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Uncovering the spatial structure of mobility networks

Author

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  • Thomas Louail

    (Institut de Physique Théorique, CEA-CNRS (URA 2306), Orme-des-Merisiers Batiment 774, F-91191 Paris, France
    Géographie-Cités, CNRS-Paris 1-Paris 7 (UMR 8504))

  • Maxime Lenormand

    (IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat de les Illes Balears)

  • Miguel Picornell

    (Nommon Solutions and Technologies)

  • Oliva García Cantú

    (Nommon Solutions and Technologies)

  • Ricardo Herranz

    (Nommon Solutions and Technologies)

  • Enrique Frias-Martinez

    (Telefonica Research)

  • José J. Ramasco

    (IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat de les Illes Balears)

  • Marc Barthelemy

    (Institut de Physique Théorique, CEA-CNRS (URA 2306), Orme-des-Merisiers Batiment 774, F-91191 Paris, France
    Centre d’Analyse et de Mathématique Sociales, EHESS-CNRS (UMR 8557))

Abstract

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

Suggested Citation

  • Thomas Louail & Maxime Lenormand & Miguel Picornell & Oliva García Cantú & Ricardo Herranz & Enrique Frias-Martinez & José J. Ramasco & Marc Barthelemy, 2015. "Uncovering the spatial structure of mobility networks," Nature Communications, Nature, vol. 6(1), pages 1-8, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7007
    DOI: 10.1038/ncomms7007
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    1. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Zhi, Danyue & Song, Dongdong & Chen, Yan & de Bok, Michiel & Tavasszy, Lóránt A. & Gao, Ziyou, 2023. "Uncovering and modeling the hierarchical organization of urban heavy truck flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    2. Mohammadi, Neda & Taylor, John E., 2017. "Urban infrastructure-mobility energy flux," Energy, Elsevier, vol. 140(P1), pages 716-728.
    3. Salvati, Luca, 2020. "Residential mobility and the local context: Comparing long-term and short-term spatial trends of population movements in Greece," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    4. Lei Dong & Paolo Santi & Yu Liu & Siqi Zheng & Carlo Ratti, 2022. "The universality in urban commuting across and within cities," Papers 2204.12865, arXiv.org.
    5. Eszter Bokányi & Zsófia Kallus & István Gódor, 2021. "Collective sensing of evolving urban structures: From activity-based to content-aware social monitoring," Environment and Planning B, , vol. 48(1), pages 115-131, January.
    6. Fabio Lamanna & Maxime Lenormand & María Henar Salas-Olmedo & Gustavo Romanillos & Bruno Gonçalves & José J Ramasco, 2018. "Immigrant community integration in world cities," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-19, March.
    7. Andrés Fielbaum & Sergio Jara-Diaz & Antonio Gschwender, 2017. "A Parametric Description of Cities for the Normative Analysis of Transport Systems," Networks and Spatial Economics, Springer, vol. 17(2), pages 343-365, June.
    8. Mirco Nanni & Leandro Tortosa & José F Vicent & Gevorg Yeghikyan, 2020. "Ranking places in attributed temporal urban mobility networks," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-25, October.
    9. 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).
    10. Jisung Yoon & Woo-Sung Jung & Hyunuk Kim, 2022. "COVID-19 confines recreational gatherings in Seoul to familiar, less crowded, and neighboring urban areas," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    11. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    12. Jungmin Kim & Juyong Park & Wonjae Lee, 2018. "Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    13. Fangye Du & Jiaoe Wang & Liang Mao & Jian Kang, 2024. "Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    14. Bíl, Michal & Vodák, Rostislav & Kubeček, Jan & Bílová, Martina & Sedoník, Jiří, 2015. "Evaluating road network damage caused by natural disasters in the Czech Republic between 1997 and 2010," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 90-103.
    15. Huang, Zhiren & Wang, Pu & Zhang, Fan & Gao, Jianxi & Schich, Maximilian, 2018. "A mobility network approach to identify and anticipate large crowd gatherings," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 147-170.
    16. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.

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