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Emergence of urban growth patterns from human mobility behavior

Nature Computational Science volume 1, pages 791–800 (2021)Cite this article

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Abstract

Cities grow in a bottom-up manner, leading to fractal-like urban morphologies characterized by scaling laws. The correlated percolation model has succeeded in modeling urban geometries by imposing strong spatial correlations; however, the origin of the underlying mechanisms behind spatially correlated urban growth remains largely unknown. Our understanding of human movements has recently been revolutionized thanks to the increasing availability of large-scale human mobility data. This paper introduces a computational urban growth model that captures spatially correlated urban growth with a micro-foundation in human mobility behavior. We compare the proposed model with three empirical datasets, discovering that strong social interactions and long-term memory effects in human movements are two fundamental principles responsible for fractal-like urban morphology, along with the three important laws of urban growth. Our model connects the empirical findings in urban growth patterns and human mobility behavior.

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Fig. 1: The paradigm of human movement models and illustration of the proposed collective mobility model.
Fig. 2: The morphologies of urban area generated by four different human movement models and in real-world city.
Fig. 3: Comparisons between the empirical urban growth patterns in the US, GB and Berlin, and the simulated urban growth patterns driven by different human movement models.
Fig. 4: The time evolution of urban occupations.
Fig. 5: Qualitative comparison between the morphology of urban area reproduced by CMM with different parameter settings.
Fig. 6: The comparison of reproducing urban growth patterns with different parameter settings.

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Data availability

The empirical urban datasets that support the findings of this study are public available. The datasets for US and GB were released by previous research52. The Berlin dataset is extracted from the telemetry images in previous works53 (see Data Collection and Calibration for details), which is available in GitHub, https://github.com/tsinghua-fib-lab/Collective-Mobility-Model56. Source data for Figs. 3, 4 and 6 are available with this manuscript.

Code availability

The source code for numeric simulation is available online: https://github.com/tsinghua-fib-lab/Collective-Mobility-Model (ref. 56).

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Acknowledgements

Y. Li, F. Xu and D. Jin were supported by the National Key Research and Development Program of China (grant no. 2020AAA0106000) and the National Natural Science Foundation of China (grant no. U1936217). We are also grateful for the insightful discussion with Prof. Z. Wang, Prof. J. Yuan and Dr. J. Ding at Tsinghua University.

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Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China

    Fengli Xu, Yong Li, Depeng Jin & Jianhua Lu

  2. Department of Physics, University of Miami, Coral Gables, FL, USA

    Chaoming Song

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Contributions

F. Xu and Y. Li contributed to the empirical implementation and evaluation of the proposed models. C. Song performed the theoretical analysis of human mobility modeling and complex urban system. D. Jin and J. Lu offered empirical motivations and insights to this research. All authors contributed to the writing of this manuscript.

Corresponding authors

Correspondence to Yong Li or Chaoming Song.

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The authors declare no competing interests.

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Peer review information Nature Computational Science thanks Pu Wang, Marta Gonzalez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Editor recognition statement: Handling editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Tables 1 and 2, and discussion.

Source data

Source Data Fig. 3

Statistical Source Data for Fig. 3.

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Statistical Source Data for Fig. 4.

Source Data Fig. 6

Statistical Source Data for Fig. 6.

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Xu, F., Li, Y., Jin, D. et al. Emergence of urban growth patterns from human mobility behavior. Nat Comput Sci 1, 791–800 (2021). https://doi.org/10.1038/s43588-021-00160-6

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