# Calculating and visualizing Isochrones In r5r: Rapid Realistic Routing with 'R5'

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>",
eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true"),
out.width = "100%"
)
```

# 1. Introduction

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 `r5r` package.

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:

1. Increase Java memory and load libraries
2. Build routable transport network
3. Calculate travel times
4. Map Isochrones

# 2. Build routable transport network with `setup_r5()`

### Increase Java memory and load libraries

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)
```

# 3. Calculate travel times

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

# 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)

```

# 4. Map Isochrones

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())
```

### 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_core)
rJava::.jgc(R.gc = TRUE)
```

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

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r5r documentation built on March 7, 2023, 6:30 p.m.