The built environment is an important modifiable determinant of human health, yet our ability to understand its effects on human health have been limited by the lack of scalable data on specific components (and exposures) of the built environment. The emergence of ubiquitous geo-referenced imagery in the United States (e.g. Google Street View Imagery), combined with recent advances in image processing using deep learning algorithms, offers unprecedented opportunity for measuring street-level built environment features at scales needed for population-based research. To develop and demonstrate the potential of deep learning algorithms for environmental health research we will: develop methods to assess green space features using street view imagery and deep learning algorithms; create new deep learning algorithms to predict urban green space quality, stress reduction and restorative potential; and apply new street view measures to 9,070 adult Twin Pairs in the Washington Twin Registry to determine associations between green space and mental health. Our proposed study will dramatically move the field of environmental health forward by provided a completely new, transferable and scalable exposure assessment method for assessing built environment exposures relevant to human health and provide robust information on how urban green space influences mental health. Overall, our new approach will provide rich new data sources for environmental epidemiologists, city planners, policy makers and neighborhoods and communities at large.

Public Health Relevance

The built environment is an important determinant of human health, yet our ability to measure specific components of the built environment relevant to health is limited. The availability of street view imagery, combined with recent advances in image processing using deep learning algorithms, offers unprecedented opportunity for measuring detailed built environment features at scales needed for population-based research. Here we develop such approaches for green space and evaluate associations with mental health using a unique Twin analysis.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21ES029722-02
Application #
9998736
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Cui, Yuxia
Project Start
2019-08-17
Project End
2021-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
2
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Oregon State University
Department
Biology
Type
Schools of Public Health
DUNS #
053599908
City
Corvallis
State
OR
Country
United States
Zip Code
97331