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

Area of Science:

• Social Sciences
• Computer Science
• Demography

Background:

• Understanding temporary migration is vital for development challenges, but data scarcity, especially in sub-Saharan Africa, hinders effective policy.
• Traditional surveys struggle to capture the nuances of temporary population movements, limiting reliable data acquisition.

Purpose of the Study:

• To introduce a novel, open-access dataset capturing temporary migration in Senegal using mobile phone data.
• To provide unprecedented spatio-temporal detail on migration flows and stocks for movements between 20 and 180 days.
• To present methodological tools for detecting and aggregating temporary migration from digital traces.

Main Methods:

• Leveraging anonymized mobile phone data to identify individual movement patterns.
• Developing algorithmic methods for detecting temporary migration events within digital traces.
• Aggregating individual trajectories to generate migration flow and stock statistics.

Main Results:

• Creation of a detailed dataset covering 151 locations in Senegal from 2013-2015.
• Quantification of temporary migration flows and stocks with half-monthly granularity.
• Demonstration of methodological adaptability for other digital trace datasets.

Conclusions:

• Mobile phone data offers a powerful tool for understanding temporary migration dynamics.
• The developed methodologies enable the creation of robust migration statistics from digital traces.
• This approach has significant implications for policy and research in developing countries.