Lai Shengjie, Zu Erbach-Schoenberg Elisabeth, Pezzulo Carla, Ruktanonchai Nick W, Sorichetta Alessandro, Steele Jessica, Li Tracey, Dooley Claire A, Tatem Andrew J
WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Flowminder Foundation, SE-113 55 Stockholm, Sweden.
Palgrave Commun. 2019 Mar 26;5. doi: 10.1057/s41599-019-0242-9.
Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date as well as urban planning, infrastructure development and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analysing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modelled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared to censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. Results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.
国内人口迁移统计数据对于保持地方人口数量估计的时效性以及城市规划、基础设施建设和影响评估等诸多应用而言至关重要。然而,迁移流动统计数据通常仍受限于不定期人口普查或调查的实际操作。目前全球手机普及率很高,且低收入国家的手机拥有量近期迅速增长。通过匿名通话记录(CDR)分析手机用户不断变化的时空分布,为在多个时空尺度上衡量人口迁移提供了可能。基于2010年10月至2014年4月纳米比亚720亿条匿名CDR数据集,我们探讨了如何在地方和年度尺度上从CDR中得出并模拟国内人口迁移估计值,以及这些估计值的精度和准确性与人口普查得出的迁移统计数据相比情况如何。我们还展示了如何使用CDR来评估迁移模式随时间的变化,与人口普查相比具有更高的时间分辨率。此外,我们展示了使用CDR构建的引力型空间相互作用模型如何能够准确捕捉迁移流动。结果表明,利用手机数据进行迁移流动估计是补充更传统的国家迁移统计数据并获取更及时和本地化数据的一个有前景的途径。