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Households Near HCT
This script measures the number of households within a buffer of high-capacity transit (HCT) stations across equity quintiles.
Activate the 'equity_tracker' Anaconda virtual environment as follows:
conda activate equity_tracker
The script relies on database tables from Elmer, layers from ElmerGeo, and equity geography definitions specific to the Equity Tracker project. Standard geographic data from ElmerGeo includes 2020 Census block group polygons ("blockgrp2020"), HCT station areas ("hct_station_areas"), and parcel centroids ("parcels_urbansim_2018_pts"). Elmer data tables includes quintile equity geography definitions in the table "equity.blockgroup_shares". This file identifies which quintile every block group is in for all equity categories. This file is produced from the equity_blockgroups.R script within this repository. Users can specify which year these quintile defintions are used by the script by changing the equity_quintiles_year variable at the top of the household_proximity_indicator.py script.
In its current form, the input "parcels_urbansim.txt" is stored on the network at Y:\Equtiy Tracker\access. This will be available as an Elmer table in the future. This file contains total households per parcel in the Soundcast base year of 2018.
From the Anaconda prompt within the equity-tracker repository, the script can now be run with python household_proximity_indicator.py. Ensure that the equity_tracker conda environment is activated.
This script will produce a file in the local working directory titled "household_proximity_indicator.csv." This file provides the total number of households within 1/2 mile of HCT stations ("households_in_buffer") for 6 equity groups and 6 transit submodes. The results also include the total number of households ("total_households") within the equity groups to allow a measure of percent of households within the proximity of HCT stations ("household_shares_in_buffer"). The table below shows a partial set of output to be expected.
| aggregation | quintile | mode | households_in_buffer | total_households | household_shares_in_buffer |
|---|---|---|---|---|---|
| poc | High | all_hct | 207389 | 326305 | 0.63556795 |
| poc | Low | all_hct | 60128 | 305583 | 0.196764872 |
| poc | Low Medium | all_hct | 114757 | 322679 | 0.355638266 |
| poc | Medium | all_hct | 139167 | 323639 | 0.43000689 |
| poc | Medium High | all_hct | 176385 | 326420 | 0.54036211 |
| poc | High | light_rail | 66611 | 326305 | 0.204137234 |
| poc | Low | light_rail | 7493 | 305583 | 0.024520343 |
| poc | Low Medium | light_rail | 29550 | 322679 | 0.091577078 |
| poc | Medium | light_rail | 42786 | 323639 | 0.132202856 |
| poc | Medium High | light_rail | 52968 | 326420 | 0.162269469 |
The calculation is based on a simple area buffering of 1/2 mile around each HCT station. Parcel-level data are intersected with the buffers to identify total number of households located on those parcels. These parcels are overlaid and aggregated at the equity geography level (block-groups). Total households are taken from the parcel household totals overlaid on equity geographies, ignoring buffers.