):
+	# Load h5 data as dataframes
+    # dataset = h5_to_df(h5file, table_list=['Household','Trip','Tour','Person','HouseholdDay','PersonDay'], name=scenario_name)
+
+    # dataset = apply_lables(dataset)
+    
+    mic_df = mic(scenario_name=scenario_name, working_dir=working_dir)
+    write_csv(mic_df, fname='mic.csv')
+
+def write_csv(df,fname):
+    '''
+    Write dataframe to file; append existing file
+    '''
+#     df.to_csv(os.path.join(output_dir,fname),mode='a')
+    if not os.path.isfile(os.path.join(output_dir,fname)):
+
+        df.to_csv(os.path.join(output_dir,fname),index=False)
+    else: # append without writing the header
+        df.to_csv(os.path.join(output_dir,fname), mode ='a', header=False, index=False)
+
+if __name__ == '__main__':
+
+    # Use root directory name as run name
+    run_name = os.getcwd().split('\\')[-1]
+
+    output_dir = r'outputs/rgc'
+
+    comparison_run_dir = r'D:\brice\sc_2040demand_2014net'
+
+    network_file_dict = {
+        run_name: os.getcwd(),
+        '2040': comparison_run_dir
+    }
+
+    # Create output directory if it doesn't exist
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    if overwrite:
+	    for fname in output_csv_list:
+	        if os.path.isfile(os.path.join(output_dir,fname+'.csv')):
+	            os.remove(os.path.join(output_dir,fname+'.csv'))
+
+	# Process all network outputs
+    for name, file_dir in network_file_dict.iteritems():
+        print name
+        process_dataset(scenario_name=name, working_dir=file_dir)
\ No newline at end of file
diff --git a/scripts/summarize/standard/net_summary_simplify.py b/scripts/summarize/standard/net_summary_simplify.py
index 25939b9a..00d38ba4 100644
--- a/scripts/summarize/standard/net_summary_simplify.py
+++ b/scripts/summarize/standard/net_summary_simplify.py
@@ -26,11 +26,12 @@
 import summary_functions as scf
 from standard_summary_configuration import *
 from input_configuration import *
+sys.path.append(os.getcwd())
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+pd.options.mode.chained_assignment = None  # mute chained assignment warnings
 
 
-
-input_file = report_output_location+'/network_summary_detailed.xlsx'
+input_file = r'outputs/network/network_summary_detailed.xlsx'
 output_file= report_output_location+'/network_summary.xlsx'
 net_summary_df = pd.io.excel.read_excel(input_file, sheetname = 'Network Summary')
 model_run_name = 'Model Run'
diff --git a/scripts/summarize/standard/network_summary.py b/scripts/summarize/standard/network_summary.py
index 60bb4538..d5b047b1 100644
--- a/scripts/summarize/standard/network_summary.py
+++ b/scripts/summarize/standard/network_summary.py
@@ -1,4 +1,4 @@
-#Copyright [2014] [Puget Sound Regional Council]
+#Copyright [2014] [Puget Sound Regional Council]
 
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.
@@ -36,43 +36,22 @@
 from datetime import datetime
 from EmmeProject import *
 from multiprocessing import Pool
+from pyproj import Proj, transform
 import pandas as pd
 sys.path.append(os.path.join(os.getcwd(),"inputs"))
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 from standard_summary_configuration import *
 from input_configuration import *
 from emme_configuration import *
+pd.options.mode.chained_assignment = None  # mute chained assignment warnings
+
+daily_network_fname = 'outputs/network/daily_network_results.csv'
 
-#network_summary_project = 'Projects/LoadTripTables/LoadTripTables.emp'
-daily_network_fname = 'outputs/daily_network_results.csv'
-#fac_type_dict = {'highway' : 'ul3 = 1 or ul3 = 2',
-#                 'arterial' : 'ul3 = 3 or ul3 = 4 or ul3 = 6',
-#                 'connectors' : 'ul3 = 5'}
-
-#extra_attributes_dict = {'@tveh' : 'total vehicles', 
-#                         '@mveh' : 'medium trucks', 
-#                         '@hveh' : 'heavy trucks', 
-#                         '@vmt' : 'vmt',\
-#                         '@vht' : 'vht', 
-#                         '@trnv' : 'buses in auto equivalents',
-#                         '@ovol' : 'observed volume', 
-#                         '@bveh' : 'number of buses'}
-
-#transit_extra_attributes_dict = {'@board' : 'total boardings', '@timtr' : 'transit line time'}
-
-#transit_tod = {'6to7' : {'4k_tp' : 'am', 'num_of_hours' : 1}, 
-#               '7to8' :  {'4k_tp' : 'am', 'num_of_hours' : 1}, 
-#               '8to9' :  {'4k_tp' : 'am', 'num_of_hours' : 1}, 
-#               '9to10' : {'4k_tp' : 'md', 'num_of_hours' : 1}, 
-#               '10to14' : {'4k_tp' : 'md', 'num_of_hours' : 4}, 
-#               '14to15' : {'4k_tp' : 'md', 'num_of_hours' : 1}}
 ## Input Files:
 aadt_counts_file = 'soundcast_aadt.csv'
 tptt_counts_file = 'soundcast_tptt.csv'
 
-#uc_list = ['@svtl1', '@svtl2', '@svtl3', '@svnt1', '@svnt2', '@svnt3', '@h2tl1', '@h2tl2', '@h2tl3',
-#           '@h2nt1', '@h2nt2', '@h2nt3', '@h3tl1', '@h3tl2', '@h3tl3', '@h3nt1', '@h3nt2', '@h3nt3', '@lttrk', '@mveh', '@hveh', '@bveh']
-
 def json_to_dictionary(dict_name):
 
     #Determine the Path to the input files and load them
@@ -143,25 +122,44 @@ def vmt_by_user_class(EmmeProject):
         uc_vmt_list.append(EmmeProject.network_calc_result['sum'])
     return uc_vmt_list
 
-def get_link_counts(EmmeProject, df_counts, tod):
+def delay_by_user_class(EmmeProject):
+    #uc_list = ['@svtl1', '@svtl2', '@svtl3', '@svnt1', '@h2tl1', '@h2tl2', '@h2tl3', '@h2nt1', '@h3tl1', '@h3tl2', '@h3tl3', '@h3nt1', '@lttrk', '@mveh', '@hveh', '@bveh']
+    uc_delay_list = []
+    link_selection = 'ul3 = 1 or ul3 = 2 or ul3 = 3 or ul3 = 4 or ul3 = 5 or ul3 = 6'
+    for item in uc_list:
+        EmmeProject.network_calculator("link_calculation", result = None, expression = item + "*(timau-(length*60/ul2))/60", selections_by_link=link_selection)
+        uc_delay_list.append(EmmeProject.network_calc_result['sum'])
+    return uc_delay_list
+
+def get_link_counts(EmmeProject, loop_id_df, tod):
     #get the network for the active scenario
      network = EmmeProject.current_scenario.get_network()
+     scenario = EmmeProject.current_scenario
      list_model_vols = []
-     for item in df_counts.index:
-         i = list(item)[0]
-         j = list(item)[1]
+     # Add/refresh screenline ID link attribute
+     if scenario.extra_attribute('@loop'):
+            scenario.delete_extra_attribute('@loop')
+     attr = scenario.create_extra_attribute('LINK', '@loop')
+     for row in loop_id_df.iterrows():
+         i = row[1].NewINode
+         j = row[1].NewJNode
          link = network.link(i, j)
          x = {}
-         x['loop_INode'] = i
-         x['loop_JNode'] = j
+         #x['NewINode'] = i
+         #x['NewJNode'] = j
+         x['CountID'] = row[1].CountID
          if link <> None:
+            #link['@loop'] = row[1].CountID
             x['vol' + tod] = link['@tveh']   
          else:
             x['vol' + tod] = None
          list_model_vols.append(x)
-     print len(list_model_vols)
+     #print len(list_model_vols)
+     #scenario.publish_network(network)
      df =  pd.DataFrame(list_model_vols)
-     df = df.set_index(['loop_INode', 'loop_JNode'])
+     df.set_index(['CountID'], inplace = True)
+     #df = df.set_index(['loop_INode', 'loop_JNode'])
+     #print df.head(10)
      return df
 
 def get_aadt_volumes(EmmeProject, df_aadt_counts, vol_dict):
@@ -316,11 +314,9 @@ def get_transit_boardings_time(EmmeProject):
 def calc_transit_link_volumes(EmmeProject):
     total_hours = transit_tod[EmmeProject.tod]['num_of_hours']
     my_expression = str(total_hours) + ' * vauteq * (60/hdw)'
-    print my_expression
+    
     EmmeProject.transit_segment_calculator(result = '@trnv', expression = my_expression, aggregation = "+")
     
-          
-        
 def writeCSV(fileNamePath, listOfTuples):
     myWriter = csv.writer(open(fileNamePath, 'wb'))
     for l in listOfTuples:
@@ -398,15 +394,229 @@ def get_link_attribute(network, attr):
     df = pd.DataFrame({'link_id': link_dict.keys(), attr: link_dict.values()})
     return df
 
-def export_corridor_results(my_project):
+
+def calc_total_vehicles(my_project):
+     '''For a given time period, calculate link level volume, store as extra attribute on the link'''
+    
+     #medium trucks
+     my_project.network_calculator("link_calculation", result = '@mveh', expression = '@metrk/1.5')
+     
+     #heavy trucks:
+     my_project.network_calculator("link_calculation", result = '@hveh', expression = '@hvtrk/2.0')
+     
+     #buses:
+     my_project.network_calculator("link_calculation", result = '@bveh', expression = '@trnv3/2.0')
+     
+     #calc total vehicles, store in @tveh 
+     str_expression = '@svtl1 + @svtl2 + @svtl3 + @h2tl1 + @h2tl2 + @h2tl3 + @h3tl1 + @h3tl2 + @h3tl3 + @lttrk + @mveh + @hveh + @bveh'
+     my_project.network_calculator("link_calculation", result = '@tveh', expression = str_expression)
+
+
+def get_aadt_trucks(my_project):
+    '''Calculate link level daily total truck passenger equivalents for medium and heavy, store in a DataFrame'''
+    
+    link_list = []
+
+    for key, value in sound_cast_net_dict.iteritems():
+        my_project.change_active_database(key)
+        
+        # Create extra attributes to store link volume data
+        for name, desc in extra_attributes_dict.iteritems():
+            my_project.create_extra_attribute('LINK', name, desc, 'True')
+        
+        ## Calculate total vehicles for each link
+        calc_total_vehicles(my_project)
+        
+        # Loop through each link, store length and truck pce
+        network = my_project.current_scenario.get_network()
+        for link in network.links():
+            link_list.append({'link_id' : link.id, '@mveh' : link['@mveh'], '@hveh' : link['@hveh'], 'length' : link.length})
+            
+    df = pd.DataFrame(link_list, columns = link_list[0].keys())       
+    grouped = df.groupby(['link_id'])
+    df = grouped.agg({'@mveh':sum, '@hveh':sum, 'length':min})
+    df.reset_index(level=0, inplace=True)
+    
+    return df
+    
+def truck_summary(df_counts, my_project, writer):
+    """ Export medium and heavy truck results where observed data is available """
+    
+    truck_volumes = get_aadt_trucks(my_project)
+    truck_compare = pd.merge(df_counts, truck_volumes, left_on='ij_id', right_on='link_id')
+    truck_compare['modeledTot'] = truck_compare['@mveh']+truck_compare['@hveh']
+    truck_compare['modeledMed'] = truck_compare['@mveh']
+    truck_compare['modeledHvy'] = truck_compare['@hveh']
+    truck_compare_grouped_sum = truck_compare.groupby(['CountID']).sum()[['modeledTot', 'modeledMed', 'modeledHvy']]
+    truck_compare_grouped_sum.reset_index(level=0, inplace=True)
+    truck_compare_grouped_min = truck_compare.groupby(['CountID']).min()[['Location', 'LocationDetail', 'FacilityType', 'length', 'observedMed',
+                                                                        'observedHvy', 'observedTot','county','LARGE_AREA','lat','lon']]
+    truck_compare_grouped_min.reset_index(level=0, inplace=True)
+    trucks_out= pd.merge(truck_compare_grouped_sum, truck_compare_grouped_min, on= 'CountID')
+    trucks_out.to_excel(excel_writer=writer, sheet_name='Truck Counts')
+
+def daily_counts(writer, my_project):
+    """Export daily network volumes and compare to observed."""
+
+    # Load observed data
+    count_id_df = pd.read_csv(r'inputs/networks/screenline_count_ids.txt', sep = ' ', header = None, names = ['NewINode', 'NewJNode','ScreenLineID'])
+    observed_count_df =  pd.read_csv(r'inputs/observed/observed_daily_counts.csv')
+    count_id_df = count_id_df.merge(observed_count_df, how = 'left', on = 'ScreenLineID')
+    # add daily bank to project if it exists
+    if os.path.isfile(r'Banks/Daily/emmebank'):
+        bank = _eb.Emmebank(r'Banks/Daily/emmebank')
+        scenario = bank.scenario(1002)
+
+        # Add/refresh screenline ID link attribute
+        if scenario.extra_attribute('@scrn'):
+            scenario.delete_extra_attribute('@scrn')
+        attr = scenario.create_extra_attribute('LINK', '@scrn')
+
+        # Add/refresh screenline count value from assignment results
+        if scenario.extra_attribute('@count'):
+            scenario.delete_extra_attribute('@count')
+        attr_count = scenario.create_extra_attribute('LINK', '@count')
+
+        network = scenario.get_network()
+
+        inode_list = []
+        jnode_list = []
+        scrn_id = []
+        facility_list = []
+        observed_volume = []
+        model_volume = []
+
+        for row in count_id_df.iterrows():
+            inode = int(row[1].NewINode) 
+            jnode = int(row[1].NewJNode) 
+            if network.link(inode, jnode):
+                link = network.link(inode, jnode)
+                link['@scrn'] = row[1]['ScreenLineID']
+                link['@count'] = row[1]['Year_2014']
+
+                inode_list.append(inode)
+                jnode_list.append(jnode)
+                facility_list.append(link['data3'])
+                scrn_id.append(link['@scrn'])
+                observed_volume.append(link['@count'])
+                model_volume.append(link['@tveh'])
+
+        scenario.publish_network(network)
+
+        df = pd.DataFrame([inode_list,jnode_list,facility_list,model_volume,scrn_id,observed_volume]).T
+        df.columns=['i','j','ul3','@tveh','@scrn','count']
+
+        df.to_excel(excel_writer=writer, sheet_name='Daily Counts')
+
+        # Export truck trip tables
+        # for matrix_name in ['mfmetrk','mfhvtrk']:
+        #     matrix_id = bank.matrix(matrix_name).id
+        #     emme_matrix = bank.matrix(matrix_id)
+        #     matrix_data = emme_matrix.get_data()
+        #     np_matrix = np.matrix(matrix_data.raw_data)
+        #     df = pd.DataFrame(np_matrix)
+        #     # Attach zone numbers
+        #     # Look up zone ID from index location
+        #     zones = my_project.current_scenario.zone_numbers
+        #     dictZoneLookup = dict((index,value) for index,value in enumerate(zones))
+        #     df.columns = [dictZoneLookup[i] for i in df.columns]
+        #     df.index = [dictZoneLookup[i] for i in df.index.values]
+
+        #     df.to_csv('outputs/'+matrix_name+'.csv')
+    else:
+        raise Exception('no daily bank found')
+
+def bike_volumes(writer, my_project, tod):
+    '''Write bike link volumes to file for comparisons to counts '''
+
+    my_project.change_active_database(tod)
+
+    network = my_project.current_scenario.get_network()
+
+    # Load bike count data from file
+    bike_counts = pd.read_csv(bike_count_data)
+
+    # Load edges file to join proper node IDs
+    edges_df = pd.read_csv(edges_file)
+
+    df = bike_counts.merge(edges_df, 
+        on=['INode','JNode'])
+    
+    # if the link is twoway, also get the other directoin IJ and JI and append to original df
+    twoway_links_df = df[df['Oneway'] == 2]
+
+    # Replace I with J node for twoway links, for Emme IJ and geodatabase IJ pairs
+    twoway_links_df.loc[:,'tempINode'] = twoway_links_df.loc[:,'JNode']
+    twoway_links_df.loc[:,'tempJNode'] = twoway_links_df.loc[:,'INode']
+    twoway_links_df.loc[:,'tempNewINode'] = twoway_links_df.loc[:,'NewJNode']
+    twoway_links_df.loc[:,'tempNewJNode'] = twoway_links_df.loc[:,'NewINode']
+    # remove old IJ values and replace with the new swapped values
+    twoway_links_df.drop(['INode','JNode','NewINode','NewJNode'],axis=1,inplace=True)
+    twoway_links_df.loc[:,'INode'] = twoway_links_df.loc[:,'tempINode']
+    twoway_links_df.loc[:,'JNode'] = twoway_links_df.loc[:,'tempJNode']
+    twoway_links_df.loc[:,'NewINode'] = twoway_links_df.loc[:,'tempNewINode']
+    twoway_links_df.loc[:,'NewJNode'] = twoway_links_df.loc[:,'tempNewJNode']
+    twoway_links_df.drop(['tempINode','tempJNode','tempNewINode','tempNewJNode'],axis=1,inplace=True)
+
+    df = pd.concat([df,twoway_links_df])
+    df = df.reset_index()
+    list_model_vols = []
+
+    for row in df.index:
+        i = df.iloc[row]['NewINode']
+        j = df.loc[row]['NewJNode']
+        link = network.link(i, j)
+        x = {}
+        x['EmmeINode'] = i
+        x['EmmeJNode'] = j
+        x['gdbINode'] = df.iloc[row]['INode']
+        x['gdbJNode'] = df.iloc[row]['JNode']
+        x['LocationID'] = df.iloc[row]['LocationID']
+        if link != None:
+            x['bvol' + tod] = link['@bvol']
+        else:
+            x['bvol' + tod] = None
+        list_model_vols.append(x)
+
+    df_count =  pd.DataFrame(list_model_vols)
+    sheet_name = 'Bike Volumes'
+    summary_file_dir = 'outputs/network/network_summary_detailed.xlsx'
+    if os.path.exists(summary_file_dir):
+        xl = pd.ExcelFile(summary_file_dir)
+        if sheet_name in xl.sheet_names:
+            '''append column to existing TOD results'''
+            # df = pd.read_csv(bike_link_vol)
+            df = pd.read_excel(io=xl, sheetname=sheet_name)
+            df['bvol'+tod] = df_count['bvol'+tod]
+            # df.to_csv(bike_link_vol,index=False)
+            df.to_excel(excel_writer=writer, sheet_name=sheet_name, index=False) 
+        else:
+            # df_count.to_csv(bike_link_vol,index=False)
+            df_count.to_excel(excel_writer=writer, sheet_name=sheet_name, index=False)
+
+def light_rail(df, writer):
+
+    # load lookup table for observed boardings and station names
+    observed = pd.read_csv(light_rail_boardings)
+    
+    # # Join total and initial boardings & sum for all hours
+
+    # # Join station information for set of nodes; report only for station in observed file
+    df = pd.merge(df, observed, left_on='inode', right_on='id', how='inner')
+    df['transfer_rate'] = df['transfers']/df['total_boardings']
+    if model_year == base_year:
+        df = df.loc[(df.observed_boardings>0)]
+    df.to_excel(excel_writer=writer, sheet_name='Light Rail')
+
+def export_corridor_results(my_project, writer):
     ''' Evaluate corridor travel time for a single AM and PM period'''
     tod = {'am': '7to8', 'pm': '16to17'}
     am_df = corridor_results(tod=tod['am'], my_project=my_project)
     pm_df = corridor_results(tod=tod['pm'], my_project=my_project)
 
