Discovering co-location patterns in multivariate spatial flow data

ABSTRACT

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results.

KEYWORDS:

• Spatial data mining
• spatial co-location patterns
• multivariate flow data
• human mobility
• uncertain geographic context problem

Acknowledgements

The authors thank the editor and the reviewers for their helpful comments.

Author contributions

All authors conceived and designed the study, and revised the manuscript. Jiannan Cai processed the data, coded methods, performed experiments, and wrote the original draft. Mei-Po Kwan contributed to the refinement of the proposed concepts, method and manuscript writing, and the discussion of the findings.

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.16570674. Considering the privacy protection of survey participants, mocked commuting flow data are provided at the link to show how the codes work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by grants from the Hong Kong Research Grants Council [General Research Fund Grant no. 14605920, 14611621; Collaborative Research Fund Grant no. C4023-20GF] and a grant from the Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes of the Chinese University of Hong Kong.

Notes on contributors

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.

Mei-Po Kwan

Mei-Po Kwan is Choh-Ming Li Professor of Geography and Resource Management and Director of the Institute of Space and Earth Information Science at the Chinese University of Hong Kong, Shatin, Hong Kong, China. Her research interests include environmental health, human mobility, sustainable cities, transport and health issues in cities, and GIScience. She is a leading researcher in deploying real-time GPS tracking and mobile sensing to collect individual-level data in environmental health research.