ArticlePreventive medicine reports2019
Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes.
Article in Preventive medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Assessing the Impact of Urban Environments on Mental Health and Perception Using Deep Learning: A Review and Text Mining Analysis.Journal of urban health : bulletin of the New York Academy of Medicine · 2024Pooled 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
- Is Population Density Associated with Non-Communicable Disease in Western Developed Countries? A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis.JMIR formative research · 2025Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Estimation of free-roaming dog populations using Google Street View: A methodological study.PloS one · 2025Article
- Commentary: A road map for future data-driven urban planning and environmental health research.Cities (London, England) · 2024Article
- Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review.Environmental research · 2024Article
- Deep Learning for Epidemiologists: An Introduction to Neural Networks.American journal of epidemiology · 2023Review
- Urban visual intelligence: Uncovering hidden city profiles with street view images.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Implementing Geospatial Science and Technology to Get to Zero New HIV Infections.Current HIV/AIDS reports · 2023Review
- 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
- Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.Big data and cognitive computing · 2022Article
- The Structural Violence Trap: Disparities in Homicide, Chronic Disease Death, and Social Factors Across San Francisco Neighborhoods.Journal of the American College of Surgeons · 2022Article
- VisualCommunity: a platform for archiving and studying communities.Journal of computational social science · 2022Article
- Role of Built Environments on Physical Activity and Health Promotion: A Review and Policy Insights.Frontiers in public health · 2022Review
- Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes.International journal of environmental research and public health · 2021Article
- Improving spatial data in health geographics: a practical approach for testing data to measure children's physical activity and food environments using Google Street View.International journal of health geographics · 2021Article
- Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study.Frontiers in public health · 2021Article
- Associations between body mass index, physical activity and the built environment in disadvantaged, minority neighborhoods: Predictive validity of GigaPan® imagery.Journal of transport & health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density. At the county level, greater presence of highways was related to lower chronic diseases and premature mortality. Areas characterized by street view images as 'rural' (having limited infrastructure) had higher obesity, diabetes, fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the census tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.