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Gravity and scaling laws of city to city migration

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  • Rafael Prieto Curiel
  • Luca Pappalardo
  • Lorenzo Gabrielli
  • Steven Richard Bishop

Abstract

Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.

Suggested Citation

  • Rafael Prieto Curiel & Luca Pappalardo & Lorenzo Gabrielli & Steven Richard Bishop, 2018. "Gravity and scaling laws of city to city migration," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    • Handle: RePEc:plo:pone00:0199892
    DOI: 10.1371/journal.pone.0199892
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    References listed on IDEAS

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    Cited by:

    1. Gauthier, Nicolas, 2019. "Trade, migration, and the dynamics of spatial interaction," SocArXiv trbf8, Center for Open Science.
    2. Inho Hong & Woo-Sung Jung & Hang-Hyun Jo, 2019. "Gravity model explained by the radiation model on a population landscape," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.
    3. Kevin McQuillan & Michael Laszlo, 2021. "An Urban Nation: The Shifting Fortunes of Canadian Cities," SPP Research Papers, The School of Public Policy, University of Calgary, vol. 4(29), November.
    4. Filippo Simini & Gianni Barlacchi & Massimilano Luca & Luca Pappalardo, 2021. "A Deep Gravity model for mobility flows generation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. Robert Kölbl & Martin Kozek, 2021. "A physiological model of human mobility: A global study," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-14, December.

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