A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references. Issue 5 (4th May 2018)

• Record Type: Journal Article
• Authors: Yao, Xiaojing; Chen, Liujia; Wen, Congcong; Peng, Ling; Yang, Liang; Chi, Tianhe; Wang, Xiaomeng; Yu, Wenhao
• Is Part Of: International journal of geographical information science. Volume 32:Issue 5(2018)
• Page Start: 980
• Page End: 1005
• Publication Date: 2018-05-04
• Subjects: Spatial data mining -- co-location pattern mining -- reward value -- Voronoi diagram -- generalized extreme value distribution; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2018.1431839
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Spatial co-location pattern mining is employed to identify a group of spatial types whose instances are frequently located in spatial proximity. Current co-location mining methods have two limitations: (1) it is difficult to set an appropriate proximity threshold to identify close instances in an unknown region, and (2) such methods neglect the effects of the distance values between instances and long-distance instance effects on pattern significance. This paper proposes a novel maximal co-location algorithm to address these problems. To remove the first constraint, the algorithm uses Voronoi diagrams to extract the most related instance pairs of different types and their normalized distances, from which two distance-separating parameters are adaptively extracted using a statistical method. To remove the second constraint, the algorithm employs a reward-based verification based on distance-separating parameters to identify the prevalent patterns. Our experiments with both synthetic data and real data from Beijing, China, demonstrate that the algorithm can identify many interesting patterns that are neglected by traditional co-location methods.