Regional Co-locations of Arbitrary Shapes

  • Song Wang
  • Yan Huang
  • Xiaoyang Sean Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8098)


In many application domains, occurrences of related spatial features may exhibit co-location pattern. For example, some disease may be in spatial proximity of certain type of pollution. This paper studies the problem of regional co-locations with arbitrary shapes. Regional co-locations represent regions in which two spatial features exhibit stronger or weaker co-location than that in other regions. Finding regional co-locations of arbitrary shapes is very challenging because: (1) statistical frameworks for mining regional co-location do not exist; and (2) testing all possible arbitrarily shaped regions is computational prohibitive even for very small dataset. In this paper, we propose frequentist and Bayesian frameworks for mining regional co-locations and develop a probabilistic expansion heuristic to find arbitrary shaped regions. Experimental results on synthetic and real world data show that both frequentist method and Bayesian statistical approach can recover the region with arbitrary shapes. Our approaches outperform baseline algorithms in terms of F measure. Bayesian statistical approach is approximately three orders of magnitude faster than the frequentist approach.


Grid Size Arbitrary Shape Expectation Maximization Algorithm Likelihood Ratio Statistic Frequentist Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Song Wang
    • 1
  • Yan Huang
    • 2
  • Xiaoyang Sean Wang
    • 3
  1. 1.Department of Computer ScienceUniversity of VermontBurlingtonUSA
  2. 2.Department of Computer ScienceUniversity of North TexasDentonUSA
  3. 3.School of Computer ScienceFudan UniversityShanghaiChina

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