Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes
Bibliographic Data
Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health
Built environment · Convolutional neural network · Environmental health · Geography · Machine learning · Mental health · Walkability · Air Quality and Health Impacts · Computer Science · Engineering · Health disparities and outcomes · Medicine · Psychology · Urban Transport and Accessibility
Past, Present, and Future Perspectives on the Integration of AI Into Walkability Assessment Tools
From intangible to tangible
Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri
Assessing the impact of maintenance condition on multifamily rents
Integrating Street Views, Satellite Imageries and Remote Sensing Data Into Economics and the Social Sciences
Would You Be Happier Living in a Greener Urban Area? A Fixed-Effects Analysis of Panel Data
Socioeconomic Status Differences in Recreational Physical Activity Levels and Real and Perceived Access to a Supportive Physical Environment
Systematic social observation of children’s neighborhoods using Google Street View
Social deprivation and premature mortality
Disparities in Urban Neighborhood Conditions
ImageNet classification with deep convolutional neural networks
Exercise for the Treatment of Depression and Anxiety
Protective Association Between Neighborhood Walkability and Depression in Older Men
Using Google Street View to Audit Neighborhood Environments
Health Care Disparities Across the Urban‐Rural Divide
Neighborhood physical disorder in New York City
Training Computers to See the Built Environment Related to Physical Activity
Neighborhood Disorder and Obesity-Related Outcomes among Women in Chicago
Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States
Health and the built environment in United States cities
Disparities and access to healthy food in the United States
Development and deployment of the Computer Assisted Neighborhood Visual Assessment System (Canvas) to measure health-related neighborhood conditions
A Local View of Informal Urban Environments
The Relationship of Built Environment to Perceived Social Support and Psychological Distress in Hispanic Elders
Does Neighborhood Collective Efficacy for Families Change Over Time? The Boston Neighborhood Survey
Neighborhood Physical Conditions and Health
Cultivating a Politics of Sight for Vacant Land Use in Cities
Neighborhood Disadvantage, Disorder, and Health
| Citations per year | 1.67 |
|---|---|
| Citation span | 2023 - 2025 (3) |
| Highly cited | No |