The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Predicting poverty and wealth from mobile phone metadata
Open accessAbstract
Predicting unmeasurable wealth: In developing countries, collecting data on basic economic quantities, such as wealth and income, is costly, time-consuming, and unreliable. Taking advantage of the ubiquity of mobile phones in Rwanda, Blumenstock et al.mapped mobile phone metadata inputs to individual phone subscriber wealth. They applied the model to predict wealth throughout Rwanda and show that the predictions matched well with those from detailed boots-on-the-ground surveys of the population. Science, this issue p.1073
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Cited by
Machine Learning: An Applied Econometric Approach
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Combining satellite imagery and machine learning to predict poverty
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Large-scale physical activity data reveal worldwide activity inequality
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Metadata
- Authors
- Blumenstock, J., Cadamuro, G., & On, R.
- Venue
- Science
- Published
- 2015-11-27
- Volume
- 350
- Issue
- 6264
- Pages
- 1073-1076
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2173315138
- Cited by
- 698
- References
- 44
- FWCI
- 85.06
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
