ArticleJournal of epidemiology and community health2018
Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research.
Article in Journal of epidemiology and community health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- 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
- Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis.JMIR formative research · 2025Article
- Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019-2021 fatal collisions in the USA.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2025Article
- Built Environment Change over Time Using Google Street View Assessments of Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Cities.Journal of urban health : bulletin of the New York Academy of Medicine · 2025Article
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study.JMIR AI · 2024Article
- 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
- Examining the Role of Passive Design Indicators in Energy Burden Reduction: Insights from a Machine Learning and Deep Learning Approach.Building and environment · 2024Article
- An Improved New YOLOv7 Algorithm for Detecting Building Air Conditioner External Units from Street View Images.Sensors (Basel, Switzerland) · 2023Article
- Article
- The Weight of Place: Built Environment Correlates of Obesity and Diabetes.Endocrine reviews · 2022Review
- Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities.Scientific reports · 2022Article
- Street images classification according to COVID-19 risk in Lima, Peru: a convolutional neural networks feasibility analysis.BMJ open · 2022Article
- Article
- Residential Exposure to Urban Trees and Medication Sales for Mood Disorders and Cardiovascular Disease in Brussels, Belgium: An Ecological Study.Environmental health perspectives · 2022Article
- Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.Big data and cognitive computing · 2022Article
- Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).American journal of epidemiology · 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
- Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment.BMC public 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
12 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundNeighbourhood quality has been connected with an array of health issues, but neighbourhood research has been limited by the lack of methods to characterise large geographical areas. This study uses innovative computer vision methods and a new big data source of street view images to automatically characterise neighbourhood built environments.
methodsA total of 430 000 images were obtained using Google's Street View Image API for Salt Lake City, Chicago and Charleston. Convolutional neural networks were used to create indicators of street greenness, crosswalks and building type. We implemented log Poisson regression models to estimate associations between built environment features and individual prevalence of obesity and diabetes in Salt Lake City, controlling for individual-level and zip code-level predisposing characteristics.
resultsComputer vision models had an accuracy of 86%-93% compared with manual annotations. Charleston had the highest percentage of green streets (79%), while Chicago had the highest percentage of crosswalks (23%) and commercial buildings/apartments (59%). Built environment characteristics were categorised into tertiles, with the highest tertile serving as the referent group. Individuals living in zip codes with the most green streets, crosswalks and commercial buildings/apartments had relative obesity prevalences that were 25%-28% lower and relative diabetes prevalences that were 12%-18% lower than individuals living in zip codes with the least abundance of these neighbourhood features.
conclusionNeighbourhood conditions may influence chronic disease outcomes. Google Street View images represent an underused data resource for the construction of built environment features.
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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.