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Co-location Patterns, Algorithms

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Encyclopedia of GIS
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Synonyms

Association; Co-occurrence; Mining collocation patterns; Mining spatial association patterns; Participation index; Participation ratio; Reference-feature centric

Definition

A spatial co-location pattern associates the co-existence of a set of non-spatial features in a spatial neighborhood. For example, a co-location pattern can associate contaminated water reservoirs with a certain disease within 5 km distance from them. For a concrete definition of the problem, consider number n of spatial datasets R1, R2, …, R n , such that each R i contains objects that have a common non-spatial feature f i . For instance, R1 may store locations of water sources, R2 may store locations of appearing disease symptoms, etc. Given a distance threshold ɛ, two objects on the map (independent of their feature labels) are neighbors if their distance is at most ɛ. We can define a co-location pattern Pby an undirected connected graph where each node corresponds to a feature and each edge...

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References

  • Agrawal R, Skrikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, pp 487–499

    Google Scholar 

  • Brinkhoff T, Kriegel HP, Seeger B (1993) Efficient processing of spatial joins using r-trees. In: Proceedings of the ACM SIGMOD international conference

    Book  Google Scholar 

  • Huang Y, Xiong H, Shekhar S, Pei J (2003) Mining confident co-location rules without a support threshold. In: Proceedings of the 18th ACM symposium on applied computing (ACM SAC) (2003)

    Google Scholar 

  • Koperski K, Han J (1995) Discovery of spatial association rules in geographic information databases. In: Proceedings of the 4th international symposium on advances in spatial databases (SSD), vol. 951, pp. 47–66

    Article  Google Scholar 

  • Mamoulis N, Papadias D (2001) Multiway spatial joins. ACM Trans Database Syst 26(4):424–475

    Article  MATH  Google Scholar 

  • Morimoto Y (2001) Mining frequent neighboring class sets in spatial databases. In: Proceedings of the ACM SIGKDD international conference knowledge discovery and data mining

    Book  Google Scholar 

  • Munro R, Chawla S, Sun P (2003) Complex spatial relationships. In: Proceedings of the 3rd IEEE international conference on data mining (ICDM)

    Google Scholar 

  • Preparata FP, Shamos MI (1985) Computational geometry: an introduction. Springer, New York

    Book  MATH  Google Scholar 

  • Salmenkivi M (2004) Evaluating attraction in spatial point patterns with an application in the field of cultural history. In: Proceedings of the 4th IEEE international conference on data mining

    Google Scholar 

  • Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: a summary of results. In: Proceedings of the 7th international symposium on advances in spatial and temporal databases (SSTD)

    Google Scholar 

  • Wang J, Hsu W, Lee ML (2005) A framework for mining topological patterns in spatio-temporal databases. In: Proceedings of the 14th ACM international conference on Information and knowledge management. Full paper in IEEE Trans. KDE 16(12), 2004

    Google Scholar 

  • Yang H, Parthasarathy S, Mehta S (2005) Mining spatial object associations for scientific data. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence

    Google Scholar 

  • Zaki MJ, Gouda K (2003) Fast vertical mining using diffsets. In: Proceedings of the ACM SIGKDD Conference

    Book  Google Scholar 

  • Zhang X, Mamoulis N, Cheung, DWL, Shou Y (2004) Fast mining of spatial collocations. In: Proceedings of the ACM SIGKDD Conference

    Book  Google Scholar 

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Correspondence to Nikos Mamoulis .

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Mamoulis, N. (2017). Co-location Patterns, Algorithms. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_152

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