Using R for spatial sampling, with selection probabilities defined in a raster

The raster package for R provides a range of GIS-like functions for analysing spatial grid data. Together with package sp, and several other spatial analysis packages, R provide a quite comprehensive set of tools for manipulating and analysing spatial data.

I needed to randomly select some locations for field sampling, with inclusion probabilities based on values contained in a raster. The code below did the job very easily.


#an example raster from the raster package
f <- system.file("external/test.grd", package="raster")


#make a raster defining the desired inclusion probabilities 
#for the all locations available for sampling
#inclusion probability for cells with value >=400 
#will be 10 times that for cells with value <400
#normalise the probability raster by dividing 
#by the sum of all inclusion weights:
probrast<-probrast/sum(getValues(probrast), na.rm=T)

#confirm sum of probabilities is one
sum(getValues(probrast), na.rm=T)

#plot the raster of inclusion probabilities
plot(probrast, col=c(gray(0.7), gray(0.3)))

#a function to select N points on a raster, with 
#inclusion probabilities defined by the raster values.
probsel<-function(probrast, N){
  #set NA cells in raster to zero
  samp<-sample(nrow(probrast)*ncol(probrast), size=N, prob=x)
  samprast[samp]<-1 #set value of sampled squares to 1
  #convert to SpatialPoints
  points<-rasterToPoints(samprast, fun=function(x){x>0}) s

#select 300 sites using the inclusion probabilities 
#defined in probrast
samppoints<-probsel(probrast, 300)
plot(probrast, col=c(gray(0.7), gray(0.3)), axes=F)
plot(samppoints, add=T, pch=16, cex=0.8, col="red")

Here’s the result. Note the higher density of sampled points (red) within the parts of the raster with higher inclusion probability (dark grey).


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