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Spatially analyzing food consumption inequalities using GIS with disaggregated data from Punjab, Pakistan.

Analyzing food patterns in Punjab using disaggregated data with GIS

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Abstract

Inequalities are usually measured in context of household income to develop policies ensuring equal opportunities to public services for well being. Sustainable Development Goal 10, however, emphasizes the uniform distribution of per-capita food and nutrition intake and percentage share of expenditure on food consumption. Concerning that, evidence-based approaches should include scientifically examining the spatial aspects of food security to evaluate geographical equity (or inequity) of its various dimensions. The main objective here is to instigate spatial inequalities in food and nutrient consumption and production in Punjab. To do so, we georeferenced the traditional local and global food and nutrition surveys in a Geographical Information System (GIS) environment. Next, we disaggregated household surveys at various sub-national levels (i.e., per km2, per district, per region with rural-urban divisions). With these levels, we analyzed food data along three categories, i.e., production, consumption, and nutrition intake. We used global Moran’s index to identify geographical disparities of production-consumption gaps among regions and commodities. The results obtained reveal clustered food patterns in the north and north-eastern regions, while southern areas face high inequality of producing and consuming cereals, meat, vegetables, fruits, and milk. High spatial autocorrelation of within-districts gaps of food production and consumption observed in the province indicates that inequality is driven by location dynamics and not by population. The evidence-based method in this research will help implicate policies through prioritizing resource allocation in terms of space and equal opportunities.

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Correspondence to Muhammad Imran.

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Khushi, S., Ahmad, S.R., Ashraf, A. et al. Spatially analyzing food consumption inequalities using GIS with disaggregated data from Punjab, Pakistan.. Food Sec. 12, 1283–1298 (2020). https://doi.org/10.1007/s12571-020-01057-4

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