A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects
Alternative title
Published Date
2003-09-22
Publisher
Type
Report
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 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.
Keywords
Description
Related to
Replaces
License
Series/Report Number
Technical Report; 03-037
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Xiong, Hui; Shekhar, Shashi; Huang, Yan; Kumar, Vipin; Ma, Xiaobin; Soung Yoo, Jin. (2003). A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215580.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.