Hi Tomislav,

Thanks for the detailed response.  I tried to use the globedem.zip from your
website, but then when I call overlay() with my data, there are many NAs
produced in the output: it seems the DEM covers  X= -180 to 180 & Y= -65 to
65, my dataset covers X= -180 to 180 & Y= -90 to 90
So when I call krige, it complains:
Error: dimensions do not match: locations 129600 and data 47029

129600 = (number of sample points) * 2
47029 = (number of sample points) - (number of NAs in overlay dem data)

Any suggestions on how to handle this?  Should I subset the data?  Find a
different dem with full global coverage?

Also when you say a global dataset <5km resolution, is that as measured at
the equator?

The current variable I am working with is solar radiation.  I have plans to
work on several other variables as well.

On Mon, Sep 14, 2009 at 1:58 AM, Tomislav Hengl
<hengl@spatial-analyst.net>wrote:

>
> Hi Abe,
>
> Did you try using the ETOPO1? This (0.1 degree) DEM can be well loaded to
> R:
>
> > URL <- "http://spatial-analyst.net/worldmaps/"
> # list of maps:
> > map.list <- c("globedem", "chlo08", "dcoast")
> # download the zipped maps one by one:
> > for(i in 1:length(map.list)) {
> >  download.file(paste(URL, map.list[i], ".zip", sep=""),
> +   destfile=paste(getwd(), "/", map.list[i], ".zip", sep=""))
> >  unzip(zipfile=paste(map.list[i], ".zip", sep=""), exdir=getwd())
> >  unlink(paste(map.list[i], ".zip", sep=""))
> > }
> # read maps to R:
> > worldmaps <- readGDAL(paste(map.list[i], ".asc", sep=""))
> # fix the layer name:
> > names(worldmaps)[1] <- map.list[1]
> > for(i in map.list[-1]) {
> > worldmaps@data[map.list[i]] <- readGDAL(paste(map.list[i], ".asc",
> sep=""))$band1
> > }
> > proj4string(meuse.grid) <- CRS("+proj=longlat +ellps=WGS84")
>
> I do not think that it will be easy to generate kriging with any global
> dataset that is <5 km resolution. You could try using the kriging in SAGA
> (it can handle maps up to 2 GB in size; it is exp(2) times faster than R).
> Here is an example:
>
>
> http://spatial-analyst.net/wiki/index.php?title=Interpolation_of_ISRIC-WISE_international_soil_profile_data
>
> T. Hengl
>
> PS: Which variable are you interpolating?
>
> > Hello All,
> >
> > My questions are regarding the best way to obtain DEMs for the entire
> > globe.  I am trying to krige a dataset of global scope.  The dataset has
> > points at all latitude/longitude intersects and so there are 64,800
> sample
> > points.  The problem that I am having is that the DEMs I have been using
> > are
> > too large (memory) to be used as R variables.  I also have had trouble
> > finding a way to download DEMs for the entire globe.
> >
> > 1.  Given the size of my sample dataset: What is the maximum resolution
> of
> > DEM that I should be using?  Do I only need values at prediction points
> or
> > will higher res yield higher accuracy?
> >
> > 2.  Is there a way to programmatically access DEMs using GDAL or R in
> > general?  ie. internally or through integration with McIDAS, NetCDF, etc.
> >
> > 3. Do I need to break the DEMs and sample dataset into smaller tiles to
> > avoid the memory issues?  If so how do I do that in R / GDAL?
> >
> > Thanks,
> > Abe
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-Geo mailing list
> > R-sig-Geo@stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >
>
>
>

	[[alternative HTML version deleted]]