     # combine am and pm into single CSV and export
-    corridor_df = pd.concat(objs=[am_df, pm_df])
-    corridor_df.to_csv('outputs/corridor_summary.csv')
+    df = pd.concat(objs=[am_df, pm_df])
+    df.to_excel(excel_writer=writer, sheet_name='Corridors')
 
 def corridor_results(tod, my_project):
     corridor_count = 12    # number of input corridor files
@@ -439,6 +649,8 @@ def corridor_results(tod, my_project):
     # join corridor flags to link travel time
     corridor_times_df = pd.merge(link_df, corridor_flags_df)
 
+    # corridor_times_df.to_csv('temp.csv')
+
     # sum corridor travel time and length for each corridor
     link_trav_time = pd.DataFrame()
     for i in range(1, corridor_count+1):    # +1 because python is zero-based
@@ -471,7 +683,128 @@ def corridor_results(tod, my_project):
 
     return df_out
 
+def freeflow_skims(my_project):
+    """
+    Attach "freeflow" (20to5) SOV skims to daysim_outputs
+    """
+
+    # Load daysim_outputs as dataframe
+    daysim = h5py.File('outputs/daysim/daysim_outputs.h5', 'r+')
+    df = pd.DataFrame()
+    for field in ['travtime','otaz','dtaz']:
+        df[field] = daysim['Trip'][field][:]
+    df['od']=df['otaz'].astype('str')+'-'+df['dtaz'].astype('str')
+
+    # Look up zone ID from index location
+    zones = my_project.current_scenario.zone_numbers
+    dictZoneLookup = dict((index,value) for index,value in enumerate(zones))
 
+    skim_vals = h5py.File(r'inputs\20to5.h5')['Skims']['svtl3t'][:]
+
+    skim_df = pd.DataFrame(skim_vals)
+    # Reset index and column headers to match zone ID
+    skim_df.columns = [dictZoneLookup[i] for i in skim_df.columns]
+    skim_df.index = [dictZoneLookup[i] for i in skim_df.index.values]
+
+    skim_df = skim_df.stack().reset_index()
+    skim_df.columns = ['otaz','dtaz','ff_travtime']
+    skim_df['od']=skim_df['otaz'].astype('str')+'-'+skim_df['dtaz'].astype('str')
+    skim_df.index = skim_df['od']
+
+    df = df.join(skim_df,on='od', lsuffix='_cong',rsuffix='_ff')
+
+    # Write to h5, create dataset if 
+    if 'sov_ff_time' in daysim['Trip'].keys():
+        del daysim['Trip']['sov_ff_time']
+    try:
+        daysim['Trip'].create_dataset("sov_ff_time", data=df['ff_travtime'].values, compression='gzip')
+    except:
+        print 'could not write freeflow skim to h5'
+    daysim.close()
+
+def jobs_transit(writer):
+    buf = pd.read_csv(r'inputs\buffered_parcels.txt', sep=' ')
+    buf.index = buf.parcelid
+
+    # distance to any transit stop
+    df = buf[['parcelid','dist_lbus','dist_crt','dist_fry','dist_lrt',
+              u'hh_p', u'stugrd_p', u'stuhgh_p', u'stuuni_p', u'empedu_p',
+           u'empfoo_p', u'empgov_p', u'empind_p', u'empmed_p', u'empofc_p',
+           u'empret_p', u'empsvc_p', u'empoth_p', u'emptot_p']]
+
+    # Use minimum distance to any transit stop
+    newdf = pd.DataFrame(df[['dist_lbus','dist_crt','dist_fry','dist_lrt']].min(axis=1))
+    newdf['parcelid'] = newdf.index
+    newdf.rename(columns={0:'nearest_transit'}, inplace=True)
+    df = pd.merge(df,newdf[['parcelid','nearest_transit']])
+
+    # only sum for parcels closer than quarter mile to stop
+    quarter_mile_jobs = pd.DataFrame(df[df['nearest_transit'] <= 0.25].sum())
+    quarter_mile_jobs.rename(columns={0:'quarter_mile_transit'}, inplace=True)
+    all_jobs = pd.DataFrame(df.sum())
+    all_jobs.rename(columns={0:'total'}, inplace=True)
+
+    df = pd.merge(all_jobs,quarter_mile_jobs, left_index=True, right_index=True)
+    df.drop(['parcelid','dist_lbus','dist_crt','dist_fry','dist_lrt','nearest_transit'], inplace=True)
+
+    df.to_excel(excel_writer=writer, sheet_name='Transit Job Access')
+
+
+def project_to_wgs84(longitude, latitude, ESPG = "+init=EPSG:2926", conversion = 0.3048006096012192):
+    '''
+    Converts the passed in coordinates from their native projection (default is state plane WA North-EPSG:2926)
+    to wgs84. Returns a two item tuple containing the longitude (x) and latitude (y) in wgs84. Coordinates
+    must be in meters hence the default conversion factor- PSRC's are in state plane feet.  
+    '''
+    #print longitude, latitude
+    # Remember long is x and lat is y!
+    prj_wgs = Proj(init='epsg:4326')
+    prj_sp = Proj(ESPG)
+    
+    # Need to convert feet to meters:
+    longitude = longitude * conversion
+    latitude = latitude * conversion
+    x, y = transform(prj_sp, prj_wgs, longitude, latitude)
+    
+    return x, y
+
+def export_network_shape(tod):
+    """
+    Loop through network components and export shape points
+    """
+
+    if os.path.isfile(r'Banks/'+tod+'/emmebank'):
+        bank = _eb.Emmebank(r'Banks/'+tod+'/emmebank')
+        scenario = bank.scenario(1002)
+        network = scenario.get_network()
+
+        inode_list = []
+        jnode_list = []
+        shape_x = []
+        shape_y = []
+        shape_loc = []
+
+        for link in network.links():
+            local_index = 0
+            for point in link.shape:
+                inode_list.append(link.i_node)
+                jnode_list.append(link.j_node)
+                shape_x.append(point[0])
+                shape_y.append(point[1])
+                shape_loc.append(local_index)
+                local_index += 1
+
+        df = pd.DataFrame([inode_list,jnode_list,shape_loc,shape_x,shape_y]).T
+        df.columns=['i','j','shape_local_index','x','y']
+
+        df['ij'] = df['i'].astype('str') + '-' + df['j'].astype('str')
+
+        # convert to lat-lon
+        df['lat_lon'] = df[['x','y']].apply(lambda row: project_to_wgs84(row['x'], row['y']), axis=1)
+        df['lon'] = df['lat_lon'].apply(lambda row: row[0])
+        df['lat'] = df['lat_lon'].apply(lambda row: row[-1])
+
+        df.to_csv('outputs/network/network_shape.csv', index=False)
 
 def main():
     ft_summary_dict = {}
@@ -479,31 +812,44 @@ def main():
     transit_atts = []
     my_project = EmmeProject(project)
 
-    # Travel times on key corridors
-    # export_corridor_results(my_project)
+    export_network_shape('7to8')
 
-    #export_corridor_results(my_project)
+    writer = pd.ExcelWriter('outputs/network/network_summary_detailed.xlsx', engine='xlsxwriter')    
 
-    # Connect to sqlite3 db
-    if run_tableau_db:
-        con = None
-        con = lite.connect(results_db)
-    
-    writer = pd.ExcelWriter('outputs/network_summary_detailed.xlsx', engine = 'xlsxwriter')#Defines the file to write to and to use xlsxwriter to do so
-    #create extra attributes:
-    
+    export_corridor_results(my_project, writer)
+    jobs_transit(writer)
        
-    #pandas dataframe to hold count table:
+    # Read observed count data
+    loop_ids = pd.read_csv(r'inputs/networks/count_ids.txt', sep = ' ', header = None, names = ['NewINode', 'NewJNode','CountID'])
+    loop_counts = pd.read_csv(r'inputs/observed/loop_counts_2014.csv')
+    loop_counts.set_index(['CountID_Type'], inplace = True)
+    #loop_ids = pd.read_csv('scripts/summarize/inputs/network_summary/' + counts_file, index_col=['loop_INode', 'loop_JNode'])
     df_counts = pd.read_csv('scripts/summarize/inputs/network_summary/' + counts_file, index_col=['loop_INode', 'loop_JNode'])
     df_aadt_counts = pd.read_csv('scripts/summarize/inputs/network_summary/' + aadt_counts_file)
     df_tptt_counts = pd.read_csv('scripts/summarize/inputs/network_summary/' + tptt_counts_file)
-    
- 
+    df_truck_counts = pd.read_csv(truck_counts_file)
+
+    daily_counts(writer, my_project)
+
+    freeflow_skims(my_project)
+
+    if run_truck_summary:
+    	truck_summary(df_counts=df_truck_counts, my_project=my_project, writer=writer)
+
+
+
     counts_dict = {}
     uc_vmt_dict = {}
+    uc_delay_dict = {}
     aadt_counts_dict = {}
     
     tptt_counts_dict = {}
+
+    # write out stop-level boardings
+    stop_df = pd.DataFrame()
+
+    # write out transit segment boardings (line and stop specific)
+    seg_df = pd.DataFrame()
     
     #get a list of screenlines from the bank/scenario
     screenline_list = get_unique_screenlines(my_project) 
@@ -513,11 +859,57 @@ def main():
         #dict where key is screen line id and value is 0
         screenline_dict[item] = 0
 
-    #loop through all tod banks and get network summaries
+   #  #loop through all tod banks and get network summaries
     for key, value in sound_cast_net_dict.iteritems():
         my_project.change_active_database(key)
         for name, desc in extra_attributes_dict.iteritems():
             my_project.create_extra_attribute('LINK', name, desc, 'True')
+
+        network = my_project.current_scenario.get_network()
+
+        tveh = []
+        i_list = []
+        j_list = []
+        speed_limit = []
+        facility_type = []
+        capacity = []
+        length = []
+        time = []
+        metrk = []
+        hvtrk = []
+        bvol = []
+
+        # Link volumes
+        for link in network.links():
+            i_list.append(link.i_node)
+            j_list.append(link.j_node)
+            tveh.append(link.auto_volume)
+            metrk.append(link['@metrk'])
+            hvtrk.append(link['@hvtrk'])
+            speed_limit.append(link.data2)
+            facility_type.append(link.data3)
+            capacity.append(link.data1)
+            length.append(link['length'])
+            time.append(link.auto_time)
+            try:
+                bvol.append(link['@bvol'])
+            except:
+                # print 'no bvol ' + key
+                pass
+
+        df = pd.DataFrame([i_list,j_list,tveh,metrk,hvtrk,speed_limit,facility_type,
+            capacity,length,time,bvol]).T
+        df.columns = ['i','j','tveh','metrk','hvtrk','speed_limit','facility_type',
+        'capacity','ij_length','time','bvol']
+        df['tod'] = key
+        df['ij'] = df['i'].astype('str') + '-' + df['j'].astype('str')
+
+        network_results_path = r'outputs/network/network_results.csv'
+        if os.path.exists(network_results_path):
+            df.to_csv(network_results_path, mode='a', index=False, header=False)
+        else:
+            df.to_csv(network_results_path, index=False)
+
         #TRANSIT:
         if my_project.tod in transit_tod.keys():
             for name, desc in transit_extra_attributes_dict.iteritems():
@@ -528,8 +920,49 @@ def main():
             transit_summary_dict[key] = transit_results[0]
             transit_atts.extend(transit_results[1])
             #transit_atts = list(set(transit_atts))
+        
+            network = my_project.current_scenario.get_network()
+            ons = {}
+            offs = {}
+            
+            for node in network.nodes():
+                ons[int(node.id)] = node.initial_boardings
+                offs[int(node.id)] = node.final_alightings
+            
+            df = pd.DataFrame() # temp dataFrame to append to stop_df
+            df['inode'] = ons.keys()
+            df['initial_boardings'] = ons.values()
+            df['final_alightings'] = offs.values()
+            df['tod'] = my_project.tod
+
+            stop_df = stop_df.append(df)
+
+            # Transit segment values
+            # boardings = {}
+            # line = {}
+
+            boardings = []
+            line = []
+            inode = []
+
+            for tseg in network.transit_segments():
+                boardings.append(tseg.transit_boardings)
+                line.append(tseg.line.id)
+                inode.append(tseg.i_node.number)
+
+                # boardings[tseg.i_node.number] = tseg.transit_boardings
+                # line[tseg.i_node.number] = tseg.line.id
+            
+            df = pd.DataFrame([inode,boardings,line]).T
+            df.columns = ['inode','total_boardings','line']
+            df['tod'] = my_project.tod
+            # df['inode'] = boardings.keys()
+            # df['line'] = line.values()
+            # df['total_boardings'] = boardings.values()
+            
+
+            seg_df = seg_df.append(df)
 
-            #print transit_summary_dict
           
         net_stats = calc_vmt_vht_delay_by_ft(my_project)
 
@@ -537,9 +970,10 @@ def main():
         ft_summary_dict[key] = net_stats
         #store vmt by user class in dict:
         uc_vmt_dict[key] = vmt_by_user_class(my_project)
+        uc_delay_dict[key] = delay_by_user_class(my_project)
 
         #counts:
-        df_tod_vol = get_link_counts(my_project, df_counts, key)
+        df_tod_vol = get_link_counts(my_project, loop_ids, key)
         counts_dict[key] = df_tod_vol
         
         #AADT Counts:
@@ -552,49 +986,29 @@ def main():
         
         #screen lines
         get_screenline_volumes(screenline_dict, my_project)
-        
-    list_of_measures = ['vmt', 'vht', 'delay']
-
-    # Write results to sqlite3 db (for Tableau)
-    if run_tableau_db:
-        facility_results = {}
 
-        for measure in list_of_measures:
-            facility_results[measure] = dict_to_df(input_dict=ft_summary_dict, 
-                                                    measure=measure)
-            # Measures by TOD
-            df = pd.DataFrame(facility_results[measure].sum(),columns=[measure]).T
-            df = stamp(df, con=con, table=measure+'_by_tod')
-            df.to_sql(name=measure+'_by_tod', con=con, if_exists='append', chunksize=1000)
-
-            # Measures by Facility Type
-            df = pd.DataFrame(facility_results[measure].T.sum(),columns=[measure]).T
-            df = stamp(df, con=con, table=measure+'_by_facility')
-            df.to_sql(name=measure+'_by_facility', con=con, if_exists='append', chunksize=1000)
-
-
-        # Re-form screenlines for export to db
-        screenline_data = process_screenlines(screenline_dict)
-        screenline_data = stamp(screenline_data, con=con, table='screenlines')
-        screenline_data.to_sql(name='screenlines', con=con, if_exists='append', chunksize=1000)
-
-        # Export DaySim results to db
-        daysim_metrics = [u'mode', u'travcost', u'travdist', u'travtime']
+        # bike volumes
+        if key in ['5to6','7to8','8to9','9to10','10to14','14to15','15to16','16to17',
+                        '17to18']:
+            bike_volumes(writer=writer, my_project=my_project, tod=key)
         
-        # Convert daysim outputs H5 to a dataframe
-        daysim_df = process_h5(data_table='Trip', h5_file=r'outputs/daysim_outputs.h5', columns=daysim_metrics)
+    list_of_measures = ['vmt', 'vht', 'delay']
 
-        # Process tables that show results by mode
-        for metric in [u'travcost', u'travdist', u'travtime']:
-            df = daysim_df.groupby('mode').mean()[[metric]].T
-            df.columns = ['Walk','Bike','SOV','HOV2','HOV3+','Transit','School Bus']
-            df = stamp(df, con=con, table=metric)
-            df.to_sql(name=metric, con=con, if_exists='append', chunksize=1000)
+    # write total boardings by inode, transit line, and tod
+    # seg_df.to_excel(excel_writer=writer, sheet_name='Segments')
+    
+    # combine initial and final boardings for transfers
 
-        # Transit Counts to db
 
+    seg_df = seg_df.groupby('inode').sum().reset_index()
+    seg_df = seg_df.drop(['tod','line'], axis=1)
+    stop_df = stop_df.groupby('inode').sum().reset_index()
+    transfer_df = pd.merge(stop_df, seg_df, on='inode')
+    transfer_df['transfers'] = transfer_df['total_boardings'] - transfer_df['initial_boardings']
+    transfer_df.to_excel(excel_writer=writer, sheet_name='Transfers by Stop')
 
-        # Mode Share to DB
+    # Compute daily boardings for light-rail and major stops    
+    light_rail(df=transfer_df, writer=writer)
 
    #write out transit:
     # print uc_vmt_dict
@@ -602,9 +1016,6 @@ def main():
     transit_df = pd.DataFrame()
 
     for tod, df in transit_summary_dict.iteritems():
-       #if transit_tod[tod] == 'am':
-       #    pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
-       #keys=None, levels=None, names=None, verify_integrity=False)
         
        workbook = writer.book
        index_format = workbook.add_format({'align': 'left', 'bold': True, 'border': True})
@@ -617,36 +1028,19 @@ def main():
         '17to18_board', '17to18_time', '18to20_board', '18to20_time']]
     transit_atts_df = pd.DataFrame(transit_atts)
     transit_atts_df = transit_atts_df.drop_duplicates(['id'], take_last=True)
-    print transit_atts_df.columns
     transit_df.reset_index(level=0, inplace=True)
-    # transit_df.to_csv('D:/transit_df.csv')
-    # transit_atts_df.to_csv('D:/transit_atts.csv')
-    #transit_df = transit_df.merge(transit_atts_df, 'inner', right_on=['id'], left_on=['id'])
     transit_atts_df = transit_atts_df.merge(transit_df, 'inner', right_on=['id'], left_on=['id'])
-  
     transit_atts_df.to_excel(excel_writer = writer, sheet_name = 'Transit Summaries')
        
-       #if col == 0:
-       #    worksheet = writer.sheets['Transit Summaries']
-       #    routes = df.index.tolist()
-       #    for route_no in range(len(routes)):
-       #        worksheet.write_string(route_no + 1, 0, routes[route_no], index_format)
-       #    col = col + 1
-       #    df.to_excel(excel_writer = writer, sheet_name = 'Transit Summaries', index = False, startcol = col)
-       #    col = col + 2
-       #else:
-       #    df.to_excel(excel_writer = writer, sheet_name = 'Transit Summaries', index = False, startcol = col)
-       #    col = col + 2
-
 
