Measuring hub locations in time-evolving spatial interaction networks based on explicit spatiotemporal coupling and group centrality

ABSTRACT

As the essence of urban spatiotemporal interaction systems, hubs and centers empower cities to enhance socioeconomic prosperity and sustainability. However, a city manifests a time-evolving spatial interaction network with latent temporal interactions and irregular spatial partitions. This phenomenon is termed the spatiotemporal inconsistency problem. The aggregate, single-layer network model is defective for capturing the importance of locations in such time-evolving spatial interaction systems. This article therefore proposes a novel multilayer network model based on the nature of inherent spatial and temporal dependencies of urban interactions. First, the spatial agglomeration and the temporal correlation are explicitly modeled in multilayer networks for alleviating the spatiotemporal inconsistency problem. Secondly, generalized centrality metrics from a single-layered static network to the multi-layered dynamic network are acquired in order to discover grouped hub locations over time. Lastly, the capability of the proposed method is evaluated by an empirical analysis of the taxi mobility networks of Beijing, China, from 2012 to 2017. The empirical analysis indicates that the proposed method enables the identification of typical hub locations clustered in space and stable over time. This ability is essential to understand the centrality of locations informed by noisy and inconsistent data in their spatial and temporal dimensions.

KEYWORDS:

• Spatial interaction
• temporal dynamics
• multilayer network
• spatiotemporal coupling
• group centrality

Acknowledgments

The authors are grateful for the financial support from the National Natural Science Foundation of China (Grants No. 41830645, 41601484 and 41625003) and the National Key Research and Development Program of China (Grant No. 2017YFB0503604). The authors also appreciate the constructive comments and suggestions from the editors Prof. David O’Sullivan, Prof. May Yuan, and the anonymous reviewers.

Data and codes availability statement

The data and codes that support the findings of this study are available with a DOI at https://doi.org/10.6084/m9.figshare.12592349.v1. Note that we implemented muxLib.R as a derivate of the MuxNetLib authored by De Domenico et al. (2015) who are not affiliated with the manuscript.

Disclosure statement

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

Supplementary materials

Supplemental data for this article can be accessed here.

Additional information

Funding

The authors are grateful for the financial support from the National Natural Science Foundation of China (Grants No. 41830645, 41601484 and 41625003) and the National Key Research and Development Program of China (Grant No. 2017YFB0503604).

Notes on contributors

Chaogui Kang

Chaogui Kang is an Associate Professor with the School of Remote Sensing and Information Engineering, Whuhan University. He received his B.S. degree in Geographcial Information Systems from Nanjing University in 2009, and his Ph.D. degree in Cartography and Geographical Information Systems from Peking University in 2015. His primary research interest lies in the intersections of Travel Behavior, Built Environment and Social Inequality with the assistance of pervasive urban sensing techniques.

Zhuojun Jiang

Zhuojun Jiang received her B.E. degree from the School of Remote Sensing and Information Engineering, Wuhan University in 2019. She is currently pursuing a Master degree in Geographical Information Systems with the Institute of Remote Sensing and Geographical Information Systems, Peking University. Her current research interest lies in urban big data analytics.

Yu Liu

Yu Liu is currently the Boya Professor of GIScience at the Institute of Remote Sensing and Geographical Information Systems, Peking University. He received his B.S., M.S., and Ph.D. degrees from Peking University in 1994, 1997, and 2003, respectively. His research interest mainly concentrates in humanities and social science based on big geo-data.