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

Quynh C. Nguyen, Yuru Huang, Abhinav Kumar, Haoshu Duan, Jessica M. Keralis, Pallavi Dwivedi, Hsien-Wen Meng, Kimberly D. Brunisholz, Jonathan Jay, Mehran Javanmardi and Tolga Tasdizen
Additional contact information
Quynh C. Nguyen: Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Yuru Huang: Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Abhinav Kumar: School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
Haoshu Duan: Department of Sociology, University of Maryland, College Park, MD 20742, USA
Jessica M. Keralis: Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Pallavi Dwivedi: Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Hsien-Wen Meng: Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
Kimberly D. Brunisholz: Intermountain Healthcare Delivery Institute, Intermountain Healthcare, Murray, UT 84107, USA
Jonathan Jay: Department of Community Health Sciences, Boston University School of Public Health, Boston, MA 02118, USA
Mehran Javanmardi: Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
Tolga Tasdizen: Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA

IJERPH, 2020, vol. 17, issue 17, 1-13

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: COVID-19; built environment; big data; GIS; computer vision; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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