     #*******write out counts:
     for value in counts_dict.itervalues():
-        df_counts = df_counts.merge(value, right_index = True, left_index = True)
-        df_counts = df_counts.drop_duplicates()
+        loop_counts = loop_counts.merge(value, left_index = True, right_index = True)
+    #loop_counts = loop_counts.drop_duplicates()
     
     #write counts out to xlsx:
     #loops
-    df_counts.to_excel(excel_writer = writer, sheet_name = 'Counts Output')
+    loop_counts.to_excel(excel_writer = writer, sheet_name = 'Counts Output')
     
     #aadt:
     aadt_df = pd.DataFrame.from_dict(aadt_counts_dict, orient="index")
@@ -694,35 +1088,14 @@ def main():
     uc_vmt_df = uc_vmt_df.sort_index()
     uc_vmt_df.to_excel(excel_writer = writer, sheet_name = 'UC VMT')
 
-    writer.save()
+    uc_delay_df = pd.DataFrame(columns = uc_list, index = uc_delay_dict.keys())
+    for colnum in range(len(uc_list)):
+        for index in uc_delay_dict.keys():
+            uc_delay_df[uc_list[colnum]][index] = uc_delay_dict[index][colnum]
+    uc_delay_df = uc_delay_df.sort_index()
+    uc_delay_df.to_excel(excel_writer = writer, sheet_name = 'UC Delay')
 
-    #checks if openpyxl is installed (or pip to install it) in order to run xlautofit.run() to autofit the columns
-    import imp
-    try:
-        imp.find_module('openpyxl')
-        found_openpyxl = True
-    except ImportError:
-        found_openpyxl = False
-    if found_openpyxl == True:
-        xlautofit.run('outputs/network_summary_detailed.xlsx')
-
-
-    #else:
-    #    try:
-    #        imp.find_module('pip')
-    #        found_pip = True
-    #    except ImportError:
-    #        found_pip = False
-    #    if found_pip == True:
-    #        pip.main(['install','openpyxl'])
-    #    else:
-    #        print('Library openpyxl needed to autofit columns')
-    
-    #writer = csv.writer(open('outputs/' + screenlines_file, 'ab'))
-    #for key, value in screenline_dict.iteritems():
-    #    print key, value
-    #    writer.writerow([key, value])
-    #writer = None
+    writer.save()
 
 if __name__ == "__main__":
     main()
diff --git a/scripts/summarize/standard/parcel_summary.py b/scripts/summarize/standard/parcel_summary.py
index 609f4145..3c877210 100644
--- a/scripts/summarize/standard/parcel_summary.py
+++ b/scripts/summarize/standard/parcel_summary.py
@@ -1,43 +1,112 @@
 # Load buffered parcel data and summarize by Regional Growth Center
+import pandana as pdna
 import pandas as pd
 import numpy as np
-import os
+import os, sys
+sys.path.append(os.getcwd())
+sys.path.append(os.path.join(os.getcwd(),'scripts/accessibility'))
+from accessibility_configuration import *
 from standard_summary_configuration import *
-import input_configuration # Import as a module to access inputs as a dictionary
+from emme_configuration import *
+import input_configuration
+import re
+import sys
+from pyproj import Proj, transform
+import h5py 
 
 print "running parcel summary"
 
-buffered_parcels = 'buffered_parcels.txt'   # Parcel data
-parcel_urbcen_map = 'parcels_in_urbcens.csv'    # lookup for parcel to RGC
-file_out = 'parcel_summary.xlsx'    # summary output file name
+def assign_nodes_to_dataset(dataset, network, column_name, x_name, y_name):
+    """Adds an attribute node_ids to the given dataset."""
+    dataset[column_name] = network.get_node_ids(dataset[x_name].values, dataset[y_name].values)
+   
+def process_dist_attribute(parcels, network, name, x, y):
+    network.set_pois(name, x, y)
+    res = network.nearest_pois(max_dist, name, num_pois=1, max_distance=999)
+    res[res != 999] = (res[res != 999]/5280.).astype(res.dtypes) # convert to miles
+    res_name = "dist_%s" % name
+    parcels[res_name] = res.loc[parcels.node_ids].values
 
-# Load files in pandas
-main_dir = os.path.abspath('')
-try:
-    parcels = pd.read_table(main_dir + "/inputs/" + buffered_parcels, sep=' ')
-except:
-    print "Missing 'buffered_parcels.dat'"
+    return parcels
 
-try:
-    map = pd.read_csv(main_dir + "/scripts/summarize/inputs/" + parcel_urbcen_map)
-except:
-    print "Missing 'parcels_in_urbcens.csv'"
+def parcel_summary():
+	"""
+	Summarize parcels by RGC for quick check of min, mean, max.
+	"""
+	main_dir = os.path.abspath('')
+	try:
+	    parcels = pd.read_table(main_dir + "/inputs/" + buffered_parcels, sep=' ')
+	except:
+	    print "Missing 'buffered_parcels.dat'"
 
+	try:
+	    map = pd.read_csv(main_dir + "/scripts/summarize/inputs/" + parcel_urbcen_map)
+	except:
+	    print "Missing 'parcels_in_urbcens.csv'"
 
-# Join the urban center location to the parcels file
-parcels = pd.merge(parcels, map, left_on='parcelid', right_on='hhparcel')
-print "Loading parcel data to summarize..."
+	# Join the urban center location to the parcels file
+	parcels = pd.merge(parcels, map, left_on='parcelid', right_on='hhparcel')
+	print "Loading parcel data to summarize..."
 
-# Summarize parcel fields by urban center
-mean_by_urbcen = pd.DataFrame(parcels.groupby('NAME').mean())
-min_by_urbcen = pd.DataFrame(parcels.groupby('NAME').min())
-max_by_urbcen = pd.DataFrame(parcels.groupby('NAME').max())
-std_by_urbcen = pd.DataFrame(parcels.groupby('NAME').std())
+	# Summarize parcel fields by urban center
+	mean_by_urbcen = pd.DataFrame(parcels.groupby('NAME').mean())
+	min_by_urbcen = pd.DataFrame(parcels.groupby('NAME').min())
+	max_by_urbcen = pd.DataFrame(parcels.groupby('NAME').max())
+	std_by_urbcen = pd.DataFrame(parcels.groupby('NAME').std())
 
-# Write results to separate worksheets in an Excel file
-excel_writer = pd.ExcelWriter(main_dir + '/outputs/' + file_out)
+	# Write results to separate worksheets in an Excel file
+	excel_writer = pd.ExcelWriter(main_dir + '/outputs/' + parcel_file_out)
 
-mean_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Mean')
-min_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Min')
-max_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Max')
-std_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Std Dev')
+	mean_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Mean')
+	min_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Min')
+	max_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Max')
+	std_by_urbcen.to_excel(excel_writer=excel_writer, sheet_name='Std Dev')
+
+def transit_access():
+	transit_df = pd.DataFrame.from_csv(transit_stops_name)
+	
+	# Load parcel data
+	parcels = pd.DataFrame.from_csv(parcels_file_name, sep = " ", index_col=None )
+
+	# Check for missing data
+	for col_name in parcels.columns:
+	    # daysim does not use EMPRSC_P
+		if col_name != 'EMPRSC_P':
+			if parcels[col_name].sum() == 0:
+				print col_name + ' column sum is zero! Exiting program.'
+				sys.exit(1)
+
+	# nodes must be indexed by node_id column, which is the first column
+	nodes = pd.DataFrame.from_csv(nodes_file_name)
+	links = pd.DataFrame.from_csv(links_file_name, index_col = None )
+
+	# assign impedence
+	imp = pd.DataFrame(links.Shape_Length)
+	imp = imp.rename(columns = {'Shape_Length':'distance'})
+
+	# create pandana network
+	net = pdna.network.Network(nodes.x, nodes.y, links.from_node_id, links.to_node_id, imp)
+	for dist in distances:
+		net.precompute(dist)
+
+	# set the number of pois on the network for the distance variables (transit + 1 for parks)
+	net.init_pois(len(transit_modes)+1, max_dist, 1)
+
+	# assign network nodes to parcels, for buffer variables
+	assign_nodes_to_dataset(parcels, net, 'node_ids', 'XCOORD_P', 'YCOORD_P')
+
+	# calc the distance from each parcel to nearest transit stop by type
+	for attr in ['frequent']:
+		print attr
+		# get the records/locations that have this type of transit:
+		transit_type_df = transit_df.loc[(transit_df[attr] == 1)]
+		parcels = process_dist_attribute(parcels, net, attr, transit_type_df["x"], transit_type_df["y"])
+		#Some parcels share the same network node and therefore have 0 distance. Recode this to .01.
+		field_name = "dist_%s" % attr
+		parcels.ix[parcels[field_name]==0, field_name] = .01
+
+	parcels.to_csv(transit_access_outfile)
+
+if __name__ == '__main__':
+	parcel_summary()
+	transit_access()
\ No newline at end of file
diff --git a/scripts/summarize/standard/roadway_base_year_validation.py b/scripts/summarize/standard/roadway_base_year_validation.py
index 2687eec5..c9210b97 100644
--- a/scripts/summarize/standard/roadway_base_year_validation.py
+++ b/scripts/summarize/standard/roadway_base_year_validation.py
@@ -1,9 +1,11 @@
-import os
-import sys
+import os, sys
 import pandas as pd
+sys.path.append(os.getcwd())
 from standard_summary_configuration import *
 from input_configuration import *
 from pandas import ExcelWriter
+pd.options.mode.chained_assignment = None  # mute chained assignment warnings
+
    
 def compare_fac_type(daily_df, out_summary):
     daily_df = pd.DataFrame([daily_df.groupby('@scrn').sum()['@tveh'].values,
diff --git a/scripts/summarize/standard/standard_summary_configuration.py b/scripts/summarize/standard/standard_summary_configuration.py
index a22520e8..bf9c5dc1 100644
--- a/scripts/summarize/standard/standard_summary_configuration.py
+++ b/scripts/summarize/standard/standard_summary_configuration.py
@@ -1,7 +1,7 @@
 #################################### NETWORK SUMMARY ####################################
 network_summary_project = 'Projects/LoadTripTables/LoadTripTables.emp'
 project = 'Projects/LoadTripTables/LoadTripTables.emp'
-report_output_location = 'Outputs'
+report_output_location = 'Outputs/network'
 fac_type_dict = {'highway' : 'ul3 = 1 or ul3 = 2',
                  'arterial' : 'ul3 = 3 or ul3 = 4 or ul3 = 6',
                  'connectors' : 'ul3 = 5'}
@@ -55,8 +55,12 @@
 
 
 # Grouped outputs
-comparison_name = '2014_pre_calibration' # define in input_config
-comparison_run_dir = r'D:\Stefan\TWG\Estimated_Models_Pre_Calibration\outputs'
+
+# To compare with 1 or more runs, add key as run name, value as location to main soundcast directory
+# e.g., comparison_runs = {'soundcast_base': 'C:/user/soundcast_base_run', 
+#                          'soundcast_2040': 'C:/user/soundcast_2040'}
+comparison_runs = {}
+compare_survey = True    # compare daysim results in Tableau and topsheet outputs
 
 #### Transit Groupings ###############################################################
 transit_time_group_file= 'scripts/summarize/inputs/network_summary/transit_time_groups.csv'
@@ -65,12 +69,22 @@
 
 
 ##### Output File Locations ######################################################
-net_summary_detailed = 'outputs/network_summary_detailed.xlsx'
-net_summary_out = 'outputs/network_summary_aggregate.xlsx'
-roadway_summary = 'outputs/roadway_validation.xlsx'
-transit_summary_out = 'outputs/transit_validation.xlsx'
+net_summary_detailed = 'outputs/network/network_summary_detailed.xlsx'
+net_summary_out = 'outputs/network/network_summary.xlsx'
+roadway_summary = 'outputs/network/roadway_summary.xlsx'
+transit_summary_out = 'outputs/transit/transit_summary.xlsx'
 
 # Bikes
-bike_link_vol = 'outputs/bike_volumes.csv'
+bike_link_vol = 'outputs/bike/bike_volumes.csv'
 bike_count_data = 'inputs/bikes/bike_count_links.csv'
-edges_file = 'inputs/bikes/edges_0.txt'
\ No newline at end of file
+edges_file = 'inputs/bikes/edges_0.txt'
+
+# Parcel Summary
+buffered_parcels = 'buffered_parcels.txt'   # Parcel data
+parcel_urbcen_map = 'parcels_in_urbcens.csv'    # lookup for parcel to RGC
+parcel_file_out = 'parcel_summary.xlsx'    # summary output file name
+parcels_file_name = 'inputs/accessibility/parcels_urbansim.txt'
+nodes_file_name = 'inputs/accessibility/all_streets_nodes_2014.csv'
+links_file_name = 'inputs/accessibility/all_streets_links_2014.csv'
+transit_access_outfile = 'outputs/transit/freq_transit_access.csv'
+max_dist = 24140.2
\ No newline at end of file
diff --git a/scripts/summarize/standard/summarize_land_use_inputs.py b/scripts/summarize/standard/summarize_land_use_inputs.py
index 64ee4127..7be8ebf5 100644
--- a/scripts/summarize/standard/summarize_land_use_inputs.py
+++ b/scripts/summarize/standard/summarize_land_use_inputs.py
@@ -1,3 +1,5 @@
+import os, sys
+sys.path.append(os.getcwd())
 from input_configuration import *
 from standard_summary_configuration import *
 import pandas as pd
diff --git a/scripts/summarize/standard/transit_base_year_validation.py b/scripts/summarize/standard/transit_base_year_validation.py
index a95ba62e..cd6e6dca 100644
--- a/scripts/summarize/standard/transit_base_year_validation.py
+++ b/scripts/summarize/standard/transit_base_year_validation.py
@@ -1,11 +1,12 @@
-import os
-import sys
+
+import os, sys
 import pandas as pd
+sys.path.append(os.getcwd())
 from standard_summary_configuration import *
 from input_configuration import *
 from pandas import ExcelWriter
+pd.options.mode.chained_assignment = None  # mute chained assignment warnings
 
-   
 
 def compare_boardings(modeled, observed, time_groups, route_groups, time_group_name, type):
 
diff --git a/scripts/summarize/standard/truck_vols.py b/scripts/summarize/standard/truck_vols.py
deleted file mode 100644
index 848fb9c0..00000000
--- a/scripts/summarize/standard/truck_vols.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import pandas as pd
-import numpy as np
-import os, sys
-import h5py
-sys.path.append(os.path.join(os.getcwd(),"scripts"))
-from EmmeProject import *
-from input_configuration import *
-from emme_configuration import *
-from standard_summary_configuration import *
-
-extra_attributes_dict = {'@tveh' : 'total vehicles', 
-                         '@mveh' : 'medium trucks', 
-                         '@hveh' : 'heavy trucks', 
-                         '@vmt' : 'vmt',\
-                         '@vht' : 'vht', 
-                         '@trnv' : 'buses in auto equivalents',
-                         '@ovol' : 'observed volume', 
-                         '@bveh' : 'number of buses'}
-
-def get_link_attribute(attr, network):
-    ''' Return dataframe of link attribute and link ID'''
-    link_dict = {}
-    for i in network.links():
-        link_dict[i.id] = i[attr]
-    df = pd.DataFrame({'link_id': link_dict.keys(), attr: link_dict.values()})
-    return df
-
-def calc_total_vehicles(my_project):
-     '''For a given time period, calculate link level volume, store as extra attribute on the link'''
-    
-     #medium trucks
-     my_project.network_calculator("link_calculation", result = '@mveh', expression = '@metrk/1.5')
-     
-     #heavy trucks:
-     my_project.network_calculator("link_calculation", result = '@hveh', expression = '@hvtrk/2.0')
-     
-     #busses:
-     my_project.network_calculator("link_calculation", result = '@bveh', expression = '@trnv3/2.0')
-     
-     #calc total vehicles, store in @tveh 
-     str_expression = '@svtl1 + @svtl2 + @svtl3 + @svnt1 +  @svnt2 + @svnt3 + @h2tl1 + @h2tl2 + @h2tl3 + @h2nt1 + @h2nt2 + @h2nt3 + @h3tl1\
-                                + @h3tl2 + @h3tl3 + @h3nt1 + @h3nt2 + @h3nt3 + @lttrk + @mveh + @hveh + @bveh'
-     my_project.network_calculator("link_calculation", result = '@tveh', expression = str_expression)
-
-
-def get_aadt_trucks(my_project):
-    '''Calculate link level daily total truck passenger equivalents for medium and heavy, store in a DataFrame'''
-    
-    link_list = []
-
-    for key, value in sound_cast_net_dict.iteritems():
-        my_project.change_active_database(key)
-        
-        # Create extra attributes to store link volume data
-        for name, desc in extra_attributes_dict.iteritems():
-            my_project.create_extra_attribute('LINK', name, desc, 'True')
-        
-        ## Calculate total vehicles for each link
-        calc_total_vehicles(my_project)
-        
-        # Loop through each link, store length and truck pce
-        network = my_project.current_scenario.get_network()
-        for link in network.links():
-            link_list.append({'link_id' : link.id, '@mveh' : link['@mveh'], '@hveh' : link['@hveh'], 'length' : link.length})
-            
-    df = pd.DataFrame(link_list, columns = link_list[0].keys())       
-    
-    grouped = df.groupby(['link_id'])
-    
-    df = grouped.agg({'@mveh':sum, '@hveh':sum, 'length':min})
-    
-    df.reset_index(level=0, inplace=True)
-    
-    return df
-    
-        
-
-def main():
-   print 'running truck_summary'
-   truck_counts = pd.read_excel(truck_counts_file)
-   filepath = r'projects/' + master_project + r'/' + master_project + '.emp'
-   my_project = EmmeProject(filepath)
-   truck_volumes =get_aadt_trucks(my_project)
-   truck_compare = pd.merge(truck_counts, truck_volumes, left_on = 'ij_id', right_on = 'link_id')
-
-   truck_compare['modeledTot'] = truck_compare['@mveh']+truck_compare['@hveh']
-   truck_compare['modeledMed'] = truck_compare['@mveh']
-   truck_compare['modeledHvy'] = truck_compare['@hveh']
-   truck_compare_grouped_sum = truck_compare.groupby(['CountID']).sum()[['modeledTot', 'modeledMed', 'modeledHvy']]
-   truck_compare_grouped_sum.reset_index(level=0, inplace=True)
-   truck_compare_grouped_min = truck_compare.groupby(['CountID']).min()[['Location', 'LocationDetail', 'FacilityType', 'length', 'observedMed',
-   																		'observedHvy', 'observedTot','county','LARGE_AREA']]
-   truck_compare_grouped_min.reset_index(level=0, inplace=True)
-   trucks_out= pd.merge(truck_compare_grouped_sum, truck_compare_grouped_min, on= 'CountID')
-   # trucks_out['ModeledVolumes'] = trucks_out['med_hvy_tot']
-   writer = pd.ExcelWriter('outputs/trucks_vol_summary.xlsx')
-   trucks_out.to_excel(writer, 'AllCounts')
-
-   # Write out counts by facility type
-   facility_counts = trucks_out.groupby('FacilityType').sum()
-   facility_counts.drop(['CountID','length'], axis=1, inplace=True)
-   facility_counts.to_excel(writer, 'CountsByFacilityType')
-
-   # Write out counts by county
-   cnty_counts = trucks_out.groupby('county').sum()
-   cnty_counts.drop(['CountID', 'length'], axis=1, inplace=True)
-   cnty_counts.to_excel(writer, 'CountsByCounty')
-
-   # Write out counts by FAZ large area (district)
-   distr_counts = trucks_out.groupby('LARGE_AREA').sum()
-   distr_counts.drop(['CountID', 'length'], axis=1, inplace=True)
-   distr_counts.to_excel(writer, 'CountsByDistrict')
-
-if __name__ == "__main__":
-	main()
\ No newline at end of file
diff --git a/scripts/summary_functions.py b/scripts/summary_functions.py
index 8e41b385..b4a9197d 100644
--- a/scripts/summary_functions.py
+++ b/scripts/summary_functions.py
@@ -12,10 +12,11 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
+import os, sys
 import pandas as pd
 import numpy as np
 import math
-
+sys.path.append(os.getcwd())
 
