Neighborhood Racial Segregation Predict the Spatial Distribution of Supermarkets and Grocery Stores Better than Socioeconomic Factors in Cleveland, Ohio: a Bayesian Spatial Approach

Abstract

Introduction: The food environment influences the availability and affordability of food options for consumers in a given neighborhood. However, disparities in access to healthy food options exist, affecting Black and low-income communities disproportionately. This study investigated whether racial segregation predicted the spatial distribution of supermarkets and grocery stores better than socioeconomic factors or vice versa in Cleveland, Ohio.

Method: The outcome measure was the count of supermarket and grocery stores in each census tract in Cleveland. They were combined with US census bureau data as covariates. We fitted four Bayesian spatial models. The first model was a baseline model with no covariates. The second model accounted for racial segregation alone. The third model looked at only socioeconomic factors, and the final model combined both racial and socioeconomic factors.

Results: Overall model performance was better in the model that considered only racial segregation as a predictor of supermarkets and grocery stores (DIC = 476.29). There was 13% decrease in the number of stores for a census tract with a higher majority of Black people compared to areas with a lower number of Black people. Model 3 that considered only socioeconomic factors was less predictive of the retail outlets (DIC = 484.80).

Conclusions: These findings lead to the conclusion that structural racism evidenced in policies like residential segregation has a significant influence on the spatial distribution of food retail in the city of Cleveland.

Keywords: Bayesian analysis; Food deserts; Neighborhood food environments; Racial segregation; Spatial analysis.

Fig. 1

Number of stores per census tract

Fig. 2

Showing the posterior mean for census tract specific rate ratio

Fig. 3

Showing the posterior exceedance probabilities