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.

项目概要/摘要 该提案代表了邻里效应研究的纵向进步，为 首次制定国家级街区建成环境指标。到目前为止，只有本地研究 由于实地考察评估的资源密集性， 社区特征以及街道图像的手动注释。随着近期的进步 计算机视觉和海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 全国所有道路交叉口和街道路段的 Google 街景图像集合 美国。我们将利用这个数据库，开发信息学算法来生成 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。创建 Neighborhood Looking Glass 后，我们将 利用医疗手段调查邻里环境对健康的影响 来自数十万患者的记录并解释了其中的易感特征 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——非常适合实现研究目标。我们的具体 目标是： 1) 开发信息技术来生成邻里质量指标； 2) 测量 数据算法的准确性并为邻域数据构建交互式地理门户 可视化和数据共享，3) 利用 Neighborhood Looking Glass 和大量数据集合 Intermountain Healthcare 的医疗记录，用于调查社区对风险的影响 肥胖和药物滥用。慢性病的流行病是最近才出现的，因此 表明其原因是社会的、文化的和人为的，而不是纯粹的生物学原因。因此，我们有 干预环境以更好地支持健康的可能性。最近的研究表明 当前的年轻人群体可能面临历史上较高的心血管疾病风险和慢性疾病 疾病负担。我们对邻里因素对慢性病影响的实质性调查 条件将进一步有助于理解背景​​对其健康的影响 处于慢性病流行前沿的队列。此外，药物过量的急剧增加， 意外中毒和导致过早死亡的心理健康问题值得进一步研究 调查诱发药物滥用风险的环境因素。邻里寻找 玻璃将为邻里效应研究人员带来重大好处，充分利用 街道图像数据在捕捉建筑环境特征方面尚未发挥的潜力。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