traveltime enables a user to create a map of travel time over an area
of interest from a user-specified set of geographic coordinates and
friction surface. The package provides convenient access to global
friction surfaces for walking and motorised travel for the year 2020.
The final result is a raster of the area of interest where the value in
each cell is the lowest travel time in minutes to any of the specified
locations.
install.packages("traveltime", repos = c("https://idem-lab.r-universe.dev"))or
remotes::install_github("idem-lab/traveltime")https://idem-lab.github.io/traveltime/
Ryan, G.E., Tierney, N., Golding, N., and Weiss, D.J (2025). traveltime: an R package to calculate travel time across a landscape from user-specified locations. EarthArXiv 10.31223/X56M74
The traveltime workflow starts with:
- a set of points you are interested in,
and either
- you supply a friction surface for the area you are interested in, or
- you supply your area of interest and use
get_friction_surfaceto retrieve a pre-prepared walking or motorised travel friction surface from Weiss et al. (2020) 1 — this will probably be the case in most applications.
Then, running calculate_travel_time produces a raster as a terra
SpatRaster with the travel time to the (temporally) nearest of the
supplied points over the area of interest.
Here we will calculate the walking travel time from the nearest mass transit station across the island nation of Singapore — specifically Mass Rapid Transit (MRT) and Light Rail Transit (LRT) stations — and create a map of this.
For this exercise, we need two items of data:
- our points to calculate travel time from — here the locations of Singapore’s MRT and LRT stations, and
- our area of interest — in this case a map of Singapore.
Our points of interest will be the stations data set included in
traveltime; a 563 row, 2 column matrix containing the longitude
(x) and latitude (y) of all LRT and MRT station exits in Singapore
from 2:
library(traveltime)
head(stations)
#> x y
#> [1,] 103.9091 1.334922
#> [2,] 103.9335 1.336555
#> [3,] 103.8493 1.297699
#> [4,] 103.8508 1.299195
#> [5,] 103.9094 1.335311
#> [6,] 103.9389 1.344999To obtain our area of interest, we download a national-level polygon
boundary of Singapore using the geodata package. Here we download only
the national boundary (level = 0) at a low resolution
(resolution = 2). Our boundary singapore_shapefile is a SpatVector
class object from the package terra.
library(terra)
#> terra 1.8.5
library(geodata)
singapore_shapefile <- gadm(
country = "Singapore",
level = 0,
path = tempdir(),
resolution = 2
)
singapore_shapefile
#> class : SpatVector
#> geometry : polygons
#> dimensions : 1, 2 (geometries, attributes)
#> extent : 103.6091, 104.0858, 1.1664, 1.4714 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> names : GID_0 COUNTRY
#> type : <chr> <chr>
#> values : SGP SingaporeNow that we have the two items of data that we require initially, the next step is to prepare a friction surface for our area of interest.
We will use the friction surface from Weiss et al (2020) that can be
downloaded by traveltime with the function get_friction_surface().
This function takes extents in a variety of formats and returns the
surface for that extent only.
We can pass in our basemap singapore_shapefile, a SpatVector,
directly as the extent. We’re interested in walking time from a
station, so we’ll download the walking friction surface by specifying
surface = "walk2020".
(Alternatively, we could use surface = "motor2020" for motorised
travel).
We’re only interested in walking on land, so we then mask out areas
outside of the land boundary of singapore_shapefile:
friction_singapore <- get_friction_surface(
surface = "walk2020",
extent = singapore_shapefile
)|>
mask(singapore_shapefile)
#> Checking if the following Surface-Year combinations are available to download:
#>
#> DATASET ID YEAR
#> - Accessibility__202001_Global_Walking_Only_Friction_Surface: DEFAULT
#>
#> Loading required package: sf
#> Linking to GEOS 3.13.0, GDAL 3.10.0, PROJ 9.5.1; sf_use_s2() is FALSE
#> No encoding supplied: defaulting to UTF-8.
