Abstract
BackgroundThe built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level.MethodsWe used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level.ResultsSingle lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes.ConclusionsStructural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.
Highlights
The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes
The indicators were selected through an iterative process of considering what the literature has found to be important built environment characteristics and what is feasible for computer vision models
Health outcomes were modeled for 20,121 census tracts with complete data on health outcomes and GSVderived built environment indicators, representing 416 cities in all 50 states and the District of Columbia
Summary
The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. The built environment has long been viewed as a structural determinant of health by social epidemiologists [1]. A substantial body of research has documented the association of built environment characteristics – such as accessibility, physical disorder, access to public transit and recreational spaces, and greenery – with health-
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