Fighting poverty with data

Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 2016-08, Vol.353 (6301), p.753-754
Main Author: Blumenstock, Joshua Evan
Format: Article
Language:English
Subjects:
Census
Data analysis
Decisions
Developing countries
Displacement
LDCs
Machine learning
PERSPECTIVES
Poverty
Public policy
Satellite imagery
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Summary:Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens. In situations where reliable survey data are missing or out of date, a novel line of research offers promising alternatives. On page 790 of this issue, Jean et al. (1) apply recent advances in machine learning to high-resolution satellite imagery to accurately measure regional poverty in Africa.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.aah5217