Applying Data Science Techniques to Model Food Insecurity

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Students: Isabel Kruse
Mentor: Dr. Walter Bialkowski

Project Background

Thirteen million U.S. households face food insecurity (>10% of all households nationwide)1 representing uncertainty in having, or inability to acquire, enough food to meet the needs of household members due to insufficient money or other resources. Whereas the prevalence of food insecurity has decreased in metropolitan areas, food insecurity is increasing in non-metropolitan and rural areas2. In part due to the SARS-CoV-2 pandemic, it is estimated that food insecurity will affect 1-in-7 Wisconsinites in rural counties during 20213. Unique dietary needs of rural populations, as well as underlying disparities by Race, Ethnicity, age, and socioeconomic status, exacerbate challenges associated with providing quality food to those in need in these rural communities. Foremost among the barriers to feeding food-insecure members of rural communities is a paucity of information about the unique needs and infrastructures of these communities.

Project Approach

This project will include gathering data from Feeding America Eastern Wisconsin to analyze inequities in food distribution throughout the state. With this information we will forecast how inflation will further exacerbate inequities and how Feeding America can adapt.