Towards geographically robust statistically significant regional colocation pattern detection

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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 (rg), a subset C of S> such that C is a statistically significant regional colocation pattern in region rg. For example Caribou Coffee and Starbucks are significantly co-located in Minneapolis but not in Dallas at present. This problem has applications in a wide variety of domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. The current literature on regional colocation pattern detection has not addressed statistical significance which can result in spurious (chance) pattern instances. In this paper, we propose a novel technique for mining statistically significant regional colocation patterns. Our approach determines regions based on geographically defined boundaries (e.g., counties) unlike previous works which employed clustering, or regular polygons to enumerate candidate regions. To reduce spurious patterns, we perform a statistical significance test by modeling the observed data points with multiple Monte Carlo simulations within the corresponding regions. Using Safegraph POI dataset, this paper provides a case study on retail establishments in Minnesota for validation of proposed ideas. The paper also provides a detailed interpretation of discovered patterns using game theory and regional economics.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2022
EditorsTaylor Anderson, Alexander Hohl, Joon-Seok Kim, Ashwin Shashidharan
PublisherAssociation for Computing Machinery, Inc
Pages11-20
Number of pages10
ISBN (Electronic)9781450395373
DOIs
StatePublished - Nov 1 2022
Event5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2022 - Seattle, United States
Duration: Nov 1 2022 → …

Publication series

NameProceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2022

Conference

Conference5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, GeoSim 2022
Country/TerritoryUnited States
CitySeattle
Period11/1/22 → …

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grants No. 2118285, 2040459, 1737633, 1901099, and 1916518, the USDOD under Grants No. HM0476-20-1-0009, the USDOE Advanced Research Projects Agency-Energy (ARPA-E) under Award No. DE-AR0000795, the NIH under Grant No. UL1 TR002494, KL2TR002492, and TL1 TR002493, the USDA under Grant No. 2021-51181-35861, and Minnesota Supercomputing Institute (MSI). The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. We also thank Kim Koffolt and the Spatial Computing Research Group for valuable comments and refinements.

Publisher Copyright:
© 2022 ACM.

Keywords

  • game theory
  • neighborhood graph
  • regional colocation pattern
  • spatial heterogeneity
  • statistical significance

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