Blanchard Paul, Rubrichi Stefania
Institut de Recherche pour le Développement, Paris, 75010, France.
Department of Economics, Trinity College Dublin, Dublin 2, Ireland.
Sci Data. 2025 Jun 20;12(1):1051. doi: 10.1038/s41597-025-04599-4.
Understanding temporary migration is crucial for addressing various socio-economic and environmental challenges in developing countries. However, traditional surveys often fail to capture such movements effectively, leading to a scarcity of reliable data, particularly in sub-Saharan Africa. This article introduces a detailed and open-access dataset that leverages mobile phone data to capture temporary migration in Senegal with unprecedented spatio-temporal detail. The dataset provides measures of migration flows and stocks across 151 locations across the country and for each half-month period from 2013 to 2015, with a specific focus on movements lasting between 20 and 180 days. The article presents a suite of methodological tools that not only includes algorithmic methods for the detection of temporary migration events in digital traces, but also addresses key challenges in aggregating individual trajectories into coherent migration statistics. These methodological advancements are not only pivotal for the intrinsic value of the dataset but also adaptable for generating systematic migration statistics from other digital trace datasets in other contexts.
了解临时移民对于应对发展中国家的各种社会经济和环境挑战至关重要。然而,传统调查往往无法有效捕捉此类流动,导致可靠数据稀缺,尤其是在撒哈拉以南非洲地区。本文介绍了一个详细且开放获取的数据集,该数据集利用手机数据以前所未有的时空细节捕捉塞内加尔的临时移民情况。该数据集提供了2013年至2015年期间全国151个地点以及每个半月期间的移民流动和存量指标,特别关注持续20至180天的流动。本文展示了一套方法工具,不仅包括用于检测数字痕迹中临时移民事件的算法方法,还解决了将个体轨迹汇总为连贯移民统计数据的关键挑战。这些方法进步不仅对数据集的内在价值至关重要,还适用于在其他背景下从其他数字痕迹数据集中生成系统的移民统计数据。