[R-sig-Geo] Universal Block Kriging covariate definition for krige in gstat

Edzer Pebesma edzer.pebesma at uni-muenster.de
Wed Jan 13 14:43:09 CET 2016



On 13/01/16 14:16, Antonio Manuel Moreno Ródenas wrote:
> Hello, I would like to rise a question on the use of predict {gstat},
> 
> I'm trying to perform the estimation of a spatially distributed variable at
> the support scale of a particular area (Block kriging). I have access to an
> additional variable, it is known that the variable of interest is
> correlated to the new variable. So I would be interested on updating my
> estimation by the use of this new information. This could be done by the
> use of a kriging with external drift (KED), but with a block support
> (Universal Block kriging). Theoretically this is included in the gstat
> library as mentioned in the documentation.
> 
> The issue comes when I try to perform the prediction:
> 
> blockprediction <- predict(gstat(formula=Variabletopredict~additionalVariable,
> data=Observed, model=vgm), newdata = shapefile)
> 
> The newdata argument should contain the prediction location. In a normal
> KED we would include a dataframe with a grid (coordinates in which to
> predict) and the values of the covariate (additionalVariable). As I'm
> trying to use a universal block kriging, I understood the newdata should be
> the region in which I'm interested to know the prediction, hence a polygon.
> How could I include in newdata the values of the covariate if its
> resolution is finer than my block?
> 
> As far as I know, what block kriging does is to predict point values inside
> the region (which I could specified with the argument sps.args
> discretization), and later average them. But I don't know how to attach the
> covariate values to the block of interest (shapefile).

maybe by

shapefile = aggregate(additionalVariable, shapefile, mean)

> 
> Thanks in advance,
> I hope I could explain it properly, but I will give more details if
> necessary.
> Kind regards,
> Antonio
> 
> 	[[alternative HTML version deleted]]
> 
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-- 
Edzer Pebesma
Institute for Geoinformatics  (ifgi),  University of Münster
Heisenbergstraße 2, 48149 Münster, Germany; +49 251 83 33081
Journal of Statistical Software:   http://www.jstatsoft.org/
Computers & Geosciences:   http://elsevier.com/locate/cageo/
Spatial Statistics Society http://www.spatialstatistics.info

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