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The scales of human mobility

Author

Listed:
  • Laura Alessandretti

    (Technical University of Denmark
    University of Copenhagen)

  • Ulf Aslak

    (Technical University of Denmark
    University of Copenhagen)

  • Sune Lehmann

    (Technical University of Denmark
    University of Copenhagen)

Abstract

There is a contradiction at the heart of our current understanding of individual and collective mobility patterns. On the one hand, a highly influential body of literature on human mobility driven by analyses of massive empirical datasets finds that human movements show no evidence of characteristic spatial scales. There, human mobility is described as scale free1–3. On the other hand, geographically, the concept of scale—referring to meaningful levels of description from individual buildings to neighbourhoods, cities, regions and countries—is central for the description of various aspects of human behaviour, such as socioeconomic interactions, or political and cultural dynamics4,5. Here we resolve this apparent paradox by showing that day-to-day human mobility does indeed contain meaningful scales, corresponding to spatial ‘containers’ that restrict mobility behaviour. The scale-free results arise from aggregating displacements across containers. We present a simple model—which given a person’s trajectory—infers their neighbourhood, city and so on, as well as the sizes of these geographical containers. We find that the containers—characterizing the trajectories of more than 700,000 individuals—do indeed have typical sizes. We show that our model is also able to generate highly realistic trajectories and provides a way to understand the differences in mobility behaviour across countries, gender groups and urban–rural areas.

Suggested Citation

  • Laura Alessandretti & Ulf Aslak & Sune Lehmann, 2020. "The scales of human mobility," Nature, Nature, vol. 587(7834), pages 402-407, November.
  • Handle: RePEc:nat:nature:v:587:y:2020:i:7834:d:10.1038_s41586-020-2909-1
    DOI: 10.1038/s41586-020-2909-1
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    Citations

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    Cited by:

    1. Clodomir Santana & Federico Botta & Hugo Barbosa & Filippo Privitera & Ronaldo Menezes & Riccardo Di Clemente, 2023. "COVID-19 is linked to changes in the time–space dimension of human mobility," Nature Human Behaviour, Nature, vol. 7(10), pages 1729-1739, October.
    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. Li, Xianghua & Deng, Yue & Yuan, Xuesong & Wang, Zhen & Gao, Chao, 2022. "Data-driven behavioral analysis and applications: A case study in Changchun, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    4. Rahul Goel & Oyinlola Oyebode & Louise Foley & Lambed Tatah & Christopher Millett & James Woodcock, 2023. "Gender differences in active travel in major cities across the world," Transportation, Springer, vol. 50(2), pages 733-749, April.
    5. Ye Tian & Xiaobai Angela Yao & Marguerite Madden & Andrew Grundstein, 2024. "Synergic effects of meteorological factors on urban form-outdoor exercise relationship: A study with crowdsourced data," Journal of Geographical Systems, Springer, vol. 26(1), pages 47-72, January.
    6. Chao Fan & Yang Yang & Ali Mostafavi, 2024. "Neural embeddings of urban big data reveal spatial structures in cities," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    7. 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).
    8. Rampazzo, Pietro, 2024. "“I want to ride my bicycle”: analysing shared mobility in Italy," SocArXiv bd8p4, Center for Open Science.
    9. Yang, Hu & Lv, Sirui & Guo, Bao & Dai, Jianjun & Wang, Pu, 2024. "Uncovering spatiotemporal human mobility patterns in urban agglomerations: A mobility field based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    10. Abhishek Senapati & Adam Mertel & Weronika Schlechte-Welnicz & Justin M. Calabrese, 2024. "Estimating Cross-Border Mobility from the Difference in Peak Timing: A Case Study of Poland–Germany Border Regions," Mathematics, MDPI, vol. 12(13), pages 1-13, July.
    11. Xiaofan Luan & Hurex Paryzat & Jun Chu & Xinyi Shu & Hengyu Gu & De Tong & Bowen Li, 2024. "Different roads take me home: the nonlinear relationship between distance and flows during China’s Spring Festival," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    12. Liu, Peng & Zheng, Yanyan, 2022. "Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    13. Xuesong Gao & Hui Wang & Lun Liu, 2021. "Profiling Residents’ Mobility with Grid-Aggregated Mobile Phone Trace Data Using Chengdu as the Case," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    14. 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.
    15. Kerstin K. Zander & Stephen T. Garnett & Harald Sterly & Sonja Ayeb-Karlsson & Barbora Šedová & Hermann Lotze-Campen & Carmen Richerzhagen & Hunter S. Baggen, 2022. "Topic modelling exposes disciplinary divergence in research on the nexus between human mobility and the environment," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    16. 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).
    17. 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.
    18. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    19. Li, Heyang & Zeng, An, 2022. "Improving recommendation by connecting user behavior in temporal and topological dimensions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

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