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Exploring the use of mobile phone data for national migration statistics

Author

Listed:
  • Shengjie Lai

    (University of Southampton
    Flowminder Foundation
    Fudan University)

  • Elisabeth zu Erbach-Schoenberg

    (University of Southampton
    Flowminder Foundation)

  • Carla Pezzulo

    (University of Southampton)

  • Nick W. Ruktanonchai

    (University of Southampton
    Flowminder Foundation)

  • Alessandro Sorichetta

    (University of Southampton
    Flowminder Foundation)

  • Jessica Steele

    (University of Southampton)

  • Tracey Li

    (Flowminder Foundation)

  • Claire A. Dooley

    (University of Southampton
    Flowminder Foundation)

  • Andrew J. Tatem

    (University of Southampton
    Flowminder Foundation)

Abstract

Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date, as well as urban planning, infrastructure development, and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analyzing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared with censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. The results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.

Suggested Citation

  • Shengjie Lai & Elisabeth zu Erbach-Schoenberg & Carla Pezzulo & Nick W. Ruktanonchai & Alessandro Sorichetta & Jessica Steele & Tracey Li & Claire A. Dooley & Andrew J. Tatem, 2019. "Exploring the use of mobile phone data for national migration statistics," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0242-9
    DOI: 10.1057/s41599-019-0242-9
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    References listed on IDEAS

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    Cited by:

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    2. Selod, Harris & Shilpi, Forhad, 2021. "Rural-urban migration in developing countries: Lessons from the literature," Regional Science and Urban Economics, Elsevier, vol. 91(C).
    3. Bo Huang & Jionghua Wang & Jixuan Cai & Shiqi Yao & Paul Kay Sheung Chan & Tony Hong-wing Tam & Ying-Yi Hong & Corrine W. Ruktanonchai & Alessandra Carioli & Jessica R. Floyd & Nick W. Ruktanonchai & , 2021. "Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities," Nature Human Behaviour, Nature, vol. 5(6), pages 695-705, June.
    4. Sida Zhuang & Gabriele Bolte & Tobia Lakes, 2022. "Exploring Environmental Health Inequalities: A Scientometric Analysis of Global Research Trends (1970–2020)," IJERPH, MDPI, vol. 19(12), pages 1-25, June.
    5. Yanchao Li & Ziyu Ran & Lily Tsai & Sarah Williams, 2023. "Using call detail records to determine mobility patterns of different socio-demographic groups in the western area of Sierra Leone during early COVID-19 crisis," Environment and Planning B, , vol. 50(5), pages 1298-1312, June.
    6. Lena Reimann & Bryan Jones & Nora Bieker & Claudia Wolff & Jeroen C.J.H. Aerts & Athanasios T. Vafeidis, 2023. "Exploring spatial feedbacks between adaptation policies and internal migration patterns due to sea-level rise," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Veronika Mooses & Siiri Silm & Tiit Tammaru & Erki Saluveer, 2020. "An ethno-linguistic dimension in transnational activity space measured with mobile phone data," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-13, December.
    8. Shenzhen Tian & Jialin Jiang & Hang Li & Xueming Li & Jun Yang & Chuanglin Fang, 2023. "Flow space reveals the urban network structure and development mode of cities in Liaoning, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    9. Hadrien Salat & Zbigniew Smoreda & Markus Schläpfer, 2020. "A method to estimate population densities and electricity consumption from mobile phone data in developing countries," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.

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