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 migrati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-05-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-022-00340-y |
| _version_ | 1818250976381894656 |
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| author | Rosa Lavelle-Hill John Harvey Gavin Smith Anjali Mazumder Madeleine Ellis Kelefa Mwantimwa James Goulding |
| author_facet | Rosa Lavelle-Hill John Harvey Gavin Smith Anjali Mazumder Madeleine Ellis Kelefa Mwantimwa James Goulding |
| author_sort | Rosa Lavelle-Hill |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-12-12T16:00:56Z |
| format | Article |
| id | doaj.art-3d32b3af294b48568faf93bf7ba1c4dd |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-12T16:00:56Z |
| publishDate | 2022-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-3d32b3af294b48568faf93bf7ba1c4dd2022-12-22T00:19:23ZengSpringerOpenEPJ Data Science2193-11272022-05-0111112310.1140/epjds/s13688-022-00340-yUsing mobile money data and call detail records to explore the risks of urban migration in TanzaniaRosa Lavelle-Hill0John Harvey1Gavin Smith2Anjali Mazumder3Madeleine Ellis4Kelefa Mwantimwa5James Goulding6University of TübingenThe University of NottinghamThe University of NottinghamThe Alan Turing InstituteThe University of NottinghamUniversity of Dar es SalaamThe University of NottinghamAbstract 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.https://doi.org/10.1140/epjds/s13688-022-00340-yMobile moneyMachine learningMigrationCall detail recordsExploitationTanzania |
| spellingShingle | Rosa Lavelle-Hill John Harvey Gavin Smith Anjali Mazumder Madeleine Ellis Kelefa Mwantimwa James Goulding Using mobile money data and call detail records to explore the risks of urban migration in Tanzania EPJ Data Science Mobile money Machine learning Migration Call detail records Exploitation Tanzania |
| title | Using mobile money data and call detail records to explore the risks of urban migration in Tanzania |
| title_full | Using mobile money data and call detail records to explore the risks of urban migration in Tanzania |
| title_fullStr | Using mobile money data and call detail records to explore the risks of urban migration in Tanzania |
| title_full_unstemmed | Using mobile money data and call detail records to explore the risks of urban migration in Tanzania |
| title_short | Using mobile money data and call detail records to explore the risks of urban migration in Tanzania |
| title_sort | using mobile money data and call detail records to explore the risks of urban migration in tanzania |
| topic | Mobile money Machine learning Migration Call detail records Exploitation Tanzania |
| url | https://doi.org/10.1140/epjds/s13688-022-00340-y |
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