Urban & Rural Divides in U.S. Food Insecurity

This project examines food insecurity patterns across rural and urban counties in the United States, merging a publicly available federal datasets with semi-private data from Feeding America. This project includes reproducible Python analysis. Completed with collaborator Lara Terpetschnig (Team: Canaries) as our final project for IS467, Data Maangement, Curation and Reproduction at the University of Illinois Urbana-Champaign.

Tech & Skills

Methods

Data from both sources were merged on standardized FIPS county codes. Tract-level FARA data was aggregated to the county level, and linear regression was used to compare population and rural/urban designation as predictors of food insecurity. Geographic visualizations were produced with geopandas using U.S. Census Bureau shapefiles.

Research Questions

Do rural families experience higher food insecurity rates than urban families — both nationally and state-by-state?
How do food access and food insecurity indicators overlap geographically, and what combined insights emerge?
Which U.S. counties face the most severe food insecurity, and what contributing factors drive this?

Findings

Rural counties consistently show higher food insecurity rates than urban counties across most U.S. states.

The Rural-Urban Continuum Code is a stronger predictor of food insecurity than raw population counts.

Poverty emerges as a critical additional factor — food access and food insecurity overlap imperfectly.

Southern states experience the highest food insecurity rates; Mississippi has the worst state-level average.

Issaquena County, MS (62.2% Black population) has the nation's highest food insecurity rate at the county level.

Native American reservations face acute vulnerability — e.g., Oglala Lakota County, South Dakota.

Data Sources

Food Access Research Atlas (FARA)

Census-tract-level food access indicators from the USDA Economic Research Service. 72,864 tracts aggregated to 3,142 counties. CC0 public domain license.

Map the Meal Gap (MMG)

County-level food insecurity estimates from Feeding America (2019 data). Restricdata must be requested from the institutional repository.

Deliverables

The project includes 8 analytical Jupyter notebooks covering data cleaning, quality assurance, and modeling; a merged county-level dataset; choropleth maps filtered by demographic category; SHA hash digests for data integrity; and a fully reproducible analysis pipeline.