 #Computation functions
 
diff --git a/scripts/supplemental/create_airport_trips_combine_all.py b/scripts/supplemental/create_airport_trips_combine_all.py
new file mode 100644
index 00000000..870aa2ec
--- /dev/null
+++ b/scripts/supplemental/create_airport_trips_combine_all.py
@@ -0,0 +1,245 @@
+import pandas as pd
+import numpy as np 
+import h5py
+import glob, os
+pd.set_option('display.float_format', lambda x: '%.3f' % x)
+import os
+#os.chdir('D:/soundcast_mode_choice/soundcast/')
+import sys
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
+from emme_configuration import *
+from input_configuration import *
+from EmmeProject import *
+
+DAYSIM = 'inputs/hh_and_persons.h5'
+PARCEL = 'inputs/accessibility/parcels_urbansim.txt'
+OUT_PUT = 'outputs/supplemental/AIRPORT_TRIPS.csv'
+daysim = h5py.File(DAYSIM,'r+')
+parcel = pd.read_csv(PARCEL, ' ')
+my_project = EmmeProject('projects/Supplementals/Supplementals.emp')
+zonesDim = len(my_project.current_scenario.zone_numbers)
+zones = my_project.current_scenario.zone_numbers
+
+# apply mode shares
+hbo_ratio = h5py.File('outputs/supplemental/hbo_ratio.h5', 'r')
+mode_dict = {'walk':'nwshwk', 'bike':'nwshbk','litrat':'nwshtw', 'sov': 'nwshda', 'hov2':'nwshs2', 'hov3':'nwshs3'}
+#Time of the day factors
+test = {'5to6' : {'sov' : .04, 'hov' : .035, 'transit' : .04}, '6to7' : {'sov' : .053, 'hov' : .056, 'transit' : .053}, '7to8' : {'sov' : .06, 'hov' : .061, 'transit' : .06}, 
+        '8to9' : {'sov' : .057, 'hov' : .057, 'transit' : .057}, '9to10' : {'sov' : .055, 'hov' : .051, 'transit' : .055}, '10to14' : {'sov' : .2250, 'hov' : .1880, 'transit' : .2250}, 
+        '14to15' : {'sov' : .061, 'hov' : .07, 'transit' : .061}, '15to16' : {'sov' : .061, 'hov' : .082, 'transit' : .061}, '16to17' : {'sov' : .06, 'hov' : .084, 'transit' : .06}, 
+        '17to18' : {'sov' : .06, 'hov' : .08, 'transit' : .06}, '18to20' : {'sov' : .10, 'hov' : .108, 'transit' : .269}, '20to5' : {'sov' : .169, 'hov' : .125, 'transit' : 0.0}}
+
+output_dir = r'outputs/supplemental/'
+# Daysim trips = pop * 0.02112
+def build_df(h5file, h5table, var_dict, nested):
+    ''' Convert H5 into dataframe '''
+    data = {}
+    if nested:
+        # survey h5 have nested data structure, different than daysim_outputs
+        for col_name, var in var_dict.iteritems():
+            data[col_name] = [i[0] for i in h5file[h5table][var][:]]
+    else:
+        for col_name, var in var_dict.iteritems():
+            data[col_name] = [i for i in h5file[h5table][var][:]]
+    return pd.DataFrame(data)
+
+
+def daysim_sort(daysim):
+    hhdict={'Household ID': 'hhno',
+        'Household Size': 'hhsize',
+        'Household TAZ': 'hhtaz',
+        'Expansion Factor': 'hhexpfac'}
+    daysim_df = build_df(h5file=daysim, h5table='Household', var_dict = hhdict, nested=False)
+    daysim_sorted = pd.DataFrame(daysim_df.groupby('Household TAZ').sum()['Household Size'])
+    del daysim_sorted.index.name
+    daysim_sorted['TAZ'] = daysim_sorted.index
+    daysim_sorted['pop_trip'] = daysim_sorted['Household Size']*0.02112
+    return daysim_sorted
+
+
+# Parcel trips = valid emp * 0.01486
+def parcel_sort(parcel):
+    parcel['Employment Size'] = parcel['EMPTOT_P'] - parcel['EMPEDU_P'] + parcel['EMPGOV_P']
+    parcel['emp_trip'] = parcel['Employment Size']*0.01486
+    parcel_sorted = pd.DataFrame(parcel.groupby('TAZ_P').sum()[['emp_trip','Employment Size']])
+    del parcel_sorted.index.name
+    parcel_sorted['TAZ'] = parcel_sorted.index
+    return parcel_sorted
+
+
+# estimated trips
+def trips_estimation(parcel_sorted, daysim_sorted):
+    trips = pd.DataFrame.merge(parcel_sorted, daysim_sorted, on='TAZ')
+    trips['est_trips'] = trips['pop_trip'] + trips['emp_trip']
+    return trips
+
+
+# Adjust estimated trips by appling observed control total 
+def trips_adjustion(trips, control_total):
+    #obs_trips = 101838
+    est_trips = trips['est_trips'].sum()
+    print 'Total number of estimated trips is', est_trips
+    adj_factor = control_total/est_trips
+    print 'The adjusted factor of observe/estimate is:', adj_factor
+    trips['adj_trips'] = trips['est_trips']*adj_factor
+
+    # Trip number validation
+    if  0 < (trips['adj_trips'].sum() - control_total) < 1:
+        print 'Total number of adjusted trips is as same as observed trips '
+    cols = ['TAZ', 'adj_trips']
+    airport_trips = trips[cols]
+    airport_trips['SeaTac_Taz'] = 983
+    return airport_trips
+
+
+#open shares for hbo:
+def create_demand_matrix(airport_trips, org_zones, dest_zones):
+    zonesDim = len(my_project.current_scenario.zone_numbers)
+    zones = my_project.current_scenario.zone_numbers
+    #demand_matrix = np.zeros((zonesDim,zonesDim), np.float16)
+    demand_matrix = np.zeros((org_zones, dest_zones), np.float16) #IndexError: index 3868 is out of bounds for axis 0 with size 3868
+    #dictZoneLookup = dict((value,index) for index,value in enumerate(zones))
+
+    #convert to matrix
+    for rec in airport_trips.iterrows():
+        #origin = dictZoneLookup[int(rec[1][0])]
+        #destination = dictZoneLookup[983]
+        demand = rec[1][1]
+        origin = int(rec[1][0]) -1
+        destination = 983-1
+        print origin 
+        print demand
+        demand_matrix[origin, destination] = demand
+    return demand_matrix
+
+
+# Input the model choice ratio matrices
+def apply_ratio_to_trips(trip_table, ratio_table):
+    result = np.zeros(shape=trip_table.shape)
+    for i in range(len(trip_table)):
+        # iterate through columns
+        for j in range(len(trip_table[0])):
+            result[i][j] = trip_table[i][j] * ratio_table[i][j]
+    return result
+
+
+# split and output trip tables by mode and directions
+def split_trips_into_modes(direction_trips):
+    table_dict = {}
+    for mode, ratio in mode_dict.iteritems():
+        print mode
+        ratio = hbo_ratio['hbo'][ratio][:]
+        mod_trip = apply_ratio_to_trips(direction_trips, ratio) 
+        # convert hov to vehicle trips (/2, /3.5)
+        if mode == 'hov2':
+            mod_trip = mod_trip / 2
+        if mode == 'hov3':
+            mod_trip = mod_trip / 3.5
+        table_dict[mode] = mod_trip
+    return table_dict
+
+
+# Split trips into time of a day: apply time of the day factors to internal trips
+def split_tod_internal(airport_trips, matrix_dict):
+    for tod, dict in test.iteritems():
+        #open work externals:
+        ixxi_work_store = h5py.File('outputs/supplemental/external_work_' + tod + '.h5', 'r')
+        sov = np.array(airport_trips['sov']) * float(test[tod]['sov']) + np.array(ixxi_work_store['svtl'])
+        hov2 = np.array(airport_trips['hov2']) * float(test[tod]['hov']) + np.array(ixxi_work_store['h2tl'])
+        hov3 = np.array(airport_trips['hov3']) * float(test[tod]['hov']) + np.array(ixxi_work_store['h3tl'])
+        ixxi_work_store.close()
+        ''' At this point, We determine to use SOV factors on transit, bike and walk '''
+        litrat = np.array(airport_trips['litrat']) * float(test[tod]['transit'])
+        walk = np.array(airport_trips['walk']) * float(test[tod]['sov'])
+        bike = np.array(airport_trips['bike']) * float(test[tod]['sov'])
+        matrix_dict[tod]= {'svtl' : sov, 'h2tl' : hov2, 'h3tl' : hov3, 'litrat': litrat, 'walk': walk, 'bike': bike}
+    return matrix_dict
+
+
+# Combine external and internal trips
+def combine_IE_trips(path, matrix_dict1, matrix_dict2, matrix_dict):
+    for tod in test.keys():
+       print tod
+       external_trips = h5py.File(path + tod + '.h5', 'r')
+       print external_trip_dir + tod + '.h5'
+       for mod in ['svtl', 'h2tl', 'h3tl']:
+           matrix_dict1[tod][mod][0:3865, 0:3865] = matrix_dict1[tod][mod][0:3865, 0:3865] + np.array(external_trips[mod])
+           matrix_dict2[tod][mod] += matrix_dict1[tod][mod]
+    matrix_dict = matrix_dict2
+    return matrix_dict
+
+
+# Output trips
+def output_trips(path, matrix_dict):
+    for tod in matrix_dict.iterkeys():
+        print tod
+        print "Exporting supplemental trips for time period: " + str(tod)
+        my_store = h5py.File(path + str(tod) + '.h5', "w")
+        for mode, value in matrix_dict[tod].iteritems():
+            my_store.create_dataset(str(mode), data=value)
+        my_store.close()
+        
+
+########################### Calculation ##########################
+# Get airport trip table  (all-in-one table) 
+daysim_sorted = daysim_sort(daysim)
+parcel_sorted = parcel_sort(parcel)
+trips = trips_estimation(parcel_sorted, daysim_sorted)
+airport_trips = trips_adjustion(trips, airport_control_total[model_year])
+print zonesDim
+demand_matrix = create_demand_matrix(airport_trips, zonesDim, zonesDim)
+print 'create airport trip demand matrix, done'
+
+# split airport trips into different modes, by appling HBO ratios
+# we would like to seperate airport trips by two directions, since the mode choice ratio are different between 'to' and 'from airport'
+to_airport = demand_matrix/2
+from_airport = to_airport.transpose()
+all = to_airport+from_airport
+airport_trips_by_mode = split_trips_into_modes(all)
+print 'split to_airport_trips into modes, done'
+airport_matrix_dict = {}
+
+#open external trip tables:
+ixxi_non_work_store = h5py.File('outputs/supplemental/external_non_work.h5', 'r')
+external_modes = ['svtl', 'h2tl', 'h3tl']
+ext_trip_table_dict = {}
+ext_tod_dict = {}
+# get the non work external trips
+for mode in external_modes:
+    ext_trip_table_dict[mode] = np.array(ixxi_non_work_store[mode])
+# add external non work to airport trips
+airport_trips_by_mode['sov'] = airport_trips_by_mode['sov'] + ext_trip_table_dict['svtl']
+airport_trips_by_mode['hov2'] = airport_trips_by_mode['hov2'] + ext_trip_table_dict['h2tl']
+airport_trips_by_mode['hov3'] = airport_trips_by_mode['hov3'] + ext_trip_table_dict['h3tl']
+
+#apply time of day factors:
+split_tod_internal(airport_trips_by_mode, airport_matrix_dict)
+print 'split internal to airpoprt trips into time periods, done'
+
+## Output final trip tables, which are by time of the day and trip mode. 
+output_trips(output_dir, airport_matrix_dict)
+#print 'congrats, your airport trip tables are ready at:', combined_bidir_airport_trip_dir
+
+
+
+##*******************************************VALIDATION/Statistic *******************************************************
+
+
+#to_airport2 = h5py.File('outputs/supplemental/mode_choice/trips_to_airport_am.h5', 'r')
+#from_airport2 = h5py.File('outputs/supplemental/mode_choice/trips_from_airport_am.h5', 'r')
+
+
+#to_total = 0
+#from_total = 0
+#for mode in ['sov', 'hov2', 'hov3', 'bike', 'walk', 'litrat']:
+#    print mode
+#    print np.sum(np.asarray(to_airport2[mode]))
+#    print np.sum(np.asarray(from_airport2[mode]))
+#    to_total += np.sum(np.asarray(to_airport2[mode]))
+#    from_total += np.sum(np.asarray(from_airport2[mode]))
+#print 'total trips to airport:', to_total 
+#print 'total trips from airport:', from_total
+#to_airport2.close()
+#from_airport2.close()
+
diff --git a/scripts/supplemental/create_ixxi_work_trips.py b/scripts/supplemental/create_ixxi_work_trips.py
new file mode 100644
index 00000000..44b672df
--- /dev/null
+++ b/scripts/supplemental/create_ixxi_work_trips.py
@@ -0,0 +1,218 @@
+import pandas as pd
+import numpy as np
+import h5py
+import sys 
+import os
+#os.chdir('D:/Stefan/TWG/Estimated_Models_Pre_Calibration')
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.path.join(os.getcwd(),"scripts/trucks"))
+sys.path.append(os.getcwd())
+from emme_configuration import *
+from input_configuration import *
+from EmmeProject import *
+from truck_configuration import *
+
+output_dir = r'outputs/supplemental/'
+my_project = EmmeProject(r'projects\Supplementals\Supplementals.emp')
+
+tod_factors = {'5to6' : .04, '6to7' : .075, '7to8' : 0.115, '8to9' : 0.091, '9to10' : 0.051, '10to14' : 0.179, '14to15' : 0.056, '15to16' : 0.071, '16to17' : 0.106, '17to18' : 0.101, 
+        '18to20' : 0.06, '20to5' : 0.055}
+
+# list of jblm taz's
+jblm_taz_list = [3061, 3070, 3346, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356]
+
+# dictionary to hold taz id and total enlisted to use to update externals
+jbml_enlisted_taz_dict = {}
+
+# it might make sense to put these file locations in a config somewhere
+parcel_file =  "inputs\\accessibility\\parcels_urbansim.txt"
+military_file = "inputs\\accessibility\\enlisted_personnel.csv"
+non_worker_file = r'inputs\supplemental\generation\externals_unadjusted.csv'
+
+parcel_emp_cols = parcel_attributes =["EMPMED_P", "EMPOFC_P", "EMPEDU_P", "EMPFOO_P", "EMPGOV_P", "EMPIND_P", "EMPSVC_P", "EMPOTH_P", "EMPTOT_P", "EMPRET_P"]
+
+def network_importer(EmmeProject):
+    for scenario in list(EmmeProject.bank.scenarios()):
+            EmmeProject.bank.delete_scenario(scenario)
+        #create scenario
+    EmmeProject.bank.create_scenario(1002)
+    EmmeProject.change_scenario()
+        #print key
+    EmmeProject.delete_links()
+    EmmeProject.delete_nodes()
+    EmmeProject.process_modes('inputs/networks/' + mode_file)
+    EmmeProject.process_base_network('inputs/networks/' + truck_base_net_name)
+
+def h5_to_data_frame(h5_file, group_name):
+    col_dict = {}
+    for col in h5_file[group_name].keys():
+        my_array = np.asarray(h5_file[group_name][col])
+        col_dict[col] = my_array
+    return pd.DataFrame(col_dict)
+
+def remove_employment_by_taz(df, taz_list, col_list):
+    for taz in taz_list:
+        for col in col_list:
+            df.loc[df['TAZ_P'] == taz, col] = 0
+    return df
+
+# bank needs a network
+network_importer(my_project)
+
+# input files
+parcels_urbansim = pd.read_csv(parcel_file, sep = " ")
+parcels_military = pd.read_csv(military_file)
+non_worker_external = pd.read_csv(non_worker_file)
+
+# Convert columns to upper case for now
+parcels_urbansim.columns = [i.upper() for i in parcels_urbansim.columns]
+
+######## Add enlisted jobs to parcels:
+
+#military file has a record for each block so need to sum, but keep first zone
+f = {}
+for col in parcels_military.columns:
+    if col == 'Zone' :
+        f[col] = ['first']
+    elif col <> 'ParcelID':
+        f[col] = ['sum']
+parcels_military = parcels_military.groupby('ParcelID').agg(f).reset_index()
+
+# go through each parcel and add enlisted job to the parcel file
+for row in parcels_military.iterrows():
+    parcel_id = int(row[1]['ParcelID'])
+    taz_id = int(row[1]['Zone'])
+    enlisted_jobs = float(row[1][model_year])
+    if taz_id in jblm_taz_list:
+        jbml_enlisted_taz_dict[taz_id] = enlisted_jobs
+    # add enlisted jobs to existing gov jobs at the parcel
+    parcels_urbansim.ix[parcels_urbansim.PARCELID==parcel_id, 'EMPGOV_P'] = float(parcels_urbansim.ix[parcels_urbansim.PARCELID==parcel_id, 'EMPGOV_P']) + enlisted_jobs
+    # add enlisted jobs to existing total jobs at the parcel
+    print 'old total ' + str(float(parcels_urbansim.ix[parcels_urbansim.PARCELID==parcel_id, 'EMPTOT_P']))
+    parcels_urbansim.ix[parcels_urbansim.PARCELID==parcel_id, 'EMPTOT_P'] = float(parcels_urbansim.ix[parcels_urbansim.PARCELID==parcel_id, 'EMPTOT_P']) + enlisted_jobs
+    print 'new total ' + str(float(parcels_urbansim.ix[parcels_urbansim.PARCELID==parcel_id, 'EMPTOT_P']))
+   
+parcels_urbansim.to_csv(parcel_file, sep = ' ', index = False)
+###############
+
+######## Create Trip Tables for IXXI jobs:
+# Get Zones
+zonesDim = len(my_project.current_scenario.zone_numbers)
+zones = my_project.current_scenario.zone_numbers
+dictZoneLookup = dict((value,index) for index,value in enumerate(zones))
+
+# read External work trips
+work = pd.read_excel('inputs/supplemental/distribution/External_Work_NonWork_Inputs.xlsx','External_Workers')
+# keep only the needed columns
+work = work [['PSRC_TAZ','External_Station','Total_IE', 'Total_EI', 'SOV_Veh_IE', 'SOV_Veh_EI','HOV2_Veh_IE','HOV2_Veh_EI','HOV3_Veh_IE','HOV3_Veh_EI']]
+
+# group trips by O-D TAZ's
+w_grp = work.groupby(['PSRC_TAZ','External_Station']).sum()
+
+# external enlisted are not included in worker flow file, so we are adding them in for JBML. assume that 30% jblm enlisted come from thurston county:
+#for taz in jblm_taz_list:
