https://orcid.org/0000-0003-4752-0153
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ABSTRACT
Regions of anomalous spatial co-locations (ROASCs) are regions where co-locations between two different features are significantly stronger or weaker than expected. ROASC discovery can provide useful insights for studying unexpected spatial associations at regional scales. The main challenges are that the ROASCs are spatially arbitrary in geographic shape and the distributions of spatial features are unknown a priori. To avoid restrictive assumptions regarding the distribution of data, we propose a distribution-free method for discovering arbitrarily shaped ROASCs. First, we present a multidirectional optimization method to adaptively identify the candidate ROASCs, whose sizes and shapes are fully endogenized. Furthermore, the validity of the candidates is evaluated through significance tests under the null hypothesis that the expected spatial co-locations between two features occur consistently across space. To effectively model the null hypothesis, we develop a bivariate pattern reconstruction method by reconstructing the spatial auto- and cross-correlation structures observed in the data. Synthetic experiments and a case study conducted using Shanghai taxi datasets demonstrate the advantages of our method, in terms of effectiveness, over an available alternative method.
Acknowledgments
The authors thank the editors, the reviewers, and the members of the spatial computing research group at the University of Minnesota for their helpful comments. We also thank Kim Koffolt for improving the readability of this paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data and codes availability statement
The synthetic data and codes that support the findings of this study are available in ‘figshare.com’ with the identifier: https://doi.org/10.6084/m9.figshare.12993146. The Shanghai taxi data cannot be made publicly available due to third party restrictions. Mocked taxi data are provided at the link to show how the codes work.
Additional information
Funding
Notes on contributors
Jiannan Cai
Jiannan Cai received his PhD in GIScience at the Central South University and was a visiting PhD student at the University of Minnesota, Twin Cities. He is currently a Postdoctoral Fellow of the Institute of Space and Earth Information Science at The Chinese University of Hong Kong. His research interests focus on spatial data science and its smart-city applications.
Min Deng
Min Deng is currently a Professor and Associate Dean of the School of Geosciences and Info-physics at the Central South University. His research interests are map generalization, spatio-temporal data analysis and mining.
Yiwen Guo
Yiwen Guo is a PhD candidate at the Central South University and her research focuses on spatio-temporal association rule mining.
Yiqun Xie
Yiqun Xie received his PhD in Computer Science at the University of Minnesota, Twin Cities. He is currently an Assistant Professor in the Department of Geographical Sciences and Center for Geospatial Information Science at the University of Maryland, College Park. His research focuses on developing novel and cutting-edge techniques for spatial data science and artificial intelligence. His work has received multiple best paper awards and was highlighted by the Great Innovative Ideas program at the Computing Community Consortium.
Shashi Shekhar
Shashi Shekhar is a McKnight Distinguished University Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He is an IEEE Fellow and AAAS Fellow. Earlier, he served as the President of the University Consortium for GIS, and on many National Academies’ committees. His research focuses on spatial data science, spatial databases and GIS.