Dear All,

I have carried out variogram analysis on geostatistical data using gstat
package of R. after rescaling my spatial coordinates, it ranges as follows
easting: min=0 and max=794.256 meters
northing: min= and max=621.8851 meters
the maximum distance between pairs of observation is 1008.754 meters.
I have fitted a number of variogram models such as spherical, exponential,
Gaussian, and linear to empirical semivariogram. Besides, I have generated
the interpolation grid for kriging. With this information, I wish to
determine optimal grid size and interpolate with kriging. The cross
validation will be used to compare the values.

### Create grid for the point coordinate

easting.range <- as.integer(range(canmod.sp@coords[,1]))
northing.range <- as.integer(range(canmod.sp@coords[,2]))

## now expand to a grid with 10 meter spacing:
grd <- expand.grid(x = seq(from=easting.range[1], to = easting.range[2], by
= 10), y = seq(from = northing.range[1], to = northing.range[2], by = 50) )
grd.attr = data.frame(grd, yield = canmod.sp@data)

## convert to SpatialPixel class
grd.pts <- SpatialPixels(SpatialPoints(grd))
proj4string(grd.pts)<-CRS("+proj = utm + datum = WGS84")


k10 <- krige(yield ~ 1, canmod.sp, newdata = grd.pts, model=sph.mod)


I ran the last statement for kriging interpolation but take quite a long
time with error message. This is probably due to large volume of the data
records (12,800 observations). Could anyone guide me on what to do when
having large data of this nature for kriging? Is there any error on the way
I defined my grid interpolation? Do I need to split the data for kirging?
If yes, how?

Thank you.

Moshood








 where cross validation. Please guide me on how to go about this using the
above details. Note that my observation size is 12,500 records.

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