Kriging is a spatial prediction method, which can predict at any location and return a measure of prediction confidence (the kriging standard error). There exist many variations of kriging, some of which can be complex, especially those that allow many of its parameters to vary spatially. To calibrate such a kriging model and to be able to interpret its results can therefore be quite daunting. We suggest that visual analytics can help with this task. In particular, we focus on the Moving Window Kriging model and three robust variants and use Star Icons to evaluate model performance and to investigate the appropriateness of the criterion used when choosing a robust model fit.
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