IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v587y2020i7834d10.1038_s41586-020-2909-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-020-2909-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-020-2909-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    5. 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.
    6. 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.
    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. 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.
    10. 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).
    11. 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.
    12. 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.
    13. 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).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:587:y:2020:i:7834:d:10.1038_s41586-020-2909-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.