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Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

Authors Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, Shashi Shekhar

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Author Details

Subhankar Ghosh
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Jayant Gupta
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Arun Sharma
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Shuai An
  • Department of Economics, University of Minnesota, Minneapolis, MN, USA
Shashi Shekhar
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA

Funding

This material is based upon work supported by the National Science Foundation under Grants No. 2118285, 2040459, 1901099, and 1916518.

Acknowledgements

We also thank Kim Koffolt, Yash Travadi, and the Spatial Computing Research Group for valuable comments and refinements.

Cite AsGet BibTex

Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar. Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.3

Abstract

Given a set 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 C of S> such that 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 [Subhankar et. al, 2022] 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.

Subject Classification

ACM Subject Classification
  • Information systems → Data mining
  • Computing methodologies → Spatial and physical reasoning
Keywords
  • Colocation pattern
  • Participation index
  • Multiple comparisons problem
  • Spatial heterogeneity
  • Statistical significance

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