knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true"), out.width = "100%" )
An isochrone of a given place includes all the areas reachable from that place within a certain amount of time. This vignette shows how to calculate and visualize isochrones 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. Our aim here is to calculate several isochrones departing from the central bus station given different travel time thresholds. We'll do this in 4 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 travel times by public transport from the central bus station in Porto Alegre to every other block in the city. With the code below we compute multiple travel time estimates departing every minute over a 120-minute time window, between 2pm and 4pm.
# read all points in the city points <- fread(file.path(data_path, "poa_hexgrid.csv")) # subset point with the geolocation of the central bus station central_bus_stn <- points[291,] # routing inputs mode <- c("WALK", "TRANSIT") max_walk_time <- 30 # in minutes max_trip_duration <- 120 # in minutes departure_datetime <- as.POSIXct("13-05-2019 14:00:00", format = "%d-%m-%Y %H:%M:%S") time_window <- 120 # in minutes percentiles <- 50 # calculate travel time matrix ttm <- travel_time_matrix(r5r_core, origins = central_bus_stn, destinations = points, mode = mode, departure_datetime = departure_datetime, max_walk_time = max_walk_time, max_trip_duration = max_trip_duration, time_window = time_window, percentiles = percentiles, progress = FALSE) head(ttm)
Now we only need to organize the travel time matrix output
ttm and plot it on the map.
# extract OSM network street_net <- street_network_to_sf(r5r_core) # add coordinates of destinations to travel time matrix ttm[points, on=c('to_id' ='id'), `:=`(lon = i.lon, lat = i.lat)] # interpolate estimates to get spatially smooth result travel_times.interp <- with(na.omit(ttm), interp(lon, lat, travel_time_p50)) %>% with(cbind(travel_time=as.vector(z), # Column-major order x=rep(x, times=length(y)), y=rep(y, each=length(x)))) %>% as.data.frame() %>% na.omit() # find isochrone's bounding box to crop the map below bb_x <- c(min(travel_times.interp$x), max(travel_times.interp$x)) bb_y <- c(min(travel_times.interp$y), max(travel_times.interp$y)) # plot ggplot(travel_times.interp) + geom_sf(data = street_net$edges, color = "gray55", size=0.01, alpha = 0.7) + geom_contour_filled(aes(x=x, y=y, z=travel_time), alpha=.7) + geom_point(aes(x=lon, y=lat, color='Central bus\nstation'), data=central_bus_stn) + scale_fill_viridis_d(direction = -1, option = 'B') + scale_color_manual(values=c('Central bus\nstation'='black')) + scale_x_continuous(expand=c(0,0)) + scale_y_continuous(expand=c(0,0)) + coord_sf(xlim = bb_x, ylim = bb_y) + labs(fill = "travel time\n(in minutes)", color='') + theme_minimal() + theme(axis.title = element_blank())
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:
r5r::stop_r5(r5r_core) rJava::.jgc(R.gc = TRUE)
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