+#    # 30% come from the two thurston county externals
+#    enlisted = jbml_enlisted_taz_dict[taz] * .3
+    
+#    # use 90% for I-5 external- 3733, put them all in SOV for now
+#    if not (taz, 3733) in list(w_grp.index.values):
+#        w_grp.ix[(taz,3733),:] = 0
+#    w_grp.ix[(taz,3733),'Total_EI'] = w_grp.ix[(taz,3733),'Total_EI'] + (.90 * enlisted)
+#    w_grp.ix[(taz,3733),'SOV_Veh_EI'] = w_grp.ix[(taz,3733),'SOV_Veh_EI'] + (.90 * enlisted)
+    
+#    # use 90% for I-5 external- 3733, put them all in SOV for now
+#    if not (taz, 3734) in list(w_grp.index.values):
+#        w_grp.ix[(taz,3734),:] = 0
+#    w_grp.ix[(taz,3734),'Total_EI'] = w_grp.ix[(taz,3734),'Total_EI'] + (.10 * enlisted)
+#    w_grp.ix[(taz,3734),'SOV_Veh_EI'] = w_grp.ix[(taz,3734),'SOV_Veh_EI'] + (.10 * enlisted)
+
+## update the exernal non worker inpout for the gravity model:
+#non_worker_external.ix[non_worker_external.taz==3733, 'hsppro'] = float(non_worker_external.ix[non_worker_external.taz==3733, 'hsppro'] - (.90 * .30 * sum(jbml_enlisted_taz_dict.values())))
+#non_worker_external.ix[non_worker_external.taz==3734, 'hsppro'] = float(non_worker_external.ix[non_worker_external.taz==3734, 'hsppro'] - (.10 * .30 * sum(jbml_enlisted_taz_dict.values())))
+
+# export to .csv
+non_worker_external.to_csv(r'inputs\supplemental\generation\externals.csv', index = False)
+
+# create empty numpy matrices for SOV, HOV2 and HOV3
+w_SOV = np.zeros((zonesDim,zonesDim), np.float16)
+w_HOV2 = np.zeros((zonesDim,zonesDim), np.float16)
+w_HOV3 = np.zeros((zonesDim,zonesDim), np.float16)
+
+# populate the numpy trips matrices
+for i in work['PSRC_TAZ'].value_counts().keys():
+    for j in work.groupby('PSRC_TAZ').get_group(i)['External_Station'].value_counts().keys(): #all the external stations for each internal PSRC_TAZ
+        #SOV
+        w_SOV[dictZoneLookup[i],dictZoneLookup[j]] = w_grp.loc[(i,j),'SOV_Veh_IE']
+        w_SOV[dictZoneLookup[j],dictZoneLookup[i]] = w_grp.loc[(i,j),'SOV_Veh_EI']
+        #HOV2
+        w_HOV2[dictZoneLookup[i],dictZoneLookup[j]] = w_grp.loc[(i,j),'HOV2_Veh_IE']
+        w_HOV2[dictZoneLookup[j],dictZoneLookup[i]] = w_grp.loc[(i,j),'HOV2_Veh_EI']
+        #HOV3
+        w_HOV3[dictZoneLookup[i],dictZoneLookup[j]] = w_grp.loc[(i,j),'HOV3_Veh_IE']
+        w_HOV3[dictZoneLookup[j],dictZoneLookup[i]] = w_grp.loc[(i,j),'HOV3_Veh_EI']
+sov = w_SOV + w_SOV.transpose()
+hov2 = w_HOV2 + w_HOV2.transpose()
+hov3 = w_HOV3 + w_HOV3.transpose()
+
+matrix_dict = {}
+matrix_dict = {'svtl' : sov, 'h2tl' : hov2, 'h3tl' : hov3}
+
+# create h5 files
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+for tod, factor in tod_factors.iteritems():
+    my_store = h5py.File(output_dir + '/' + 'external_work_' + tod + '.h5', "w")
+    for mode, matrix in matrix_dict.iteritems():
+        matrix = matrix * factor
+        my_store.create_dataset(str(mode), data=matrix)
+    my_store.close()	
+
+###############
+
+######Create ixxi file
+w_grp.reset_index(inplace= True)
+w_grp.reset_index(inplace= True)
+observed_ixxi = w_grp.groupby('PSRC_TAZ').sum()
+observed_ixxi = observed_ixxi.reindex(zones, fill_value=0)
+observed_ixxi.reset_index(inplace = True)
+
+parcel_df = pd.read_csv(r'inputs\accessibility\parcels_urbansim.txt',  sep = ' ')
+parcel_df = remove_employment_by_taz(parcel_df, jblm_taz_list, parcel_emp_cols)
+hh_persons = h5py.File(r'inputs\hh_and_persons.h5', "r")
+parcel_grouped = parcel_df.groupby('TAZ_P')
+emp_by_taz = pd.DataFrame(parcel_grouped['EMPTOT_P'].sum())
+emp_by_taz.reset_index(inplace = True)
+
+person_df = h5_to_data_frame(hh_persons, 'Person')
+print len(person_df)
+person_df = person_df.loc[(person_df.pwtyp > 0)]
+hh_df = h5_to_data_frame(hh_persons, 'Household')
+merged = person_df.merge(hh_df, how= 'left', on = 'hhno')
+print len(merged)
+merged_grouped = merged.groupby('hhtaz')
+
+workers_by_taz = pd.DataFrame(merged_grouped['pno'].count())
+workers_by_taz.rename(columns={'pno' :'workers'}, inplace = True)
+workers_by_taz.reset_index(inplace = True)
+
+final_df = emp_by_taz.merge(workers_by_taz, how= 'left', left_on = 'TAZ_P', right_on = 'hhtaz')
+final_df = observed_ixxi.merge(final_df, how= 'left', left_on = 'PSRC_TAZ', right_on = 'TAZ_P')
+final_df['Worker_IXFrac'] = final_df.Total_IE/final_df.workers
+final_df['Jobs_XIFrac'] = final_df.Total_EI/final_df.EMPTOT_P
+
+final_df.loc[final_df['Worker_IXFrac'] > 1, 'Worker_IXFrac'] = 1
+final_df.loc[final_df['Jobs_XIFrac'] > 1, 'Jobs_XIFrac'] = 1
+
+final_df = final_df.replace([np.inf, -np.inf], np.nan) 
+final_df = final_df.fillna(0)
+final_cols = ['PSRC_TAZ', 'Worker_IXFrac', 'Jobs_XIFrac']
+
+for col_name in final_df.columns:
+    if col_name not in final_cols:
+        final_df.drop(col_name, axis=1, inplace=True)
+final_df = final_df.round(3)
+
+final_df.to_csv('inputs/psrc_worker_ixxifractions.dat', sep = '\t', index = False, header = False)
+parcel_df.to_csv(r'inputs\accessibility\parcels_urbansim.txt',  sep = ' ', index = False)
+###############
diff --git a/scripts/supplemental/distribute_non_work_ixxi.py b/scripts/supplemental/distribute_non_work_ixxi.py
new file mode 100644
index 00000000..3b09fd99
--- /dev/null
+++ b/scripts/supplemental/distribute_non_work_ixxi.py
@@ -0,0 +1,572 @@
+import array as _array
+import os
+import shutil
+import json
+import csv
+import pandas as pd
+import h5py
+import numpy as np
+#os.chdir(r"D:\soundcast_mode_choice\soundcast")
+import sys
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.path.join(os.getcwd(),"scripts/trucks"))
+sys.path.append(os.getcwd())
+from emme_configuration import *
+from EmmeProject import *
+from truck_configuration import *
+#from mode_choice_supplemental import *
+
+# Global variable to hold taz id/index; populated in main
+
+print os.getcwdu()
+#dictZoneLookup = {}
+
+def json_to_dictionary(dict_name):
+    ''' Load supplemental input files as dictionary '''
+    input_filename = os.path.join('inputs/supplemental/',dict_name+'.json').replace("\\","/")
+    my_dictionary = json.load(open(input_filename))
+
+    return(my_dictionary)
+
+# Load the trip productions and attractions
+trip_table = pd.read_csv(r'outputs\supplemental\prod_att.csv', index_col="taz")  # total 4K Ps and As by trip purpose
+gq_trip_table = pd.read_csv(r'outputs\supplemental\gq_prod_att.csv', index_col="taz")  # only group quarter Ps and As
+
+# Import JSON inputs as dictionaries
+coeff = json_to_dictionary('gravity_model')
+mode_dict = json_to_dictionary('mode_dict')
+# Need a new time dictionary! (updated)
+'''time_dict = json_to_dictionary('time_dict')'''
+time_dict = json_to_dictionary('time_dict')
+purp_tod_dict = json_to_dictionary('purp_tod_dict')
+mode_list = ['svtl2', 'h2tl2', 'h3tl2', 'trnst', 'walk', 'bike']
+time_periods = time_dict['svtl'].keys()
+
+# Import new and old trip mode 
+new_mode_list = {'hbw1': ['w1shda', 'w1shs2', 'w1shs3', 'w1shtw', 'w1shbk', 'w1shwk'],
+                       'hbw2': ['w2shda', 'w2shs2', 'w2shs3', 'w2shtw', 'w2shbk', 'w2shwk'],
+                       'hbw3': ['w3shda', 'w3shs2', 'w3shs3', 'w3shtw', 'w3shbk', 'w3shwk'],
+                       'nhb': ['nhshda', 'nhshs2', 'nhshs3', 'nhshtw',  'nhshbk', 'nhshwk'],
+                       'hbo': ['nwshda', 'nwshs2', 'nwshs3', 'nwshtw',  'nwshbk', 'nwshwk']}
+
+old_mode_list = ['svtl', 'h2tl', 'h3tl', 'trnst', 'bike', 'walk']
+final_mode_list = ['svtl1', 'svtl2', 'svtl3', 
+                   'h2tl1', 'h2tl2', 'h2tl3', 
+                   'h3tl1', 'h3tl2', 'h3tl3', 
+                   'transit', 'walk', 'bike'] #That's it, for everyone!!!
+                     
+                   
+# Trip purposes lists - group quarters trips are only given for home-based inc class 1 (hw1)
+#trip_purp_full = ("hsp", "hbo", "sch", "wko", "col", "oto", "hw1", "hw2", "hw3", "hw4")
+
+#Putting all external trips to hps
+trip_purp_full = ('hsp',)
+
+
+trip_purp_gq = ("hsp", "hbo", "sch", "wko", "col", "oto", "hw1")
+# Replace old trip purpose into new purp structure
+new_purp_list = ['hbw1', 'hbw2', 'hbw3', 'hbo', 'nhb']
+# For old trip purposes ['wko', 'hsp', 'oto'] are combined to one trip purpose: 'nhb'
+new_to_old_purp_list = {'hbw1' : 'hw1', 'hbw2' : 'hw2', 'hbw3' : 'hw3', 'hbo' : 'hbo', 'nhb' : 'nhb'}
+# Final structure of trip purp
+trip_purps =['hbo', 'nhb', 'hw2', 'hw3', 'hw1']
+
+# Other inputs 
+supplemental_loc = 'outputs/supplemental/'
+output_dir = 'outputs/supplemental/'
+ext_spg_dir = 'outputs/supplemental/ext_spg' # External and special generator trips generated by zone
+gq_directory = 'outputs/supplemental/group_quarters' # Temporarily store group quarters trip table
+
+
+
+def init_dir(directory):
+    if os.path.exists(directory):
+        shutil.rmtree(directory)
+    os.mkdir(directory)
+
+def load_skims(skim_file_loc, mode_name, divide_by_100=False):
+    ''' Loads H5 skim matrix for specified mode. '''
+    with h5py.File(skim_file_loc, "r") as f:
+        skim_file = f['Skims'][mode_name][:]
+    # Divide by 100 since decimals were removed in H5 source file through multiplication
+    if divide_by_100:
+        return skim_file.astype(float)/100
+    else:
+        return skim_file
+
+def calc_fric_fac(cost_skim, dist_skim):
+    ''' Calculate friction factors for all trip purposes '''
+    friction_fac_dic = {}
+    for purpose, coeff_val in coeff.iteritems():
+        friction_fac_dic[purpose] = np.exp((coeff[purpose])*(cost_skim + (dist_skim * autoop * avotda)))
+        ## Set external zones to zero to prevent external-external trips
+        friction_fac_dic[purpose][LOW_STATION:] = 0
+        friction_fac_dic[purpose][:,[x for x in range(LOW_STATION, len(cost_skim))]] = 0
+
+    return friction_fac_dic
+
+def delete_matrices(my_project, matrix_type):
+    ''' Deletes all Emme matrices of specified type in emmebank '''
+    for matrix in my_project.bank.matrices():
+        if matrix.type == matrix_type:
+            my_project.delete_matrix(matrix)
+
+def load_matrices_to_emme(trip_table_in, trip_purps, fric_facs, my_project):
+    ''' Loads data to Emme matrices: Ps and As and friction factor by trip purpose.
+        Also initializes empty trip distribution and O-D result tables. '''
+    #print 'in load_matrices_to_emme function: ' + trip_purps
+    # list of matrix names
+    matrix_name_list = [matrix.name for matrix in my_project.bank.matrices()]
+    zonesDim = len(my_project.current_scenario.zone_numbers)
+    zones = my_project.current_scenario.zone_numbers
+
+    # Create Emme matrices if they don't already exist
+    for purpose in trip_purps:
+        print purpose
+        if purpose + 'pro' not in matrix_name_list:
+            my_project.create_matrix(str(purpose)+ "pro" , str(purpose) + " productions", "ORIGIN")
+        if purpose + 'att' not in matrix_name_list:
+            my_project.create_matrix(str(purpose) + "att", str(purpose) + " attractions", "DESTINATION")
+        if purpose + 'fri' not in matrix_name_list:
+            my_project.create_matrix(str(purpose) + "fri" , str(purpose) + "friction factors", "FULL")
+        if purpose + 'dis' not in matrix_name_list:
+            my_project.create_matrix(str(purpose) + "dis" , str(purpose) + "distributed trips", "FULL")
+        if purpose + 'od' not in matrix_name_list:
+            my_project.create_matrix(str(purpose) + "od" , str(purpose) + "O-D tables", "FULL")
+
+        for p_a in ['pro', 'att']:
+            # Load zonal production and attractions from CSV (output from trip generation)
+            
+            trips = np.array(trip_table_in[purpose + p_a])
+            trips.resize(zonesDim)
+            #code below does not work for GQs because there are only 3700 records in the csv file. Not sure if code above is ideal.
+            #trips = np.array(trip_table_in.loc[0:zonesDim - 1][purpose + p_a])    # Less 1 because NumPy is 0-based\
+            matrix_id = my_project.bank.matrix(purpose + p_a).id    
+            emme_matrix = my_project.bank.matrix(matrix_id)  
+            emme_matrix = ematrix.MatrixData(indices=[zones],type='f')    # Access Matrix API
+
+             # Update Emme matrix data
+            emme_matrix.raw_data = _array.array('f', trips)    # set raw matrix data equal to prod/attr data
+            my_project.bank.matrix(matrix_id).set_data(emme_matrix, my_project.current_scenario)
+
+        # Load friction factors by trip purpose
+        fri_fac = fric_facs[purpose][0:zonesDim,0:zonesDim]
+        emme_matrix = ematrix.MatrixData(indices=[zones,zones],type='f')    # Access Matrix API
+        emme_matrix.raw_data = [_array.array('f',row) for row in fri_fac]
+        matrix_id = my_project.bank.matrix(purpose + "fri").id    
+        my_project.bank.matrix(matrix_id).set_data(emme_matrix, my_project.current_scenario)
+                  
+def balance_matrices(trip_purps, my_project):
+    ''' Balances productions and attractions by purpose for all internal zones '''
+    for purpose in trip_purps:
+        # For friction factors, have to make sure 0s in Externals are actually 0, otherwise you will get intrazonal trips
+        my_project.matrix_calculator(result = 'mf' + purpose + 'fri', expression = '0', 
+                                 constraint_by_zone_destinations = str(LOW_STATION) + '-' + str(HIGH_STATION), 
+                                 constraint_by_zone_origins = str(LOW_STATION) + '-' + str(HIGH_STATION))
+        print "Balancing trips for purpose: " + str(purpose)
+        my_project.matrix_balancing(results_od_balanced_values = 'mf' + purpose + 'dis', 
+                                    od_values_to_balance = 'mf' + purpose + 'fri', 
+                                    origin_totals = 'mo' + purpose + 'pro', 
+                                    destination_totals = 'md' + purpose + 'att', 
+                                    constraint_by_zone_destinations = '1-' + str(HIGH_STATION), 
+                                    constraint_by_zone_origins = '1-' + str(HIGH_STATION))
+
+def calculate_daily_trips_externals(trip_purps, my_project):
+    # Accounting for out- and in-bound trips.
+    # The distribution matrices (e.g. 'mfcoldis') are in PA format. Need to convert to OD format by transposing
+    #Stefan- changing code for externals- we are sending half the daily trips from external to internal. Now we need add the transpose to get the other direction. 
+    for purpose in trip_purps:
+        my_project.matrix_calculator(result = 'mf' + purpose + 'od', 
+                                     expression = 'mf' + purpose + 'dis + mf' + purpose + 'dis'+ "'")
+
+def calculate_daily_trips(trip_purps, my_project):
+    # Accounting for out- and in-bound trips.
+    # The distribution matrices (e.g. 'mfcoldis') are in PA format. Need to convert to OD format by transposing
+    for purpose in trip_purps:
+        my_project.matrix_calculator(result = 'mf' + purpose + 'od', 
+                                     expression = '0.5*mf' + purpose + 'dis + 0.5*mf' + purpose + 'dis'+ "'")
+
+def export_trips(split_by_mode_tod, output_dir):
+    ''' Export combined trips to H5 '''
+    # re-structure the mode dictionary
+    # adding income levels of sov, hov
+    for mode in ['svtl', 'h2tl', 'h3tl']:
+            for i in ['1', '2', '3']:
+                new_mode = mode + i
+                mode_dict[new_mode] = mode_dict[mode]
+    #deleteing the general sov, hov
+    del mode_dict['svtl']
+    del mode_dict['h2tl']
+    del mode_dict['h3tl']
+
+    for tod in time_periods:
+        print "Exporting supplemental trips for time period: " + str(tod)
+        my_store = h5py.File(output_dir + str(tod) + '.h5', "w-")
+        for mode in mode_dict.keys(): 
+            my_store.create_dataset(str(mode), data=split_by_mode_tod[mode][tod])
+        my_store.close()
+
+# Input the model choice ratio matrices
+def apply_ratio_to_trips(trip_table, ratio_table):
+    result = np.zeros(shape=trip_table.shape)
+    for i in range(len(trip_table)):
+        # iterate through columns
+        for j in range(len(trip_table[0])):
+            result[i][j] = trip_table[i][j] * ratio_table[i][j]
+    print result[:1]
+    print result[-1:]
