Entropy-Based Weights for MultiCriteria Spatial Decision-Making

This paper presents an integration of entropy-based weights and a modified TOPSIS algorithm within a GIS-based multicriteria decision analysis (MCDA). Geospatial multicriteria decision-making (MCDM) problems typically involve a set of spatially feasible alternatives that are evaluated by multiple and conflicting evaluation criteria that vary in importance to the decision-maker(s). However, often in complex spatial decision-making problems the decision-maker(s) may be unable or unwilling to provide cohesive and exact numerical judgments regarding the relative importance or weights of criteria. An entropy-based object weighting scheme determines the weights for a set of criteria by quantifying the amount of information within the decision matrix and based on evaluation values. Information entropy is a measure of the degree of disorder within a system. It can quantify the amount of expected and useful information content within criterion values, and it measures the contrast intensity among a set of spatial criteria. This paper will present the implementation of entropy-based weights within a vector-based spatial problem for calculating the Heat Vulnerability Index within San Fernando Valley, Los Angeles.