(P20-045-24) Baltimore’s Urban Grocery Gap: A Mixed-Methods Geo-Spatial Analysis of the Retail Food Environment and Community Perspectives

Objectives: Under-resourced urban communities like Baltimore, Maryland often lack access to healthy foods and face high rates of obesity and diet-related chronic disease. Given that the retail food environment (RFE) influences food purchasing and consumption, it is crucial to understand local food retail density and accompanying community perspectives. This study sought to: (1) geographically characterize the RFEs of nine neighborhoods and (2) examine extant data from community members on food sourcing and desired changes to their RFEs.

Methods: Food retail location data were retrieved from the Center for a Livable Future. Spatial distribution of food retail density was analyzed in neighborhoods predominantly deemed to be food deserts, or ‘Healthy Food Priority Areas’ (HFPAs), using ArcGIS. Qualitative in-depth interview (IDI) data were collected as part of a larger study of dollar store food environments in Baltimore City among a sample of community members (n=12). IDI transcripts were coded and analyzed thematically using ATLAS-ti.

Results: Of the 184 food retailers identified across all sampled neighborhoods, corner/convenience stores accounted for 63.04% followed by small grocers (27.17%), dollar stores (5.43%), and supermarkets (4.35%). Supermarkets had the lowest density, ranging from 0 to 0.23 per 1,000 people. Secondary analysis of IDIs revealed community concerns regarding the limited availability of fresh produce and meat in dollar stores. Despite higher prices and the necessity to drive there, supermarkets were often preferred for their variety of options. Community members from HFPAs expressed a particularly strong desire for supermarkets to be established in their neighborhoods, over other food retail source types.

Conclusions: We combined geo-spatial and thematic analyses to uncover insights into the existing urban grocery gap in Baltimore. Findings from this unique triangulation of data will help inform the ongoing development of programs and policies targeting Baltimore’s RFE and incorporation of community-generated ideas, especially for those with financial and transportation constraints.

Funding Sources: Extant data collected with funding by The Center for Science in the Public Interest.