This group of methods pertains to evaluating the spatial distribution of criterion evaluation scores. Such sensitivity could be analyzed based on traditional value measurement uncertainty combined with the spatial uncertainty stemming from the geographical distribution of a given criterion. For example, if the decision maker uses rainfall as a decision criterion in raster-based multicriteria analysis, she should consider the uncertainty of the rainfall measure at a particular location, together with the uncertainty associated with a selected interpolation method. One way of analyzing the spatial sensitivity of a criterion involves adding an uncertainty surface to the criterion surface. Another method involves attribute generalization similar to traditional rescaling (Voogd, 1983).
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