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The universal visitation law of human mobility

Author

Listed:
  • Markus Schläpfer

    (Massachusetts Institute of Technology
    Santa Fe Institute
    Singapore-ETH Centre, ETH Zurich)

  • Lei Dong

    (Massachusetts Institute of Technology
    Peking University)

  • Kevin O’Keeffe

    (Massachusetts Institute of Technology)

  • Paolo Santi

    (Massachusetts Institute of Technology
    Istituto di Informatica e Telematica del CNR)

  • Michael Szell

    (Massachusetts Institute of Technology
    IT University of Copenhagen
    ISI Foundation)

  • Hadrien Salat

    (Singapore-ETH Centre, ETH Zurich
    Sociology and Economics of Networks and Services, Orange Labs)

  • Samuel Anklesaria

    (Massachusetts Institute of Technology)

  • Mohammad Vazifeh

    (Massachusetts Institute of Technology)

  • Carlo Ratti

    (Massachusetts Institute of Technology)

  • Geoffrey B. West

    (Santa Fe Institute)

Abstract

Human mobility impacts many aspects of a city, from its spatial structure1–3 to its response to an epidemic4–7. It is also ultimately key to social interactions8, innovation9,10 and productivity11. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models—such as the gravity law12,13 or the radiation model14—concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf’s law15. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory16 and Weber’s theory of emergent optimality10) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.

Suggested Citation

  • Markus Schläpfer & Lei Dong & Kevin O’Keeffe & Paolo Santi & Michael Szell & Hadrien Salat & Samuel Anklesaria & Mohammad Vazifeh & Carlo Ratti & Geoffrey B. West, 2021. "The universal visitation law of human mobility," Nature, Nature, vol. 593(7860), pages 522-527, May.
  • Handle: RePEc:nat:nature:v:593:y:2021:i:7860:d:10.1038_s41586-021-03480-9
    DOI: 10.1038/s41586-021-03480-9
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    Citations

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    Cited by:

    1. Yuyang Wu & Yao Yao & Shuliang Ren & Shiyi Zhang & Qingfeng Guan, 2023. "How do urban services facilities affect social segregation among people of different economic levels? A case study of Shenzhen city," Environment and Planning B, , vol. 50(6), pages 1502-1517, July.
    2. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    3. Francesco Filippi, 2022. "A Paradigm Shift for a Transition to Sustainable Urban Transport," Sustainability, MDPI, vol. 14(5), pages 1-27, March.
    4. Sevtsuk, Andres & Basu, Rounaq, 2022. "The role of turns in pedestrian route choice: A clarification," Journal of Transport Geography, Elsevier, vol. 102(C).
    5. Pierre Magontier, Maximilian v. Ehrlich, Markus Schl pfer, 2022. "The Fragility of Urban Social Networks - Mobility as a City Glue -," Diskussionsschriften credresearchpaper38, Universitaet Bern, Departement Volkswirtschaft - CRED.
    6. Laura Alessandretti & Luis Guillermo Natera Orozco & Meead Saberi & Michael Szell & Federico Battiston, 2023. "Multimodal urban mobility and multilayer transport networks," Environment and Planning B, , vol. 50(8), pages 2038-2070, October.
    7. Jiang, Jincheng & Xu, Zhihua & Zhang, Zhenxin & Zhang, Jie & Liu, Kang & Kong, Hui, 2023. "Revealing the fractal and self-similarity of realistic collective human mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Pietro Folco & Laetitia Gauvin & Michele Tizzoni & Michael Szell, 2023. "Data-driven micromobility network planning for demand and safety," Environment and Planning B, , vol. 50(8), pages 2087-2102, October.
    9. Liu, Jianmiao & Li, Junyi & Chen, Yong & Lian, Song & Zeng, Jiaqi & Geng, Maosi & Zheng, Sijing & Dong, Yinan & He, Yan & Huang, Pei & Zhao, Zhijian & Yan, Xiaoyu & Hu, Qinru & Wang, Lei & Yang, Di & , 2023. "Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management," Applied Energy, Elsevier, vol. 331(C).
    10. João Monteiro & Nuno Sousa & Eduardo Natividade-Jesus & João Coutinho-Rodrigues, 2022. "Benchmarking City Layouts—A Methodological Approach and an Accessibility Comparison between a Real City and the Garden City," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    11. Li, Wen-Jing & Chen, Zhi & Jin, Ke-Zhong & Wang, Jun & Yuan, Lin & Gu, Changgui & Jiang, Luo-Luo & Perc, Matjaž, 2022. "Options for mobility and network reciprocity to jointly yield robust cooperation in social dilemmas," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    12. Hongwei Guo & Ji Han & Jian Wang, 2021. "Population mobility, urban centrality and subnetworks in China revealed by social sensing big data," Environment and Planning A, , vol. 53(8), pages 1855-1858, November.
    13. Paulsen, Mads & Rich, Jeppe, 2023. "Societally optimal expansion of bicycle networks," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    14. Cardoso, M. & Souza, J.T.G. & Neli, R.R. & Souza, W.E., 2023. "Scaling laws from Brazilian state election results point out that, the candidate’s chance to win increases by investing more campaign efforts in smaller electorates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    15. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    16. Toger, Marina & Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2023. "Inequality in leisure mobility: An analysis of activity space segregation spectra in the Stockholm conurbation," Journal of Transport Geography, Elsevier, vol. 111(C).
    17. Lee, Wang-Sheng & Tran, Trang My & Yu, Lamont Bo, 2022. "Dual Circulation and Population Mobility during the Pandemic in China," IZA Discussion Papers 15269, Institute of Labor Economics (IZA).
    18. Hadrien Salat & Dustin Carlino & Fernando Benitez-Paez & Anna Zanchetta & Daniel Arribas-Bel & Mark Birkin, 2023. "Synthetic population Catalyst: A micro-simulated population of England with circadian activities," Environment and Planning B, , vol. 50(8), pages 2309-2316, October.

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