Open AccessA general approach to detecting migration events in digital trace data
Author(s) -
Guanghua Chi,
Fengyang Lin,
Guangqing Chi,
Joshua Blumenstock
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0239408
Subject(s) - trace (psycholinguistics) , data science , metadata , computer science , social media , mobile phone , benchmark (surveying) , phone , big data , data mining , world wide web , telecommunications , geography , cartography , philosophy , linguistics
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.