Geotargeting in Urban Areas Informed by
Community-Driven Insights
Using community-informed spatial features to predict food insecure and asset-deprived households in urban areas

Abstract
Recent advances in poverty prediction at a national scale employ new data sources and machine learning. However, the performance of these models for households experiencing poverty, food insecurity, and nutritional deficiency, in urban areas is unknown. This research explores how geospatial indicators, particularly those informed by community insights, improve poverty prediction and household targeting for social transfers in urban settings. We conducted a survey of 300households in Lusaka, Zambia. We combine this information with geo-features, such as neighborhood structure, access to infrastructure and services (based on Point of Interest data from crowd-sourced maps), satellite imagery features, connectivity data, and Twitter. Using machine learning techniques, these features are used to predict welfare based on our four different indicators: assets, food security, iron deficiency, and a combination of asset and food dimensions. We find that using community information in the selection of geographic predictors provides a parsimonious way to identify households in need. Geographic features combined with household-level information can efficiently predict asset-based deprivation, food insecurity and iron deficiency, and households with multi-deprivation. Our best models also reduce targeting errors by 20-22 percentage points compared to baseline models that only use socio demographic characteristics to identify asset, food, and nutrition- deficient households. Our results show that community-informed spatial features can be used for geographic targeting for asset and food-related dimensions of poverty to effectively reach vulnerable populations while improving the legitimacy of targeting methods.
Citation
Jung, W., Stoeffler, Q., Kim, A. H., Goudarzi, S., Benotsmane, R., Shah, V., & Sartorius, M. (Working paper). Geographic targeting in urban areas informed by community-driven insights in Zambia.

RESEARCH TEAM .................................................................
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Woojin Jung (PI) - Rutgers, School of Social Work
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Quentin Stoeffler - University of Bordeaux, Economics
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Andrew H. Kim - Rutgers, School of Social Work
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Sajedeh Goudarzi - Rutgers, Global Urban Studies
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Rofaida Benotsmane - Istanbul Technical Univ., Economics
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Vatsal Shah - Rutgers, Computer Science, Statistics
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Melissa Sartorius - Rutgers, School of Social Work