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Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Author

Listed:
  • Xiaohe Yue

    (Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA)

  • Anne Antonietti

    (Walt Whitman High School, Bethesda, MD 20817, USA)

  • Mitra Alirezaei

    (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)

  • Dapeng Li

    (Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA)

  • Leah Nguyen

    (Department of Health Policy and Management, University of Maryland School, College Park, MD 20742, USA)

  • Heran Mane

    (Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA)

  • Abby Sun

    (Public Health Science Program, University of Maryland School, College Park, MD 20742, USA)

  • Ming Hu

    (School of Architecture, Planning & Preservation, University of Maryland School, College Park, MD 20742, USA)

  • Ross T. Whitaker

    (School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA)

  • Quynh C. Nguyen

    (Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA)

Abstract

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

Suggested Citation

  • Xiaohe Yue & Anne Antonietti & Mitra Alirezaei & Tolga Tasdizen & Dapeng Li & Leah Nguyen & Heran Mane & Abby Sun & Ming Hu & Ross T. Whitaker & Quynh C. Nguyen, 2022. "Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes," IJERPH, MDPI, vol. 19(19), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12095-:d:924038
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    References listed on IDEAS

    as
    1. Lynn Phan & Weijun Yu & Jessica M. Keralis & Krishay Mukhija & Pallavi Dwivedi & Kimberly D. Brunisholz & Mehran Javanmardi & Tolga Tasdizen & Quynh C. Nguyen, 2020. "Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States," IJERPH, MDPI, vol. 17(10), pages 1-10, May.
    2. Stephanie L. Mayne & Angelina Jose & Allison Mo & Lynn Vo & Simona Rachapalli & Hussain Ali & Julia Davis & Kiarri N. Kershaw, 2018. "Neighborhood Disorder and Obesity-Related Outcomes among Women in Chicago," IJERPH, MDPI, vol. 15(7), pages 1-12, July.
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