Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proc. VLDB, pp. 487–499 (1994)
Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A.: Mixed-Drove Spatiotemporal Co-occurence Pattern Mining. IEEE TKDE 20(10), 1322–1335 (2008)
Cressie, N.A.C.: Statistics for Spatial Data. Wiley, Chichester (1993)
Diggle, P.J., Gratton, R.J.: Monte Carlo Methods of Inference for Implicit Statistical Models. J. of the Royal Statist. Society, Series B 46(2), 193–227 (1984)
Harkness, R.D., Isham, V.: A Bivariate Spatial Point Pattern of Ants’ Nests. J. of the Royal Statist. Society, Series C (Appl. Statist.) 32(3), 293–303 (1983)
Huang, Y., Shekhar, S., Xiong, H.: Discovering Colocation Patterns from Spatial Data Sets: A General Approach. IEEE TKDE 16(12), 1472–1485 (2004)
Illian, J., Penttinen, A., Stoyan, H., Stoyan, D.: Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, Chichester (2008)
Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Mane, S., Murray, C., Shekhar, S., Srivastava, J., Pusey, A.: Spatial Clustering of Chimpanzee Locations for Neighborhood Identification. In: Proc. ICDM, pp. 737–740 (2005)
Morimoto, Y.: Mining Frequent Neighboring Class Sets in Spatial Databases. In: Proc. SIGKDD, pp. 353–358 (2001)
Ripley, B.: The Second-Order Analysis of Stationary Point Processes. J. of Appl. Probability 13(2), 255–266 (1976)
Shekhar, S., Huang, Y.: Discovering Spatial Co-location Patterns: A Summary of Results. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 236–256. Springer, Heidelberg (2001)
Xiao, X., Xie, X., Luo, Q., Ma, W.Y.: Density Based Co-location Pattern Discovery. In: Proc. GIS, pp. 250–259 (2008)
Yoo, J.S., Shekhar, S.: A Partial Join Approach for Mining Co-location Patterns. In: Proc. GIS, pp. 241–249 (2004)
Yoo, J.S., Shekhar, S.: A Joinless Approach for Mining Spatial Colocation Patterns. IEEE TKDE 18(10), 1323–1337 (2006)
Yoo, J.S., Shekhar, S., Kim, S., Celik, M.: Discovery of Co-evolving Spatial Event Sets. In: Proc. SDM, pp. 306–315 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barua, S., Sander, J. (2011). SSCP: Mining Statistically Significant Co-location Patterns. In: Pfoser, D., et al. Advances in Spatial and Temporal Databases. SSTD 2011. Lecture Notes in Computer Science, vol 6849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22922-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-22922-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22921-3
Online ISBN: 978-3-642-22922-0
eBook Packages: Computer ScienceComputer Science (R0)