Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes

• Published in: Public health reports (1974) 2021-03, Vol.136 (2), p.201-211
• Main Authors: Nguyen, Quynh C., Keralis, Jessica M., Dwivedi, Pallavi, Ng, Amanda E., Javanmardi, Mehran, Khanna, Sahil, Huang, Yuru, Brunisholz, Kimberly D., Kumar, Abhinav, Tasdizen, Tolga
• Format: Article
• Language: English
• Subjects: Big Data; Built environment; Built Environment - statistics & numerical data; Computer vision; Construction; Counties; Data quality; Death & dying; Decision making; Diabetes; Diabetes mellitus; Diabetes Mellitus - epidemiology; Environment Design; Families & family life; Health Behavior; Health Status; Humans; Internet; Machine learning; Mental health; Mortality, Premature - trends; Neighborhoods; Obesity; Obesity - epidemiology; Quality assessment; Regression analysis; Regression models; Residence Characteristics - statistics & numerical data; Roads; Roads & highways; Sedentary Behavior; Spatial Analysis; Stress, Psychological - epidemiology; Urban environments
• ISSN: 0033-3549; 1468-2877
• DOI: 10.1177/0033354920968799

Objectives Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. Methods We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non–single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). Results Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non–single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. Conclusions The use of computer vision and big data image sources makes possible national studies of the built environment’s effects on health, producing data and results that may inform national and local decision-making.