Multi-level method for discovery of regional co-location patterns. Issue 9 (2nd September 2017)

• Record Type: Journal Article
• Authors: Deng, Min; Cai, Jiannan; Liu, Qiliang; He, Zhanjun; Tang, Jianbo
• Is Part Of: International journal of geographical information science. Volume 31:Issue 9(2017)
• Page Start: 1846
• Page End: 1870
• Publication Date: 2017-09-02
• Subjects: Spatial heterogeneity -- multi-level -- regional co-location patterns -- adaptive spatial clustering -- spatial data mining; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1334890
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Regional co-location patterns represent subsets of feature types that are frequently located together in sub-regions in a study area. These sub-regions are unknown a priori, and instances of these co-location patterns are usually unevenly distributed across a study area. Regional co-location patterns remain challenging to discover. This study developed a multi-level method to identify regional co-location patterns in two steps. First, global co-location patterns were detected, and other non-prevalent co-location patterns were identified as candidates for regional co-location patterns. Second, an adaptive spatial clustering method was applied to detect the sub-regions where regional co-location patterns are prevalent. To improve computational efficiency, an overlap method was developed to deduce the sub-regions of ( k + 1)-size co-location patterns from the sub-regions of k -size co-location patterns. Experiments based on both synthetic and ecological data sets showed that the proposed method is effective in the detection of regional co-location patterns.