Discovery of statistically significant regional co-location patterns on urban road networks

ABSTRACT

Detecting regional co-location patterns on urban road networks is challenging because it is computationally prohibitive to search all potential co-location patterns and their localities, and effective statistical methods for evaluating the prevalence of regional co-location patterns are lacking. To overcome these challenges, this study developed an adaptive method for detecting network-constrained regional co-location patterns. Specifically, an alternate prevalence measure of regional co-location patterns was defined based on the likelihood ratio statistic. A network-constrained k-nearest neighbor method was used to construct instances of candidate co-location patterns, and a heuristic two-phase expansion method was proposed to identify candidate localities of regional co-location patterns. The statistical significance of regional co-location patterns was evaluated using a Monte Carlo simulation. Experiments using extensive simulated datasets showed that our method was superior to three state-of-the-art methods. The proposed method was also applied to a Beijing points of interest (POI) dataset. The identified regional POI co-location patterns could support a better understanding of the spatial organization of urban functions and may be useful for facilitating urban planning.

KEYWORDS:

• Combinational optimization
• regional co-location pattern
• road network
• significance test

Author contributions

Wenkai Liu and Qiliang Liu conceived and designed the presented idea. Wenkai Liu implemented the experiments and analyzed the results. Qiliang Liu and Wenkai Liu wrote the manuscript. Min Deng and Jiannan Cai reviewed the manuscript, and provided comments. Jie Yang collected the research data and checked the code of the proposed method.

Acknowledgements

The authors gratefully acknowledge the comments from the editor and the reviewers.

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: https://doi.org/10.6084/m9.figshare.14782002.v1.

Additional information

Funding

This study was funded through support from National Natural Science Foundation of China (NSFC) [No. 41730105 and 41971353], and Natural Science Foundation of Hunan Province [No. 2021JJ20058 and 2020JJ4695].

Notes on contributors

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.

Qiliang Liu

Qiliang Liu 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.

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 is a Postdoctoral Fellow of the Institute of Space and Earth Information Science at The Chinese University of Hong Kong. He received his PhD in GIScience from the Central South University and was a visiting PhD student at the University of Minnesota, Twin Cities. His research interests include spatial data science, human mobility and environmental health.

Jie Yang

Jie Yang is currently a Ph.D. candidate at Central South University and his research interests focus on spatio-temporal clustering.