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Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data

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  • Santi Phithakkitnukoon
  • Zbigniew Smoreda
  • Patrick Olivier

Abstract

A relationship between people’s mobility and their social networks is presented based on an analysis of calling and mobility traces for one year of anonymized call detail records of over one million mobile phone users in Portugal. We find that about 80% of places visited are within just 20 km of their nearest (geographical) social ties’ locations. This figure rises to 90% at a ‘geo-social radius’ of 45 km. In terms of their travel scope, people are geographically closer to their weak ties than strong ties. Specifically, they are 15% more likely to be at some distance away from their weak ties than strong ties. The likelihood of being at some distance from social ties increases with the population density, and the rates of increase are higher for shorter geo-social radii. In addition, we find that area population density is indicative of geo-social radius where denser areas imply shorter radii. For example, in urban areas such as Lisbon and Porto, the geo-social radius is approximately 7 km and this increases to approximately 15 km for less densely populated areas such as Parades and Santa Maria da Feira.

Suggested Citation

  • Santi Phithakkitnukoon & Zbigniew Smoreda & Patrick Olivier, 2012. "Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
    • Handle: RePEc:plo:pone00:0039253
    DOI: 10.1371/journal.pone.0039253
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    5. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
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    9. Pan, Yu & He, Sylvia Y., 2022. "Analyzing COVID-19’s impact on the travel mobility of various social groups in China’s Greater Bay Area via mobile phone big data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 263-281.
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    11. Traag, V.A. & Quax, R. & Sloot, P.M.A., 2017. "Modelling the distance impedance of protest attendance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 171-182.
    12. Sanja Šćepanović & Igor Mishkovski & Pan Hui & Jukka K Nurminen & Antti Ylä-Jääski, 2015. "Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    13. Abdullah Almaatouq, 2016. "Complex Systems and a Computational Social Science Perspective on the Labor Market," Papers 1606.08562, arXiv.org.
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    15. Claudio Gariazzo & Armando Pelliccioni & Maria Paola Bogliolo, 2019. "Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy," Data, MDPI, vol. 4(1), pages 1-25, January.
    16. A. N. Bochkarev, 2019. "Methodological Aspects of Studying Labor Commuting," Regional Research of Russia, Springer, vol. 9(2), pages 173-180, April.
    17. Patrick Bonnel & Etienne Hombourger & Ana-Maria Olteanu-Raimond & Zbigniew Smoreda, 2015. "Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations," Post-Print halshs-01664219, HAL.
    18. Erlström, Andreas & Grillitsch, Markus & Hall, Ola, 2020. "The Geography of Connectivity: Trails of Mobile Phone Data," Papers in Innovation Studies 2020/6, Lund University, CIRCLE - Centre for Innovation Research.
    19. Wang, Jing & Cai, Jianping & Yue, Xiaohang & Suresh, Nallan C., 2021. "Pre-positioning and real-time disaster response operations: Optimization with mobile phone location data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    20. Marra, Alessio D. & Sun, Linghang & Corman, Francesco, 2022. "The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area," Transport Policy, Elsevier, vol. 116(C), pages 258-268.
    21. Przemyslaw A Grabowicz & José J Ramasco & Bruno Gonçalves & Víctor M Eguíluz, 2014. "Entangling Mobility and Interactions in Social Media," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.
    22. Yandong Wang & Teng Wang & Ming-Hsiang Tsou & Hao Li & Wei Jiang & Fengqin Guo, 2016. "Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China," Sustainability, MDPI, vol. 8(11), pages 1-19, November.

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