I am currently using a raster that is a land cover class raster (with 50 different land cover classes at a resolution of 30m). I would like to obtain 50 different rasters (essentially predictors for a future regression analysis) that are data of a continuous nature.

Previously, I used the focal statistics tool in ArcMap but I ended up obtaining either a 0 or a 1 based on areas where a particular land cover type was present.

See image below (All areas of White are 1 and Areas in black are 0):

enter image description here

Is there a way to obtain multiple rasters from a single land cover class raster, with the proportion of each land cover class calculated around a window of 5*5 from a particular point (instead of a 0 or a 1) in R?

I noticed that there is a focal function in the raster package.

  • By nature, the data is nominal. What would a continuous range [0-1] represent? Are you want to calculate the proportion of each landcover class within a specified window? If this is the case then yes, the focal function is what you are after. Please search the site as, I have answered variations of this question at least twice. Commented Apr 10, 2017 at 15:20
  • unless are you intending to change the resolution (coarser), how are you going to change the categorical land cover class to continuous 0-1 values?
    – Sam
    Commented Apr 10, 2017 at 15:31
  • @JeffreyEvans Thank you for the correction. Yes, I am trying to calculate the proportion of each landcover class within a particular window. Commented Apr 10, 2017 at 15:36
  • @Sam,I am trying to resample it (coarser) to a 1 km to match the extent and resolution of the rest of my predictors. Commented Apr 10, 2017 at 15:36
  • ok thanks, is this 1km res on the same CRS and spatial extent?
    – Sam
    Commented Apr 10, 2017 at 15:38

2 Answers 2


For calculating proportions around points you can define a function in the raster::extract function.

First, create an example raster with values of [1,2] and generate a random point sample.

 r <- raster(nrows=180, ncols=360, xmn=571823.6, xmx=616763.6, ymn=4423540, 
             ymx=4453690, resolution=270, crs = CRS("+proj=utm +zone=12 +datum=NAD83 
             +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"))
 r[] <- rpois(ncell(r), lambda=1)
 r <- calc(r, fun=function(x) { x[x >= 1] <- 1; return(x+1) } )
 x <- sampleRandom(r, 10, na.rm = TRUE, sp = TRUE)
plot(x, add=TRUE, pch=20)

Now we can pull the proportions of each raster class, using a 500m buffer around each point, and store them in a list object. The iterator "i" is defining the raster class value in the loop and the function being passed to raster::extract.

 landcover.prop <- list()
   for( i in 1:2) {
     landcover.prop[[i]] <- extract(r, x, buffer=500, small=TRUE, fun=function(x, p = i) 
                                   { prop.table( ifelse(x == p, 1, 0) ) } )

The elements in the list object are vectors that are ordered the same as your points so, you can just add them to the SpatialPointsDataFrame object.

  • Thank you @Jeffrey Evans. This is precisely what I was looking for. To be clear, the iterator i is moving through the different land cover classes as you mentioned right? Currently running your example code. Commented Apr 11, 2017 at 12:37

based on the info you have given, i hope this helps (R):

# your 30m raster with various land cover classes (here dummy data with 5)
r <- raster(ncol=10,nrow=10)
r[] <- sample(seq(from = 1, to = 5, by = 1), size = 100, replace = TRUE)

# create an empty raster stack ready to receive rasters
st <- stack() 

# Then, loop through the land cover classes, convert to binary (at original resolution) 
# and aggregate up to new desired resolution.

for(i in 1:maxValue(r)){
  d <- r
  d[d!=i] <- NA
  d[!is.na(d)] <- 1
  dagg <- aggregate(d,fact=c(5,5),fun=sum)

The result of each cellStats(sum) should be the same as freq(r).

However, if you are indeed doing 1km aggregation or 'window' over 30m data, which obviously isn't a factor, then you will have to consider converting the 1km summary raster into polygons and doing some summary stats instead (more involved, a separate answer i think).

  • Thanks, this is definitely an interesting solution. In essence, this would give me the total proportion of land-cover at every 5 *5 window right? I think I might post a separate question again, as I have begun to think about the same, but would love to obtain the proportion of each land cover class around a given point, in a 5 * 5 km window. Commented Apr 10, 2017 at 16:02
  • Hi @Sam, I have modified the question to essentially get at the proportion of each land cover class around a given point. Commented Apr 10, 2017 at 20:24
  • @Vijay, i can't answer now, but essentially what my very simple example did was subset the raster for 1 land class, turn it binary (1 = land class exists, NA it doesn't), then aggregated to a desired new resolution (my example was 25 times coarser). Then you can divide the new raster by the aggregation factor to get a 0 to 1 value (i didnt do this above).
    – Sam
    Commented Apr 10, 2017 at 20:33
  • @Vijay, you have a slightly different qu now though, a moving focal window? is this what you want? to me that sounds very odd but if it's what you want....
    – Sam
    Commented Apr 10, 2017 at 20:34
  • 3
    Here is where you really need to be much clearer. In R the method for extracting data around a point is much different than creating new rasters representing proportion(s). Please look at ?extract. You can pass a custom function and specify a window around each point. This is computationally, much more efficient than deriving new rasters using focal. Commented Apr 10, 2017 at 20:37

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