+    return result
+
+
+
+# Apply supplemental mode choice ratios into the trip tables, which were created from trip distribution step
+def repalce_previous_trip_table(new_purp_list, new_to_old_purp_list, new_mode_list, old_mode_list, trip_table_dict):
+    new_trip_dict = {}
+    for new_purp in new_purp_list:
+        print 'trip purp', new_purp, 'is transfering...'
+        # read in the mode choice ratios from h5 file, which was created in "mode_choice_supplemental.py"
+        mode_choice = h5py.File('outputs/supplemental/mode_choice/' + new_purp + '_ratio.h5', 'r')
+        
+        # re-catergory 10 purposes into 7 purposes
+        # previous 10 trips purposes: ['hbo', 'sch', 'wko', 'oto', 'hw3', 'hsp', 'hw4', 'hw2', 'col', 'hw1']
+        # updated to 7 purposes: ['hw1', 'hw2', 'hw3', 'nhb', 'hbo', 'sch', 'col']
+        new_mode = new_mode_list[new_purp]
+        old_purp = new_to_old_purp_list[new_purp]
+        # hw1, hw2, are same
+        if new_purp in ['hbw1', 'hbw2']:
+            old_trip_table = trip_table_dict[old_purp]
+        # combined hw3 and hw4 into hw3
+        if new_purp in ['hw3']:
+            old_trip_table = trip_table_dic['hw3'] + trip_table_dic['hw4']
+        # ['wko', 'hsp', 'oto'] are combined to 'nhb'
+        if new_purp in ['nhb']:
+            old_trip_table = trip_table_dic['wko'] + trip_table_dic['oto']
+        # ['hbo', 'hsp'] are combined to 'hbo'
+        if new_purp in ['hbo']:
+            old_trip_table = trip_table_dic['hbo'] + trip_table_dic['hsp']
+
+        # compute the new trip table by applying mode choice ratio
+        for i in range(6):
+            # prepare the mode choice table by trip purpose
+            if i > 0 and i < 3: 
+                # for the hov2, hov3, divide ratio by three to account for going from 1 income category to 3
+                new_ratio = mode_choice[new_purp][new_mode[i]][:] / (3)
+            else: 
+                new_ratio = mode_choice[new_purp][new_mode[i]][:]
+
+            # calculate new trip table
+            new_trip_table = apply_ratio_to_trips(old_trip_table , new_ratio)
+            if i == 0:
+                new_trip_dict[old_purp] = {old_mode_list[i] : new_trip_table }
+            else:
+                new_trip_dict[old_purp][old_mode_list[i]] = new_trip_table
+        print new_purp, 'finished transfered to', new_trip_dict[old_purp].keys(), 'done'
+
+        mode_choice.close()
+    return new_trip_dict
+
+
+
+def trips_by_mode(trip_dict, trip_purp, my_project):
+    inits_results = {}
+    results = {}
+    #divide SOV, HOV into three income levels
+    for p in trip_purp:
+        for m in trip_dict[p].keys():
+            # home based work by income level: 1,2,3
+            if m in ['svtl', 'h2tl', 'h3tl'] and p[-1:] in ['1', '2', '3']:
+                n = m + p
+                inits_results[n] = trip_dict[p][m]
+            # nonhome based work (nhb), homebased other (hbo) (break down hov and sov by income level: 1,2,3)
+            if m in ['svtl', 'h2tl', 'h3tl'] and p[-1:] in ['b', 'o']:
+                for i in ['1', '2', '3']:
+                    n = m + p + i
+                    inits_results[n] = trip_dict[p][m]/3
+            new_n = n[:4] + n[-1:]       
+            # combine sov, hov by income level
+            if new_n not in results.keys():
+                    results[new_n] = inits_results[n]
+            else:
+                    results[new_n] += inits_results[n]
+   
+    # combine Bike, Walk, Transit
+    for p in trip_purp:
+        for m in trip_dict[p].keys():
+            if m in ['trnst', 'walk', 'bike']:
+                if m not in results.keys():
+                    results[m] = trip_dict[p][m]
+                else:
+                    results[m] += trip_dict[p][m]
+    return results
+  
+
+
+def trips_by_tod(trips_by_mode, trip_purps):
+    ''' Distribute modal trips across times of day '''
+    tod_df = {}
+    trips_by_tod = {}
+    #re-structural the time dictionary
+    # adding factor tables for hov, sov by income levels
+    for mode in ['svtl', 'h2tl', 'h3tl']:
+        for i in ['1', '2', '3']:
+            new_mode = mode + i
+            time_dict[new_mode] = time_dict[mode]
+    # deleting the general fator tables of hov, sov
+    del time_dict['svtl']
+    del time_dict['h2tl']
+    del time_dict['h3tl']
+
+    for mode, tod_shares in time_dict.iteritems():
+        for tod in time_periods:
+            if mode in trips_by_mode.keys():
+                tod_df[tod] = trips_by_mode[mode] * time_dict[mode][tod]
+                print tod
+        trips_by_tod[mode] = tod_df
+        tod_df = {}
+        print mode
+    return trips_by_tod
+
+
+
+def distribute_trips_externals(trip_table_in, results_dir, trip_purps, fric_facs, my_project):
+    ''' Load data in Emme, balance trips by purpose, and produce O-D trip tables '''
+    #print 'in distribute trips function: ' + trip_purps
+    # Clear all existing matrices
+    delete_matrices(my_project, "ORIGIN")
+    delete_matrices(my_project, "DESTINATION")
+    delete_matrices(my_project, "FULL")
+
+    # Load data into fresh Emme matrices
+    load_matrices_to_emme(trip_table_in, trip_purps, fric_facs, my_project)
+
+    # Balance matrices
+    balance_matrices(trip_purps, my_project)
+
+    # Calculate daily trips
+    calculate_daily_trips_externals(trip_purps, my_project)
+
+
+def split_trips(trip_dict, trip_purps, my_project):
+    ''' Distribute trips by Soundcast user classes and TOD, using 2006 Survey shares '''
+    by_mode = trips_by_mode(trip_dict, trip_purps, my_project)  # Distribute by mode
+    by_tod = trips_by_tod(by_mode, trip_purps)  # Distribute by time of day
+
+    return by_tod
+
+def sum_by_purp(trip_purps, my_project):
+    ''' For error checking, sum trips by trip purpose '''
+    total_sum_by_purp = {}
+    for purpose in trip_purps:
+        print purpose
+        # Load Emme O-D total trip data by purpose
+        matrix_id = my_project.bank.matrix(purpose + 'od').id    
+        emme_matrix = my_project.bank.matrix(matrix_id)  
+        emme_data = emme_matrix.get_data() # Access emme data as numpy matrix
+        emme_data = np.array(emme_data.raw_data, dtype='float64')
+        total_sum_by_purp[purpose] = emme_data
+    return total_sum_by_purp
+
+def summarize_all_by_purp(ext_spg_summary, gq_summary, trip_purps):
+    ''' For error checking, sum external, special generator, and group quarters tirps by purpose '''
+    total_sum_by_purp = {}
+    for purpose in trip_purps:   
+    # Select only externals and special generators
+        filtered = np.zeros_like(ext_spg_summary[purpose])
+        # Add only special generator rows
+        for loc_name, loc_zone in SPECIAL_GENERATORS.iteritems():
+            # Add rows (minus 1 for zero-based NumPy index)
+            #filtered[[loc_zone - 1],:] = ext_spg_summary[purpose][[loc_zone - 1],:]
+            filtered[[dictZoneLookup[loc_zone]],:] = ext_spg_summary[purpose][[dictZoneLookup[loc_zone]],:]
+            # Add columns (minus 1 for zero-based NumPy index)
+            #filtered[:,[loc_zone - 1]] = ext_spg_summary[purpose][:,[loc_zone - 1]]
+            filtered[:,[dictZoneLookup[loc_zone]]] = ext_spg_summary[purpose][:,[dictZoneLookup[loc_zone]]]
+            # Combine with group quarters array
+            if purpose not in ['hw2', 'hw3', 'hw4']:
+                filtered += gq_summary[purpose]
+        # Add only external rows and columns
+        filtered[3700:,:] = ext_spg_summary[purpose][3700:,:]
+        filtered[:,3700:] = ext_spg_summary[purpose][:,3700:]
+        total_sum_by_purp[purpose] = filtered
+    return total_sum_by_purp
+
+def write_csv(data, file_name):
+    with open(supplemental_loc + 'supplemental_summary.csv', 'wb') as f:
+        csv_writer = csv.writer(f)
+        csv_writer.writerows(data)
+
+def ext_spg_selected(trip_purps):
+    ''' Select only external and special generator zones '''
+    total_sum_by_purp = {}
+    for purpose in trip_purps:
+        print purpose
+        # Load Emme O-D total trip data by purpose
+        matrix_id = my_project.bank.matrix(purpose + 'od').id    
+        emme_matrix = my_project.bank.matrix(matrix_id)  
+        emme_data = emme_matrix.get_data() # Access emme data as numpy matrix
+        emme_data = np.array(emme_data.raw_data, dtype='float64')
+        filtered = np.zeros_like(emme_data)
+        # Add only special generator rows
+        #for loc_name, loc_zone in SPECIAL_GENERATORS.iteritems():
+        #    # Add rows (minus 1 for zero-based NumPy index)
+        #    #filtered[[loc_zone - 1],:] = emme_data[[loc_zone - 1],:]
+        #    filtered[[dictZoneLookup[loc_zone]],:] = emme_data[[dictZoneLookup[loc_zone]],:]
+        #    # Add columns (minus 1 for zero-based NumPy index)
+        #    #filtered[:,[loc_zone - 1]] = emme_data[:,[loc_zone - 1]]
+        #    filtered[:,[dictZoneLookup[loc_zone]]] = emme_data[:,[dictZoneLookup[loc_zone]]]
+        # Add only external rows and columns
+        filtered[3700:,:] = emme_data[3700:,:]
+        filtered[:,3700:] = emme_data[:,3700:]
+
+        total_sum_by_purp[purpose] = filtered
+    return total_sum_by_purp
+
+def supplementals_report(ext_spg_trimmed, gq_summary, combined, split_by_mode_tod):
+
+    # Create an array to hold summary trips by purpose 
+    sum_p = [[purpose for purpose in trip_purp_full]]
+    sum_p[len(sum_p)-1].insert(0,"")    # Insert space for row title
+    sum_p.append([ext_spg_trimmed[purp].sum() for purp in trip_purp_full]) 
+    sum_p[len(sum_p)-1].insert(0,'Externals, Special Generators')  # Add row title
+    sum_p.append([gq_summary[purp].sum() for purp in trip_purp_gq])
+    sum_p[len(sum_p)-1].insert(0,'Group Quarters')  # Add row title
+    sum_p.append([combined[purp].sum() for purp in trip_purp_full])
+    sum_p[len(sum_p)-1].insert(0,'Totals')
+
+    # Create array to hold summary trips by tod and mode
+    sum_tm = [[purpose for purpose in split_by_mode_tod.keys()]]
+    sum_tm[len(sum_tm)-1].insert(0,"")
+    for tod in split_by_mode_tod['svtl1'].keys():
+        sum_tm.append([split_by_mode_tod[mode][tod].sum() for mode in split_by_mode_tod.keys()])
+        sum_tm[len(sum_tm)-1].insert(0, tod)    # Insert TOD row heading
+
+    write_csv(sum_p + [[]] + sum_tm, file_name='supplemental_summary.csv')
+
+
+#def main():
+#    global dictZoneLookup
+#    dictZoneLookup = dict((value,index) for index,value in enumerate(my_project.current_scenario.zone_numbers))
+    
+#    # Overwrite previous trip tables 
+#    init_dir(supplemental_loc)
+
+#    # Load skim data
+#    am_cost_skim = load_skims(r'inputs\7to8.h5', mode_name='svtl2g')
+#    am_dist_skim = load_skims(r'inputs\7to8.h5', mode_name='svtl1d', divide_by_100=True)
+#    pm_cost_skim = load_skims(r'inputs\17to18.h5', mode_name='svtl2g')
+#    pm_dist_skim = load_skims(r'inputs\17to18.h5', mode_name='svtl1d', divide_by_100=True)
+#    cost_skim = (am_cost_skim + pm_cost_skim) * .5
+#    dist_skim = (am_cost_skim + pm_dist_skim) * .5
+   
+#    # Compute friction factors by trip purpose
+#    fric_facs = calc_fric_fac(cost_skim, dist_skim)
+
+#    # Create trip table for externals and special generators by purpose and summarize
+#    distribute_trips(trip_table, ext_spg_dir, trip_purp_full, fric_facs, my_project)
+#    ext_spg_trimmed = ext_spg_selected(trip_purp_full)    # Include only external and special gen. zones
+#    print 'dinished created trip tables_______________'
+#    # Distribute group quarters trips by purpose and summarize results
+#    distribute_trips(gq_trip_table, gq_directory, trip_purp_gq, fric_facs, my_project)
+#    gq_summary = sum_by_purp(trip_purp_gq, my_project)
+#    print 'distributed______________'
+#    # Combine external, special gen., and group quarters trips
+#    #combined = {}
+#    print 'start combined trips.........'
+#    for purp in trip_purp_full:
+#        if purp not in ['hw2', 'hw3', 'hw4']:   # These purposes don't exist for GQ trips, use only for ext_spg
+#            combined[purp] = ext_spg_trimmed[purp] + gq_summary[purp]
+#        else:
+#            combined[purp] = ext_spg_trimmed[purp]
+#        print purp, 'is done............'
+#    print 'combined trip is created_____________'
+
+#    # Replace old trip tables into new trip tables
+#    trip_dict = repalce_previous_trip_table(new_purp_list, new_to_old_purp_list, new_mode_list, old_mode_list)
+#    print 'finished replace the trip tables________________'
+#     # Split by mode and TOD
+#    #split_by_mode_tod = split_trips(combined, trip_purp_full, my_project)
+#    split_by_mode_tod = split_trips(trip_dict, trip_purps, my_project)
+
+#    # Export results to H5
+#    export_trips(split_by_mode_tod, output_dir = 'outputs/supplemental/')
+
+#    # Report results in CSV summary
+#    supplementals_report(ext_spg_trimmed, gq_summary, combined, split_by_mode_tod)
+
+#my_project = EmmeProject(r'projects\Supplementals\Supplementals.emp')
+#combined = {}    
+#if __name__ == "__main__":
+#    main()
+
+
+
+
+
+my_project = EmmeProject(r'projects\Supplementals\Supplementals.emp')
+
+global dictZoneLookup
+dictZoneLookup = dict((value,index) for index,value in enumerate(my_project.current_scenario.zone_numbers))
+    
+# Overwrite previous trip tables 
+#init_dir(supplemental_loc)
+
+# Load skim data
+am_cost_skim = load_skims(r'inputs\7to8.h5', mode_name='svtl2g')
+am_dist_skim = load_skims(r'inputs\7to8.h5', mode_name='svtl1d', divide_by_100=True)
+pm_cost_skim = load_skims(r'inputs\17to18.h5', mode_name='svtl2g')
+pm_dist_skim = load_skims(r'inputs\17to18.h5', mode_name='svtl1d', divide_by_100=True)
+cost_skim = (am_cost_skim + pm_cost_skim) * .5
+dist_skim = (am_cost_skim + pm_dist_skim) * .5
+   
+# Compute friction factors by trip purpose
+fric_facs = calc_fric_fac(cost_skim, dist_skim)
+
+# Create trip table for externals 
+distribute_trips_externals(trip_table, ext_spg_dir, trip_purp_full, fric_facs, my_project)
+
+#removed special generators for now
+ext_spg_trimmed = ext_spg_selected(trip_purp_full)    # Include only external and special gen. zones
+
+#Just IXXI trips for now:
+ixxi_trips = ext_spg_trimmed['hsp']
+svtl = ixxi_trips * .8
+h2tl = ixxi_trips * .13
+h3tl = ixxi_trips * .07
+
+#write out trip table for now:
+my_store = h5py.File(output_dir + '/' + 'external_non_work' + '.h5', "w")
+my_store.create_dataset('svtl', data=svtl)
+my_store.create_dataset('h2tl', data=h2tl)
+my_store.create_dataset('h3tl', data=h3tl)
+
+my_store.close()
+
+
+## Distribute group quarters trips by purpose and summarize results
+#distribute_trips(gq_trip_table, gq_directory, trip_purp_gq, fric_facs, my_project)
+#gq_trip_tables = sum_by_purp(trip_purp_gq, my_project)
+
+
+
+
+
+
+## Combine external, special gen., and group quarters trips
+#combined = {}
+#for purp in trip_purp_full:
+#        if purp not in ['hw2', 'hw3', 'hw4']:   # These purposes don't exist for GQ trips, use only for ext_spg
+#            combined[purp] = ext_spg_trimmed[purp] + gq_summary[purp]
+#        else:
+#            combined[purp] = ext_spg_trimmed[purp]
+
+
+### Input the model choice ratio matrices
+#trip_dict = repalce_previous_trip_table(new_purp_list, new_to_old_purp_list, new_mode_list, old_mode_list, gq_trip_tables)
+
+## Split by mode and TOD
+#split_by_mode_tod = split_trips(trip_dict, trip_purps, my_project)
+
+## Export results to H5
+#export_trips(split_by_mode_tod, output_dir = 'outputs/supplemental')
+
+## Report results in CSV summary
+#supplementals_report(ext_spg_trimmed, gq_summary, combined, split_by_mode_tod)
diff --git a/scripts/supplemental/distribution.py b/scripts/supplemental/distribution.py
index 2b01f2ec..d739276b 100644
--- a/scripts/supplemental/distribution.py
+++ b/scripts/supplemental/distribution.py
@@ -6,10 +6,11 @@
 import pandas as pd
 import h5py
 import numpy as np
-from emme_configuration import *
 import sys
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
 sys.path.append(os.path.join(os.getcwd(),"scripts/trucks"))
+sys.path.append(os.getcwd())
+from emme_configuration import *
 from EmmeProject import *
 from truck_configuration import *
 
