Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research

Neighborhood Looking Glass：用于邻里效应研究的建筑环境的 360 度自动表征

• 批准号: 10217256
• 负责人: QUYNH NGUYEN
• 金额: $ 32.97万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-06 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10217256
• 关键词: Accounting; Algorithms; Bioinformatics; Biological; Characteristics; Chronic; Chronic Disease; Cities; Collection; Communities; Community Health; Computer Vision Systems; Consumption; Data; Data Collection; Data Sources; Development; Disease; Environment; Environmental Risk Factor; Epidemic; Epidemiology; Face; Family; Food; Food Access; Geographic Information Systems; Geography; Glass; Grant; Happiness; Health; Health Food; Health Personnel; Health behavior; Health care facility; Healthcare; Image; Individual; Informatics; Investigation; Label; Literature; Manuals; Measures; Medical Records; Mental Health; Methods; Nature; Neighborhoods; Obesity; Outcome; Overdose; Patients; Physical activity; Physical environment; Premature Mortality; Process; Public Health; Quality Indicator; Research; Research Personnel; Resources; Risk; Sampling; Signal Transduction; Site Visit; Social Environment; Social Processes; Source; Structure; Substance abuse problem; System; Techniques; Testing; Time; United States; Visit; built environment; burden of illness; cardiovascular disorder risk; cohort; computer science; contextual factors; convolutional neural network; cost; crowdsourcing; data management; data mining; data repository; data resource; data sharing; data visualization; density; health care availability; health disparity; improved; land use; obesity risk; object recognition; physical conditioning; population based; social; social media; substance use; walkability; young adult

PROJECT SUMMARY/ABSTRACT This proposal represents a vertical advancement in neighborhood effects research, producing for the first time, national neighborhood indicators of the built environment. Thus far, only local studies have been conducted due to the resource-intensive nature of site visits to conduct assessments of community features and also manual annotations of street images. With the recent advancement of computer vision and the emergence of massive sources of image data, we will leverage our team’s abilities to develop a data collection strategy utilizing geographic information systems to assemble a national collection of Google Street View images of all road intersections and street segments in the United States. We will utilize this data bank, and develop informatics algorithms to produce neighborhood summaries of built environment that have been theoretically and empirically identified to be important for health outcomes. After the creation of Neighborhood Looking Glass, we will conduct investigations into the impact of neighborhood environments on health utilizing medical records from hundreds of thousands of patients and accounting for predisposing characteristics in analyses. Our investigative team—comprised of experts in the field of epidemiology, computer vision, bioinformatics, and computer science—is uniquely suited to implement the study aims. Our Specific Aims are: 1) Develop informatics techniques to produce neighborhood quality indicators; 2) Measure the accuracy of data algorithms and construct an interactive geoportal for neighborhood data visualization and data sharing, 3) Utilize Neighborhood Looking Glass and a large collection of medical records from Intermountain Healthcare to investigate neighborhood influences on the risk of obesity and substance abuse. The epidemic rise in chronic health conditions is recent and as such suggests its cause is social, cultural, and constructed rather than purely biological. Thus, we have the possibility of intervening on the environment to better support health. Recent studies suggest that the current cohort of young adults may face historically high cardiovascular disease risk and chronic disease burden. Our substantive investigation of the impact of neighborhood factors on chronic conditions will contribute further to the understanding of contextual influences on the health of this cohort at the forefront of a chronic disease epidemic. Moreover, the dramatic rise in overdoses, accidental poisonings, and mental health issues contributing to premature mortality warrants further investigation into risk-inducing environmental factors for substance abuse. Neighborhood Looking Glass will be a significant benefit to neighborhood effects researchers, harnessing the largely untapped potential of street image data to capture built environment characteristics. Results can be utilized to inform population-based strategies to reduce health disparities and improve health.

项目摘要/摘要 这项建议代表了邻里效应研究的垂直进步，为 第一次，全国街区建成环境指标。到目前为止，只有当地的研究人员 由于现场访问的资源密集型性质，进行了评估 社区功能以及街道图像的手动注释。随着最近的进步， 计算机视觉和大量图像数据源的出现，我们将利用我们团队的 利用地理信息系统制定数据收集策略的能力 全国范围内所有道路交叉口和路段的谷歌街景图像收集 美国。我们将利用这个数据库，并开发信息学算法来产生 已从理论和经验上确定的建成环境的邻里总结 对健康结果很重要。在创建了Neighborhood Look Glass之后，我们将 利用医疗资源调查社区环境对健康的影响 来自数十万名患者的记录，并说明了 分析。我们的调查团队-由流行病学、计算机视觉、 生物信息学和计算机科学--是唯一适合实现研究目标的学科。我们的特定 目标是：1)发展信息学技术以产生邻里质量指标；2)测量 数据精确度算法及构建邻域数据的交互式地理门户 可视化和数据共享，3)利用Neighbor Look Glass和大量的 来自山间医疗保健的医疗记录，以调查社区对疾病风险的影响 肥胖和药物滥用。慢性健康状况的流行是最近才出现的，因此 表明其原因是社会的、文化的和构建的，而不是纯粹的生物学。因此，我们有了 有可能对环境进行干预，以更好地支持健康。最近的研究表明， 当前的年轻人可能面临历史上较高的心血管疾病风险和慢性 疾病负担。邻里因素对慢性病影响的实证研究 条件将进一步有助于理解背景对健康的影响 处于慢性病流行前沿的人群。此外，吸毒过量的戏剧性上升， 意外中毒，以及导致过早死亡的精神健康问题，进一步证明 物质滥用危险诱发环境因素调查。邻里相望 玻璃将为邻居效应研究人员带来显著的好处，主要是利用 街道图像数据在捕捉建成环境特征方面尚未开发的潜力。结果可以是 用于为基于人口的战略提供信息，以减少健康差距和改善健康。

项目成果

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

• DOI: 10.3390/ijerph17176359
• 发表时间: 2020-09-01
• 期刊: International journal of environmental research and public health
• 作者: Nguyen QC;Huang Y;Kumar A;Duan H;Keralis JM;Dwivedi P;Meng HW;Brunisholz KD;Jay J;Javanmardi M;Tasdizen T
• 通讯作者: Tasdizen T