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. © 2011 Springer-Verlag.
CITATION STYLE
Barua, S., & Sander, J. (2011). SSCP: Mining statistically significant co-location patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6849 LNCS, pp. 2–20). https://doi.org/10.1007/978-3-642-22922-0_2
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