[R-sig-Geo] Kriging

Jon Olav Skoien j.skoien at geo.uu.nl
Fri Dec 4 11:26:05 CET 2009

```Hi Greg,

Variogram modelling is slower with large data sets, but 8-10.000
observations should not be a problem, unless you need the results
extremely fast. On my computer (3 years old) it takes about 4 seconds
with 8.000 random observations, using the variogram function in gstat.
Time increases approximately with the square of the number of observations:

data(meuse)
coordinates(meuse) = ~x+y

samp = 1000
for (i in 1:5) {
samp = samp*2
m2 = SpatialPointsDataFrame(spsample(meuse,samp,"random"),data =
data.frame(value = rnorm(samp)))
tvar = system.time(vario <-variogram(value~1,m2))
print(paste(samp,tvar[1]))
}

What you could consider when you get to the kriging part, is to limit
the number of neighbours to e.g. 50 (argument nmax). Otherwise you have
to invert a matrix that is e.g. 10.000*10.000, which is slow, and can
also get you close to the memory limit if you are using Windows.

Cheers,
Jon

GREGORY GERARD LUNA wrote:
> Hi, I have a very non-specific question about the number of sample points that
> can be used
> for developing experimental variograms and kriging in R/gstat/etc. Does anyone
> have experience
> using a very large number of data points in the kriging processes with R?  It
> has been a few
> years since my last spatial stats course but I recall that I was limited in the
> number of data
> points that R could handle.  I have a regional archaeological dataset with many
> data points.
> My sub-regions that I would like to use for kriging can contain 8,000-10,000
> data points.  Many
> of these are zero values (not no data).  Am I going to have sample my sample if
> I want to
> generate experimental variograms in R? Thanks in advance for any ideas,
> Greg Luna
>
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```