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SMP: scalable max-P regionalization

MP-regions is an NP-hard problem that groups spatial areas to produce a maximum number of regions by enforcing a user-defined constraint at the regional level. Existing approximate algorithms for MP-regions do not scale for large datasets due to their high computational cost. This paper introduces SMP; a scalable technique to support MP-regions on large datasets. SMP works on two stages. The first stage finds an initial solution through randomized search, and the second stage improves this solution through efficient heuristic search. SMP optimizes the region building efficiency and quality by tuning the randomized area selection to trade-off runtime with region homogeneity. The experimental evaluation shows the superiority of our technique to support an order of magnitude larger datasets efficiently compared to the state-of-the-art techniques while producing high-quality solutions.