Context-conditioned normalizing flows refine subnational survey distributions under severe data scarcity when conditioning covariates capture local heterogeneity.
Continental-scale assessment of spatial food market accessibility in Africa using open geospatial data
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abstract
Food market accessibility is a critical yet underexplored dimension of food systems, particularly in low- and middle-income countries. In this paper, we present a continent-wide assessment of spatial food market accessibility in Africa, integrating open geospatial data from OpenStreetMap and the World Food Programme. We compare three complementary metrics: travel time to the nearest market, market availability within a 30-minute threshold, and an entropy-based measure of spatial distribution, to quantify accessibility across diverse settings. Our analysis reveals pronounced disparities: rural and economically disadvantaged populations face substantially higher travel times, limited market reach, and less spatial redundancy. These accessibility patterns align with socioeconomic stratification, as measured by the Relative Wealth Index, and moderately correlate with food insecurity levels, assessed using the Integrated Food Security Phase Classification. We find pronounced disparities in accessibility: rural and economically disadvantaged populations face substantially longer travel times and reduced market availability, with some areas requiring several hours of travel. Overall, results suggest that access to food markets reflects broader geographic and economic inequalities and plays a relevant role in shaping food security outcomes. This framework provides a scalable, data-driven approach for identifying underserved regions and supporting equitable infrastructure planning and policy design across diverse African contexts.
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cs.CY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Context-Conditioned Generative Models Enable Subnational Refinement of Sparse Humanitarian Surveys
Context-conditioned normalizing flows refine subnational survey distributions under severe data scarcity when conditioning covariates capture local heterogeneity.