A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2004 SIAM International Conference on Data Mining (SDM)A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial ObjectsHui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, and Jin Soung YocHui Xiong, Shashi Shekhar, Yan Huang, Vipin Kumar, Xiaobin Ma, and Jin Soung Yocpp.78 - 89Chapter DOI:https://doi.org/10.1137/1.9781611972740.8PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 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. Previous chapter Next chapter RelatedDetails Published:2004ISBN:978-0-89871-568-2eISBN:978-1-61197-274-0 https://doi.org/10.1137/1.9781611972740Book Series Name:ProceedingsBook Code:PR117Book Pages:xiv + 537Key words:Spatial Data Mining, Co-location Patterns, GIS Buffer Operation, Spatial Association Rules