This short article demonstrates how to get data from the UK’s Department for Transport funded JTS project with R and Python. It accompanies a more detailed paper on the subject. The R and Python versions are hosted on github at datasciencecampus/jtstats-r and datasciencecampus/jtstats-py.
For further details see the official JTS home page at https://www.gov.uk/government/statistical-data-sets/journey-time-statistics-data-tables-jts
If the information provided is of use and interest, please cite the accompanying paper as follows (TBC):
Install the package as follows:
remotes::install_github("datasciencecampus/jtstats-r")From the readme of the R package we can download a specific dataset as follows:
library(jtstats)
jts_geo = get_jts(type = "jts04", purpose = "GPs", sheet = 2017, geo = TRUE)Matching tables by type (jts04):
Travel time, destination and origin indicators for employment centres by mode of travel and local authority, England
Travel time, destination and origin indicators for primary schools by mode of travel and local authority, England
Travel time, destination and origin indicators for secondary schools by mode of travel and local authority, England
Travel time, destination and origin indicators for further education by mode of travel and local authority, England
Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
Travel time, destination and origin indicators for hospitals by mode of travel and local authority, England
Travel time, destination and origin indicators for food stores by mode of travel and local authority, England
Travel time, destination and origin indicators for town centres by mode of travel and local authority, England
Travel time, destination and origin indicators to Pharmacy by cycle and car, local authority, England
Matching tables by purpose (jts04):
Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
Matching tables by sheet (jts04, GPs, 2017):
jts0405-2017.csv
# We'll print data for Hartlepool and Middlesbrough:
jts_hartlepool_middlesbrough = jts_geo[jts_geo$LA_Name %in% c("Hartlepool", "Middlesbrough"), ]
jts_hartlepool_middlesbrough[, c("LA_Name", "GP_pop")]Simple feature collection with 2 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -1.383764 ymin: 54.50113 xmax: -1.137319 ymax: 54.72717
Geodetic CRS: WGS 84
# A tibble: 2 × 3
LA_Name GP_pop geometry
<chr> <dbl> <MULTIPOLYGON [°]>
1 Hartlepool 41484. (((-1.270252 54.72717, -1.268225 54.72609, -1.267405 54.…
2 Middlesbrough 58295. (((-1.23003 54.58411, -1.230163 54.58374, -1.229838 54.5…
Install the package as follows from the jtstats-py directory:
cd ../jtstats-py # if already there
curl -sSL https://install.python-poetry.org | python3 -
# See https://python-poetry.org/docs/#installing-with-the-official-installer
poetry install
# To open a Python session with all the dependencies you can run the following:
poetry shell
python
# From the Python shell, load the package to test it's installed:
import jtspy as jts
# Access the equivalent dataset from the Python package:
jts_geo = jts.get_jts(type_code = "jts04", spec = "gp", sheet = "2017")
# Print the values for Hartlepool and Middlesbrough:
jts_hartlepool_middlesbrough = jts_geo[jts_geo["LA_Name"].isin(["Hartlepool", "Middlesbrough"])]
# Print the first 5 columns:
print(jts_hartlepool_middlesbrough[["LA_Name", "GP_pop"]].head(4))0 LA_Name GP_pop
2 Hartlepool 41483.728979
3 Middlesbrough 58295.046099
The Python package allows easy retrieval of the JTS data using the
get_jts() function. The key parameters that can be specified in order
to specify what data to retrieve are: type_code, spec, sheet and
table_code (even though this last parameter is only rarely needed).
The table here reports the values to be used for these parameters in
order to retrieve each of the JTS data. In some cases, the spec
parameter should be set to empty and the sheet parameter should be
used instead. These occurrences are indicated with sheet parameter should be simply
set to the year for which data is needed, e.g. sheet = '2019'. The
get_jts() function returns a pandas dataframe. The size of the
returned dataframe is given in the last column (in the format #rows
sheet parameter in the
get_jts() function, and the spec parameter should be left empty.
table_code parameter in
the get_jts() function, and the type_code parameter should be left
unspecified.
