ArticleInternational journal of environmental research and public health2020
Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 92 citations in OpenAlex.
- Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.The Science of the total environment · 2022Pooled it
- Pooled 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
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Using internet-assisted geocoding of 1940 census addresses to reconstruct enumeration districts for use with redlining and longitudinal health datasets.PLOS global public health · 2025Article
- Analysis and prediction of infectious diseases based on spatial visualization and machine learning.Scientific reports · 2024Article
- Article
- A Global Meta-Analysis of the Effects of Greenspaces on COVID-19 Infection and Mortality Rates.GeoHealth · 2024Article
- 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
- Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.JAMA cardiology · 2024Article
- Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.Scientific reports · 2024Article
- Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city.Sustainable cities and society · 2023Article
- Exploring the spatial pattern of community urban green spaces and COVID-19 risk in Wuhan based on a random forest model.Heliyon · 2023Article
- COVID-19 infection rate but not severity is associated with availability of greenness in the United States.Landscape and urban planning · 2023Article
- Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities.Scientific data · 2023Article
- Associations Between Built Environment Factors and SARS-CoV-2 Infections at the Neighbourhood Level in a Metropolitan Area in Germany.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review.Progress in planning · 2023Article
- An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables.Journal of computational social science · 2023Article
- Evaluating the public acceptance of sustainable mobility interventions responding to Covid-19: The case of the Great Walk of Athens and the importance of citizen engagement.Cities (London, England) · 2023Article
- Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.
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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.