"Downsample" a dense grouping of points onto a sparse grid

Question

I have a SpatialPointsDataFrame with thousands of datapoints in a relatively small geographic area. The points are grouped around regions of physical measurements and are hence irregularly spaced:

plot(old)

I'd like to resample these data onto a coarser grid that is sparsely populated so as not to overextrapolate observations. Each point in the grid grid should take on the value of the closest point in old --- unless there are no data within (say) a 500m radius, in which case the grid point in new should take on the value of NA.

ggg <-raster(crs = old@proj4string,
               ext = extent(old@bbox),
               resolution = 1000)
gg <- as(pppp, 'SpatialGrid')
plot(gg)

I have played around with creating an empty SpatialGrid object as shown above and using over(), but I get a vector of NAs. I assume that's because the points in the grid do not precisely align with my data.

I am currently trying to see if there's a way to do it with aggregate but no dice so far. Note that I don't want to average all values in each grid cell---instead, I want to use the nearest single observation to each grid cell center / point.

Can anyone point me in the right direction?

Answer 1

You could treat this as a (degenerate) interpolation problem, using a neighbourhood of one, try something along the lines of

library(gstat)
i = idw(var~1, old, ggg, nmax = 1, maxdist = 500)
library(sp)
spplot(i[1])

where var refers to the name of the variable you sampled.

Answer 2

I found I was able to solve the problem using the nncross() function in the package spatstat. I don't know if this was the simplest solution, since it required first converting my SpatialPoints to class ppp. In any case here it is:

library('raster')
library('spatstat')

# Make the grid. Use raster() and then convert.
# Not the most direct but it works.
gridRes = 1000 # meters
ggg <-raster(crs = old@proj4string,
             ext = extent(old@bbox),
             resolution = gridRes)
gg <- as(ggg, 'SpatialGrid')
g <- as(gg, 'SpatialPoints')

grd <- ppp(x = g@coords[,1],
           y = g@coords[,2],
           window = owin(xrange=g@bbox[1,], yrange = g@bbox[2,]))
old_ppp <- ppp(x = old@coords[,1],
           y = old@coords[,2],
           window = owin(xrange=old@bbox[1,],
           yrange = old@bbox[2,]))

distz <- nncross(grd, old_ppp)
# distz is a dataframe with a row for each item in grd,
# and each row contains (1) distance and (2) reference to the nearest
# point in old_ppp.