360-Degree Automated Characterization of the Built Environment

February 20, 2020
Mission Hall 2109

Dr. Nguyen will discuss the use of Google Street View images for creating national data on neighborhoods and the examination of neighborhood effects on health.

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 31 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density.

At the county level, we found that features of the built environment (walkability, physical disorder, urban development) have significant impacts on health behaviors and health outcomes. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

Check out the pilot study.

Event Type: 
PhD Program Sponsored Event