Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

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

SUBMITTER: Nguyen QC 

PROVIDER: S-EPMC7504319 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature