Local co-location pattern detection: A summary of results

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

1 Scopus citations

Abstract

Given a set of spatial objects of different features (e.g., mall, hospital) and a spatial relation (e.g., geographic proximity), the problem of local co-location pattern detection (LCPD) pairs co-location patterns and localities such that the co-location patterns tend to exist inside the paired localities. A co-location pattern is a set of spatial features, the objects of which are often related to each other. Local co-location patterns are common in many fields, such as public security, and public health. For example, assault crimes and drunk driving events co-locate near bars. The problem is computationally challenging because of the exponential number of potential co-location patterns and candidate localities. The related work applies data-unaware or clustering heuristics to partition the study area, which results in incomplete enumeration of possible localities. In this study, we formally defined the LCPD problem where the candidate locality was defined using minimum orthogonal bounding rectangles (MOBRs). Then, we proposed a Quadruplet & Grid Filter-Refine (QGFR) algorithm that leveraged an MOBR enumeration lemma, and a novel upper bound on the participation index to efficiently prune the search space. The experimental evaluation showed that the QGFR algorithm reduced the computation cost substantially. One case study using the North American Atlas-Hydrography and U.S. Major City Datasets was conducted to discover local co-location patterns which would be missed if the entire dataset was analyzed or methods proposed by the related work were applied.

Original languageEnglish (US)
Title of host publication10th International Conference on Geographic Information Science, GIScience 2018
EditorsAmy L. Griffin, Stephan Winter, Monika Sester
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Print)9783959770835
DOIs
StatePublished - Aug 1 2018
Event10th International Conference on Geographic Information Science, GIScience 2018 - Melbourne, Australia
Duration: Aug 28 2018Aug 31 2018

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume114
ISSN (Print)1868-8969

Other

Other10th International Conference on Geographic Information Science, GIScience 2018
CountryAustralia
CityMelbourne
Period8/28/188/31/18

Keywords

  • Co-location pattern
  • Participation index
  • Spatial heterogeneity

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  • Cite this

    Li, Y., & Shekhar, S. (2018). Local co-location pattern detection: A summary of results. In A. L. Griffin, S. Winter, & M. Sester (Eds.), 10th International Conference on Geographic Information Science, GIScience 2018 (Leibniz International Proceedings in Informatics, LIPIcs; Vol. 114). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.GIScience.2018.10