Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA.
School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA.
Int J Environ Res Public Health. 2020 Sep 1;17(17):6359. doi: 10.3390/ijerph17176359.
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.
新冠疫情的传播并非均匀分布。社区环境可能会对风险和资源进行结构划分，从而导致新冠疫情的差异。那些允许更多人流进入某个区域或阻碍社交距离措施的社区建设环境，可能会增加居民感染病毒的风险。我们利用谷歌街景（GSV）图像和计算机视觉来检测建筑环境特征（是否有横道线、是否为非独门独户住宅、是否为单行道、是否有破旧建筑和可见的电线）。我们利用泊松回归模型来确定建筑环境特征与新冠疫情病例之间的关联。混合土地利用（非独门独户住宅）、可步行性（人行道）和物理无序（破旧建筑和可见电线）等指标与更高的新冠疫情病例数有关。较低的城市发展水平（单行道和绿色街道）等指标与较少的新冠疫情病例数有关。黑人和受教育程度低于高中的人口比例与更多的新冠疫情病例数有关。我们的研究结果表明，建筑环境特征可以帮助描述社区层面的新冠疫情风险。社会人口差异也凸显了不同人群之间的新冠疫情风险差异。计算机视觉和大数据图像源使对新冠疫情风险的建筑环境影响进行全国性研究成为可能，从而为地方决策提供信息。
