Regional Co-locations of Arbitrary Shapes
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.
KeywordsGrid Size Arbitrary Shape Expectation Maximization Algorithm Likelihood Ratio Statistic Frequentist Method
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google Scholar
- 3.Celik, M., Kang, J.M., Shekhar, S.: Zonal co-location pattern Discovery with dynamic parameters. In: ICDM 2007, pp. 433–438. IEEE Computer Society, Washington, DC (2007)Google Scholar
- 4.Eick, C.F., Parmar, R., Ding, W., Stepinski, T.F., Nicot, J.-P.: Finding regional co-location patterns for sets of continuous variables in spatial datasets. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2008, pp. 30:1–30:10. ACM, New York (2008)Google Scholar
- 7.Huang, Y., Xiong, H., Shekhar, S., Pei, J.: Mining confident co-location rules without a support threshold. In: Proceedings of the 2003 ACM Symposium on Applied Computing, SAC 2003, pp. 497–501. ACM, New York (2003)Google Scholar
- 11.Neill, D.B., Moore, A.W., Cooper, G.F.: A bayesian spatial scan statistic. In: NIPS (2005)Google Scholar
- 12.Powell, J.W., Huang, Y., Bastani, F., Ji, M.: Towards reducing taxicab cruising time using spatio-temporal profitability maps. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 242–260. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 15.Welsh, D.: Approximate Counting. Cambridge University Press (2007)Google Scholar
- 16.Zhang, X., Mamoulis, N., Cheung, D.W., Shou, Y.: Fast mining of spatial collocations. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 384–393 (2004)Google Scholar