Abstract
Given a spatial dataset containing instances of a set of spatial Boolean feature-types, the problem of spatial co-location pattern mining aims to determine a subset of feature-types which are frequently co-located in space. Spatial Co-location patterns have a wide range of applications in the domains such as ecology, public health and public safety. For instance, in an ecological dataset containing event instances corresponding to different bird species and vegetation types, spatial co-location patterns may revel that a particular species of birds prefer a particular kind of trees for their nests. Similarly, in a crime dataset, spatial co-location may revel a pattern that drunk-driving cases are co-located with bar locations. This article presents a gentle introduction to spatial co-location pattern mining. It introduces a well studied interest measure called participation index for co-location mining and, then discusses an algorithm to determine patterns having high participation index in a spatial dataset.
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Notes
- 1.
Whenever the context is clear, we drop the keyword “Spatial” from“Spatial Co-location” to maintain clarity of text.
References
Agarwal, P., Verma, R., Gunturi, V.M.V.: Discovering spatial regions of high correlation. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 1082–1089 (2016)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD 1993, pp. 207–216 (1993)
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 (1994)
Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J.S.: Scalable sweeping-based spatial join. In: Proceedings of the 24th International Conference on Very Large Data Bases, VLDB 1998, pp. 570–581 (1998)
Barua, S., Sander, J.: Mining statistically significant co-location and segregation patterns. IEEE Trans. Knowl. Data Eng. 26(5), 1185–1199 (2014)
Barua, S., Sander, J.: SSCP: mining statistically significant co-location patterns. In: Pfoser, D., et al. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 2–20. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22922-0_2
Cao, H., Mamoulis, N., Cheung, D.W.: Mining frequent spatio-temporal sequential patterns. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), pp. 82–89 (2005)
Celik, M., Kang, J.M., Shekhar, S.: Zonal co-location pattern discovery with dynamic parameters. In: Seventh IEEE International Conference on Data Mining (ICDM), pp. 433–438 (2007)
Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A.: Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Trans. Knowl. Data Eng. 20(10), 1322–1335 (2008)
Güting, R.H.: An introduction to spatial database systems. VLDB J. 3(4), 357–399 (1994)
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). https://doi.org/10.1007/3-540-60159-7_4
Li, X., Han, J., Lee, J.-G., Gonzalez, H.: Traffic density-based discovery of hot routes in road networks. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 441–459. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73540-3_25
Liu, Z., Huang, Y.: Mining co-locations under uncertainty. In: Nascimento, M.A., Sellis, T., Cheng, R., Sander, J., Zheng, Y., Kriegel, H.-P., Renz, M., Sengstock, C. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 429–446. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40235-7_25
Mohan, P., Shekhar, S., Shine, J.A., Rogers, J.P.: Cascading spatio-temporal pattern discovery: a summary of results. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp. 327–338 (2010)
Mohan, P., Shekhar, S., Shine, J.A., Rogers, J.P.: Cascading spatio-temporal pattern discovery. IEEE Trans. Knowl. Data Eng. 24(11), 1977–1992 (2012)
Ripley, B.D.: The second-order analysis of stationary point processes. J. Appl. Probab. 13(2), 255–266 (1976)
Sainju, A.M., Aghajarian, D., Jiang, Z., Prasad, S.K.: Parallel grid-based colocation mining algorithms on GPUs for big spatial event data. IEEE Transactions on Big Data (2018). https://doi.org/10.1109/TBDATA.2018.2871062
Sainju, A.M., Jiang, Z.: Grid-based colocation mining algorithms on GPU for big spatial event data: a summary of results. In: Gertz, M., et al. (eds.) SSTD 2017. LNCS, vol. 10411, pp. 263–280. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64367-0_14
Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice Hall (2003). (ISBN 013-017480-7)
Shekhar, S., Chawla, S., Ravada, S., Fetterer, A., Liu, X., Lu, C.T.: Spatial databases - accomplishments and research needs. IEEE Trans. Knowl. Data Eng. 11(1), 45–55 (1999)
Shekhar, S., Feiner, S.K., Aref, W.G.: Spatial computing. Commun. ACM 59(1), 72–81 (2015)
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). https://doi.org/10.1007/3-540-47724-1_13
Wang, S., Huang, Y., Wang, X.S.: Regional co-locations of arbitrary shapes. In: Nascimento, M.A., et al. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 19–37. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40235-7_2
Worboys, M., Duckham, M.: GIS: A computing perspective. CRC (2004). ISBN: 0415283752
Yoo, J.S., Shekhar, S., Celik, M.: A join-less approach for co-location pattern mining: a summary of results. In: Fifth IEEE International Conference on Data Mining (ICDM) (2005)
Yoo, J.S., Shekhar, S., Smith, J., Kumquat, J.P.: A partial join approach for mining co-location patterns. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, GIS 2004, pp. 241–249 (2004)
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Gunturi, V.M.V. (2018). Spatial Co-location Pattern Mining. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_29
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