  Given a collection of Boolean spatial feature types, their instances, a
neighborhood relation (e.g., proximity), and a hierarchical taxonomy of the
feature types, the goal is to find the subsets of feature types or their
parents whose spatial interaction is statistically significant. This problem is
for taxonomy-reliant applications such as ecology (e.g., finding new symbiotic
relationships across the food chain), spatial pathology (e.g., immunotherapy
for cancer), retail, etc. The problem is computationally challenging due to the
exponential number of candidate co-location patterns generated by the taxonomy.
Most approaches for co-location pattern detection overlook the hierarchical
relationships among spatial features, and the statistical significance of the
detected patterns is not always considered, leading to potential false
discoveries. This paper introduces two methods for incorporating taxonomies and
assessing the statistical significance of co-location patterns. The baseline
approach iteratively checks the significance of co-locations between leaf nodes
or their ancestors in the taxonomy. Using the Benjamini-Hochberg procedure, an
advanced approach is proposed to control the false discovery rate. This
approach effectively reduces the risk of false discoveries while maintaining
the power to detect true co-location patterns. Experimental evaluation and case
study results show the effectiveness of the approach.
