A Supervised Heuristic for a Balanced Approach to Regionalization

Regionalization refers to the design of areal zones by spatially aggregating smaller units into larger clusters. Algorithms to conduct regionalization typically require the desired number of clusters to be specified a priori, though a reasonable number is not always clear. Therefore, a heuristic is proposed to endogenously determine the number of clusters in a supervised setting (i.e., model-driven) by balancing the fit of a spatial model and the average area of clusters used as input. The heuristic is applied in a spatial interaction modeling context and a workflow is presented for integrating regionalization algorithms into larger spatial analysis frameworks.