Advertisement

Discovering Spatial Co-location Patterns: A Summary of Results

  • Shashi Shekhar
  • Yan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2121)

Abstract

Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type feature and a drought feature. The spatial co-location rule problem is different from the association rule problem. Even though boolean spatial feature types (also called spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. This creates difficulty in using traditional measures (e.g. support, confidence) and applying association rule mining algorithms which use support based pruning. We propose a notion of user-specified neighborhoods in place of transactions to specify groups of items. New interest measures for spatial co-location patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future.

Keywords

Association Rule Spatial Feature Association Rule Mining Participation Ratio Participation Index 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R. Agarwal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In In Proc. of the ACM SIGMOD Conference on Management of Data, pages 207–216, may 1993.Google Scholar
  2. 2.
    R. Agarwal and R. Srikant. Fast algorithms for Mining association rules. VLDB, may 1994.Google Scholar
  3. 3.
    P.S. Albert and L.M. McShane. A Generalized Estimating Equations Approach for Spatially Correlated Binary Data: Applications to the Analysis of Neuroimaging Data. Biometrics (Publisher: Washington, Biometric Society, Etc.), 1:627–638, 1995.Google Scholar
  4. 4.
    L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter. Scalable Sweeping-Based Spatial Join. In Proc. of the Int’l Conference on Very Large Databases, 1998.Google Scholar
  5. 5.
    Y. Chou. In Exploring Spatial Analysis in Geographic Information System, Onward Press, (ISBN: 1-56690-119-7), 1997.Google Scholar
  6. 6.
    N. Cressie. In Statistics for Spatial Data, Wiley-Interscience, (ISBN:0-471-00255-0), 1993.Google Scholar
  7. 7.
    G. Greenman. Turning a map into a cake layer of information. New York Times, Feb 12 2000.Google Scholar
  8. 8.
    R.H. Guting. An Introduction to Spatial Database Systems. In Very Large Data Bases Jorunal(Publisher: Springer Verlag), October 1994.Google Scholar
  9. 9.
    R.J. Haining. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Cambfidge, U.K, 1989.Google Scholar
  10. 10.
    J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In In Proc. 1995 Int. Conf. Very Large Data Bases, pages 420–431, Zurich, Switzerland, September 1995.Google Scholar
  11. 11.
    J. Hipp, U. Guntzer, and G. Nakaeizadeh. Algorithms for Association Rule Mining-A General Survey and Comparison. In In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.Google Scholar
  12. 12.
    Issaks, Edward, and M. Svivastava. Applied Geostatistics. In Oxford University Press, Oxford, 1989.Google Scholar
  13. 13.
    K. Koperski, J. Adhikary, and J. Han. Spatial Data Mining: Progress and Challenges. In In Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’96), 1996.Google Scholar
  14. 14.
    K. Koperski and J. Han. Discovery of Spatial Association Rules in Geographic Informa tion Databases. In In Proc. Fourth International Symposium on Large Spatial Data bases, Maine. 47–66, 1995.Google Scholar
  15. 15.
    P. Krugman. Development, Geography, and Economic theory. In MIT Press, Camgridge, MA, 1995.Google Scholar
  16. 16.
    Z. Li, J. Cihlar, L. Moreau, F. Huang, and B. Lee. Monitoring Fire Activities in the Boreal Ecosystem. Journal Geophys. Res,. 102(29):611–629, 1997.Google Scholar
  17. 17.
    D. Mark. Geographical Information Science: Critical Issues in an Emerging Cross-disciplinary Research Domain. In In NSF Workshop, February 1999.Google Scholar
  18. 18.
    D.C. Nepstad, A. Verissimo, A. Alencar, C. Nobre, E. Lima, P. Lefebvre, P. Schlesinger, C. Potter, P. Moutinho, E. Mendoza, M. Cochrane, and V. Brooks. Large-scale Improverishment of Amazonian Forests by Logging and Fire. Nature, 398:505–508, 1999.CrossRefGoogle Scholar
  19. 19.
    J.S. Park, M. Chen, and P.S. Yu. Using a Hash-Based Method with Transaction Trimming for Mining Association Rules. In IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 5, pp. 813–825, Sep–Oct 1997.CrossRefGoogle Scholar
  20. 20.
    J.F. Roddick and M. Spiliopoulou. A Bibliography of Temporal, Spatial and Spatio-temporal Data Mining Research. ACM Special Interest Group on Knowledge Discovery in Data Mining(SIGKDD) Explorations, 1999.Google Scholar
  21. 21.
    S. Shekhar and S. Chawla. Spatial Databases: Issues, Implementation and Trends. Prentice Hall (under contract), 2001.Google Scholar
  22. 22.
    S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C.T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, 11(1), Jan-Feb 1999.Google Scholar
  23. 23.
    S. Shekhar, T.A. Yang, and P. Hancock. An Intelligent Vehicle Highway Information Management System. Intl Jr. on Microcomputers in Civil Engineering (Publisher: Blackwell Publishers), 8(3), 1993.Google Scholar
  24. 24.
    R. Srikant and R. Agrawal. Mining Generalized Association Rules. In Proc. of the 21st Int’l Conference on Very Large Databases, Zurich, Switzerland, 1997.Google Scholar
  25. 25.
    R. Srikant, Q. Vu, and R. Agrawal. Mining Association Rules with Item Constraints. In Proc. of the 3rd Int’l Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, Aug 1997.Google Scholar
  26. 26.
    P. Stolorz, H. Nakamura, E. Mesrobian, R.R. Muntz, E.C. Shek, J.R. Santos, J. Yi, K. Ng, S.Y. Chien, R. Mechoso, and J.D. Farrara. Fast Spatio-Temporal Data Mining of Large Geophysical Datasets. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, AAAI Press, 300–305, 1995.Google Scholar
  27. 27.
    C. Tsur, J. Ullman, C. Clifton, S. Abiteboul, R. Motwani, S. Nestorov, and A. Rosenthal. Query Flocks: a Generalization of Association-Rule Mining. In Proceedings of 1998 ACM SIGMOD, Seattle, 1998.Google Scholar
  28. 28.
    M.F. Worboys. GIS: A Computing Perspective. In Taylor and Francis, 1995.Google Scholar
  29. 29.
    Y. Yasui and S.R. Lele. A Regression Method for Spatial Disease Rates: An Estimating Function Approach. Journal of the American Statistical Association, 94:21–32, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Shashi Shekhar
    • 1
  • Yan Huang
    • 1
  1. 1.Computer Science DepartmentUniversity of MinnesotaMinneapolisUSA

Personalised recommendations