Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.
Int J Environ Res Public Health
; 17(17)2020 09 01.
Article
in English
| MEDLINE | ID: covidwho-742787
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Pandemics
/
Satellite Imagery
/
Built Environment
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Year:
2020
Document Type:
Article
Affiliation country:
Ijerph17176359
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