Hello,
I am sorry to post this question a second time, but I still got no solution. My
starting point are the local densities from a kernel density estimation (my
object called k1) using the splancs package, now I want to calculate the mean
value for every superimposed polygon (called tsids). The example code below
should help to reproduce my question.
Any help would be greatly appreciated!!!
Thanks and kind regards
Marco
-----------
library(rgdal); library(splancs); library(sp);
library(maptools); library(gpclib)
# test data + window study site
sids <- readOGR(system.file("shapes/sids.shp", package = "maptools"), "sids")
crs <- CRS("+proj=longlat +datum=NAD27")
proj4string(sids) <- crs
plot(sids)
ncrs <- CRS("+proj=utm +zone=18 +datum=NAD27")
tsids <- spTransform(sids, ncrs)
plot(tsids)
win <- unionSpatialPolygons(tsids, rep("x", length(slot(tsids, "polygons"))))
proj4string(win) <- ncrs
tcoord <- cbind(coordinates(tsids)[,1], coordinates(tsids)[,2])
tpoi <- SpatialPoints(tcoord)
proj4string(tpoi) <- ncrs
plot(win)
plot(tpoi, add=T)
# define splancs objects
tpoi.pts <- as.points(coordinates(tpoi))
win.pol = as.points(list(x=...@polygons[[1]]@polygons[[...@coords[,1],
y=...@polygons[[1]]@polygons[[...@coords[,2]))
# define grid, bandwith for kernel density estimation
pixs <- 15000
dimx <- ceiling(abs((bbox(win)[1,1] - bbox(win)[1,2])/pixs))
dimy <- ceiling(abs((bbox(win)[2,1] - bbox(win)[2,2])/pixs))
grd <- GridTopology(cellcentre.offset=c(bbox(win)[1,1], bbox(win)[2,1]),
cellsize=c(pixs, pixs), cells.dim=c(dimx,dimy))
summary(grd)
# kernel density estimation
kde <- spkernel2d(tpoi.pts, win.pol, 2000, grd)
kdf <- data.frame(k0=kde)
k <- SpatialGridDataFrame(grd, data=kdf)
spplot(k, sp.layout = list(list("sp.polygons", tsids)))
k1 <- as(k, "SpatialPointsDataFrame")
k1[["x"]] <- coordinates(k1)[,1]
k1[["y"]] <- coordinates(k1)[,2]
# the following part shows my problem: how can i aggregate the kernel
estimations (using the mean) for every polygon in the tsids object? using the
overlay function: overlay(k1, tsids, fn = mean) ?
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