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Abstract
The Supplemental Nutrition Assistance Program (SNAP) is among the most participated in public assistance programs in the United States. In 2023, 12.6% of the United States participated in the program (USDA, 2025). Research on the varying participation rates across U.S. states has largely been limited to local study areas and identifying influential demographic and economic characteristics. By applying these findings from past studies, this study intends to expand the work of spatially analyzing SNAP participation rates by testing two different spatial attributes’ influence on SNAP participation rates in the state of North Carolina. This study hypothesizes that North Carolina counties with low population density and fewer mileage of paved interstate will reflect a lower participation rate in SNAP. These spatial attributes were chosen because of their indication of accessibility and ruralness. Accessibility does not have a universally agreed upon definition, but this paper will understand accessibility as “people’s overall ability to reach desired services and activities” (Litman, 2024). Similarly, ruralness does not have a set definition but this study will understand it through population density as it “serves as a better general definition for rural areas in North Carolina and better captures the county-centric identities many North Carolinians hold" (NC RuralCenter, 2025). Using these attributes, a linear regression analysis will be conducted to find the statistical significance of this group of variables in participation rates. To inform this model, data from the North Carolina Rural Center, Democratic Party, and Office of State Budget and Management will be used. To fully understand the concentration of these variables across North Carolina, this paper will produce three choropleth maps that visualize the population density, SNAP participation rates, and miles of interstate per North Carolina county. In addition to this set of choropleth maps, this study will conduct an optimized hot spot analysis in ArcGIS Pro to visualize statistically significant hotspots of SNAP participation rates. Finally, to test the hypothesis set forth by this study, I will run linear regressions to find out if population density or miles of paved interstate have a statistically significant or insignificant relationship between SNAP participation.