A quantitative comparison of regionalization methods

Regionalization is the task of partitioning a set of contiguous areas into spatial clusters or regions. The theoretical and empirical literature focusing on regionalization is extensive, yet few quantitative comparisons have been conducted. We present a simulation study and explore the quality of frequently used and state-of-the-art regionalization algorithms, namely AZP, AZP-SA, AZPTabu, ARISEL, REDCAP, and SKATER, where the number of regions is an exogenous variable. The simulated benchmark data set consists of model realizations that represent various complexities in spatial data. Model families are defined with respect to regions' shapes, value-mixing between regions, and the number of underlying spatial clusters. We evaluate the performance of different regionalization methods for realizations families using internal and external measures of regionalization quality. A large number of regionalization quality metrics expose a detailed profile of the analyzed methods' strengths and weaknesses. We investigate the computational efficiency of every method as a function of the number of spatial units studied. We summarize results for different region families and discuss circumstances that make a certain method more desirable. We illustrate different regionalization algorithms' implications on defining ecological regions for the conterminous US and compare them against expert-defined ecoregions.

Automated summary

This study investigates the performance of regionalization algorithms on a simulated benchmark dataset, utilizing various metrics to evaluate quality. The analysis reveals strengths and weaknesses of each method, demonstrating computational efficiency and preferred circumstances.