Implements methods from Weiss et al. 2018, 2020 to calculate travel time from given locations over a friction surface.
Citations:
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
D. J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181.
You can install traveltime with:
install.packages("traveltime", repos = c("https://idem-lab.r-universe.dev"))First download a friction surface — here using the motorised travel time from Weiss et al. 2020.
library(traveltime)
library(terra)
#> terra 1.7.79friction_surface <- get_friction_surface(
surface = "motor2020",
extent = c(111,112,0,1)
)
#> Checking if the following Surface-Year combinations are available to download:
#>
#> DATASET ID YEAR
#> - Explorer__2020_motorized_friction_surface: DEFAULT
#>
#> Loading required package: sf
#> Linking to GEOS 3.12.1, GDAL 3.9.0, PROJ 9.4.0; sf_use_s2() is FALSE
#> <GMLEnvelope>
#> ....|-- lowerCorner: 0 111
#> ....|-- upperCorner: 1 112friction_surface
#> class : SpatRaster
#> dimensions : 120, 120, 1 (nrow, ncol, nlyr)
#> resolution : 0.008333333, 0.008333333 (x, y)
#> extent : 111, 112, 1.387779e-17, 1 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source : Explorer__2020_motorized_friction_surface_0,111,1,112.tif
#> name : friction_surfaceLet’s have a look at that
plot(friction_surface)
Prepare points we would like to calculate travel time from
from_here <- tibble::tibble(
x = c(111.2, 111.9),
y = c(0.2, 0.35)
)
from_here
#> # A tibble: 2 × 2
#> x y
#> <dbl> <dbl>
#> 1 111. 0.2
#> 2 112. 0.35And calculate the travel time
travel_time <- calculate_travel_time(
friction_surface = friction_surface,
points = from_here
)
travel_time
#> class : SpatRaster
#> dimensions : 120, 120, 1 (nrow, ncol, nlyr)
#> resolution : 0.008333333, 0.008333333 (x, y)
#> extent : 111, 112, 1.387779e-17, 1 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> source(s) : memory
#> name : travel_time
#> min value : 0.0000
#> max value : 582.1882Et voila!
plot(travel_time)
points(from_here, pch = 19)
Here it is:
# install.packages("sdmtools", repos = "https://idem-lab.r-universe.dev")
library(sdmtools)
sin <- sdmtools::make_africa_mask(
type = "vector",
countries = "SGP"
)
#> Please Note: Because you did not provide a version, by default the version being used is 202403 (This is the most recent version of admin unit shape data. To see other version options use function listShpVersions)
#> although coordinates are longitude/latitude, st_union assumes that they are
#> planarplot(sin)
We’re going to see how long it takes to walk home from Changi Airport. So we’ll download the walking-only friction surface this time.
We can feed in our sin SpatVector directly as the extent, instead
of specifying by hand as above.
library(traveltime)
library(terra)
friction_singapore <- get_friction_surface(
surface = "walk2020",
extent = sin
)|>
mask(sin)
#> Checking if the following Surface-Year combinations are available to download:
#>
#> DATASET ID YEAR
#> - Explorer__2020_walking_only_friction_surface: DEFAULT
#>
#> <GMLEnvelope>
#> ....|-- lowerCorner: 1.164 103.6383
#> ....|-- upperCorner: 1.4713 104.09friction_singapore
#> class : SpatRaster
#> dimensions : 37, 54, 1 (nrow, ncol, nlyr)
#> resolution : 0.008333333, 0.008333333 (x, y)
#> extent : 103.6417, 104.0917, 1.166667, 1.475 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> varname : Explorer__2020_walking_only_friction_surface_1.164,103.6383,1.4713,104.09
#> name : friction_surface
#> min value : 0.01200000
#> max value : 0.06192715And where is Changi Airport?
changi_airport <- tibble::tibble(
x = c(103.984),
y = c(1.355)
)
changi_airport
#> # A tibble: 1 × 2
#> x y
#> <dbl> <dbl>
#> 1 104. 1.36Let’s look at those.
plot(friction_singapore)
plot(sin, add = TRUE)
points(changi_airport, pch = 19)
And calculate the travel time
travel_time_sin <- calculate_travel_time(
friction_surface = friction_singapore,
points = changi_airport
)
travel_time_sin
#> class : SpatRaster
#> dimensions : 37, 54, 1 (nrow, ncol, nlyr)
#> resolution : 0.008333333, 0.008333333 (x, y)
#> extent : 103.6417, 104.0917, 1.166667, 1.475 (xmin, xmax, ymin, ymax)
#> coord. ref. :
#> source(s) : memory
#> name : travel_time
#> min value : 0
#> max value : InfEt voilah!
plot(travel_time_sin)
points(changi_airport, pch = 19)
plot(sin, add = TRUE)
library(tidyterra)
#> Registered S3 method overwritten by 'tidyterra':
#> method from
#> autoplot.SpatRaster malariaAtlas
#>
#> Attaching package: 'tidyterra'
#> The following object is masked from 'package:stats':
#>
#> filterlibrary(ggplot2)
ggplot() +
# plot the spatraster first
geom_spatraster(
data = travel_time_sin
) +
theme_minimal() +
scale_fill_whitebox_c(palette = "deep") +
# overlay the vector outline
geom_spatvector(
data = sin,
colour = "grey70",
linewidth = 1,
fill = NA
) +
# add the points from tibble
geom_point(
data = changi_airport,
aes(x = x, y = y),
colour = "hotpink"
) +
labs(x = NULL, y = NULL, fill = "Travel time\n(minutes)") 