Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment

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

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.

Spatial Statistical Models: An Overview under the Bayesian Approach

Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.