School of Information, University of California, Berkeley, Berkeley, CA, United States of America.
Department of Statistics, Columbia University, New York, NY, United States of America.
PLoS One. 2020 Oct 2;15(10):e0239408. doi: 10.1371/journal.pone.0239408. eCollection 2020.
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.
迁移的实证研究历来存在测量挑战。最近，移动电话、社交媒体和相关“大数据”来源的数字跟踪数据日益普及，为迁移的定量分析创造了新的机会。然而，大多数现有工作依赖于推断迁移的相对特定方法。在这里，我们开发并验证了一种在跟踪数据中检测迁移事件的新颖且通用的方法。我们使用两个不同的跟踪数据集对该方法进行基准测试：一个国家垄断运营商的四年移动电话元数据，以及三年带有地理标记的 Twitter 数据。新的衡量标准更准确地反映了人类对迁移事件的理解和评估，并且比标准调查工具所捕获的更深入地洞察了迁移模式和类型。
