Using mobile money data and call detail records to explore the risks of urban migration in Tanzania.

ABSTRACT: 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 poore

SUBMITTER: Lavelle-Hill R 

PROVIDER: S-EPMC9079216 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature