SSCP: Mining Statistically Significant Co-location Patterns

  • Sajib Barua
  • Jörg Sander
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)


Co-location pattern discovery searches for subsets of spatial features whose instances are often located at close spatial proximity. Current algorithms using user specified thresholds for prevalence measures may report co-locations even if the features are randomly distributed. In our model, we look for subsets of spatial features which are co-located due to some form of spatial dependency but not by chance. We first introduce a new definition of co-location patterns based on a statistical test. Then we propose an algorithm for finding such co-location patterns where we adopt two strategies to reduce computational cost compared to a naïve approach based on simulations of the data distribution. We propose a pruning strategy for computing the prevalence measures. We also show that instead of generating all instances of an auto-correlated feature during a simulation, we could generate a reduced number of instances for the prevalence measure computation. We evaluate our algorithm empirically using synthetic and real data and compare our findings with the results found in a state-of-the-art co-location mining algorithm.


Spatial Dependency Randomization Test Frequent Itemset Feature Instance Pruning Technique 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sajib Barua
    • 1
  • Jörg Sander
    • 1
  1. 1.Dept. of Computing ScienceUniversity of AlbertaEdmontonCanada

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