A method for estimating physical and economic food access at high spatial resolution

Florencio Campomanes (), Michael Marshall and Andrew Nelson
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Florencio Campomanes: University of Twente
Michael Marshall: University of Twente
Andrew Nelson: University of Twente

Food Security: The Science, Sociology and Economics of Food Production and Access to Food, 2024, vol. 16, issue 1, No 4, 47-64

Abstract: Abstract Physical and economic access to food vary spatially. Methods to map that variability at high levels of spatial detail over large areas are scarce, even though suitable datasets and methods exist. Using open-access data for Ethiopia, we developed a method to map the disparities in physical and economic food access at 1-km resolution. We selected 25 access-related variables for 486 geo-located communities from the 2018 Ethiopian Living Standards Measurement Study to create a food access index (FAI). The index was based on a weighted summation of the 25 variables from a principal component analysis (PCA). We then extrapolated the FAI to the rest of Ethiopia using a generalized additive model (GAM) to produce a 1-km resolution FAI map and used that to describe the spatial variability of food access. Economic access had a heavier weight than physical access in the FAI reflecting the fact that proximity to food markets alone is insufficient if one cannot afford food. The GAM had an R2 of 0.57 and a normalized root mean square error of 22.2% which are comparable to measures of model performance in studies that provided micro-level estimates of relative wealth. Peri-urban areas, representing 67% of the population, had relatively low food access, suggesting that these areas should be a priority for infrastructure or economic intervention. The scarcity of detailed spatial information on food access may limit the effectiveness of targeted policymaking to improve food security. The methodology developed in this study uses widely available and carefully selected datasets and can contribute to more spatially detailed estimates of food access in other countries.

Keywords: Food prices; Food security; Sustainable development; Spatial analysis; Household surveys; Sub-Saharan Africa; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12571-023-01404-1

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