https://orcid.org/0000-0003-4752-0153
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ABSTRACT
Multilevel co-location patterns embedded in spatial datasets are difficult to discern due to the complexity of neighboring relationships among spatial features. The neighboring relationships are used to determine whether instances of different spatial features are located in close geographic proximity. When spatial features are distributed unevenly, the neighboring relationships among spatial features cannot be constructed appropriately. Correspondingly, the instances of co-location patterns cannot be generated correctly, and the prevalence of multilevel co-location patterns cannot be measured accurately. To overcome this challenge, this study develops a method to adaptively detect multilevel co-location patterns based on natural neighborhoods. First, locally adaptive neighboring relationships for instances of different spatial features, called ‘natural neighborhoods’, are defined by considering the formation mechanism of co-location patterns and the local-distribution characteristics of spatial features. Using the natural neighborhoods, we propose a multilevel refining method to identify all global and local co-location patterns algorithmically. We compare the proposed method against three state-of-the-art methods using both simulated and real-life datasets. The comparison shows that the proposed method can discover multilevel co-location patterns from unevenly distributed spatial features more completely and accurately with less a priori knowledge for the construction of the natural neighborhoods.
Acknowledgments
The authors gratefully acknowledge the comments from the editor and the reviewers. This study was funded through support from the National Key Research and Development Foundation of China (No. 2017YFB0503601), National Science Foundation of China (NSFC) (No. 41730105 and 41971353), Natural Science Foundation of Hunan Province (No.2020JJ40669) and Innovation-Driven Project of Central South University (No. 2018CX015).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data and codes availability statement
The data and codes that support the findings of this study are available in ‘figshare.com’ with the identifier at the permanent link: http://doi.org/10.6084/m9.figshare.12061701
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Notes on contributors
Qiliang Liu
Qiliang Liu received the Ph.D. degree in geographical information science from The Hong Kong Polytechnic University in 2015. He is currently an associate professor at Central South University,Hunan, China. His research interests focus on multi-scale spatio-temporal data mining and spatiotemporal statistics. He has published more than 30 peer-reviewed journal articles in these areas.
Wenkai Liu
Wenkai Liu is currently a Ph.D. candidate at Central South University and his research interests focus on spatio-temporal clustering and association rule mining.
Min Deng
Min Deng is currently a professor at Central South University and the associate dean of School of Geosciences and info-physics. His research interests are map generalization, spatio-temporal data analysis and mining.
Jiannan Cai
Jiannan Cai received the Ph.D. degree in geographical information science from Central South University in 2019. His research interests focus on spatio-temporal association rule mining.
Yaolin Liu
Yaolin Liu is currently a Professor at Wuhan University. His research interests include Geographic Information Science, geographic data mining, and spatial analysis and decision making.