Easily streamline accessibility measures for Great Britain.
AccessUK is an open-source tool for the R programming language
designed to streamline the integration of accessibility measures into R
workflows focusing on Great Britain (GB), thereby unlocking the
potential of spatial data and analysis by informing decision-making
processes and supporting sustainable practices across diverse sectors.
The main aim of this tool is to manage and distribute a series of pre-computed Accessibility-related measures for small geographic areas in GB described here (PTAI dataset). It offers additional functions that enable users to customise or create new accessibility measures.
AccessUK uses DuckDB, a relational database
management system optimised for analytical workloads, in the background.
This facilitates the rapid execution of queries without loading the full
data into virtual memory. This mitigates common computational challenges
associated with large travel matrices.
To install the latest version of AccessUK directly from GitHub, please
follow the steps below:
You’ll need the devtools package to install R packages from GitHub. If
you don’t have it installed, run:
install.packages("devtools")With devtools installed, you can now install the AccessUK package:
devtools::install_github("urbanbigdatacentre/AccessUK")AccessUK does three main things: (1) get ready-to-use accessibility
measures for a range of urban and regional services; (2) tailor
accessibility measures to destinations not previously included in the
dataset based on pre-computed travel matrices; (3) estimate new
accessibility measures based on user-generated TTMs and service
locations.
library(AccessUK)
library(tidyverse)
library(sf)To get precomputed measures, the first step is to define a list 2011 lower super output areas (LSOA) or data zones (DZ) code for the desired locations. the example below selects a few areas in central London,
lsoa_london <- c('E01000919', 'E01002726', 'E01003014', 'E01033490', 'E01000914', 'E01000937', 'E01032583', 'E01000855', 'E01003016', 'E01000918', 'E01000002', 'E01032739', 'E01000916', 'E01004734', 'E01000936', 'E01004735', 'E01002724', 'E01000850', 'E01033596', 'E01000852', 'E01000853', 'E01003927', 'E01033595', 'E01004731', 'E01000001', 'E01003935', 'E01004763', 'E01004736', 'E01000917', 'E01003929', 'E01003013', 'E01032740', 'E01032582', 'E01000915', 'E01003930', 'E01000920', 'E01003017', 'E01003934', 'E01004733', 'E01032584')The desired locations are passed to the get_accessibility() function.
Also, the type of destination can be specified in the ‘service’
argument. It can be chosen from “employment”, “supermarkets”, “school”,
“gp” (general practice), “hospital”, and “cities”. The example below
retrieves accessibility to employment in central London.
accessibility_london <- get_accessibility(
from = lsoa_london,
service = 'employment',
mode = "public_transport"
)
glimpse(accessibility_london)
## Rows: 40
## Columns: 18
## $ geo_code <chr> "E01032584", "E01032583", "E01032582", "E01003927",…
## $ geo_label <chr> "Southwark 034E", "Southwark 034D", "Lambeth 036E",…
## $ employment_15 <dbl> 116600, 146950, 106650, 140075, 475675, 326450, 201…
## $ employment_30 <dbl> 2006530, 2265690, 2120155, 1924470, 1930935, 216792…
## $ employment_45 <dbl> 3382235, 3474700, 3508980, 3219590, 3187045, 325164…
## $ employment_60 <dbl> 4532830, 4630565, 4701855, 4311400, 4307635, 452590…
## $ employment_75 <dbl> 5937880, 5992060, 6095340, 5729995, 5717505, 599671…
## $ employment_90 <dbl> 7356745, 7474240, 7465010, 7140865, 7082720, 756973…
## $ employment_105 <dbl> 8488575, 8515250, 8598215, 8312200, 8297765, 879248…
## $ employment_120 <dbl> 9596665, 9628955, 9690610, 9379390, 9323300, 992857…
## $ employment_pct_15 <dbl> 0.3877880, 0.4887260, 0.3546963, 0.4658611, 1.58199…
## $ employment_pct_30 <dbl> 6.673313, 7.535226, 7.051206, 6.400398, 6.421899, 7…
## $ employment_pct_45 <dbl> 11.24863, 11.55615, 11.67016, 10.70770, 10.59947, 1…
## $ employment_pct_60 <dbl> 15.07528, 15.40032, 15.63742, 14.33884, 14.32632, 1…
## $ employment_pct_75 <dbl> 19.74819, 19.92838, 20.27187, 19.05680, 19.01526, 1…
## $ employment_pct_90 <dbl> 24.46705, 24.85781, 24.82711, 23.74907, 23.55569, 2…
## $ employment_pct_105 <dbl> 28.23128, 28.32000, 28.59592, 27.64470, 27.59669, 2…
## $ employment_pct_120 <dbl> 31.91657, 32.02396, 32.22901, 31.19395, 31.00741, 3…The results includes both the absolute employment available for a range of travel times in minutes as well as the relative employment in percent.
Given new point locations in GB, the user can tailor its own accessibility measures at the LSOA/DZ level. This uses 2011 LSOA/DZ geometries in the background. This is combined with pre-computed travel times in the PTAI dataset. The example below uses retail points and for various time cuts.
