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

Date of Submission: 
September 22, 2003
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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 mine 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 controlling of related geometric computation costs. Based on a buffer-based definition of neighborhoods, a new model of finding co-location patterns over extended spatial objects has been proposed. Furthermore, this paper presents two pruning approaches, namely a prevalence-based pruning approach and a geometric filter-and-refine approach. Experimental evaluation with a real data set (the roadmap of 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, the extended co-location mining algorithm proposed in this paper has been used to select most challenging field test routes for a novel GPS-based approach to accessing road user charges.