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Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of “ground truth” data from surveys, and then predicting poverty levels in areas where imagery exists but surveys do not. Using survey and satellite data from ten countries, this paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines, and shows how these phenomena affect the validity of policies based on predicted maps. Our findings highlight the importance of careful error and bias analysis before using satellite-based poverty maps in real-world policy decisions.
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What is the Future of Survey-Based Data Collection for Local Government Research? Trends, Strategies, and Recommendations
Surveys are an important vehicle for advancing research on urban policy and governance. The introduction of online tools eased survey-based data collection, making it cheaper and easier to obtain data from key informants like local elected officials or public administrators. However, the utility of web-based survey administration may be diminishing. To investigate this dynamic and search for strategies to support survey research in urban studies, we perform a systematic review of survey research in urban policy and administration scholarship and conduct an original survey follow-up experiment. Our findings identify a clear downward trend in survey response rates that was accentuated during the COVID-19 pandemic. Results from our survey experiment show distinctly different costs per solicitation and per completed survey, depending on administration mode. These findings stimulate discussion on how scholars may continue to use surveys to generate high-quality, empirically rigorous research on urban affairs in light of recent trends.
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- PAR ID:
- 10416635
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Urban Affairs Review
- ISSN:
- 1078-0874
- Page Range / eLocation ID:
- Article No. 107808742311758
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of “ground truth” data from surveys, and then predicting poverty levels in areas where imagery exists but surveys do not. Using survey and satellite data from ten countries, this paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines, and shows how these phenomena affect the validity of policies based on predicted maps. Our findings highlight the importance of careful error and bias analysis before using satellite-based poverty maps in real-world policy decisions.more » « less
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