# New destination points for this example
data_dir = system.file('data', package = "AccessUK")
load(file.path(data_dir, 'retail_points.RData'))
retail_points <- retail_points %>%
st_as_sf(coords = c('long_wgs', 'lat_wgs'), crs = 4326)
# Run my_accessibility
timecuts <- c(10, 20, 30)
retail_accessibility <- my_accessibility(
destinations = retail_points,
time_cut = timecuts
)
## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries
glimpse(retail_accessibility)
## Rows: 41,729
## Columns: 4
## $ from_id <chr> "E01000001", "E01000002", "E01000003", "E01000005", "E0100…
## $ access_n_10 <dbl> 6, 4, 6, 0, 0, 3, 0, 3, 4, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 1…
## $ access_n_20 <dbl> 27, 16, 25, 24, 5, 11, 6, 11, 11, 9, 8, 4, 3, 2, 3, 6, 3, …
## $ access_n_30 <dbl> 109, 74, 89, 95, 12, 15, 14, 17, 17, 13, 12, 18, 13, 9, 8,…The result includes accessibility for all of GB, given that the retail points cover all the territory. However, we visualise a subset in central London.
# Read LSOA geometries
lsoa_geoms <- st_read(file.path(data_dir, 'lsoa_geoms/infuse_lsoa_lyr_2011_clipped.shp'))
## Reading layer `infuse_lsoa_lyr_2011_clipped' from data source
## `C:\Users\jvt3d\AppData\Local\Programs\R\R-4.3.0\library\AccessUK\data\lsoa_geoms\infuse_lsoa_lyr_2011_clipped.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 42619 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -69.1254 ymin: 5337.9 xmax: 655604.7 ymax: 1220302
## Projected CRS: OSGB36 / British National Grid
# Map
retail_accessibility %>%
filter(from_id %in% lsoa_london) %>%
left_join(lsoa_geoms, by = c('from_id' = 'geo_code')) %>%
st_as_sf() %>%
ggplot() +
geom_sf(aes(fill = access_n_30), col = NA) +
scale_fill_viridis_c() +
labs(
title = 'Accessibility to retail points in London by public transport within 30 minutes',
subtitle = 'Contains retail points larger than 280 m2',
fill = 'Retail points'
) +
theme_void()
The user can estimate completely new accessibility measures using
DuckDB in the background for any region by providing a travel matrix
in CSV or Parquet format. The user should input aggregated locations
using the same ID as the travel matrix. This can contain different types
of locations in each column with the first column named “id”.
# Aggregate locations in Manchester by LSOA
aggregated_retail <- retail_points %>%
filter(town == 'Manchester') %>%
my_accessibility(0)
## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries
# First column as 'id'
aggregated_retail <- aggregated_retail %>%
rename(retail = access_n_0, id = from_id)
# Specify the location of a travel matrix. This can be a folder with multiple files or a single file
ttm_path <- file.path(data_dir, 'accessibility_indicators_gb/ttm/ttm_pt_20211122.csv')
# Estimate accessibility for various thresholds
new_accessibility <- estimate_accessibility(
travel_matrix = ttm_path,
travel_cost = 'travel_time_p50',
weights = aggregated_retail,
time_cut = timecuts
)
glimpse(new_accessibility)
## Rows: 41,729
## Columns: 4
## $ from_id <chr> "E01000001", "E01000002", "E01000003", "E01000005", "…
## $ access_retail_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ access_retail_20 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ access_retail_30 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…This returns all LSOA/DZ in GB. As we are providing data for Manchester only, we can filter relevant observations only.
new_accessibility %>%
filter(access_retail_20 > 0) %>%
left_join(lsoa_geoms, by = c('from_id' = 'geo_code')) %>%
st_as_sf() %>%
ggplot() +
geom_sf(aes(fill = access_retail_20), col = NA) +
scale_fill_viridis_c() +
labs(
title = 'Accessibility to retail points in Manchester by public transport within 20 minutes',
subtitle = 'Contains retail points larger than 280 m2',
fill = 'Retail points'
) +
theme_void()
If you use this package, please cite as following:
- Verduzco Torres, J. R., & McArthur, D. P. (2024). Public transport accessibility indicators to urban and regional services in Great Britain. Scientific Data, 11(1), Article 1. https://doi.org/10.1038/s41597-023-02890-w
- Verduzco Torres, J. R. (2023). AccessUK (v0.0.1-alpha) [R]. University of Glasgow. https://github.com/urbanbigdatacentre/AccessUK
- DuckDB - https://duckdb.org/
- Public transport accessibility indicators (PTAI) in Great Britain repo - https://github.com/urbanbigdatacentre/access_uk_open
accessibilityR package - https://github.com/ipeaGIT/accessibility
- Verduzco Torres, J. R., & McArthur, D. (2022). Public Transport Accessibility Indicators for Great Britain [dataset]. Zenodo. https://doi.org/10.5281/zenodo.8037156
- Verduzco Torres, J. R., & McArthur, D. P. (2024). Public transport accessibility indicators to urban and regional services in Great Britain. Scientific Data, 11(1), Article 1. https://doi.org/10.1038/s41597-023-02890-w