https://doi.org/10.1140/epjds/s13688-022-00340-y
Regular Article
Using mobile money data and call detail records to explore the risks of urban migration in Tanzania
1
University of Tübingen, Tübingen, Germany
2
The Alan Turing Institute, London, UK
3
The University of Nottingham, Nottingham, UK
4
University of Dar es Salaam, Dar es Salaam, Tanzania
a
rosa.lavelle-hill@uni-tuebingen.de
Received:
21
December
2021
Accepted:
14
April
2022
Published online:
8
May
2022
Understanding what factors predict whether an urban migrant will end up in a deprived neighbourhood or not could help prevent the exploitation of vulnerable individuals. This study leveraged pseudonymized mobile money interactions combined with cell phone data to shed light on urban migration patterns and deprivation in Tanzania. Call detail records were used to identify individuals who migrated to Dar es Salaam, Tanzania’s largest city. A street survey of the city’s subwards was used to determine which individuals moved to more deprived areas. t-tests showed that people who settled in poorer neighbourhoods had less money coming into their mobile money account after they moved, but not before. A machine learning approach was then utilized to predict which migrants will move to poorer areas of the city, making them arguably more vulnerable to poverty, unemployment and exploitation. Features indicating the strength and location of people’s social connections in Dar es Salaam before they moved (‘pull factors’) were found to be most predictive, more so than traditional ‘push factors’ such as proxies for poverty in the migrant’s source region.
Key words: Mobile money / Machine learning / Migration / Call detail records / Exploitation / Tanzania / Vulnerability
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-022-00340-y.
© The Author(s) 2022
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.