Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data

Abstract

A geographical flow (hereafter flow) is defined as a movement between locations at two different times. A group of spatiotemporal flows can be viewed as a cluster if their origins and destinations are both spatiotemporally concentrated. Identifying spatiotemporal flow clusters may help reveal underlying spatiotemporal mobility trends or intensive relationships between regions. Despite recent advances in flow clustering methods, most only consider spatial attributes and ignore temporal information, and may fail to differentiate space-close but time-separated clusters. To this end, we derive global and local versions of the Spatiotemporal Flow L-function, extended from the classical L-function for points, and thereby construct a clustering method. First, the global version is utilized to check whether flow data contain clusters and estimate the spatial and temporal scales of the clusters. The local version is then employed to extract the clusters with the estimated scales. Experiments of simulated data demonstrate that our method outperforms three state-of-the-art methods in identifying spatiotemporal flow clusters with arbitrary shapes and different densities and reducing subjectivity in the parameter selection process. A case study with taxi data shows that our method reveals residents’ spatiotemporal moving patterns, including rush-hour commuting and whole-daytime transferring among railway stations.

Keywords:

• Geographical flow
• spatiotemporal
• L-function
• flow cluster
• spatiotemporal statistics

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 on ‘figshare.com’ with the identifier at the public link: https://doi.org/10.6084/m9.figshare.20217581.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 42071436] and the Innovation Project of LREIS [Grant No. KPI002].

Notes on contributors

Xiaorui Yan

Xiaorui Yan is a doctoral candidate at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. Her research interests include spatiotemporal statistics and geographical big data mining. She developed the idea, developed the methodology, implemented the experiments, and wrote the manuscript.

Tao Pei

Prof. Tao Pei is a professor at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests include spatial big data mining and geostatistics. He conceived and designed the presented idea, developed the methodology, and reviewed the manuscript and provided constructive comments.

Hua Shu

Dr. Hua Shu is a postdoctoral researcher at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests include spatial statistics and spatial big data mining about human movements in the city. He collected the research data and helped perform the analysis with constructive discussions.

Ci Song

Dr. Ci Song is an assistant professor at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests include spatial data mining, spatial analysis, and geographic information science. He contributed to the conception of the study and helped analyse the results.

Mingbo Wu

Mingbo Wu is a doctoral candidate at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His current research focuses on big data application in environmental behaviour analysis and urban analysis. He checked the code of the proposed method and helped perform the analysis with constructive discussions.

Zidong Fang

Zidong Fang is a doctoral candidate at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. Her primary research interests include spatial analysis and data mining about human movements in the city. She checked the code of the proposed method and helped perform the analysis with constructive discussions.

Jie Chen

Dr. Jie Chen is an assistant professor at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. Her research interests include spatial big data mining and urban computing. She contributed to the conception of the study and helped analyse the results.