ArticlePublic health reports (Washington, D.C. : 1974)
Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes.
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments.International journal of obesity (2005) · 2023Pooled it
- Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Investigating the Role of the Environment on Physical Activity Interventions (the InSPACE Project): Protocol for a Pooled Secondary Analysis of Randomized Controlled Trials.JMIR research protocols · 2026Article
- A study on the coupling mechanism between the urban environment and depression perception based on deep learning and street view image.Scientific reports · 2026Article
- Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis.JMIR formative research · 2025Article
- The role of sidewalk availability in gentrification: A longitudinal study of U.S. neighborhoods and racial/ethnic composition.SSM - population health · 2025Article
- Advancing equitable and sustainable urban health.Nature medicine · 2025Review
- Data driven assessment of built environment impacts on urban health across United States cities.Scientific reports · 2025Article
- Intersecting Paths to Health: A Factor Analysis Approach to Socioeconomic and Environmental Determinants in Indiana.International journal of environmental research and public health · 2025Article
- Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.International journal of environmental research and public health · 2024Article
- Article
- Utilizing big data without domain knowledge impacts public health decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Potential of artificial intelligence in injury prevention research and practice.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2024Article
- Article
- Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities.Scientific data · 2023Article
- Multi-Task Classification for Improved Health Outcome Prediction Based on Environmental Indicators.IEEE access : practical innovations, open solutions · 2023Article
- Playgrounds are for children: Investigating developmentally-specific "Green Space" and child mental health.SSM. Mental health · 2022Article
- Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities.Scientific reports · 2022Article
- Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes.International journal of environmental research and public health · 2022Article
- Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis.BMJ open · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesBuilt 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.
methodsWe 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]).
resultsCompared 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.
conclusionsThe 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.
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