  Given a set \emph{S} of spatial feature types, its feature instances, a study
area, and a neighbor relationship, the goal is to find pairs $<$a region
($r_{g}$), a subset \emph{C} of \emph{S}$>$ such that \emph{C} is a
statistically significant regional-colocation pattern in $r_{g}$. This problem
is important for applications in various domains including ecology, economics,
and sociology. The problem is computationally challenging due to the
exponential number of regional colocation patterns and candidate regions.
Previously, we proposed a miner \cite{10.1145/3557989.3566158} that finds
statistically significant regional colocation patterns. However, the numerous
simultaneous statistical inferences raise the risk of false discoveries (also
known as the multiple comparisons problem) and carry a high computational cost.
We propose a novel algorithm, namely, multiple comparisons regional colocation
miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis,
experimental evaluation, and case study results show that the proposed method
reduces both the false discovery rate and computational cost.
