# Isochrones

#### 2026-05-19

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.

The `r5r::isochrone()` function allows you to build both polygon- and line-based isochrones. We will cover both approaches in this vignette, where we will be calculating isochrones by public transport from the central bus station in Porto Alegre.

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)

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. Calculating and visualizing isochrones

## 3.1 Polygon-based isochrones

The most common approach here is to create polygon-based isochrones. To do this, you need to pass the arguments `polygon_output = TRUE` and choose the `zoom` level. The polygon-based isochrone in {r5r} are built on top of a regular grid based on Web Mercator pixels. The zoom level can be controlled with the `zoom` parameter; higher zooms lead to more detailed isochrones at the expense of computational time. The default is 10 (which uses cells of 153m at the Equator). In large networks, high zooms may not be possible and will give an error.

With the code below, `r5r` determines the isochrones considering the median travel time of multiple travel time estimates calculated departing every minute over a 60-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 <- 90  # in minutes
    time_window <- 60        # 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,
      polygon_output = TRUE,
      cutoffs = time_intervals,
      departure_datetime = departure_datetime,
      max_walk_time = max_walk_time,
      max_trip_duration = max_trip_duration,
      time_window = time_window,
      progress = FALSE,
      zoom = 10
      )

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

    head(iso1)

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_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())

## 3.1 Line-based isochrones

Alternatively, you can build line-based isochrones by simply passing `polygon_output = FALSE` to the `isochrone()` function. Note that you do not need the `zoom` parameter here, and that the output is `LINESTRING "sf" "data.frame"`.

    # calculate travel time matrix
    iso2 <- r5r::isochrone(
      r5r_network,
      origins = central_bus_stn,
      mode = mode,
      polygon_output = FALSE,
      cutoffs = time_intervals,
      departure_datetime = departure_datetime,
      max_walk_time = max_walk_time,
      max_trip_duration = max_trip_duration,
      time_window = time_window,
      progress = FALSE
      )

    head(iso2)

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

    ggplot() +
      geom_sf(data = iso2, aes(color=factor(isochrone)), alpha = .7) +
      scale_color_manual(values = rev(colors) ) +
      geom_point(data = central_bus_stn, aes(x=lon, y=lat), color='black') +
      labs(color = "Travel time\n(in minutes)", color='sadasd') +
      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.