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

Abstract

Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed.

Food Swamps Surrounding Schools in Three Areas of Guatemala

“…In the United States, food swamps are defined as areas with 4 or more corner stores within 0.4 km (0.25 miles) of home or where the ratio of unhealthy to healthy food establishments exceeds 3.89 ( 2 , 3 ). In the United States, low-income and racial/ethnic minority populations are more likely than high-income white populations to live in food swamps ( 4 ). Living in a food swamp has been associated with unhealthy dietary behaviors and obesity among adults and young adolescents ( 3 ).…”

“…Food swamps are environments saturated with unhealthy foods because of the large numbers of corner stores and fast-food outlets in them (2). In the United States, food swamps are defined as areas with 4 or more corner stores within 0.4 km (0.25 miles) of home or where the ratio of unhealthy to healthy food establishments exceeds 3.89 (2,3). In the United States, low-income and racial/ethnic minority populations are more likely than high-income white populations to live in food swamps (4).…”

“…In the United States, low-income and racial/ethnic minority populations are more likely than high-income white populations to live in food swamps (4). Living in a food swamp has been associated with unhealthy dietary behaviors and obesity among adults and young adolescents (3). Consequently, some US municipalities have adopted zoning or permitting laws to improve the food environment and reduce disparities in obesity prevalence (2,3).…”

“…Living in a food swamp has been associated with unhealthy dietary behaviors and obesity among adults and young adolescents (3). Consequently, some US municipalities have adopted zoning or permitting laws to improve the food environment and reduce disparities in obesity prevalence (2,3).…”

Vulnerability Analysis for Reusing an Old Industrial Area: A Case Study of the Winter Olympic Park

“…e methods used for vulnerability assessments depend on the characteristics of what is being assessed. Luan et al used the Bayesian spatial factor analysis model to evaluate the environmental health of adjacent restaurants, but the fuzziness of the indicators led to deviations between the assessed and actual values [10].…”