  Regionalization is the act of breaking a dataset into contiguous homogeneous
regions that are heterogeneous from each other. Many different algorithms exist
for performing regionalization; however, using these algorithms on large real
world data sets have only become feasible in terms of compute power in recent
years. Very few studies have been done comparing different regionalization
methods, and those that do lack analysis in memory, scalability, geographic
metrics, and large-scale real-world applications. This study compares
state-of-the-art regionalization methods, namely, Agglomerative Clustering,
SKATER, REDCAP, AZP, and Max-P-Regions using real world social determinant of
health (SDOH) data. The scale of real world SDOH data, up to 1 million data
points in this study, not only compares the algorithms over different data sets
but provides a stress test for each individual regionalization algorithm, most
of which have never been run on such scales previously. We use several new
geographic metrics to compare algorithms as well as perform a comparative
memory analysis. The prevailing regionalization method is then compared with
unconstrained K-Means clustering on their ability to separate real health data
in Virginia and Washington DC.
