# Isochrones

#### 2025-08-21

Abstract

This vignette shows how to calculate and visualize isochrones in R using the `r5r` package.

# 1. Introduction

An isochrone map shows how far one can travel from a given place within a certain amount of time. In other other words, it shows all the areas reachable from that place within a maximum travel time. This vignette shows how to calculate and visualize isochrones in R using the `r5r` package using a reproducible example. In this 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.

There are two ways to calculate / visualize isochrones using `r5r`. The quick and easy option is using the `r5r::isochrone()` function. The other alternative requires one to first calculate travel time estimates, and then to do some spatial interpolation operations. We will cover both approaches in this vignette.

Before we start, we need to increase Java memory + load a few libraries, and to build routable transport network.

Warning: If you want to calculate how many opportunities (e.g. jobs, or schools or hospitals) are located inside each isochrone, we strongly recommend you NOT to use the `isochrone()` function. You will find much more efficient ways to do this in the Accessibility vignette.

# 2. Build routable transport network with `build_network()`

### Increase Java memory and load libraries

First, we need to increase the memory available to Java and load the packages used in this vignette. Please note we allocate RAM memory to Java before loading our libraries.

    options(java.parameters = "-Xmx2G")

    library(r5r)
    library(sf)
    library(data.table)
    library(ggplot2)
    library(interp)

To build a routable transport network with `r5r`, the user needs to call `build_network()` 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 running this example
    data_path <- system.file("extdata/poa", package = "r5r")

    r5r_network <- build_network(data_path)

# 3. Isochrone: quick and easy approach

The quick and easy approach to estimate / visualize an isochrone is to use the `isochrone()` function built in the `r5r` package. In this example, we will be calculating the isochrones by public transport from the central bus station in Porto Alegre. The `isochrone()` function calculates isochrones considering the travel times from the origin point to a random sample of `80%` of all nodes in the transport network (default). The size of the sample can be fine tuned with the `sample_size` parameter.

With the code below, `r5r` determines the isochrones considering the median travel time of multiple travel time estimates calculated 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,]

    # isochrone intervals
    time_intervals <- seq(0, 100, 10)

    # routing inputs
    mode <- c("WALK", "TRANSIT")
    max_walk_time <- 30      # in minutes
    max_trip_duration <- 100 # in minutes
    time_window <- 120       # in minutes
    departure_datetime <- as.POSIXct("13-05-2019 14:00:00",
                                     format = "%d-%m-%Y %H:%M:%S")

    # calculate travel time matrix
    iso1 <- r5r::isochrone(r5r_network,
                           origins = central_bus_stn,
                           mode = mode,
                           cutoffs = time_intervals,
                           sample_size = 1,
                           departure_datetime = departure_datetime,
                           max_walk_time = max_walk_time,
                           max_trip_duration = max_trip_duration,
                           time_window = time_window,
                           progress = FALSE)

As you can see, the `isochrone()` functions works very similarly to the `travel_time_matrix()` function, but instead of returning a table with travel time estimates, it returns a `POLYGON "sf" "data.frame"` for each isochrone of each origin.

    head(iso1)
    #> Simple feature collection with 6 features and 2 fields
    #> Geometry type: POLYGON
    #> Dimension:     XY
    #> Bounding box:  xmin: -51.2648 ymin: -30.1133 xmax: -51.1322 ymax: -29.9966
    #> Geodetic CRS:  WGS 84
    #>                id isochrone                       polygons
    #> 1 89a90128a8fffff       100 POLYGON ((-51.2572 -30.111,...
    #> 2 89a90128a8fffff        90 POLYGON ((-51.2572 -30.111,...
    #> 3 89a90128a8fffff        80 POLYGON ((-51.2572 -30.111,...
    #> 4 89a90128a8fffff        70 POLYGON ((-51.2572 -30.111,...
    #> 5 89a90128a8fffff        60 POLYGON ((-51.2558 -30.1105...
    #> 6 89a90128a8fffff        50 POLYGON ((-51.2483 -30.0831...

Now it becomes super simple to visualize our isochrones on a map:

    # extract OSM network
    street_net <- street_network_to_sf(r5r_network)
    main_roads <- subset(street_net$edges, street_class %like% 'PRIMARY|SECONDARY')

    colors <- c('#ffe0a5','#ffcb69','#ffa600','#ff7c43','#f95d6a',
                '#d45087','#a05195','#665191','#2f4b7c','#003f5c')

    ggplot() +
      geom_sf(data = iso1, aes(fill=factor(isochrone)), color = NA, alpha = .7) +
      geom_sf(data = main_roads, color = "gray55", size=0.01, alpha = 0.2) +
      geom_point(data = central_bus_stn, aes(x=lon, y=lat, color='Central bus\nstation')) +
      # scale_fill_viridis_d(direction = -1, option = 'B') +
      scale_fill_manual(values = rev(colors) ) +
      scale_color_manual(values=c('Central bus\nstation'='black')) +
      labs(fill = "Travel time\n(in minutes)", color='') +
      theme_minimal() +
      theme(axis.title = element_blank())

# 4 Isochrone alternative

This second approach to calculating isochrones with `r5r` takes a few more steps because it requires the spatial interpolation of travel time estimates, but it generates more refined maps. It takes two steps.

# 4.1 Calculate travel times

First, we calculate the travel times by public transport from the central bus station in Porto Alegre to multiple destinations we input to the function. Here, we input the `points` data frame, which comprises the centroids of a hexagonal grid at a fine spatial resolution.

    # calculate travel time matrix
    ttm <- travel_time_matrix(r5r_network,
                              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,
                              progress = FALSE)

    head(ttm)
    #>            from_id           to_id travel_time_p50
    #>             <char>          <char>           <int>
    #> 1: 89a90128a8fffff 89a901291abffff              60
    #> 2: 89a90128a8fffff 89a9012a3cfffff              83
    #> 3: 89a90128a8fffff 89a901295b7ffff              62
    #> 4: 89a90128a8fffff 89a901284a3ffff              65
    #> 5: 89a90128a8fffff 89a9012809bffff              54
    #> 6: 89a90128a8fffff 89a901285cfffff              45

# 4.2 Spatial interpolation of travel times

Now we need to bring the spatial coordinates information to our travel time matrix output `ttm`, and do some spatial interpolation of travel time estimates.

    # 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()

With just a few more lines of code, we get our isochrones on a map:

    # 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 = main_roads, 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_fill_manual(values = rev(colors) ) +
      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())

### Cleaning up after usage

`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_network)
    rJava::.jgc(R.gc = TRUE)

If you have any suggestions or want to report an error, please visit the package GitHub page.