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The scales of human mobility
Open accessAbstract
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 free 1-3. 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 dynamics 4,5. 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.
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“Still, human mobility is known to follow certain universal principles, such as scaling laws of mobility (Alessandretti et al. 2020;Pappalardo et al. 2016;Song et al. 2010b), which suggests that our findings may reflect more general behavioral regularities.”
From Visitation patterns across mobility groups: wandering, commuting, and exploring (2026) · cites this paper as Alessandretti et al. 2020
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Cites
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,710
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
Newman (2005) · doi:10.1080/00107510500052444 · Cited by 5,749
Location and Land Use: Toward a General Theory of Land Rent
Alonso, W. (1964) · Cited by 3,820
Alonso, W. (1964) · Harvard University Press eBooks · doi:10.4159/harvard.9780674730854 · Cited by 3,673
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
The effect of human mobility and control measures on the COVID-19 epidemic in China
Kraemer, M. U. G., Yang, C.-H., Gutiérrez, B., Wu, C., Klein, B., Pigott, D. M., open COVID-19 data working group, du Plessis, L., Faria, N. R., Li, R., et al. (2020) · Science · doi:10.1126/science.abb4218 · Cited by 2,987
On the Move: Mobility in the Modern Western World
Cresswell, T. (2006) · Cited by 2,468
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,182
Research works printed but not linked: 4
- Larsen, J. & Urry, J. Mobilities, Networks, Geographies (Routledge, 2016).
- Alessandretti, L., Sapiezynski, P., Sekara, V., Lehmann, S. & Baronchelli, A. Evidence for a conserved quantity in human mobility. Nat. Hum. Behav. 2, 485–491 (2018). · DOI
- Batty, M. in Hierarchy in Natural and Social Sciences (ed. Pumain, D.) 143–168 (Springer, 2006).
- Train, K. E. Discrete Choice Methods with Simulation (Cambridge Univ. Press, 2009).
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Wang, Y., Yao, X., Wang, J., Kang, C., Meng, X., Hu, G., Liu, Y., & Li, X. (2024) · Cities · doi:10.1016/j.cities.2024.105047 · Cited by 9
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Bannister, A., & Botta, F. (2021) · Royal Society Open Science · doi:10.1098/rsos.211069 · Cited by 7
Wu, M., & Huang, Q. (2022) · Annals of GIS · doi:10.1080/19475683.2022.2026471 · Cited by 7
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Samuelsson, K., Rivas, I., Raimbault, B., Domínguez, A., Galmés, T., Valentín, A., Foraster, M., Gascón, M., Persavento, C., Psyllidis, A., et al. (2025) · Health & Place · doi:10.1016/j.healthplace.2025.103413 · Cited by 6
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Enfield, N. J. (2023) · Cognitive Science · doi:10.1111/cogs.13341 · Cited by 6
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Pang, X.-Z., Lin, X.-J., Yang, Y., Liu, E., Jia, X., Lv, Y.-Y., Guo, Z., Wang, Y., & Yan, X.-Y. (2024) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/ad9630 · Cited by 5
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Liu, L., Pei, T., Fang, Z., Yan, X., Zheng, C., Wang, X., Song, C., Luan, W., & Chen, J. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104654 · Cited by 4
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Shuai, C., Liao, C., Qu, S., Chen, X., Zhao, B., Zou, J.-P., & Xu, M. (2024) · iScience · doi:10.1016/j.isci.2024.110497 · Cited by 4
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Donges, J. F., Lochner, J., Kitzmann, N. H., Heitzig, J., Lehmann, S., Wiedermann, M., & Vollmer, J. (2021) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-021-00279-7 · Cited by 5
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Berragan, C., Singleton, A. D., Calafiore, A., & Morley, J. G. (2024) · Transactions of the Institute of British Geographers · doi:10.1111/tran.12669 · Cited by 4
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Metadata
- Authors
- Alessandretti, L., Aslak, U., & Lehmann, S.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Lehmann, Sune
- Venue
- Nature
- Published
- 2020-11-18
- Volume
- 587
- Issue
- 7834
- Pages
- 402-407
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3098208509
- Cited by
- 361
- References
- 90
- Citations by year
- 2026: 74 · 2025: 71 · 2024: 76 · 2023: 48 · 2022: 55 · 2021: 34 · 2020: 3
- FWCI
- 31.45
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Urban Studies › Urban Design and Spatial Analysis
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- human mobility, spatial scale, neighborhood size, city size, single-cell trajectory analysis, power-law distribution, mobility behavior, urban-rural differences, gender differences
- MeSH terms
- Built Environment, Denmark, Humans, Models, Theoretical, Reproducibility of Results, Human Migration
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