2 Search Results for "Ghosh, Subhankar"

Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.25
Towards Statistically Significant Taxonomy Aware Co-Location Pattern Detection (Short Paper)

Authors: Subhankar Ghosh, Arun Sharma, Jayant Gupta, and Shashi Shekhar

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

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.
Cite as

Subhankar Ghosh, Arun Sharma, Jayant Gupta, and Shashi Shekhar. Towards Statistically Significant Taxonomy Aware Co-Location Pattern Detection (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 25:1-25:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{ghosh_et_al:LIPIcs.COSIT.2024.25,
  author =	{Ghosh, Subhankar and Sharma, Arun and Gupta, Jayant and Shekhar, Shashi},
  title =	{{Towards Statistically Significant Taxonomy Aware Co-Location Pattern Detection}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{25:1--25:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.25},
  URN =		{urn:nbn:de:0030-drops-208404},
  doi =		{10.4230/LIPIcs.COSIT.2024.25},
  annote =	{Keywords: Co-location patterns, spatial data mining, taxonomy, hierarchy, statistical significance, false discovery rate, family-wise error rate}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2023.3
Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

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

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

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.
Cite as

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)

Copy BibTex To Clipboard

@InProceedings{ghosh_et_al:LIPIcs.GIScience.2023.3,
  author =	{Ghosh, Subhankar and Gupta, Jayant and Sharma, Arun and An, Shuai and Shekhar, Shashi},
  title =	{{Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{3:1--3:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.3},
  URN =		{urn:nbn:de:0030-drops-188985},
  doi =		{10.4230/LIPIcs.GIScience.2023.3},
  annote =	{Keywords: Colocation pattern, Participation index, Multiple comparisons problem, Spatial heterogeneity, Statistical significance}
}
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