A framework for discovering co-location patterns in data sets with extended spatial objects

Hui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, Jin Soung Yoo

Research output: Contribution to conferencePaperpeer-review

44 Scopus citations

Abstract

Co-location patterns are subsets of spatial features (e.g. freeways, frontage roads) usually located together in geographic space. Recent literature has provided a transaction-free approach to discover co-location patterns over spatial point data sets to avoid potential loss of proximity relationship information in partitioning continuous geographic space into transactions. This paper provides a more general transaction-free approach to mining data sets with extended spatial objects, e.g. line-strings and polygons. Key challenges include modeling of neighborhood and relationships among extended spatial objects as well as control of related geometric computation costs. The approach we propose is based on a new buffer-based definition of neighborhoods. Furthermore, we introduce and compare two pruning approaches, namely a prevalence-based pruning approach and a geometric filter-and-refine approach. Experimental evaluation with a real data set (a digital roadmap of the Minneapolis and St. Paul metropolitan area) shows that the geometric filter-and-refine approach can speed up the prevalence-based pruning approach by a factor of 30 to 40. Finally, we show how the extended co-location mining algorithm proposed in this paper has been used to find line-string co-location patterns, which can help with decision-makings on selecting most challenging field test routes. These field test routes are important for evaluating a GPS-based approach to accessing road user charges.

Original languageEnglish (US)
Pages78-89
Number of pages12
DOIs
StatePublished - 2004
EventProceedings of the Fourth SIAM International Conference on Data Mining - Lake Buena Vista, FL, United States
Duration: Apr 22 2004Apr 24 2004

Other

OtherProceedings of the Fourth SIAM International Conference on Data Mining
CountryUnited States
CityLake Buena Vista, FL
Period4/22/044/24/04

Keywords

  • Co-location Patterns
  • GIS Buffer Operation
  • Spatial Association Rules
  • Spatial Data Mining

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