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 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.

Association of built environment attributes with the spread of COVID-19 at its initial stage in China

“…Road attributes, building and housing attributes, population or its density, medical facilities and services, and schools, etc., have been examined. For instance, based on street view images, Nguyen et al (2020) explored how sidewalks, dilapidated buildings and visible wires were associated with COVID-19 infection cases in USA. Through a multi-level linear model, Hamidi, Ewing, et al (2020) examined the impacts of population, activity density (population & employment per square mile), ICU beds, primary care physicians on infection rates and higher mortality rates in USA.…”

A higher ratio of green spaces means a lower racial disparity in severe acute respiratory syndrome coronavirus 2 infection rates: A nationwide study of the United States

“…Significant associations between socioeconomic, demographic, and pre-existing chronic disease factors and the racial disparity in SARS-CoV-2 infection rates have been reported by a number of ecological studies 3, 4, 5, 13, 14 . Some urban environmental factors, such as overcrowding housing conditions, living in senior living communities, living in high-density urban areas, and a long commute distance, are associated with a greater racial disparity in SARS-CoV-2 infection rates 67, 68, 69, 70, 71 . However, no study examined the relationship between green spaces and racial disparity in SARS-CoV-2 infection rates.…”

“…best of our knowledge, this study is the first to measure whether and to what extent green spaces within and beyond developed urban areas are associated with racial disparities in rates of contagious disease infection. Although a few studies have identified some built environment factors, such as crowded living conditions, staying in senior living communities, and dense urban areas, are related to racial disparity in SARS-CoV-2 infection rates67,68,70,71 , none have addressed green spaces.…”

A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques

“…Lin et al [98], and Ibrahim et al [99] figured out the meteorological factors that influence coronavirus transmission. Environmental predictors that influence the COVID-19 can be determined by surveillance of the infected area's street view image [79]. Spatiotemporal data can reveal the distribution pattern of PM2.5 air pollution during the pandemic [100].…”