The raster package for R provides a variety of functions for the analysis of raster GIS data. The *focal()* function is very useful for applying moving window filters to such data. I wanted to calculate a moving window mean for cells within a specified radius, but *focal()* did not provide a built-in option for this. The following code generates an appropriate weights matrix for implementing such a filter, by using the matrix as the *w* argument of *focal()*.

require(raster) #function to make a circular weights matrix of given radius and resolution #NB radius must me an even multiple of res! make_circ_filter<-function(radius, res){ circ_filter<-matrix(NA, nrow=1+(2*radius/res), ncol=1+(2*radius/res)) dimnames(circ_filter)[[1]]<-seq(-radius, radius, by=res) dimnames(circ_filter)[[2]]<-seq(-radius, radius, by=res) sweeper<-function(mat){ for(row in 1:nrow(mat)){ for(col in 1:ncol(mat)){ dist<-sqrt((as.numeric(dimnames(mat)[[1]])[row])^2 + (as.numeric(dimnames(mat)[[1]])[col])^2) if(dist<=radius) {mat[row, col]<-1} } } return(mat) } out<-sweeper(circ_filter) return(out) }

This example uses a weighs matrix generated by *make_circ_filter()* to compute a circular moving average on the Meuse river grid data. For a small raster like this, the function is more than adequate. For large raster datasets, it’s quite slow though.

#make a circular filter with 120m radius, and 40m resolution cf<-make_circ_filter(120, 40) #test it on the meuse grid data f <- system.file("external/test.grd", package="raster") r <- raster(f) r_filt<-focal(r, w=cf, fun=mean, na.rm=T) plot(r, main="Raw data") #original data plot(r_filt, main="Circular moving window filter, 120m radius") #filtered data