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A general approach to detecting migration events in digital trace data.

Guanghua Chi1, Fengyang Lin2, Guangqing Chi3

  • 1School of Information, University of California, Berkeley, Berkeley, CA, United States of America.

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This study introduces a new method for detecting migration events using digital trace data. It offers more accurate and detailed insights into migration patterns than traditional surveys.

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Area of Science:

  • Sociology
  • Demography
  • Data Science

Background:

  • Empirical migration research faces significant measurement challenges.
  • Digital trace data (e.g., mobile phone, social media) offer new quantitative analysis opportunities.
  • Existing methods for inferring migration from trace data are often ad hoc.

Purpose of the Study:

  • To develop and validate a novel, generalizable approach for detecting migration events in digital trace data.
  • To improve the accuracy and granularity of migration measurement using big data.
  • To provide a more robust alternative to traditional survey instruments for migration studies.

Main Methods:

  • Developed and validated a novel method for inferring migration events from digital trace data.
  • Benchmarked the method using mobile phone metadata (4 years, single operator) and geo-tagged Twitter data (3 years).
  • Compared the novel measures against human evaluation and standard survey instruments.

Main Results:

  • The novel method accurately detects migration events in trace data.
  • The approach provides more granular insights into migration spells and types compared to standard surveys.
  • Validated the method's effectiveness across two distinct trace datasets.

Conclusions:

  • The developed method offers a significant advancement in quantifying migration using digital trace data.
  • This approach enhances the accuracy and detail of migration analysis.
  • Digital trace data, analyzed with robust methods, can overcome traditional measurement challenges in migration research.