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Mapping global variation in human mobility

Nature Human Behaviour volume 4, pages 800–810 (2020)Cite this article

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Abstract

The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth’s populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.

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Fig. 1: Aggregated human movements show strong geographic and temporal variability.
Fig. 2: Aggregated human movement data from 70,010,784 unique location flows (~300 million participants) covering 65% of Earth’s populated surface reveal variability in human movements across the world.
Fig. 3: Formalization of geographic variation in human movement patterns explained by sociodemographic indicators.
Fig. 4: Relative frequency of international travel varies by country and is influenced by the proximity to an international border.

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Data availability

Human movement summary data for 242 countries and territories presented in this work are available online at https://bit.ly/2L7FXxc. The aggregated dataset used for this study is available upon reasonable request to the corresponding authors and with permission of Google, LLC. Interested parties should contact the corresponding authors via email to obtain a request form, which will be reviewed by Google, LLC, and the corresponding authors.

Code availability

Standard R-packages were used to produce results for this analysis. No custom code was developed.

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Acknowledgements

We thank A. Bar, C. Black, A. Broder, S. Cadrecha, S. Cason, C. Cattuto, C. Chou, K. Chou, I. Conroy, L. Davidoff, J. Dean, J. Degener, D. Desfontaines, X. Dotiwalla, P. Eastham, J. Freidenfelds, E. Gabrilovich, V. Hoang, S. Holland, M. Howell, P.-P. Jiang, A. Lange, B. Mehta, C. Niedermeyer, G. Park, O. Pybus, P. Ramaswami, C. Rigby, K. Rough, F. Sekles, C. Seto, A. Stein, C. Thota, M. Tizzoni, A. Vespignani and A. Zlatinov for their insights and guidance. M.U.G.K. is supported by the Society in Science, the Branco Weiss Fellowship, administered by the ETH Zurich and acknowledges funding from a Training Grant from the National Institute of Child Health and Human Development (T32HD040128) and the Oxford Martin School. J.S.B. and M.U.G.K. acknowledge support from the National Library of Medicine of the National Institutes of Health (R01LM010812, R01LM011965) and a Google Faculty Award (to M.U.G.K.). Q.Z., T.A.P. and D.L.S. are supported by a BMGF grant (OPP1110495). T.A.P. also acknowledges support from the Defense Advanced Research Projects Agency (D16AP00114). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Moritz U. G. Kraemer, Adam Sadilek, Qian Zhang.

  2. These authors jointly supervised this work: Robert C. Reiner Jr, John S. Brownstein, Moritz U. G. Kraemer.

Authors and Affiliations

  1. Harvard Medical School, Harvard University, Boston, MA, USA

    Moritz U. G. Kraemer & John S. Brownstein

  2. Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA, USA

    Moritz U. G. Kraemer, Gaurav Tuli, Emily L. Cohn, Yulin Hswen & John S. Brownstein

  3. Department of Zoology, University of Oxford, Oxford, UK

    Moritz U. G. Kraemer

  4. Google Inc., Mountain View, CA, USA

    Adam Sadilek

  5. Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA

    Qian Zhang, David L. Smith & Robert C. Reiner Jr

  6. Oxford Internet Institute, University of Oxford, Oxford, UK

    Nahema A. Marchal

  7. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Yulin Hswen

  8. Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, USA

    T. Alex Perkins

  9. Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA

    David L. Smith & Robert C. Reiner Jr

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Contributions

M.U.G.K., A.S., R.C.R. and J.S.B. conceived and planned the study. M.U.G.K., A.S., R.C.R., T.A.P. and Q.Z. analysed the data. M.U.G.K. wrote the first draft of the manuscript, and all authors contributed to subsequent revisions. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Moritz U. G. Kraemer, Robert C. Reiner Jr or John S. Brownstein.

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The authors declare no competing interests.

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Peer review information Primary handling editor: Aisha Bradshaw.

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Supplementary Information

Supplementary Tables 2–4 and 7 and Supplementary Figs. 1–16.

Reporting Summary

Supplementary Tables 1, 5 and 6.

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Kraemer, M.U.G., Sadilek, A., Zhang, Q. et al. Mapping global variation in human mobility. Nat Hum Behav 4, 800–810 (2020). https://doi.org/10.1038/s41562-020-0875-0

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