Extracting Prevalent Co-location Patterns from Historic Spatial Data

  • Lizhen Wang
  • Pingping Wu
  • Gaofeng Fan
  • Yongheng Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7901)


A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. The data from which the patterns are derived is often historic, yet little or no attention has been paid to the time aspects of this data. In this paper we study the problem of finding prevalent co-location patterns from time constrained spatial data. We define spatial instances with time constraints brought to a net present value, and then define weighted row instances, weighted table instances and a weighted participation index for the spatial co-location patterns. We propose two algorithms to extract prevalent co-locations from spatial data with time constraints, a w-join-based algorithm that can find all prevalent patterns, and a top-k-w algorithm to find the top k most prevalent co-location patterns. Optimization strategies for the two algorithms are presented. Finally, we show the performance of the proposed algorithms using “real+synthetic” data sets, including the effect of various parameters on the algorithms.


Spatial co-location pattern mining net present value time constraints weighted participation index top-k 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lizhen Wang
    • 1
  • Pingping Wu
    • 1
  • Gaofeng Fan
    • 1
  • Yongheng Zhou
    • 1
  1. 1.Department of Computer Science and Engineering, Dianchi CollegeYunnan UniversityKunmingChina

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