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Urban dynamics through the lens of human mobility

Nature Computational Science volume 3, pages 611–620 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. Although it usually evolves slowly, it can change quickly during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. Here we present an approach to delineate such urban dynamics in quasi-real time through a human mobility metric, the mobility centrality index ΔKS. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. The results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, KSHBT, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.

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Fig. 1: Socioeconomic segregation and its relation to the radius of gyration.
Fig. 2: Schematic illustration of aggregate metrics relating human mobility to urban structure.
Fig. 3: Defining urban structure via the mobility behavior of the population.
Fig. 4: Changes in urban spatial structure and mobility behavior during the COVID-19 pandemic in the 11 Spanish cities.

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Data availability

All data needed to evaluate the conclusions in the paper are described in the paper and the Supplementary Information. For contractual and privacy reasons, we cannot make the raw mobile-phone data available. One can contract Kido Dynamics SA to try to get access to the raw mobile-phone data. A sample of the data is available in ref. 50. Source data are provided with this paper.

Code availability

The implementation of this work is available at GitHub (https://github.com/humnetlab/Urban_Dynamics) and Zenodo51.

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Acknowledgements

We thank P. Wang for the data provided. This work was supported by Berkeley DeepDrive (BDD) and the ITS-SB1 Berkeley Statewide Transportation Research Program. Y.X. and X.Y. were also supported by the National Natural Science Foundation of China (62102258), the Shanghai Pujiang Program (21PJ1407300), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102) and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China

    Yanyan Xu & Xiaokang Yang

  2. Department of City and Regional Planning, University of California, Berkeley, CA, USA

    Yanyan Xu, Luis E. Olmos & Marta C. González

  3. Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Yanyan Xu & Marta C. González

  4. Facultad de Ciencias Básicas, Universidad de Medellín, Medellín, Colombia

    Luis E. Olmos

  5. Kido Dynamics SA, Lausanne, Switzerland

    David Mateo & Alberto Hernando

  6. Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA

    Marta C. González

Authors
  1. Yanyan Xu
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Contributions

All contributors who fulfill the authorship criteria are listed as co-authors in this paper. Other contributors who do not meet all criteria for authorship are listed in the Acknowledgements. Y.X., L.E.O., X.Y. and M.C.G. conceived the research and designed the analyses. D.M. and A.H. processed the Spanish data. Y.X. and L.E.O. performed the analyses. M.C.G. and Y.X. wrote the paper. M.C.G. supervised the research.

Corresponding author

Correspondence to Marta C. González.

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Peer review information

Nature Computational Science thanks Elsa Arcaute, Benjamin F. Maier, and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary sections 1–6, Figs. 1–19 and Tables 1 and 2.

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Xu, Y., Olmos, L.E., Mateo, D. et al. Urban dynamics through the lens of human mobility. Nat Comput Sci 3, 611–620 (2023). https://doi.org/10.1038/s43588-023-00484-5

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