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

Rosa Lavelle-Hill, John Harvey, Gavin Smith, Anjali Mazumder, Madeleine Ellis, Kelefa Mwantimwa, James Goulding

Abstract read

In one paragraph

Article in EPJ data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Rosa Lavelle-HillUniversity of Tübingen, Tübingen, Germany.ORCID 0000-0002-1767-9828

John HarveyThe University of Nottingham, Nottingham, UK.

Gavin SmithThe University of Nottingham, Nottingham, UK.

Anjali MazumderThe Alan Turing Institute, London, UK.

Madeleine EllisThe University of Nottingham, Nottingham, UK.

Kelefa MwantimwaUniversity of Dar es Salaam, Dar es Salaam, Tanzania.

James GouldingThe University of Nottingham, Nottingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-022-00340-y.

Indexed as

Call detail recordsExploitationMachine learningMigrationMobile moneyTanzaniaVulnerability

Identifiers

PMID35571071

DOI10.1140/epjds/s13688-022-00340-y

PMCPMC9079216

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.