IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i17p6359-d407130.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/17/6359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/17/6359/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Borck, Rainald & Schrauth, Philipp, 2021. "Population density and urban air quality," Regional Science and Urban Economics, Elsevier, vol. 86(C).
    2. David Cyranoski & Alison Abbott, 2003. "Apartment complex holds clues to pandemic potential of SARS," Nature, Nature, vol. 423(6935), pages 3-4, May.
    3. Powdthavee, Nattavudh & Oswald, Andrew J., 2020. "Is there a link between air pollution and impaired memory? Evidence on 34,000 english citizens," Ecological Economics, Elsevier, vol. 169(C).
    4. Burdette, Amy M. & Hill, Terrence D., 2008. "An examination of processes linking perceived neighborhood disorder and obesity," Social Science & Medicine, Elsevier, vol. 67(1), pages 38-46, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carozzi, Felipe & Roth, Sefi, 2023. "Dirty density: Air quality and the density of American cities," Journal of Environmental Economics and Management, Elsevier, vol. 118(C).
    2. Frisha Abkar & Sajjad ur Rahman & Ahsan Naveed & Hira Rasheed & Syed Ashar Mehfooz, 2019. "Evaluation of Oral Microflora in Obese and Non- Obese Humans from District Faisalabad, Pakistan," Current Research in Diabetes & Obesity Journal, Juniper Publishers Inc., vol. 10(1), pages 12-16, March.
    3. Aidana Tleuken & Galym Tokazhanov & Mert Guney & Ali Turkyilmaz & Ferhat Karaca, 2021. "Readiness Assessment of Green Building Certification Systems for Residential Buildings during Pandemics," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
    4. Ahlfeldt, Gabriel M. & Gobillon, Laurent, 2021. "Introduction to the Special issue: “Emerging Trends in Urban Economics”," Regional Science and Urban Economics, Elsevier, vol. 90(C).
    5. Grace Lordan & Debayan Pakrashi, 2015. "Do All Activities “Weigh” Equally? How Different Physical Activities Differ as Predictors of Weight," Risk Analysis, John Wiley & Sons, vol. 35(11), pages 2069-2086, November.
    6. Higney, Anthony & Hanley, Nick & Moro, Mirko, 2022. "The lead-crime hypothesis: A meta-analysis," Regional Science and Urban Economics, Elsevier, vol. 97(C).
    7. Walker, David M. & Allingham, David & Lee, Heung Wing Joseph & Small, Michael, 2010. "Parameter inference in small world network disease models with approximate Bayesian Computational methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 540-548.
    8. Carozzi, Felipe & Roth, Sefi, 2019. "Dirty density: air quality and the density of American cities," LSE Research Online Documents on Economics 103393, London School of Economics and Political Science, LSE Library.
    9. von Hippel, Paul T. & Lynch, Jamie L., 2014. "Why are educated adults slim—Causation or selection?," Social Science & Medicine, Elsevier, vol. 105(C), pages 131-139.
    10. Zakaria Sorgho & Joe Tharakan, 2022. "Do PTAs with Environmental Provisions Reduce GHG Emissions? Distinguishing the Role of Climate-Related Provisions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(3), pages 709-732, November.
    11. Blaudin de Thé, Camille & Carantino, Benjamin & Lafourcade, Miren, 2021. "The carbon ‘carprint’ of urbanization: New evidence from French cities," Regional Science and Urban Economics, Elsevier, vol. 89(C).
    12. Carozzi, Felipe & Roth, Sefi, 2020. "Dirty Density: Air Quality and the Density of American Cities," IZA Discussion Papers 13191, Institute of Labor Economics (IZA).
    13. Galym Tokazhanov & Aidana Tleuken & Mert Guney & Ali Turkyilmaz & Ferhat Karaca, 2020. "How is COVID-19 Experience Transforming Sustainability Requirements of Residential Buildings? A Review," Sustainability, MDPI, vol. 12(20), pages 1-20, October.
    14. O'Brien, Daniel T. & Farrell, Chelsea & Welsh, Brandon C., 2019. "Broken (windows) theory: A meta-analysis of the evidence for the pathways from neighborhood disorder to resident health outcomes and behaviors," Social Science & Medicine, Elsevier, vol. 228(C), pages 272-292.
    15. Rainald Borck & Philipp Schrauth, 2022. "Urban pollution: A global perspective," CEPA Discussion Papers 60, Center for Economic Policy Analysis.
    16. Dulin-Keita, Akilah & Clay, Olivio & Whittaker, Shannon & Hannon, Lonnie & Adams, Ingrid K. & Rogers, Michelle & Gans, Kim, 2015. "The influence of HOPE VI neighborhood revitalization on neighborhood-based physical activity: A mixed-methods approach," Social Science & Medicine, Elsevier, vol. 139(C), pages 90-99.
    17. Echeverría, Lucía & Gimenez-Nadal, J. Ignacio & Molina, José Alberto, 2022. "Green mobility and well-being," Ecological Economics, Elsevier, vol. 195(C).
    18. Bellani, Luna & Ceolotto, Stefano & Elsner, Benjamin & Pestel, Nico, 2021. "Air Pollution Affects Decision-Making: Evidence from the Ballot Box," IZA Discussion Papers 14718, Institute of Labor Economics (IZA).
    19. Klingen, Joris & van Ommeren, Jos, 2020. "Urban air pollution and time losses: Evidence from cyclists in London," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    20. Johnston, D.W. & Lordan, G., 2012. "My body is fat and my wallet is thin: The link between weight perceptions, weight control and income," Health, Econometrics and Data Group (HEDG) Working Papers 12/27, HEDG, c/o Department of Economics, University of York.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6359-:d:407130. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.