A novel hierarchical aggregation algorithm for optimal repartitioning of statistical regions

Abstract

Data regionalisation allows spatial inference over a population. The statistical regions must be updated to account for population changes, but this update process is more restrictive and iterative than ab initio regionalisation. This creates a need for an algorithmic solution that minimises human-in-the-loop involvement in population-driven regionalisation. The new method must address the basic regionalisation criteria – contiguity, compactness, homogeneity, equinumeriosity, and temporal consistency. We present a novel validation metric to assess the quality of partition based on these criteria. We have developed a novel hybrid aggregation algorithm (HeLP), combining elements of hierarchical and graph-theoretic approaches, for the primary purpose of repartitioning. This algorithm operates in average computational time complexity. HeLP was tested on simulated data and the Australian Statistical Geography Standard. The method can emulate the human operator successfully, providing statistically significant results in repartitioning parcel-based systems, such as the Cadastre.