diff --git a/scripts/supplemental/generation.py b/scripts/supplemental/generation.py
index af4d83d9..59b6303b 100644
--- a/scripts/supplemental/generation.py
+++ b/scripts/supplemental/generation.py
@@ -5,6 +5,7 @@
 import h5py
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
 sys.path.append(os.path.join(os.getcwd(),"scripts/trucks"))
+sys.path.append(os.getcwd())
 from emme_configuration import *
 from EmmeProject import *
 from truck_configuration import *
@@ -161,25 +162,27 @@ def calc_hhs(master_taz):
 
 def add_special_gen(trip_table):
     ''' Loads additional productions and attraction values for special generator zones. '''
+
+    df = pd.read_csv(special_gen_trips)
+
     # Note: Airport trips are assumed 75% home-based and 25% work-based
     airport_hb_share = 0.75
     airport_wb_share = 1 - airport_hb_share
 
     # Add special generator home-based (spghbo) other and 75% of airport (spgapt) trips
     # to general home-based attractions (hboatt)
-    for key, value in spg_general.iteritems():
-        #trip_table.iloc[key - 1]["hboatt"] += value
-        trip_table.iloc[dictZoneLookup[key]]["hboatt"] += value
-
-    # Add 25% of airport trips to work-based attractions
-    #trip_table.iloc[spg_airport.keys()[0] - 1]["hboatt"] += airport_hb_share * spg_airport.values()[0]
-    trip_table.iloc[dictZoneLookup[spg_airport.keys()[0]]]["hboatt"] += airport_hb_share * spg_airport.values()[0]
-    #trip_table.iloc[spg_airport.keys()[0] - 1]["wkoatt"] += airport_wb_share * spg_airport.values()[0]
-    trip_table.iloc[dictZoneLookup[spg_airport.keys()[0]]]["wkoatt"] += airport_wb_share * spg_airport.values()[0]
-
+    for i in xrange(len(df)):
+        taz = df.iloc[i]['taz']
+        trips = df.iloc[i]['trips']
+        if i not in airport_zone_list:
+            trip_table.iloc[dictZoneLookup[taz]]["hboatt"] += trips
+        else:
+            # Add 25% of airport trips to work-based attractions
+            trip_table.iloc[dictZoneLookup[taz]]["hboatt"] += airport_hb_share * trips
+            trip_table.iloc[dictZoneLookup[taz]]["wkoatt"] += airport_wb_share * trips
+ 
     # Add (unbalanced) externals
     externals = pd.DataFrame(pd.read_csv(externals_loc, index_col="taz"))
-    #externals.index = [taz_num-1 for taz_num in externals.index]    # convert to index from TAZ?
     externals.columns = trip_col
     trip_table = trip_table.append(externals)
 
diff --git a/scripts/supplemental/military_parcel_loading.py b/scripts/supplemental/military_parcel_loading.py
index b1113708..70c52b41 100644
--- a/scripts/supplemental/military_parcel_loading.py
+++ b/scripts/supplemental/military_parcel_loading.py
@@ -1,9 +1,9 @@
 import pandas as pd
 import sys
 import os
-from input_configuration import *
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
-
+sys.path.append(os.getcwd())
+from input_configuration import *
 
 #it might make sense to put these file locations in a config somewhere
 parcel_file =  "inputs\\accessibility\\parcels_urbansim.txt"
diff --git a/scripts/supplemental/mode_choice_supplemental.py b/scripts/supplemental/mode_choice_supplemental.py
new file mode 100644
index 00000000..836d58e8
--- /dev/null
+++ b/scripts/supplemental/mode_choice_supplemental.py
@@ -0,0 +1,541 @@
+
+import pandas as pd
+import array as _array
+import inro.emme.desktop.app as app
+import inro.modeller as _m
+import inro.emme.matrix as ematrix
+import inro.emme.database.matrix
+import inro.emme.database.emmebank as _eb
+import json
+import numpy as np
+import time
+import os,sys
+import Tkinter, tkFileDialog
+import multiprocessing as mp
+import subprocess
+from multiprocessing import Pool
+import h5py
+sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.path.join(os.getcwd(),"scripts/trucks"))
+sys.path.append(os.getcwd())
+#os.chdir('D:\\soundcast_mode_choice\\soundcast')
+from emme_configuration import *
+from EmmeProject import *
+#from truck_configuration import *
+
+
+def init_dir(directory):
+    if os.path.exists(directory):
+        shutil.rmtree(directory)
+    os.mkdir(directory)
+
+def json_to_dictionary(dict_name):
+    #Determine the Path to the input files and load them
+    input_filename = os.path.join('inputs/supplemental/',dict_name).replace("\\","/")
+    my_dictionary = json.load(open(input_filename))
+    return(my_dictionary)
+
+
+# Help check current matrix ids and names in the bank
+def matrix_name_list(my_project):
+    for id in my_project.bank.matrices():
+        print id, my_project.bank.matrix(id).name
+
+
+# Create scalar matrices/initialize scalar matrices:
+def create_scalar_matrices():
+    for y in range(0, len(origin_destination_dict["Scalar_Matrices"])):
+        my_project.create_matrix(origin_destination_dict['Scalar_Matrices'][y]['Name'], 
+                                 origin_destination_dict['Scalar_Matrices'][y]['Description'], 
+                                 'SCALAR')
+
+
+# Creat origin and destination matrices
+def create_origin_destination_matrices():
+    for y in range (0, len(origin_destination_dict["Origin_Matrices"])):
+        my_project.create_matrix(origin_destination_dict['Origin_Matrices'][y]['Name'],
+                                 origin_destination_dict['Origin_Matrices'][y]['Description'], 
+                                 'ORIGIN')
+    for y in range (0, len(origin_destination_dict["Destination_Matrices"])):
+        my_project.create_matrix(origin_destination_dict['Destination_Matrices'][y]['Name'],
+                                 origin_destination_dict['Destination_Matrices'][y]['Description'], 
+                                 'DESTINATION')
+
+
+# Create full matrices:
+def create_full_matrices():
+    for y in range(0, len(origin_destination_dict["Full_Matrices"])):
+        my_project.create_matrix(origin_destination_dict['Full_Matrices'][y]['Name'],
+                                 origin_destination_dict['Full_Matrices'][y]['Description'], 
+                                 'FULL')
+
+
+# Create skim matrices: cost, time, distance, transit, bike, walk, park&ride
+def create_skim_matrices():
+    for y in range(0, len(origin_destination_dict["Skim_Matrices"])):
+        my_project.create_matrix(origin_destination_dict['Skim_Matrices'][y]['Name'],
+                                 origin_destination_dict['Skim_Matrices'][y]['Description'], 
+                                 'FULL')
+
+# Create terminal matrices:
+def create_terminal_matrices():
+    for y in range(0, len(origin_destination_dict["Terminal_Matrices"])):
+        my_project.create_matrix(origin_destination_dict['Terminal_Matrices'][y]['Name'],
+                                 origin_destination_dict['Terminal_Matrices'][y]['Description'], 
+                                 'FULL')
+
+         
+# Initialize partitions
+def create_ensembles_dict():
+    ensembles = pd.read_csv(ensembles_path)
+    ensembles_dict = {}
+    i = 0
+    for i in range(len(ensembles)):
+        short_name = ensembles.iat[i, 0]
+        file_name = 'inputs\\supplemental\\generation\\ensembles\\' + ensembles.iat[i, 1]
+        ensembles_dict[short_name] = file_name
+    return ensembles_dict
+
+def initialize_process_zone_partition():
+    ensembles_dict = create_ensembles_dict()
+    for short_name, file_name in ensembles_dict.items():
+        #print short_name
+        #print file_name
+        my_project.initialize_zone_partition(short_name)
+        my_project.process_zone_partition(file_name)
+
+
+# Reset utility to zero 
+def reset_utility():
+    utilities = ['euda', 'eus2', 'eus3', 'eutw', 'eutd', 'eubk', 'euwk', 'eusm', 'dabct', 's2bct', 's3bct']
+    for u in utilities:
+        my_project.matrix_calculator(result = u, expression = "0")
+
+
+# Import data (skim data) from external database (1.31: IMPORT DATA FROM EXTERNAL DATABASE)
+def load_skims(skim_file_loc, mode_name, divide_by_100=False):
+    ''' Loads H5 skim matrix for specified mode. '''
+    with h5py.File(skim_file_loc, "r") as f:
+        skim_file = f['Skims'][mode_name][:]
+    # Divide by 100 since decimals were removed in H5 source file through multiplication
+    if divide_by_100:
+        return skim_file.astype(float)/100.0
+    else:
+        return skim_file
+
+# SKIM: bi-directional cost, distance, time;
+def load_skim_data(np_matrix_name_input, np_matrix_name_output, TrueOrFalse):
+    # get am and pm skim
+    am_skim = load_skims(r'inputs\7to8.h5', 
+                         mode_name=np_matrix_name_input, 
+                         divide_by_100=TrueOrFalse)
+    pm_skim = load_skims(r'inputs\17to18.h5', 
+                         mode_name=np_matrix_name_input, 
+                         divide_by_100=TrueOrFalse)
+
+    # calculate the bi_dictional skim
+    np_matrix_dic = {}
+    np_matrix_dic[np_matrix_name_output] = (am_skim + pm_skim) * .5
+    #print np_matrix_dic
+
+    # import the final skim into emme bank
+    for np_matrix_name_output, np_matrix in np_matrix_dic.iteritems():
+        targeted_matrix = my_project.bank.matrix(np_matrix_name_output)
+        targeted_matrix.set_numpy_data(np_matrix)
+        #print my_project.bank.matrix(np_matrix_name_output).id
+
+
+def get_cost_time_distance_skim_data():
+    input_skim = {'hbw1' : {'cost' : {'svt' : 'svtl1c', 'h2v' : 'h2tl1c', 'h3v' : 'h3tl1c'}, 
+                            'time' : {'svt' : 'svtl1t', 'h2v' : 'h2tl1t', 'h3v' : 'h3tl1t'}, 
+                            'distance' : {'svt' : 'svtl1d', 'h2v' : 'h2tl1d', 'h3v' : 'h3tl1d'}},
+                  'hbw2' : {'cost' : {'svt' : 'svtl2c', 'h2v' : 'h2tl2c', 'h3v' : 'h3tl2c'}, 
+                            'time' : {'svt' : 'svtl2t', 'h2v' : 'h2tl2t', 'h3v' : 'h3tl2t'}, 
+                            'distance' : {'svt' : 'svtl2d', 'h2v' : 'h2tl2d', 'h3v' : 'h3tl2d'}},
+                  'hbw3' : {'cost' : {'svt' : 'svtl3c', 'h2v' : 'h2tl3c', 'h3v' : 'h3tl3c'}, 
+                           'time' : {'svt' : 'svtl3t', 'h2v' : 'h2tl3t', 'h3v' : 'h3tl3t'}, 
+                           'distance' : {'svt' : 'svtl3d', 'h2v' : 'h2tl3d', 'h3v' : 'h3tl3d'}},
+                  'nhb' : {'cost' : {'svt' : 'svtl1c', 'h2v' : 'h2tl1c', 'h3v' : 'h3tl1c'}, 
+                           'time' : {'svt' : 'svtl1t', 'h2v' : 'h2tl1t', 'h3v' : 'h3tl1t'}, 
+                           'distance' : {'svt' : 'svtl1d', 'h2v' : 'h2tl1d', 'h3v' : 'h3tl1d'}},
+                  'hbo' : {'cost' : {'svt' : 'svtl1c', 'h2v' : 'h2tl1c', 'h3v' : 'h3tl1c'}, 
+                           'time' : {'svt' : 'svtl1t', 'h2v' : 'h2tl1t', 'h3v' : 'h3tl1t'}, 
+                           'distance' : {'svt' : 'svtl1d', 'h2v' : 'h2tl1d', 'h3v' : 'h3tl1d'}}} 
+    output_skim = {'cost' : {'svt' : 'dabcs', 'h2v' : 's2bcs', 'h3v' : 's3bcs'}, 
+                   'time' : {'svt' : 'dabtm', 'h2v' : 's2btm', 'h3v' : 's3btm'}, 
+                   'distance' : {'svt' : 'dabds', 'h2v' : 's2bds', 'h3v' : 's3bds'}}
+    
+    for skim_name in ['cost', 'time', 'distance']:
+        for sov_hov in ['svt', 'h2v', 'h3v']:
+            #print sov_hov
+            if skim_name == 'cost':
+                load_skim_data(input_skim[trip_purpose][skim_name][sov_hov],
+                               output_skim[skim_name][sov_hov], True)
+            else:
+                load_skim_data(input_skim[trip_purpose][skim_name][sov_hov], 
+                               output_skim[skim_name][sov_hov], True)
+
+
+
+# calculate full matrix terminal times
+def get_terminal_skim_data():
+        my_project.matrix_calculator(result = 'termti', expression = 'prodtt + attrtt' )
+
+
+
+# Walk and Bike Skims emmebank
+def load_walk_bike_skim_data(np_matrix_name_input, np_matrix_name_output, TrueOrFalse):
+    np_matrix_dic = {}
+    np_matrix_dic[np_matrix_name_output] = load_skims(r'inputs\5to6.h5', 
+                                                      mode_name=np_matrix_name_input, 
+                                                      divide_by_100=TrueOrFalse)
+
+    for np_matrix_name, np_matrix in np_matrix_dic.iteritems():
+        targeted_matrix = my_project.bank.matrix(np_matrix_name_output)
+        targeted_matrix.set_numpy_data(np_matrix)
+        #print my_project.bank.matrix(np_matrix_name_output).id
+
+def get_walk_bike_skim_data():
+    walk_bike_skim_dict = {'walkt' : 'walkt', 'biket' : 'biket'}
+    for input, output in walk_bike_skim_dict.iteritems():
+        load_walk_bike_skim_data(input, output, True)
+
+
+
+# Transit Skims emmebank
+def load_transit_skim_data(np_matrix_name_input, np_matrix_name_output, TrueOrFalse):
+    '''
+    #auxwa_skim :?
+    #iwtwa : First waiting time
+    #brdwa : ?
+    #nbdwa : ABs all mode
+    #xfrwa : Transfer time
+    #farbx : Ferry?
+    #farwa : Ferry?
+    np_matrix_dic = {}
+    '''
+    np_matrix_dic = {}
+
+    if np_matrix_name_input in ['ivtwa', 'iwtwa', 'brdwa', 'nbdwa', 'xfrwa']:
+        np_matrix_dic[np_matrix_name_input] = load_skims(r'inputs\10to14.h5', 
+                                                         mode_name= np_matrix_name_output, 
+                                                         divide_by_100=TrueOrFalse) # Actual in vehicle time
+   
+    if np_matrix_name_input in ['farbx', 'farwa']:
+        np_matrix_dic[np_matrix_name_input] = load_skims(r'inputs\6to7.h5', 
+                                                         mode_name= np_matrix_name_output, 
+                                                         divide_by_100=TrueOrFalse)
+
+    for np_matrix_name, np_matrix in np_matrix_dic.iteritems():
+        targeted_matrix = my_project.bank.matrix(np_matrix_name)
+        targeted_matrix.set_numpy_data(np_matrix)
+        #print np_matrix_name, my_project.bank.matrix(np_matrix_name).id
+
+def get_transit_skim_data():
+    transit_skim_dict = {'ivtwa' : 'ivtwa', 
+                         'iwtwa' : 'iwtwa', 
+                         'brdwa' : 'iwtwa', 
+                         'nbdwa' : 'ndbwa', 
+                         'xfrwa' : 'xfrwa', 
+                         'farwa' : 'mfafarps', 
+                         'farbx' : 'mfafarbx'}
+    for input, output in transit_skim_dict.iteritems():
+        #print input, output
+        load_transit_skim_data(input, output, True)
+
+
+
+# Calculate Auto Cost
+def calculate_auto_cost():
+    input_paramas_vot_name = {'hbw1': {'svt': 'avot1v', 'h2v': 'avots2', 'h3v': 'avots3'},
+                          'hbw2': {'svt': 'avot3v', 'h2v': 'avots2', 'h3v': 'avots3'},
+                          'hbw3': {'svt': 'avot4v', 'h2v': 'avots2', 'h3v': 'avots3'},
+                          'nhb': {'svt': 'mvotda', 'h2v': 'mvots2', 'h3v':'mvots3'},
+                          'hbo': {'svt': 'mvotda', 'h2v': 'mvots2', 'h3v':'mvots3'}}
+    output_auto_cost_name = {'svt': 'dabct', 'h2v': 's2bct', 'h3v': 's3bct'}
+
+    # SOV
+    my_project.matrix_calculator(result = output_auto_cost_name['svt'], 
+                                          expression = '(mf"dabds"*' \
+                                              + str(parameters_dict[trip_purpose]['global']['autoop']) + \
+                                              ') + (mf"dabcs")/' \
+                                              + str(parameters_dict[trip_purpose]['vot'][input_paramas_vot_name[trip_purpose]['svt']]) + \
+                                              '+ (md"daily"/2)')
+    #print output_auto_cost_name['svt'], input_paramas_vot_name[trip_purpose]['svt']
+
+    # HOV 2 passenger               
+    my_project.matrix_calculator(result = output_auto_cost_name['h2v'], 
+                                          expression = '((mf"s2bds"*' \
+                                              + str(parameters_dict[trip_purpose]['global']['autoop']) + \
+                                              ') + (mf"s2bcs")/' \
+                                              + str(parameters_dict[trip_purpose]['vot'][input_paramas_vot_name[trip_purpose]['h2v']]) + \
+                                              '+ (md"daily"/2))/2')
+    #print output_auto_cost_name['h2v'], input_paramas_vot_name[trip_purpose]['h2v']
+
+    # HOV 3+ passengers
+    my_project.matrix_calculator(result = output_auto_cost_name['h3v'], 
+                                          expression = '((mf"s3bds"*' \
+                                              + str(parameters_dict[trip_purpose]['global']['autoop']) + \
+                                              ') + (mf"s3bcs")/' \
+                                              + str(parameters_dict[trip_purpose]['vot'][input_paramas_vot_name[trip_purpose]['h3v']])+ \
+                                              '+ (md"daily"/2))/3.5')
+    #print output_auto_cost_name['h3v'], input_paramas_vot_name[trip_purpose]['h3v']
+
+
+
+
+def calculate_mode():
+    '''
+    some submatrices restrictions:
+    mode_choice.bat : %hightaz% %lowstation% %highstation% %lowpnr% %highpnr%
+    %1%: 3700 - regional TAZ
+    %2%: 3733 - external TAZ
+    %3%: 3750 - external TAZ
+    %4%: 3751 - PNR TAZ
+    %5%: 4000 - PNR TAZ
+
+    there are -e+21 values in the TAZ zone after 3751, it is because the ln(0)
+    will come back fix it later
+    '''
+    
+    #os.chdir(r'D:\\Angela\\soundcast_mode_choice')
+    # Calculate Drive Alone Utility
+    my_project.matrix_calculator(result = 'euda', 
+                                     expression = 'exp(' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['autivt']) + \
+                                         '*mf"dabtm" +'\
+                                         + str(parameters_dict[trip_purpose]['modechoice']['autcos']) + \
+                                         '* mf"dabct")',
+                                     constraint_by_zone_origins = '1-3750', # 1, %3%
+                                     constraint_by_zone_destinations = '1-3750') # 1, %3% 
+
+    print 'euda done'
+    # Calculate Shared Ride 2, 3+ Utility
+    my_project.matrix_calculator(result = 'eus2', 
+                                     expression = 'exp(' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['asccs2']) + '+' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['autivt']) + \
+                                         '* mf"s2btm" + ' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['autcos']) + \
+                                         '* mf"s2bct")',
+                                     constraint_by_zone_origins = '1-3750', # 1, %3%
+                                     constraint_by_zone_destinations = '1-3750') # 1, %3% 
+    print 'eus2 done'
+    my_project.matrix_calculator(result = 'eus3', 
+                                     expression = 'exp('\
+                                         + str(parameters_dict[trip_purpose]['modechoice']['asccs3']) + '+ ' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['autivt']) + \
+                                         '* mf"s3btm" + ' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['autcos']) + \
+                                         '* mf"s3bct")',
+                                     constraint_by_zone_origins = '1-3750', # 1, %3%
+                                     constraint_by_zone_destinations = '1-3750') # 1, %3% 
+    print 'eus3 done'
+    # Calculate Walk, drive to Transit Utility
+    my_project.matrix_calculator(result = 'eutw', 
+                                     expression = 'exp(' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['ascctw']) + '+' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['trwivt']) + \
+                                         '* mf"ivtwa" +' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['trwovt']) + \
+                                         '* (mf"auxwa"+mf"iwtwa"+mf"xfrwa") +' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['trwcos']) + \
+                                         '* mf"farwa")',
+                                     constraint_by_zone_origins = '1-3700', # 1, %1%
+                                     constraint_by_zone_destinations = '1-3700') # 1, %1% 
+    print 'eutw done'
+    # Calculate  Walk, Bike Utility
+    my_project.matrix_calculator(result = 'euwk', 
+                                     expression = 'exp(' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['asccwk']) + '+'\
+                                         + str(parameters_dict[trip_purpose]['modechoice']['walktm']) + \
+                                         '* mf"walkt")',
+                                     constraint_by_zone_origins = '1-3700', # 1, %1%
+                                     constraint_by_zone_destinations = '1-3700') # 1, %1%
+    print 'euwk done'
+    my_project.matrix_calculator(result = 'eubk', 
+                                     expression = 'exp(' + str(parameters_dict[trip_purpose]['modechoice']['asccbk']) + '+' \
+                                         + str(parameters_dict[trip_purpose]['modechoice']['biketm']) + '*mf"biket")',
+                                     constraint_by_zone_origins = '1-3700', # 1, %1%
+                                     constraint_by_zone_destinations = '1-3700') # 1, %1%
+
+    print 'eubk done'
+
+
+
+
+# Calculate Mode Shares
+
+def calculate_mode_shares():
+
+    output_mode_share_name = {'hbw1': ['eusm', 'w1shda', 'w1shs2', 'w1shs3', 'w1shtw', 'w1shtd', 'w1shbk', 'w1shwk'],
+                          'hbw2': ['eusm', 'w2shda', 'w2shs2', 'w2shs3', 'w2shtw', 'w2shtd', 'w2shbk', 'w2shwk'],
+                          'hbw3': ['eusm', 'w3shda', 'w3shs2', 'w3shs3', 'w3shtw', 'w3shtd', 'w3shbk', 'w3shwk'],
+                          'nhb': ['eusm', 'nhshda', 'nhshs2', 'nhshs3', 'nhshtw', 'nhshtd', 'nhshbk', 'nhshwk'],
+                          'hbo': ['eusm', 'nwshda', 'nwshs2', 'nwshs3', 'nwshtw', 'nwshtd', 'nwshbk', 'nwshwk']}
+
+    # Calculate the sum of utility: eusm
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][0], 
+                                     expression = 'mf"euda"+mf"eus2"+mf"eus3"+mf"eutw"+mf"eubk"+mf"euwk"')
+    #print output_mode_share_name[trip_purpose][0]
+
+    # Auto, shda
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][1],
+                                     expression = 'mf"euda"/mf"eusm"',
+                                     constraint_by_value = {
+                                         "interval_min": 0,
+                                         "interval_max": 0.00000001,
+                                         "condition": "EXCLUDE",
+                                         "od_values": 'mf"eusm"'})
+    #print output_mode_share_name[trip_purpose][1]
+
+    # 2 passengers auto, shs2
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][2], 
+                                     expression = 'mf"eus2"/mf"eusm"',
+                                     constraint_by_value = {
+                                         "interval_min": 0,
+                                         "interval_max": 0.00000001,
+                                         "condition": "EXCLUDE",
+                                         "od_values": 'mf"eusm"'})
+    #print output_mode_share_name[trip_purpose][2]
+
+    # 3 passenger auto, shs3
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][3], 
+                                     expression = 'mf"eus3"/mf"eusm"',
+                                     constraint_by_value = {
+                                         "interval_min": 0,
+                                         "interval_max": 0.00000001,
+                                         "condition": "EXCLUDE",
+                                         "od_values": 'mf"eusm"'})
+    #print output_mode_share_name[trip_purpose][3]
+
+    # Transit to walk, shtw
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][4], 
+                                     expression = 'mf"eutw"/mf"eusm"',
+                                     constraint_by_value = {
+                                         "interval_min": 0,
+                                         "interval_max": 0.00000001,
+                                         "condition": "EXCLUDE",
+                                         "od_values": 'mf"eusm"'})
+    #print output_mode_share_name[trip_purpose][4]
+
+    # Bike, shbk
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][6], 
+                                     expression = 'mf"eubk"/mf"eusm"',
+                                     constraint_by_value = {
+                                         "interval_min": 0,
+                                         "interval_max": 0.00000001,
+                                         "condition": "EXCLUDE",
+                                         "od_values": 'mf"eusm"'})
+    #print output_mode_share_name[trip_purpose][6]
+
+    # Walk, shwk
+    my_project.matrix_calculator(result = output_mode_share_name[trip_purpose][7], 
+                                     expression = 'mf"euwk"/mf"eusm"',
+                                     constraint_by_value = {
+                                         "interval_min": 0,
+                                         "interval_max": 0.00000001,
+                                         "condition": "EXCLUDE",
+                                         "od_values": 'mf"eusm"'})
+    #print output_mode_share_name[trip_purpose][7]
+
+
+# Validate, test the results 
+def test_results():
+    shda = my_project.bank.matrix('mf68').get_numpy_data()
+    shs2 = my_project.bank.matrix('mf69').get_numpy_data()
+    shs3 = my_project.bank.matrix('mf70').get_numpy_data()
+    shtw = my_project.bank.matrix('mf71').get_numpy_data()
+    shtd = my_project.bank.matrix('mf72').get_numpy_data()
+    shbk = my_project.bank.matrix('mf73').get_numpy_data()
+    shwk = my_project.bank.matrix('mf74').get_numpy_data()
+    
+    sum = shda + shs2 + shs3 + shtw + shtd +shbk + shwk
+    error = 0
+    for i in range(0,3868):
+        for j in range(0, 3868):
+            if sum[i][j] > 0.1 and sum[i][j] < 0.9:
+               # every value should be very close to 1, so that means nothing would print out at this step.
+               error += 1
+    print 'there are', error, 'cells might have error.'
+
+
+def mode_choice_to_h5(trip_purpose):
+    output_mode_share_name = {'hbw1': ['eusm', 'w1shda', 'w1shs2', 'w1shs3', 'w1shtw', 'w1shtd', 'w1shbk', 'w1shwk'],
+                          'hbw2': ['eusm', 'w2shda', 'w2shs2', 'w2shs3', 'w2shtw', 'w2shtd', 'w2shbk', 'w2shwk'],
+                          'hbw3': ['eusm', 'w3shda', 'w3shs2', 'w3shs3', 'w3shtw', 'w3shtd', 'w3shbk', 'w3shwk'],
+                          'nhb': ['eusm', 'nhshda', 'nhshs2', 'nhshs3', 'nhshtw',  'nhshbk', 'nhshwk'],
+                          'hbo': ['eusm', 'nwshda', 'nwshs2', 'nwshs3', 'nwshtw',  'nwshbk', 'nwshwk']}
+
+    #my_store = h5py.File('/outputs/supplemental/' + trip_purpose + '_ratio.h5', 'w')
+    my_store = h5py.File(output_dir + '/' + trip_purpose + '_ratio.h5', "w")
+    grp = my_store.create_group(trip_purpose)
+    for mod in output_mode_share_name[trip_purpose]:
+            mod_np = my_project.bank.matrix(mod).get_numpy_data()
+            grp.create_dataset(mod, data = mod_np)
+            print mod
+    my_store.close()
+
+
+def delete_matrices(my_project, matrix_type):
+    for matrix in my_project.bank.matrices():
+        if matrix.type == matrix_type:
+            my_project.delete_matrix(matrix)
+
+
+
+def main():
+    #init_dir('outputs/supplemental/mode_choice')
+    my_project.delete_matrices("ALL")
+    create_scalar_matrices()
+    print 'scalar done'
+    create_origin_destination_matrices()
+    print 'OD done'
+    create_full_matrices()
+    print 'full done'
+    create_skim_matrices()
+    print 'skim done'
+    create_terminal_matrices()
+    print 'terminal done'
+    initialize_process_zone_partition()
+    print 'initialize done'
+    reset_utility()
+    print 'reset utility done'
+
+    get_cost_time_distance_skim_data()
+    print 'get auto skim done'
+    get_terminal_skim_data()
+    print 'get terminal skim done'
+    get_walk_bike_skim_data()
+    print 'get walk bike skim done'
+    get_transit_skim_data()
+    print 'transit skim done'
+
+    calculate_auto_cost()
+    print 'calculate auto cost done'
+    calculate_mode()
+    print 'calculate mode done'
+    calculate_mode_shares()
+    print 'calculate mode shares done'
+
+    test_results()
+    mode_choice_to_h5(trip_purpose)
+    print trip_purpose, 'is done'
+
+my_project = EmmeProject(r'projects\Supplementals\Supplementals.emp')
+origin_destination_dict = json_to_dictionary(r'supplemental_matrices_dict.txt')
+parameters_dict = json_to_dictionary('parameters.json')
+ensembles_path = r'inputs\supplemental\generation\ensembles\ensembles_list.csv'
+#trip_purpose_list = ['hbw1', 'hbw2', 'hbw3', 'hbo', 'nhb']
+#for trip_purp in trip_purpose_list:
+#     trip_purpose = trip_purp
+trip_purpose = 'hbo'
+
+if __name__ == "__main__":
+    main()
+
+
+print 'end'
+
+
+
diff --git a/scripts/trucks/truck_model.py b/scripts/trucks/truck_model.py
index bf4b5478..9b05ed34 100644
--- a/scripts/trucks/truck_model.py
+++ b/scripts/trucks/truck_model.py
@@ -16,6 +16,7 @@
 import h5py
 sys.path.append(os.path.join(os.getcwd(),"inputs"))
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 #from truck_model import *
 from EmmeProject import * 
 from truck_configuration import *
@@ -45,16 +46,18 @@ def json_to_dictionary(dict_name):
 def skims_to_hdf5(EmmeProject):
     truck_od_matrices = ['lttrk', 'mdtrk', 'hvtrk']
   
