期刊
PLOS ONE
卷 15, 期 10, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0239408
关键词
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资金
- National Science Foundation [CMMI-1541136, SES-1823633]
- Office of Naval Research [N00014-17-1-2313]
- Eunice Kennedy Shriver National Institute of Child Health and Human Development [P2C HD041025]
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 relativelyad hocmethods 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.
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