mdsumner/fasterize

High performance raster conversion for modern spatial data 🚀🌏▦

fasterize

Fast sf-to-raster conversion

fasterize is a high-performance replacement for the rasterize() function in the raster package.

Functionality is currently limited to rasterizing polygons in sf-type data frames.

Installation

Install the current version of fasterize from CRAN:

install.packages('fasterize')

Install the development version of fasterize with devtools:

devtools::install_github("ecohealthalliance/fasterize")

fasterize uses Rcpp and thus requires a compile toolchain to install from source. Testing (and for normal use of sf objects) requires sf, which requires GDAL, GEOS, and PROJ to be installed.

Usage

The main function, fasterize(), takes the same inputs as raster::rasterize() but currently has fewer options and is is limited to rasterizing polygons.

A raster() and plot() methods for rasters are re-exported from the raster package.

library(raster)
library(fasterize)
library(sf)
p1 <- rbind(c(-180,-20), c(-140,55), c(10, 0), c(-140,-60), c(-180,-20))
hole <- rbind(c(-150,-20), c(-100,-10), c(-110,20), c(-150,-20))
p1 <- list(p1, hole)
p2 <- list(rbind(c(-10,0), c(140,60), c(160,0), c(140,-55), c(-10,0)))
p3 <- list(rbind(c(-125,0), c(0,60), c(40,5), c(15,-45), c(-125,0)))
pols <- st_sf(value = c(1,2,3),
             geometry = st_sfc(lapply(list(p1, p2, p3), st_polygon)))
r <- raster(pols, res = 1)
r <- fasterize(pols, r, field = "value", fun="sum")
plot(r)

Performance

Let’s compare fasterize() to raster::rasterize():

pols_r <- as(pols, "Spatial")
bench <- microbenchmark::microbenchmark(
  rasterize = r <- raster::rasterize(pols_r, r, field = "value", fun="sum"),
  fasterize = f <- fasterize(pols, r, field = "value", fun="sum"),
  unit = "ms"
)
print(bench, digits = 3)

#> Unit: milliseconds
#>       expr      min       lq     mean   median      uq     max neval cld
#>  rasterize 1033.587 1110.270 1136.372 1128.716 1152.55 1523.47   100   b
#>  fasterize    0.696    0.872    0.959    0.924    0.99    1.42   100  a

It’s also quite a bit faster than terra, see the vignette.

How does fasterize() do on a large set of polygons? Here I download the IUCN shapefile for the ranges of all terrestrial mammals and generate a 1/6 degree world map of mammalian biodiversity by rasterizing all the layers.

if(!dir.exists("Mammals_Terrestrial")) {
  download.file(
    "https://s3.amazonaws.com/hp3-shapefiles/Mammals_Terrestrial.zip",
    destfile = "Mammals_Terrestrial.zip") # <-- 383 MB
  unzip("Mammals_Terrestrial.zip", exdir = ".")
  unlink("Mammals_Terrestrial.zip")
}

mammal_shapes <- st_read("Mammals_Terrestrial")
mammal_raster <- raster(mammal_shapes, res = 1/6)
bench2 <- microbenchmark::microbenchmark(
  mammals = mammal_raster <- fasterize(mammal_shapes, mammal_raster, fun="sum"),
  times=20, unit = "s")
print(bench2, digits=3)
par(mar=c(0,0.5,0,0.5))
plot(mammal_raster, axes=FALSE, box=FALSE)

#> Unit: seconds
#>     expr   min    lq  mean median    uq   max neval
#>  mammals 0.847 0.857 0.883  0.886 0.894 0.963    20

About

fasterize is developed openly at EcoHealth Alliance under the USAID PREDICT project. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.