Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. For example, MacDonald's and Burger Kings are likely co-located in a local business map. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships. A large fraction of the computation time is devoted to identifying the instances of co-location patterns. We propose a novel join-less approach for co-location pattern mining, which materializes spatial neighbor relationships with no loss of co-location instances and reduces the computational cost of identifying the instances. The join-less co-location mining algorithm is efficient since it uses an instance-lookup scheme instead of an expensive spatial or instance join operation for identifying co-location instances. We prove the join-less algorithm is correct and complete in finding co-location rules. The experimental evaluations using synthetic datasets and real world datasets show the join-less algorithm performs more efficiently than a current join-based algorithm and is scalable in dense spatial datasets.
Yoo, Jin Soung; Shekhar, Shashi; Celik, Mete.
A Join-less Approach for Co-location Pattern Mining: A Summary of Results.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.