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 rg. 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 [8] 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.
Original language | English (US) |
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Title of host publication | 12th International Conference on Geographic Information Science, GIScience 2023 |
Editors | Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, Sarah Wise |
Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
ISBN (Electronic) | 9783959772884 |
DOIs | |
State | Published - Sep 2023 |
Event | 12th International Conference on Geographic Information Science, GIScience 2023 - Leeds, United Kingdom Duration: Sep 12 2023 → Sep 15 2023 |
Publication series
Name | Leibniz International Proceedings in Informatics, LIPIcs |
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Volume | 277 |
ISSN (Print) | 1868-8969 |
Conference
Conference | 12th International Conference on Geographic Information Science, GIScience 2023 |
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Country/Territory | United Kingdom |
City | Leeds |
Period | 9/12/23 → 9/15/23 |
Bibliographical note
Publisher Copyright:© Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar.
Keywords
- Colocation pattern
- Multiple comparisons problem
- Participation index
- Spatial heterogeneity
- Statistical significance