Travel Time to Healthcare or Cities (Oxford Dataset) — l4h_travel

Retrieves the travel time raster (in minutes) to the nearest healthcare facility or populated city, based on the Oxford Global Map of Accessibility datasets.

Usage

l4h_travel_time(
  region,
  destination = "cities",
  transport_mode = "all",
  fun = "mean",
  sf = FALSE,
  quiet = FALSE,
  force = FALSE,
  ...
)

Arguments

region: A spatial object defining the region of interest. Can be an sf, sfc object, or a SpatVector (from the terra package).
destination: Character. Target destination for travel time. Use "healthcare" (default) for travel time to the nearest healthcare facility, or "cities" for travel time to the nearest populated urban center.
transport_mode: Character. Mode of transportation. Use "all" (default) for general travel time (mixed modes), or "walking_only" for walking-only accessibility (only valid when destination = "healthcare").
fun: Character. Summary function to apply. Values include "mean", "sum","median" , etc. Default is "mean".
sf: Logical. If TRUE, returns the result as an sf object. If FALSE, returns an Earth Engine object. Default is FALSE.
quiet: Logical. If TRUE, suppress the progress bar (default FALSE).
force: Logical. If TRUE, skips the internal representativity check of the input region. Defaults to FALSE.
...: arguments of ee_extract of rgee packages.

Value

A spatial object containing the computed RAI value for the region in an sf or tibble object.

Credits

Pioneering geospatial health analytics and open‐science tools. Developed by the Innovalab Team, for more information send a email to imt.innovlab@oficinas-upch.pe

References

Weiss, D.J. et al. (2018). A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688), 333–336. DOI: 10.1038/nature25181
Weiss, D.J. et al. (2020). Global maps of travel time to healthcare facilities. Nature Medicine, 26, 1835–1838. DOI: 10.1038/s41591-020-1059-1

Examples

if (FALSE) { # \dontrun{
library(land4health)
library(sf)
ee_Initialize()

# Define a bounding-box region in Ucayali, Peru
region <- st_as_sf(
  st_sfc(
    st_polygon(list(matrix(
      c(
        -74.1, -4.4,
        -74.1, -3.7,
        -73.2, -3.7,
        -73.2, -4.4,
        -74.1, -4.4
      ), ncol = 2, byrow = TRUE
    )))
  ),
  crs = 4326
)

# Travel time to nearest healthcare facility (all modes)
result_hosp_all <- l4h_travel_time(region = region)
head(result_hosp_all)

# Travel time to nearest healthcare facility (walking only)
result_hosp_walk <- l4h_travel_time(
  region        = region,
  destination   = "healthcare",
  transport_mode = "walking_only")

head(result_hosp_walk)

# Mean travel time to nearest cities (mixed modes)
result_city_mean <- l4h_travel_time(
  region      = region,
  destination = "cities",
  fun         = "mean")

head(result_city_mean)

# Sum of travel time to nearest cities
result_city_sum <- l4h_travel_time(
  region      = region,
  destination = "cities",
  fun         = "sum")

head(result_city_sum)
} # }