A general approach to detecting migration events in digital trace data

• DOI: 10.1371/journal.pone.0239408
• Publication Date: 2020-10-02T17:27:16Z
• Authors: Guanghua Chi; Fengyang Lin; Guangqing Chi; Joshua Blumenstock
• Keywords: 0303 health sciences; 330; General Science & Technology; Science; Human Migration; Q; Statistics as Topic; R; Uncertainty; 004; 03 medical and health sciences; Networking and Information Technology R&D (NITRD); Medicine; Humans; Social Media; Algorithms; Research Article

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.