  This paper explores the application of machine learning to enhance our
understanding of water accessibility issues in underserved communities called
Colonias located along the northern part of the United States - Mexico border.
We analyzed more than 2000 such communities using data from the Rural Community
Assistance Partnership (RCAP) and applied hierarchical clustering and the
adaptive affinity propagation algorithm to automatically group Colonias into
clusters with different water access conditions. The Gower distance was
introduced to make the algorithm capable of processing complex datasets
containing both categorical and numerical attributes. To better understand and
explain the clustering results derived from the machine learning process, we
further applied a decision tree analysis algorithm to associate the input data
with the derived clusters, to identify and rank the importance of factors that
characterize different water access conditions in each cluster. Our results
complement experts' priority rankings of water infrastructure needs, providing
a more in-depth view of the water insecurity challenges that the Colonias
suffer from. As an automated and reproducible workflow combining a series of
tools, the proposed machine learning pipeline represents an operationalized
solution for conducting data-driven analysis to understand water access
inequality. This pipeline can be adapted to analyze different datasets and
decision scenarios.
