By: Stephanie Cuningham, G'20
“Food environment” is the culmination of socioeconomic and physical factors which shape the food habits of a population. The USDA’s 2020 “Food Environmental Atlas” (FEA) contains county-level data on 281 of these factors. A central theme in the discussion of food environment has been the idea of a “food desert,” generally defined as an area that is both low-income and low-access. However, recent research calls this question, with claims that choices drive poor health outcomes, rather than lack of nutritional access. In this analysis, the FEA along with 2018 Census data on labor participation rates and educational attainment were examined to determine whether clustering areas by common food environment can reveal how underlying factors and health outcomes (here, diabetes rates) vary beyond food desert classification.
Bayesian Principal Component Analysis (PCA) and K-means clustering were performed with counties stratified by metro status. This yielded six clusters, plotted against the strongest loadings for each component to create biplots, and on choropleths. Clusters’ relationships to food desert diabetes rate were statistically analyzed. Random forests were run against these two outcomes to compare variable importance rankings. Findings showed that demographics (age, race, educational attainment) are more important to health outcome than to food desert status, and labor participation is more important to food desert status than to health outcome. SNAP benefit participation is important to both outcomes. Overall, results indicated that while food environment, food desert status, and diabetes rate are inter-related, certain factors associated with food desert status are more correlated with poor health outcomes than others. While poverty and access are important to food environment, this is not a simple causal relationship and further clustering work could reveal more personalized solutions for populations in need.
Keywords: non-profit, data processing, Alteryx, data integration