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: 9979947
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9979947
• 关键词: Accounting; Alcohol or Other Drugs use; 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 resource; data sharing; data visualization; data warehouse; density; health care availability; health disparity; improved; land use; obesity risk; object recognition; physical conditioning; population based; social; social media; 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 该提案代表了邻里效应研究的垂直进步，为您生产 第一次，到目前为止，国家的邻里指标。 由于现场访问的资源启发性性质而进行的，以进行评估 社区功能以及街头图像的手动注释。 计算机视觉和大量图像数据来源的出现，我们将利用团队的 利用地理信息系统制定数据收集策略的能力来组装 全国谷歌街道查看图像的图像 美国。 在理论上和经验上都确定了建筑环境的邻里摘要 为了在创建邻里玻璃之后，对健康很重要，我们将 对使用医疗的邻国环境对健康的影响进行调查 数十万患者的记录，并考虑到易感性特征 分析我们的调查团队 - 流行病学领域的专家组成 生物信息学和计算机科学非常适合实施我们的特定研究 目的是：1）开发信息，以产生邻里质量指标； 2）措施 数据算法的垂直性并为邻里数据构建交互式地球 可视化和数据共享，3）利用邻里玻璃和大量集合 来自Intermountain Healthcare的医疗记录，以调查邻居对邻居的影响 肥胖和药物滥用。 暗示它的原因是社会，文化和构建，而不是纯粹的生物学。 介入环境更好地支持健康的可能性。 当前的年轻人队列可能会心血管疾病风险和慢性 疾病负担。我们对邻里因素对慢性的影响 条件将进一步有助于理解上下文对此健康的影响 在慢性疾病流行的最前沿。 意外中毒和心理健康健康卫生造成的促进预期的疾病进一步保证 调查风险诱发滥用毒品的环境因素 玻璃将对研究人员的邻居效果构成重大好处，从而使较大的利用 街道图像数据的未开发的潜力可以捕获构建的环境特征 用于为减少健康差异并改善健康的基于人群的策略提供信息。