Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A highly granular temporary migration dataset derived from mobile phone data in Senegal

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月20日

查看全文
  • Paul Blanchard,
  • Stefania Rubrichi

摘要

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.