+    # if h5 exists, delete it and re-write
+    try:
+        os.remove(truck_trips_h5_filename)
+    except OSError:
+        pass
+
     #open h5 container, delete existing truck trip matrices:
-    my_store = h5py.File(truck_trips_h5_filename, "r+")
+    my_store = h5py.File(truck_trips_h5_filename, 'w')
     for tod in tod_list:
+    	my_store.create_group(tod)
         for name in truck_od_matrices:
             matrix_name = tod[0] + name       
-            #delete if matrix exists
-            e = matrix_name in my_store[tod]
-            if e:
-                del my_store[tod][matrix_name]
-                'deleted ' + str(e)
             #export to hdf5
             print 'exporting' 
             matrix_name = tod[0] + name
@@ -63,6 +66,8 @@ def skims_to_hdf5(EmmeProject):
             print matrix_id
             matrix = EmmeProject.bank.matrix(matrix_id)
             matrix_value = np.matrix(matrix.raw_data)
+            if name == 'lttrk':
+                matrix_value = matrix_value * 0
             my_store[tod].create_dataset(matrix_name, data=matrix_value.astype('float32'),compression='gzip')
             print matrix_name+' was transferred to the HDF5 container.'
             matrix_value = None
@@ -384,11 +389,11 @@ def write_summary():
     # Write production and attraction totals
     truck_pa = {'prod': {}, 'attr': {}}
 
-    for truck_type in ['lt','mt','ht']:
+    for truck_type in ['mt','ht']:
         truck_pa['prod'][truck_type] = my_project.bank.matrix('mo' + truck_type + 'prof').get_numpy_data().sum()
         truck_pa['attr'][truck_type] = my_project.bank.matrix('md' + truck_type + 'attf').get_numpy_data().sum()
 
-    pd.DataFrame.from_dict(truck_pa).to_csv(r'outputs/trucks.csv')
+    pd.DataFrame.from_dict(truck_pa).to_csv(r'outputs/network/trucks.csv')
 
 
 def main():
diff --git a/scripts/utils/4kTripTables_to_h5.py b/scripts/utils/4kTripTables_to_h5.py
index b19ee97d..27b2459f 100644
--- a/scripts/utils/4kTripTables_to_h5.py
+++ b/scripts/utils/4kTripTables_to_h5.py
@@ -7,8 +7,7 @@
 import json
 import numpy as np
 import time
-import os
-import sys
+import os, sys
 import h5py
 import Tkinter
 import tkFileDialog
@@ -16,6 +15,7 @@
 import multiprocessing as mp
 import subprocess
 from multiprocessing import Pool
+sys.path.append(os.getcwd())
 
 # This script converts the 4k emmebank skims into HDF5 skims to be used in Daysim for model estimation.
 
diff --git a/scripts/utils/convert_hhinc_2000_2010.py b/scripts/utils/convert_hhinc_2000_2010.py
index 38ebb55e..41a7afbe 100644
--- a/scripts/utils/convert_hhinc_2000_2010.py
+++ b/scripts/utils/convert_hhinc_2000_2010.py
@@ -4,6 +4,7 @@
 import h5py
 import numpy as np
 import os, sys
+sys.path.append(os.getcwd())
 
 print 'Converting synthetic population income from 2000$ to 2010$'
 INCOME_FACTOR_00_10 = 1.26
diff --git a/scripts/utils/create_node_to_node_index.py b/scripts/utils/create_node_to_node_index.py
index 4550199a..b231b591 100644
--- a/scripts/utils/create_node_to_node_index.py
+++ b/scripts/utils/create_node_to_node_index.py
@@ -1,7 +1,8 @@
+import os, sys
 import pandas as pd
 import h5py
 import numpy as np
-
+sys.path.append(os.getcwd())
 # This file creates node to node index file and node to node distance file from DTAlite output. 
 
 
diff --git a/scripts/utils/h5toDF.py b/scripts/utils/h5toDF.py
index 9c917a5b..67b67b7f 100644
--- a/scripts/utils/h5toDF.py
+++ b/scripts/utils/h5toDF.py
@@ -20,6 +20,7 @@
 import json
 import sys, os
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
 
 def negative_check(array,variable):
     if pd.Series.min(array)<0:
diff --git a/scripts/utils/shadow_pricing_check.py b/scripts/utils/shadow_pricing_check.py
index 4fd2207b..53270fd1 100644
--- a/scripts/utils/shadow_pricing_check.py
+++ b/scripts/utils/shadow_pricing_check.py
@@ -16,9 +16,10 @@
 import h5toDF
 import math
 import os.path
-from input_configuration import *
 import sys
 sys.path.append(os.path.join(os.getcwd(),"scripts"))
+sys.path.append(os.getcwd())
+from input_configuration import *
 
 def get_percent_rmse(urbansim_file, daysim_file, guide_file):
     urbansim_data = pd.io.parsers.read_table(urbansim_file, sep = ' ') #Read in UrbanSim data
diff --git a/scripts/utils/skims_to_csv.py b/scripts/utils/skims_to_csv.py
new file mode 100644
index 00000000..67233bb9
--- /dev/null
+++ b/scripts/utils/skims_to_csv.py
@@ -0,0 +1,22 @@
+# this is an example script for when people ask for skims in csv
+import h5py
+import pandas as pd
+
+user_class_auto = 'svtl1t'
+user_class_transit='ivtwa'
+
+pk_skims_loc = r'Z:\brice\soundcast\inputs\7to8.h5'
+op_skims_loc = r'Z:\brice\soundcast\inputs\10to14.h5'
+
+
+h5_pk= h5py.File(pk_skims_loc)
+h5_op = h5py.File(op_skims_loc)
+
+#travel time skims for transit (peak hours, in-vehicle time only) 
+transit = h5_pk['Skims'][user_class_transit]
+transit_df = pd.DataFrame(transit[:])
+transit_df.to_csv(r'C:\Users\SChildress\Documents\data_request\am_transit_ivt_100.csv')
+#car (off-peak hours) for 2014 
+auto = h5_op['Skims'][user_class_transit]
+auto_df = pd.DataFrame(auto[:])
+auto_df.to_csv(r'C:\Users\SChildress\Documents\data_request\md_auto_ivt_100.csv')
diff --git a/scripts/utils/update_parking.py b/scripts/utils/update_parking.py
index c211fd45..3dde2104 100644
--- a/scripts/utils/update_parking.py
+++ b/scripts/utils/update_parking.py
@@ -8,16 +8,17 @@
 # Run this script from the same location as the parcels.txt file
 # Parcels.txt can be converted from .dbf format with ArcGIS.
 
+import os, sys
 import pandas as pd
 import h5py
 import numpy as np
+sys.path.append(os.getcwd())
 from input_configuration import *
 
-
-daily_parking_cost = "inputs\\parcel_buffer\\daily_parking_costs.csv"
-hourly_parking_cost = "inputs\\parcel_buffer\\hourly_parking_costs.csv"
-input_ensemble = "inputs\\parking_gz.csv"
-input_parcels = "inputs\\parcel_buffer\\parcels_urbansim.txt"
+daily_parking_cost = r'inputs\parking\daily_parking_costs.csv'
+hourly_parking_cost = r'inputs\parking\hourly_parking_costs.csv'
+input_ensemble = r'inputs\parking_gz.csv'
+input_parcels = r'inputs\accessibility\parcels_urbansim.txt'
 
 # Combine data columns
 df_parcels = pd.read_csv(input_parcels, delim_whitespace=True)
@@ -29,12 +30,14 @@
 merged_df = pd.merge(left=join_ensemble_to_parcel,
                                 right=df_daily_parking_cost, 
                                 left_on="ENS",
-                                right_on="ENS")
+                                right_on="ENS",
+                                how='left')
 # Join hourly costs with parcel data
 merged_df = pd.merge(left = merged_df,
                                 right = df_hourly_parking_cost,
                                 left_on="ENS",
-                                right_on="ENS")
+                                right_on="ENS",
+                                how='left')
 
 
 # Clean up the results and store in same format as original parcel.txt file