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Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases
Quynh C Nguyen1, Yuru Huang1, Abhinav Kumar2
1Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA.
International Journal of Environmental Research and Public Health
|September 5, 2020
Summary
Neighborhood built environments significantly impact COVID-19 spread. Features like mixed land use and disorder increased cases, while lower urban development decreased them, highlighting the need for data-driven local decision-making.
Area of Science:
- Environmental Science
- Public Health
- Epidemiology
Background:
- COVID-19 disparities are linked to neighborhood environments.
- Built environments influence virus transmission through population flow and social distancing.
- Understanding these links is crucial for public health interventions.
Purpose of the Study:
- To investigate the association between neighborhood built environment features and COVID-19 cases.
- To identify specific built environment characteristics that increase or decrease COVID-19 risk.
- To explore the role of sociodemographic factors in COVID-19 disparities.
Main Methods:
- Utilized Google Street View (GSV) images and computer vision to identify built environment features.
- Employed Poisson regression models to analyze associations between built environment characteristics and COVID-19 cases.
- Incorporated sociodemographic data (percent Black, education level) into the analysis.
Main Results:
- Mixed land use, walkability (sidewalks), and physical disorder (dilapidated buildings, visible wires) were associated with higher COVID-19 cases.
- Lower urban development indicators (single-lane roads, green streets) were linked to fewer COVID-19 cases.
- Higher percentages of Black residents and those with less than a high school education correlated with increased COVID-19 cases.
Conclusions:
- Built environment characteristics can effectively characterize community-level COVID-19 risk.
- Sociodemographic disparities significantly contribute to differential COVID-19 risk.
- Computer vision and big data enable national-scale studies to inform local public health decisions.
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