GitHub - hypertidy/fasterize: High performance raster conversion for modern spatial data 🚀🌏▦ (original) (raw)

fasterize

Fast polygon-to-raster conversion, burn polygon shapes and/or values into pixels.

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

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

Installation

Install the current version of fasterize from CRAN:

install.packages('fasterize')

Install the development version of fasterize withdevtools:

devtools::install_github("hypertidy/fasterize")

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

Usage

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

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

library(raster) library(fasterize) library(wk) library(fasterize) p123 <- c(paste0("POLYGON ((-180 -20, -140 55, 10 0, -140 -60, -180 -20),", "(-150 -20, -100 -10, -110 20, -150 -20))"), "POLYGON ((-10 0, 140 60, 160 0, 140 -55, -10 0))", "POLYGON ((-125 0, 0 60, 40 5, 15 -45, -125 0))") pols <- data.frame(value = seq_along(p123), geometry = wk::as_wkt(p123)) ex <- as.numeric(wk_bbox(pols))[c(1, 3, 2, 4)] r <- raster::raster(raster::extent(ex), res = 1) r <- fasterize(pols, r, field = "value", fun="sum") plot(r)

Performance

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

pols_t <- terra::vect(p123)

#> dialect must be 'OGRSQL' or 'SQLITE', default value '' means 'OGRSQL'

pols_t$value <- 1:3 #pols_r <- as(pols_t, "Spatial") tr <- terra::rast(r)

bench <- microbenchmark::microbenchmark(

rasterize = r <- raster::rasterize(pols_r, r, field = "value", fun="sum"),

terrarize = tr <- terra::rasterize(pols_t, tr, 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
#>  terrarize 9.464 9.761 11.306 10.022 10.375 82.0   100  a 
#>  fasterize 0.647 0.684  0.839  0.845  0.971  1.1   100   b

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.

(this doesn’t work anymore because the source data is gone, left as a record 2024-09-25).

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 was developed openly at EcoHealth Alliance under the USAID PREDICT project by Noam Ross. The repository for hosting fasterize was taken over by Michael Sumner in December 2022, and was later migrated from Github ‘ecohealthalliance/fasterize’ tohttps://github.com/hypertidy/fasterize in March 2025.

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.