I have a set of points in a 2d space representing location of animals. I am interested in a probability heatmap which give lower values for cells far from these locations.
I have seen many references in biology using kernel density estimation to achieve that. However, my dataset is fairly large (continental scale) and I am using R, which means traditional R functions such as kde2d() run out of memory, or require some complex manuvering with overlay() in the raster package.
There is a more memory efficient and simpler code for doing gaussian blur, i.e. a convolution two grids one with data, and one representing an approximation of a normal distribution.
Intuitively these two concepts seem related. Both are based on (1) draw a normal distribution around each point, (2) sum those normals, (3) get new value for each location. Plus Gaussian blur tutorials seems to use the term kernel quite frequently. But I could not find any source saying they are similar.
Would a gaussian blur on a binary map of locations (1,0) be equivalent to a kernel density estimation? (ignoring, of course the error due to making the process in a discrete cells).