Geostatistical Prediction/Simulation of Point Values From Areal Data
P. C. Kyriakidis and E.-H. Yoo
The spatial prediction/simulation of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support (the term support referring to the domain informed by each measurement). It is shown that the geostatistical framework: (i) can explicitly and consistently account for the support difference between the available areal data and the sought-after point predictions, (ii) yields coherent (mass-preserving or pycnophylactic) predictions, and (iii) provides a measure of reliability (standard error) associated with each prediction. In the case of stochastic simulation, the resulting simulated realizations reproduce a point-support histogram (Gaussian in this work) and a semivariogram model (possibly including anisotropic nested structures). A case study using simulated areal data illustrates the application of the proposed methodology in a remote sensing context, whereby the areal data are available at a regular pixel support. It is demonstrated that point-support (sub-pixel scale) predictions and simulated realizations can be readily obtained, and that such predictions/simulations are consistent with the available information at the coarser (pixel-level) resolution. The point-support predictions/simulations obtained by the proposed methodology can be used to assess the uncertainty in spatially distributed environmental model outputs due to uncertain input parameters linked to coarser resolution data.
Reference: Proceedings of the 7th International Conference on GeoComputation
, University of Southampton, United Kingdom, 8 - 10 September 2003.
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