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Field theory for recurrent mobility

Author

Listed:
  • Mattia Mazzoli

    (Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB)

  • Alex Molas

    (Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB)

  • Aleix Bassolas

    (Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB)

  • Maxime Lenormand

    (Irstea, UMR TETIS)

  • Pere Colet

    (Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB)

  • José J. Ramasco

    (Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB)

Abstract

Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions so far unexplored. In this work, we show for a series of worldwide cities that commuting daily flows can be mapped into a well behaved vector field, fulfilling the divergence theorem and which is, besides, irrotational. This property allows us to define a potential for the field that can become a major instrument to determine separate mobility basins and discern contiguous urban areas. We also show that empirical fluxes and potentials can be well reproduced and analytically characterized using the so-called gravity model, while other models based on intervening opportunities have serious difficulties.

Suggested Citation

  • Mattia Mazzoli & Alex Molas & Aleix Bassolas & Maxime Lenormand & Pere Colet & José J. Ramasco, 2019. "Field theory for recurrent mobility," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11841-2
    DOI: 10.1038/s41467-019-11841-2
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    Citations

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    Cited by:

    1. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. 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).
    3. Lv, Ying-Yue & Yan, Xiao-Yong & Jia, Bin & Yang, Yitao & Liu, Erjian, 2024. "Quantifying the overall spatial distribution characteristics of urban heavy truck trips: The case of China," Journal of Transport Geography, Elsevier, vol. 115(C).
    4. Pengjun Zhao & Hao Wang & Qiyang Liu & Xiao-Yong Yan & Jingzhong Li, 2024. "Unravelling the spatial directionality of urban mobility," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Siddharth Patwardhan & Marc Barthelemy & Şirag Erkol & Santo Fortunato & Filippo Radicchi, 2024. "Symmetry breaking in optimal transport networks," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. Sizhe Yang & Xiaoru Sun & Li Jin & Menghan Zhang, 2024. "Inferring language dispersal patterns with velocity field estimation," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    7. Mattia Mazzoli & Boris Diechtiareff & Antònia Tugores & Willian Wives & Natalia Adler & Pere Colet & José J Ramasco, 2020. "Migrant mobility flows characterized with digital data," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.

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