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Built Environment, Pedestrian Injuries and Deep Learning (BEPIDL) Study

建筑环境、行人伤害和深度学习 (BEPIDL) 研究

基本信息

批准号：
10453821
负责人：
Duane Alexander Quistberg
金额：
$ 8.3万
依托单位：
DREXEL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-18 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10453821
关键词：
Adopted Adoption Affect Artificial Intelligence Big Data Cessation of life Characteristics Cities Classification Collaborations Colombia Competence Complex Computer Vision Systems Country Data Data Analyses Data Collection Data Set Data Sources Databases Discipline Education Future Goals Grant Health Human Image Image Analysis Infrastructure Injury Label Latin America Lead Location Machine Learning Mathematics Mentors Methods Modeling Neighborhoods Perception Persons Public Health Research Research Methodology Research Personnel Research Project Grants Research Training Risk Safety Social Environment Statistical Methods Time Training Transportation Typology Urban Developments Urban Health Vehicle crash Writing automated image analysis built environment burden of illness career career development computer science cost data infrastructure deep learning design digital imaging experience health inequalities high risk improved injured learning strategy low and middle-income countries neighborhood association neural network pedestrian injury population health skills social socioeconomics spatiotemporal virtual

项目摘要

PROJECT SUMMARY Road traffic injuries are a major contributor to the burden of disease globally with nearly 1.3 million deaths globally and as many as 50 million injured annually with pedestrians and cyclists in low and middle-income countries (LMICs) among the most affected. Road infrastructure of the built environment (e.g., sidewalks), neighborhood design (e.g., street connectivity) and urban development (e.g., urban sprawl) are key determinants of the risk of pedestrian injuries. In LMICs, poor road infrastructure and neighborhood design are acknowledged as being important contributors to rising numbers of road traffic injuries and deaths, but there are few studies systematically identifying and quantifying what specific features of the built environment are contributing to motor vehicle collisions in these settings. Within LMIC cities, there are often large disparities where infrastructure is improved that reflect socioeconomic characteristics, leading to health inequities in road traffic injury. The paucity of georeferenced data on the built environment in LMICs has made research on road traffic injuries more difficult, though recent advances in computer vision and image analysis combined with Big Data of publicly available, georeferenced, images of roads worldwide (e.g., Google Street View, GSV) can help overcome the paucity of data and the cost and time limitations of collecting and analyzing data on the built environment in LMICs. Automated image analysis has largely been made possible via deep learning, a subfield of artificial intelligence and machine learning and relies on training neural networks to detect and label specific objects within images. These methods can drastically reduce the barriers to citywide built environment and traffic safety research in LMIC cities, thus substantially increasing research capacity and generalizability. My career goal is to become an independent investigator in global urban health with a focus on road safety and the built environment in LMICs. I propose undertaking research and training in deep learning methods applied to public health in the setting of Bogota, Colombia: 1) Develop neural networks to create a database of BE features of the road infrastructure from image data and to create neighborhood typologies from those features; 2) Assess the association between neighborhood-level BE features and typologies and pedestrian collisions and fatalities and road safety perceptions; 3) Assess the association of neighborhood social environment characteristics with pedestrian collision and fatalities, perceptions, and BE features and typologies. I am seeking additional training in 1) developing competency in deep learning methods applied to public health; 2) creating neighborhood indictors and typologies of health and the built environment; 3) applying Bayesian spatiotemporal models to understand how neighborhood characteristics and typologies influence health; 4) develop skills in multi-country collaboration, grant writing and overseeing research projects in LMICs.

项目总结