Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.
Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.
复制标题
DOI：
10.3390/ijerph17176359
复制
发表时间：
2020-09-01
影响因子：
--
通讯作者：
Tasdizen T
中科院分区：
文献类型：
--
作者：
Nguyen QC;Huang Y;Kumar A;Duan H;Keralis JM;Dwivedi P;Meng HW;Brunisholz KD;Jay J;Javanmardi M;Tasdizen T
COVID-19的传播分布并不均匀。邻里环境可能会构成产生COVID-19差异的风险和资源。允许更多人流入一个地区或阻碍社交距离做法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（人行横道、非独户住宅、单车道道路、破旧建筑和可见电线）。我们利用泊松回归模型来确定建筑环境特征与COVID-19病例的关联。混合土地使用（非单一家庭住宅）、步行能力（人行道）和物理障碍（破旧的建筑物和可见的电线）的指标与COVID-19病例增加有关。城市发展水平较低的指标（单车道道路和绿色街道）与COVID-19病例减少有关。黑人百分比和高中以下教育程度的百分比与更多的COVID-19病例有关。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COVID-19风险。社会人口差异也凸显了不同人群之间的COVID-19风险差异。计算机视觉和大数据图像源使国家研究建筑环境对COVID-19风险的影响成为可能，为当地决策提供信息。
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.
登录
查看更多内容
DOI：
10.15585/mmwr.mm6915e3
发表时间：
2020-04-17
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Garg S;Kim L;Whitaker M;O'Halloran A;Cummings C;Holstein R;Prill M;Chai SJ;Kirley PD;Alden NB;Kawasaki B;Yousey-Hindes K;Niccolai L;Anderson EJ;Openo KP;Weigel A;Monroe ML;Ryan P;Henderson J;Kim S;Como-Sabetti K;Lynfield R;Sosin D;Torres S;Muse A;Bennett NM;Billing L;Sutton M;West N;Schaffner W;Talbot HK;Aquino C;George A;Budd A;Brammer L;Langley G;Hall AJ;Fry A
通讯作者：
Fry A
DOI：
10.1007/s11524-017-0134-5
发表时间：
2017-04-01
影响因子：
6.6
作者：
Marco, Miriam;Gracia, Enrique;Lopez-Quilez, Antonio
通讯作者：
Lopez-Quilez, Antonio
影响因子：
3.5
作者：
Nesoff, Elizabeth D.;Milam, Adam J.;Furr-Holden, C. Debra M.
通讯作者：
Furr-Holden, C. Debra M.
影响因子：
5.5
作者：
Boarnet, MG;Day, K;Oakes, M
通讯作者：
Oakes, M
影响因子：
5.4
作者：
Burdette, Amy M.;Hill, Terrence D.
通讯作者：
Hill, Terrence D.