Cell declustering examples in Gstat

Dylan Beaudette wrote:

On Monday 22 February 2010, Edzer Pebesma wrote:

Carlos, please try:

library(gstat)
data(meuse)
meuse$rn = 1:nrow(meuse)
coordinates(meuse)=~x+y
spplot(meuse["rn"])
data(meuse.grid)
gridded(meuse.grid)=~x+y
out = krige(rn~1, meuse, meuse.grid, nmax=1)
spplot(out[1])
meuse$w = as.vector(table(out[[1]])/sum(table(out[[1]])))
spplot(out[1],col.regions=bpy.colors(),sp.layout=list("sp.points",meuse)
) spplot(meuse["w"], col.regions=bpy.colors())

Having a somewhat finer interpolation grid might give slightly more
satisfactory results.

Best regards,
--
Edzer

Thanks for the example Edzer. This is a really interesting demonstration
of cell declustering... however, pardon my lack of understanding, I can't
quite figure out how the resulting are obtained by interpolating the row
numbers. Can you please elaborate on how/why the use of row numbers and
setting nmax=1 produces these results?

Hi Dylan,

When "interpolating" with nmax=1, you basically assign the value of the
nearest observation to each grid cell, so, honoustly, it's hard to call
this interpolation, it is rather something of a discretized Thiessen
polygon. At least, this is the case for inverse distance (used in the
example above) and ordinary/universal kriging.

Challenged to avoid the krige function, the following script assigns row
indexes to all nearest grid cells (when executed after the one above),
using spDists from sp to find the right index:

d = spDists(meuse, meuse.grid)
dim(d)
out$x = apply(d, 2, function(x) { which(x == min(x)) })
sum(abs(out[[1]]-out$x))
# should give 0

following that you could use the table stuff. Of course the spDists
function computes all n x N distances, and is not recommended for large
/ massive data sets. The idw / krige functions in gstat use a PR bucket
quadtree (not written by me!) to efficiently find the nearest nmax
points; this does scale up.

Best wishes,
--
Edzer

Thanks!

Dylan

Carlos Alberto Gutierrez Carvajal wrote:

Hello everybody in the list.

My question have been treated previously by Edzer Pebesma in his
communications with Stefano Pegoretti in july 2007, however I would
find very useful some examples.

Basically, Edzer Pebesma pointed to two methods to perform cell
declustering.

One of these methods is voronoi diagrams (avaliable in package deldir).

The other method is, in words of Edzer Pebesma, by  'using the number
of nearest cells based on a regular discretization of the study area;
for the latter you could misuse package gstat, interpolate record
number with nmax=1 and compute a table of the resulting "prediction"
grid'.

Could you provide me some examples for each one of this methods for an
 R user,please?.

Regards,

Carlos Gutierrez Carvajal.