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A general approach to detecting migration events in digital trace data

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  • Guanghua Chi
  • Fengyang Lin
  • Guangqing Chi
  • Joshua Blumenstock

Abstract

Empirical research on migration has historically been fraught with measurement challenges. Recently, the increasing ubiquity of digital trace data—from mobile phones, social media, and related sources of ‘big data’—has created new opportunities for the quantitative analysis of migration. However, most existing work relies on relatively ad hoc methods for inferring migration. Here, we develop and validate a novel and general approach to detecting migration events in trace data. We benchmark this method using two different trace datasets: four years of mobile phone metadata from a single country’s monopoly operator, and three years of geo-tagged Twitter data. The novel measures more accurately reflect human understanding and evaluation of migration events, and further provide more granular insight into migration spells and types than what are captured in standard survey instruments.

Suggested Citation

  • Guanghua Chi & Fengyang Lin & Guangqing Chi & Joshua Blumenstock, 2020. "A general approach to detecting migration events in digital trace data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    • Handle: RePEc:plo:pone00:0239408
    DOI: 10.1371/journal.pone.0239408
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    Cited by:

    1. Xiao Hui Tai & Shikhar Mehra & Joshua E. Blumenstock, 2022. "Mobile phone data reveal the effects of violence on internal displacement in Afghanistan," Nature Human Behaviour, Nature, vol. 6(5), pages 624-634, May.
    2. Kirchberger, Martina, 2021. "Measuring internal migration," Regional Science and Urban Economics, Elsevier, vol. 91(C).

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