Impact of proximity-adjusted preferences on rank-order stability in geographical multicriteria decision analysis

This paper presents a new approach to deriving preferences assigned to evaluation criteria in geographical multicriteria decision analysis. In this approach, the preferences, expressed by numeric weights, are adjusted by distance measures derived from the explicit consideration of a locational structure. The structure is given by locations of decision options and high importance reference objects. The approach is demonstrated on the example of a house selection case study in San Diego, California. The results show that proximity-adjusted preferences for the evaluation criteria can alter significantly the rank order of decision options. Consequently, the explicit modeling of spatial preference variability may be needed in order to better account for decision-maker’s preferences.