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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