#> <GMLEnvelope>
#> ....|-- lowerCorner: 1.1664 103.6091
#> ....|-- upperCorner: 1.4714 104.0858Start tag expected, '<' not foundThus we have our friction surface as a SpatRaster:
friction_singapore
#> class : SpatRaster
#> dimensions : 37, 57, 1 (nrow, ncol, nlyr)
#> resolution : 0.008333333, 0.008333333 (x, y)
#> extent : 103.6083, 104.0833, 1.166667, 1.475 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> varname : Accessibility__202001_Global_Walking_Only_Friction_Surface_1.1664,103.6091,1.4714,104.0858
#> name : friction_surface
#> min value : 0.01200000
#> max value : 0.06192715Below we plot the friction surface raster friction_singapore, with the
vector boundary singapore_shapefile as a grey line, and stations as
grey points. traveltime takes resistance values of friction (see paper
for more details), so higher values of friction indicate more time
travelling across a given cell.
library(tidyterra)
#> Registered S3 method overwritten by 'tidyterra':
#> method from
#> autoplot.SpatRaster malariaAtlas
#>
#> Attaching package: 'tidyterra'
#> The following object is masked from 'package:stats':
#>
#> filter
library(ggplot2)
ggplot() +
geom_spatraster(
data = friction_singapore
) +
geom_spatvector(
data = singapore_shapefile,
fill = "transparent",
col = "grey50"
) +
geom_point(
data = stations,
aes(
x = x,
y = y
),
col = "grey60",
size = 0.5
) +
scale_fill_viridis_c(
option = "A",
na.value = "transparent",
direction = -1
) +
labs(
fill = "Resistance",
x = element_blank(),
y = element_blank()
) +
theme_minimal()
Friction surface raster of Singapore, showing Singapore boundary in grey, and station locations as grey points.
With all the data collected, the function calculate_travel_time()
takes the friction surface friction_singapore and the points of
interest in stations, and returns a SpatRaster of walking time in
minutes to each cell from the nearest station:
trave_time_singapore <- calculate_travel_time(
friction_surface = friction_singapore,
points = stations
)
trave_time_singapore
#> class : SpatRaster
#> dimensions : 37, 57, 1 (nrow, ncol, nlyr)
#> resolution : 0.008333333, 0.008333333 (x, y)
#> extent : 103.6083, 104.0833, 1.166667, 1.475 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> source(s) : memory
#> name : travel_time
#> min value : 0
#> max value : InfWe present the resulting calculated travel times below where (as
expected) the travel times are lowest near station exits and
progressively higher further away. Note that the results in
trave_time_singapore include infinite values (Inf above). The
islands to the south and north-east are shown as filled cells in the
figure above, i.e., they are not masked out by singapore_shapefile.
But because those islands they are not connected to any cells with a
station, the calculated travel time is infinite, and so these cells do
not appear in the figure below.
ggplot() +
geom_spatraster(
data = trave_time_singapore
) +
scale_fill_viridis_c(
option = "A",
direction = -1,
na.value = "transparent"
) +
theme_minimal() +
labs(fill = "Minutes") +
geom_spatvector(
data = singapore_shapefile,
fill = "transparent",
col = "grey20"
)
Map of walking travel time in Singapore, in minutes from nearest MRT or LRT station.
Footnotes
-
D. J. Weiss, A. Nelson, C. A. Vargas-Ruiz, K. Gligoric, S., Bavadekar, E. Gabrilovich, A. Bertozzi-Villa, J. Rozier, H. S. Gibson, T., Shekel, C. Kamath, A. Lieber, K. Schulman, Y. Shao, V. Qarkaxhija, A. K. Nandi, S. H. Keddie, S. Rumisha, P. Amratia, R. Arambepola, E. G. Chestnutt, J. J. Millar, T. L. Symons, E. Cameron, K. E. Battle, S. Bhatt, and P. W. Gething. Global maps of travel time to healthcare facilities. (2020) Nature Medicine. https://doi.org/10.1038/s41591-020-1059-1 ↩
-
Land Transport Authority. (2019). LTA MRT Station Exit (GEOJSON) (2024) [Dataset]. data.gov.sg. Retrieved December 10, 2024 from https://data.gov.sg/datasets/d_b39d3a0871985372d7e1637193335da5/view. ↩