../vignettes/calculating_accessibility.Rmd
calculating_accessibility.Rmd
Abstract
This vignette shows how to calculate and visualize accessibility in R using ther5r
package.
Accessibility indicators measure the ease with which opportunities, such as jobs, can be reached by a traveler from a particular location (Levinson and et al. 2020). One of the simplest forms of accessibility metrics is the cumulative-opportunities, which counts all of the opportunities accessible from each location in less than a cutoff time. Using a travel time matrix and information on the number of opportunities available at each location, we can calculate and map accessibility. This vignette shows how to do that in R using the r5r
package.
In this reproducible example, we will be using a sample data set for the city of Porto Alegre (Brazil) included in r5r
. We can compute accessibility in 5 quick steps:
setup_r5()
Before we start, we need to increase the memory available to Java and load the packages used in this vignette
options(java.parameters = "-Xmx2G")
library(r5r)
library(sf)
library(data.table)
library(ggplot2)
library(interp)
library(dplyr)
To build a routable transport network with r5r and load it into memory, the user needs to call setup_r5
with the path to the directory where OpenStreetMap and GTFS data are stored.
# system.file returns the directory with example data inside the r5r package
# set data path to directory containing your own data if not using the examples
data_path <- system.file("extdata/poa", package = "r5r")
r5r_core <- setup_r5(data_path)
In this example, we will be calculating the number of schools and public healthcare facilities accessible by public transport within a travel time of up to 20 minutes. The sample data provided contains information on the spatial distribution of schools in Porto Alegre in the points$schools
column, and healthcare facilities in the points$healthcare
column.
With the code below we compute the number of schools and healthcare accessible considering median of multiple travel time estimates departing every minute over a 60-minute time window, between 2pm and 3pm. The accessibility()
function can calculate access to multiple opportunities in a single call, which is much more efficient and convenient than producing a travel time matrix of the study area and manually computing accessibility.
# read all points in the city
points <- fread(file.path(data_path, "poa_hexgrid.csv"))
# routing inputs
mode <- c("WALK", "TRANSIT")
max_walk_time <- 30 # in minutes
travel_time_cutoff <- 21 # in minutes
departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
format = "%d-%m-%Y %H:%M:%S")
time_window <- 60 # in minutes
percentiles <- 50
# calculate travel time matrix
access <- accessibility(r5r_core,
origins = points,
destinations = points,
mode = mode,
opportunities_colnames = c("schools", "healthcare"),
decay_function = "step",
cutoffs = travel_time_cutoff,
departure_datetime = departure_datetime,
max_walk_time = max_walk_time,
time_window = time_window,
percentiles = percentiles,
progress = FALSE)
head(access)
#> id opportunity percentile cutoff accessibility
#> 1: 89a901291abffff schools 50 21 3
#> 2: 89a901291abffff healthcare 50 21 5
#> 3: 89a9012a3cfffff schools 50 21 0
#> 4: 89a9012a3cfffff healthcare 50 21 0
#> 5: 89a901295b7ffff schools 50 21 5
#> 6: 89a901295b7ffff healthcare 50 21 2
The final step is mapping the accessibility results calculated earlier. The code below demonstrates how to do that, with some extra steps to produce a prettier map by doing a spatial interpolation of accessibility estimates.
# interpolate estimates to get spatially smooth result
access_schools <- access %>%
filter(opportunity == "schools") %>%
inner_join(points, by='id') %>%
with(interp::interp(lon, lat, accessibility)) %>%
with(cbind(acc=as.vector(z), # Column-major order
x=rep(x, times=length(y)),
y=rep(y, each=length(x)))) %>% as.data.frame() %>% na.omit() %>%
mutate(opportunity = "schools")
access_health <- access %>%
filter(opportunity == "healthcare") %>%
inner_join(points, by='id') %>%
with(interp::interp(lon, lat, accessibility)) %>%
with(cbind(acc=as.vector(z), # Column-major order
x=rep(x, times=length(y)),
y=rep(y, each=length(x)))) %>% as.data.frame() %>% na.omit() %>%
mutate(opportunity = "healthcare")
access.interp <- rbind(access_schools, access_health)
# find results' bounding box to crop the map
bb_x <- c(min(access.interp$x), max(access.interp$x))
bb_y <- c(min(access.interp$y), max(access.interp$y))
# extract OSM network, to plot over map
street_net <- street_network_to_sf(r5r_core)
# plot
ggplot(na.omit(access.interp)) +
geom_contour_filled(aes(x=x, y=y, z=acc), alpha=.8) +
geom_sf(data = street_net$edges, color = "gray55", size=0.1, alpha = 0.9) +
scale_fill_viridis_d(direction = -1, option = 'B') +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
coord_sf(xlim = bb_x, ylim = bb_y, datum = NA) +
labs(fill = "Facilities within\n20 minutes\n(median travel time)") +
theme_minimal() +
theme(axis.title = element_blank()) +
facet_wrap(~opportunity)
If you have any suggestions or want to report an error, please visit the package GitHub page.
Levinson, David, and et al. 2020. “Transport Access Manual: A Guide for Measuring Connection Between People and Places,” January. https://ses.library.usyd.edu.au/handle/2123/23733.