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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Quynh C. Nguyen & Yuru Huang & Abhinav Kumar & Haoshu Duan & Jessica M. Keralis & Pallavi Dwivedi & Hsien-Wen Meng & Kimberly D. Brunisholz & Jonathan Jay & Mehran Javanmardi & Tolga Tasdizen, 2020. "Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases," IJERPH, MDPI, vol. 17(17), pages 1-13, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6359-:d:407130
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    References listed on IDEAS

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    Cited by:

    1. Jinyao Lin & Yaye Zhuang & Yang Zhao & Hua Li & Xiaoyu He & Siyan Lu, 2022. "Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    2. Bopaki Phogole & Kowiyou Yessoufou, 2023. "Greener Neighbourhoods Show Resilience to the Spread but Not Severity of COVID-19 Infection in South Africa," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    3. Md Amiruzzaman & Ye Zhao & Stefanie Amiruzzaman & Aryn C. Karpinski & Tsung Heng Wu, 2023. "An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables," Journal of Computational Social Science, Springer, vol. 6(1), pages 315-337, April.
    4. Jingjing Wang & Xueying Wu & Ruoyu Wang & Dongsheng He & Dongying Li & Linchuan Yang & Yiyang Yang & Yi Lu, 2021. "Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    5. Aminou Arouna & Souleïmane A. Adekambi & Rachidi Aboudou & Wilfried Gnipabo Yergo & Gaudiose Mujawamariya, 2024. "Impact assessment of the COVID-19 pandemic on the income and food security of rice smallholder producers in five African countries," SN Business & Economics, Springer, vol. 4(11), pages 1-20, November.
    6. Thu T. Nguyen & Quynh C. Nguyen & Anna D. Rubinsky & Tolga Tasdizen & Amir Hossein Nazem Deligani & Pallavi Dwivedi & Ross Whitaker & Jessica D. Fields & Mindy C. DeRouen & Heran Mane & Courtney R. Ly, 2021. "Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes," IJERPH, MDPI, vol. 18(19), pages 1-13, October.

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