Towards Statistically Significant Taxonomy Aware Co-location Pattern Detection

TL;DR

This paper presents new methods for detecting statistically significant co-location patterns in spatial data that incorporate hierarchical taxonomies, reducing false discoveries and improving pattern relevance in applications like ecology and retail.

Contribution

It introduces two methods that integrate taxonomies and significance testing into co-location pattern detection, with an advanced approach controlling false discovery rate.

Findings

The advanced method effectively reduces false positives.

Experimental results demonstrate improved detection accuracy.

Case studies confirm practical applicability.

Abstract

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.