| Table title | Description | Type code | Spec/Sheet | Size |
|---|---|---|---|---|
| JTS0101 | Travel time to reach nearest key services by mode of travel (England) | jts01 | JTS0101$^\S$ | |
| JTS0102 | Travel time to reach nearest key services by mode of travel (by urban/rural) | jts01 | urban | |
| JTS0103 | Travel time to reach nearest key services by mode of travel (by region) | jts01 | region | |
| JTS0104 | Travel time to reach nearest key services by mode of travel (by local authority) | jts01 | local authority | |
| JTS0201 | Access to key services within journey times by mode of travel (England) | jts02 | 32$\times$ 13 | |
| JTS0202 | Access to key services within journey times by mode of travel (by urban/rural) | jts02 | urban | 64$\times$ 14 |
| JTS0203 | Access to key services within journey times by public transport$^\star$ (by region) | jts02 | public transport | 71$\times$ 13 |
| JTS0204 | Access to key services within journey times by cycle (by region) | jts02 | cycle | 71$\times$ 13 |
| JTS0205 | Access to key services within journey times by car (by region) | jts02 | car | 71$\times$ 13 |
| JTS0206 | Access to key services within journey times by walking (by region) | jts0206$^\dagger$ | 71$\times$ 13 | |
| JTS0301 | Number of sites within journey time by key services and mode of travel (England) | jts03 | 19$\times$ 13 | |
| JTS0302 | Number of sites within journey time by key services and mode of travel (urban/rural) | jts03 | urban | 32$\times$ 15 |
| JTS0303 | Number of sites within journey time by key services and public transport$^\star$ (regions) | jts03 | public transport | 36$\times$ 14 |
| JTS0304 | Number of sites within journey time by key services and cycle (regions) | jts03 | cycle | 36$\times$ 14 |
| JTS0305 | Number of sites within journey time by key services and car (regions) | jts03 | car | 36$\times$ 14 |
| JTS0306 | Number of sites within journey time by key services and walking (regions) | jts0306$^\dagger$ | 36$\times$ 14 | |
| JTS0401 | Journey times for employment centres by mode of travel (local authority) | jts04 | employment | 361$\times$ 112 |
| JTS0402 | Journey times for primary schools by mode of travel (local authority) | jts04 | primary | 361$\times$ 40 |
| JTS0403 | Journey times for secondary schools by mode of travel (local authority) | jts04 | secondary | 361$\times$ 40 |
| JTS0404 | Journey times for further education by mode of travel (local authority) | jts04 | further | 361$\times$ 40 |
| JTS0405 | Journey times for GPs by mode of travel (local authority) | jts04 | gp | 361$\times$ 40 |
| JTS0406 | Journey times for hospitals by mode of travel (local authority) | jts04 | hospital | 361$\times$ 40 |
| JTS0407 | Journey times for food stores by mode of travel (local authority) | jts04 | food | 361$\times$ 40 |
| JTS0408 | Journey times for town centres by mode of travel (local authority) | jts04 | town | 361$\times$ 40 |
| JTS0409 | Journey times to pharmacy by cycle and car (local authority) | jts04 | pharmacy | 361$\times$ 22 |
| JTS0501 | Journey times for employment centres by mode of travel (LSOA) | jts05 | employment | 32844$\times$ 113 |
| JTS0502 | Journey times for primary schools by mode of travel (LSOA) | jts05 | primary | 32844$\times$ 41 |
| JTS0503 | Journey times for secondary schools by mode of travel (LSOA) | jts05 | secondary | 32844$\times$ 41 |
| JTS0504 | Journey times for further education by mode of travel (LSOA) | jts05 | further | 32844$\times$ 41 |
| JTS0505 | Journey times for GPs by mode of travel (LSOA) | jts05 | gp | 32844$\times$ 41 |
| JTS0506 | Journey times for hospitals by mode of travel (LSOA) | jts05 | hospital | 32844$\times$ 41 |
| JTS0507 | Journey times for food stores by mode of travel (LSOA) | jts05 | food | 32844$\times$ 41 |
| JTS0508 | Journey times for town centres by mode of travel (LSOA) | jts05 | town | 32844$\times$ 41 |
| JTS0509 | Journey times to pharmacy by cycle and car (LSOA) | jts05 | pharmacy | 32844$\times$ 23 |
As in the table above, we report here how to retrieve the JTS data using
the Python package. In particular, this table focuses on the journey
times connectivity (JTS09) data, which have a slightly different format
than the rest. For this reason, the table includes separately the spec
and sheet parameters, since in most cases here they both need to be
set in order to retrieve the correct data.
| Table title | Description | Type code | Spec | Sheet | Size |
|---|---|---|---|---|---|
| JTS0901 | Journey times to nearest airports (local authority) | jts09 | airports | JTS0901_Nearest | 128$\times$ 6 |
| JTS0901 | Journey times to selected airports (local authority) | jts09 | airports | JTS0901_Selected | 128$\times$ 44 |
| JTS0902 | Catchment population measures for airports (local authority) | jts09 | airports | JTS0902 | 32$\times$ 14 |
| JTS0903 | Population within journey time of airports (local authority) | jts0903 | airports | JTS0903 | 130$\times$ 9 |
| JTS0904 | Number airports within journey time (local authority) | jts09 | airports | JTS0904 | 129$\times$ 9 |
| JTS0905 | Journey times to selected airports (LSOA) | jts09 | airports | JTS0905_Selected | 32844$\times$ 41 |
| JTS0905 | Journey times to airports, summary (LSOA) | jts09 | airports | JTS0905_Summary | 32844$\times$ 9 |
| JTS0921 | Journey times to nearest rail stations (local authority) | jts09 | rail | JTS0921_Nearest | 119$\times$ 7 |
| JTS0921 | Journey times to rail stations by car (local authority) | jts09 | rail | car | 120$\times$ 81 |
| JTS0921 | Journey times to rail stations by public transport (local authority) | jts09 | rail | PT | 120$\times$ 81 |
| JTS0922 | Catchment population measures for rail stations (England) | jts09 | rail | JTS0922 | 79$\times$ 15 |
| JTS0923 | Population within journey time of rail stations (local authority) | jts09 | rail | JTS0923 | 130$\times$ 10 |
| JTS0924 | Number of rail stations within journey time (local authority) | jts09 | rail | JTS0924 | 129$\times$ 9 |
| JTS0925 | Journey times by public transport for selected rail stations (LSOA) | jts09 | rail | JTS0925_Selected | 32844$\times$ 84 |
| JTS0925 | Journey times by public transport for rail stations, summary (LSOA) | jts09 | rail | JTS0926_Summary | 32844$\times$ 7 |
| JTS0926 | Journey times by car for selected rail stations (LSOA) | jts09 | rail | JTS0926_Selected | 32844$\times$ 84 |
| JTS0926 | Journey times by car for rail stations, summary (LSOA) | jts09 | rail | JTS0926_Summary | 32844$\times$ 7 |
| JTS0930 | Travel time to city centres (local authority) | jts09 | JTS0930_LA | 361$\times$ 5 | |
| JTS0930 | Travel time to city centres (LSOA) | jts09 | JTS0930_LSOA | 32844$\times$ 6 |