AbstractThis vignette shows how to calculate and visualize accessibility in R using the
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
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:
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
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 6 #> 3: 89a9012a3cfffff schools 50 21 0 #> 4: 89a9012a3cfffff healthcare 50 21 0 #> 5: 89a901295b7ffff schools 50 21 6 #> 6: 89a901295b7ffff healthcare 50 21 4
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$y)) # extract OSM network, to plot over map street_net <- street_network_to_sf(r5r_core) # plot ggplot(na.omit(access.interp)) + geom_sf(data = street_net$edges, color = "gray55", size=0.01, alpha = 0.7) + geom_contour_filled(aes(x=x, y=y, z=acc), alpha=.7) + 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)
r5r objects are still allocated to any amount of memory previously set after they are done with their calculations. In order to remove an existing
r5r object and reallocate the memory it had been using, we use the
stop_r5 function followed by a call to Java’s garbage collector, as follows:
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.