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Article

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

1
Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
2
School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
3
Department of Sociology, University of Maryland, College Park, MD 20742, USA
4
Intermountain Healthcare Delivery Institute, Intermountain Healthcare, Murray, UT 84107, USA
5
Department of Community Health Sciences, Boston University School of Public Health, Boston, MA 02118, USA
6
Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(17), 6359; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17176359
Received: 7 July 2020 / Revised: 24 August 2020 / Accepted: 29 August 2020 / Published: 1 September 2020
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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. View Full-Text
Keywords: COVID-19; built environment; big data; GIS; computer vision; machine learning COVID-19; built environment; big data; GIS; computer vision; machine learning
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MDPI and ACS Style

Nguyen, Q.C.; Huang, Y.; Kumar, A.; Duan, H.; Keralis, J.M.; Dwivedi, P.; Meng, H.-W.; Brunisholz, K.D.; Jay, J.; Javanmardi, M.; Tasdizen, T. Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases. Int. J. Environ. Res. Public Health 2020, 17, 6359. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17176359

AMA Style

Nguyen QC, Huang Y, Kumar A, Duan H, Keralis JM, Dwivedi P, Meng H-W, Brunisholz KD, Jay J, Javanmardi M, Tasdizen T. Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases. International Journal of Environmental Research and Public Health. 2020; 17(17):6359. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17176359

Chicago/Turabian Style

Nguyen, Quynh C., Yuru Huang, Abhinav Kumar, Haoshu Duan, Jessica M. Keralis, Pallavi Dwivedi, Hsien-Wen Meng, Kimberly D. Brunisholz, Jonathan Jay, Mehran Javanmardi, and Tolga Tasdizen. 2020. "Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases" International Journal of Environmental Research and Public Health 17, no. 17: 6359. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17176359

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