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

Xiaohe Yue, Anne Antonietti, Mitra Alirezaei, Tolga Tasdizen, Dapeng Li, Leah Nguyen, Heran Mane, Abby Sun, Ming Hu, Ross T. Whitaker and Quynh C. Nguyen ()
Additional contact information
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

IJERPH, 2022, vol. 19, issue 19, 1-18

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.

Keywords: built environment; big data; GIS; computer vision; structural determinants of health; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/19/12095/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/19/12095/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:19:p:12095-:d:924038

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().