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 stock 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 include 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.

翻译：理解临时迁移对于应对发展中国家面临的各种社会经济与环境挑战至关重要。然而，传统调查方法往往无法有效捕捉此类流动，导致可靠数据稀缺，在撒哈拉以南非洲地区尤为突出。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，以前所未有的时空粒度捕捉塞内加尔的临时迁移。该数据集提供了2013年至2015年间、每半个月周期、覆盖全国151个地点的迁移流量与存量测度，特别关注持续时间为20至180天的流动。本文提出了一套方法论工具，不仅包括从数字轨迹中检测临时迁移事件的算法方法，还解决了将个体轨迹聚合为连贯迁移统计数据的关键挑战。这些方法学进展不仅对于数据集的内在价值至关重要，也能适用于其他情境下从其他数字轨迹数据集生成系统性迁移统计数据。