Multi-level method for discovery of regional co-location patterns

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.

KEYWORDS:

• Spatial heterogeneity
• multi-level
• regional co-location patterns
• adaptive spatial clustering
• spatial data mining

Acknowledgments

The authors gratefully acknowledge the comments from the editor and the reviewers. This study was funded through support from the National Science Foundation of China (NSFC), No. 41601410 and 41471385, the Natural Foundation of Hunan Province, No. 2017JJ3379 and the Postgraduate Research and Innovation Foundation of Central South University, No. 2016zzts430.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental material

The supplemental material for this article can be accessed here.

Additional information

Funding

This study was funded through support from the Postgraduate Research and Innovation Foundation of Central South University [2016zzts430], the National Science Foundation of China (NSFC) [41471385, 41601410] and the Natural Foundation of Hunan Province [2017JJ3379].