Non-Euclidean distance measures in spatial data decision analysis: investigations for mineral potential mapping

As a powerful tool for decision analysis, fifteen distance measures are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) to carry out a knowledge-driven approach in mineral exploration, whereby a multi-criterion decision making problem is solved for spatial data analysis. The kernel of the original version of the TOPSIS method as an integral part of the analysis will be defined on Euclidean distance. This research investigates to define the kernel on each of 15 distance measures from four general families of functions including L1, intersection, inner product and fidelity. To check the performance of each distance measure, the North Narbaghi porphyry copper mine in Iran was chosen. The significance of kernel substitution lies in the improvement of synthesized evidence maps in comparison to the Euclidean-based TOPSIS method in this study.