[R-sig-Geo] Kriging with uncertain data

[Bede-Fazekas Ákos]

Dear Santiago,

you mean you have two values at each location (observed value and 
uncertainty)? Or you have an observed value that is the sum of the real 
value and the observation error (uncertainty). If the last, then I think 
using the gstat::krige() function is straightforward, since the result 
of the function contains the variance of the prediction ("Attributes 
columns contain prediction and
prediction variance"; 
https://cran.r-project.org/web/packages/gstat/gstat.pdf).

HTH,
Ákos Bede-Fazekas
Hungarian Academy of Sciences



2016.10.06. 11:52 keltezéssel, Santiago Beguería írta:
> Dear R-sig-geo list members,
>
> I am curious about what are sensible approaches to spatial interpolation, most especially by using kriging, in the context of uncertain data.
>
> Suppose one has a dataset of values observed at different locations, and each value consists on the expected value and its variance. Variance here represents the uncertainty related to the observation, and shows spatial variation due to external factors, for instance the geological setting affecting the quality of the measurement.
>
> How would you proceed to model the spatial distribution of this variable, including propagation of the (spatially varying)?
>
> I suppose one approach could be by simulation, but at there other ways of propagating the uncertainty that do not involve potentially expensive (in computation time) simulation approaches?
>
> Cheers,
>
> Santiago Beguería
> CSIC
> Spain
>
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