Using Satellite Data to Guide Urban Poverty Reduction
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Using Satellite Data to Guide Urban Poverty Reduction. / Sohnesen, Thomas Pave; Fisker, Peter; Malmgren-Hansen, David.
In: Review of Income and Wealth, Vol. 68, No. S2 Special Issue, 2022, p. S282-S294.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Using Satellite Data to Guide Urban Poverty Reduction
AU - Sohnesen, Thomas Pave
AU - Fisker, Peter
AU - Malmgren-Hansen, David
PY - 2022
Y1 - 2022
N2 - Poverty reduction in low- and middle-income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution of poverty within cities. Utilizing existing household survey data in combination with Convolutional Neural Networks (CNN) applied to high-resolution satellite images of cities, this study shows that existing data can generate detailed neighborhood-level maps providing key targeting information for an anti-poverty program. The approach is highly automatic, applicable at scale, and cost-effective. The method also provides direct support for policy development, as illustrated by the case study, where the Government of Mozambique is implementing an urban social safety net program, targeting poor urban neighborhoods, utilizing the estimated poverty maps.
AB - Poverty reduction in low- and middle-income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution of poverty within cities. Utilizing existing household survey data in combination with Convolutional Neural Networks (CNN) applied to high-resolution satellite images of cities, this study shows that existing data can generate detailed neighborhood-level maps providing key targeting information for an anti-poverty program. The approach is highly automatic, applicable at scale, and cost-effective. The method also provides direct support for policy development, as illustrated by the case study, where the Government of Mozambique is implementing an urban social safety net program, targeting poor urban neighborhoods, utilizing the estimated poverty maps.
KW - poverty
KW - social protection
KW - remote sensing
KW - convolutional neural networks
KW - image recognition
U2 - 10.1111/roiw.12552
DO - 10.1111/roiw.12552
M3 - Journal article
VL - 68
SP - S282-S294
JO - Review of Income and Wealth
JF - Review of Income and Wealth
SN - 0034-6586
IS - S2 Special Issue
ER -
ID: 291122513