I want to compare the cell values of two kernel density estimations in R. As a first step, i am using the function density from the Package "raster".

x_dens <- density(x_ppp, sd, eps=rw, edge=TRUE, at="pixels")
x_ras <- raster(x_dens, crs="+proj=utm +zone=35 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0")

y_dens <- density(y_ppp, sd, eps=rw, edge=TRUE, at="pixels")
y_ras <- raster(y_dens, crs="+proj=utm +zone=35 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0")

The next step includes the calculation of the cell differences:

xy_comp <- sqrt(((x_ras - y_ras)^2 / x_ras))
xy_comp [!is.finite(xy_comp )] <- 0
xy_comp_result <- sum(xy_comp @data@values)

xy_comp shall result in a new raster layer, which shows the differences in both point pattern, but the resulting cell values are enormously high at some points and as a consequence xy_comp_result has a illogically high value. It is due to the fact, that some pixel values of x_ras much to small compared with y_ras. Do you have got any suggestions, how to solve this problem. I've tried to remove outliers, but I did not get proper results.