Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment

被引:63
作者
Keralis, Jessica M. [1 ]
Javanmardi, Mehran [2 ]
Khanna, Sahil [3 ]
Dwivedi, Pallavi [1 ]
Huang, Dina [1 ]
Tasdizen, Tolga [2 ]
Nguyen, Quynh C. [1 ]
机构
[1] Univ Maryland, Dept Epidemiol & Biostat, Sch Publ Hlth, 4200 Valley Dr 2242, College Pk, MD 20742 USA
[2] Univ Utah, Dept Elect & Comp Engn, 50 S Cent Campus Dr 2110, Salt Lake City, UT 84112 USA
[3] Univ Maryland, Masters Telecommun Program, 2433 AV Williams Bldg, College Pk, MD 20742 USA
基金
美国国家卫生研究院;
关键词
Google Street View; Machine learning; Computer vision; Built environment; Structural determinants of health; PHYSICAL-ACTIVITY; NEIGHBORHOOD DISORDER; LAND-USE; OUTCOMES; OBESITY; AUDIT;
D O I
10.1186/s12889-020-8300-1
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background The 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. Methods We 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 alpha = 0.05 level. Results Single 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. Conclusions Structural 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.
引用
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页数:10
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