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

项目摘要 道路交通伤害是造成全球疾病负担的主要因素，有近130万人死亡 在全球范围内，每年有多达5000万人受伤，其中包括低收入和中等收入的行人和骑自行车的人。 受影响最严重的国家之一。建筑环境的道路基础设施（例如，人行道）， 邻域设计（例如，街道连通性）和城市发展（例如，城市蔓延）是关键 行人受伤风险的决定因素。在中低收入国家，糟糕的道路基础设施和社区设计是 被认为是造成道路交通伤亡人数不断上升的重要因素， 很少有研究系统地确定和量化建筑环境的具体特征， 在这些环境中导致机动车碰撞。在低收入国家城市内部， 在基础设施得到改善，反映社会经济特征的地方， 交通伤害。由于中低收入国家建成环境地理参考数据的缺乏， 交通伤害更困难，虽然最近在计算机视觉和图像分析结合大 全球公开可用的地理参考道路图像数据（例如，谷歌街景，GSV）可以帮助 克服数据缺乏以及收集和分析建筑物上数据的成本和时间限制 LMIC的环境。自动图像分析在很大程度上是通过深度学习实现的， 人工智能和机器学习，并依赖于训练神经网络来检测和标记特定的 图像中的物体。这些方法可以大大减少城市建筑环境的障碍， 交通安全研究在LMIC城市，从而大大提高了研究能力和推广。我 职业目标是成为全球城市健康的独立调查员，重点是道路安全， 低收入国家的建筑环境。我建议进行深度学习方法的研究和培训， 哥伦比亚波哥大的公共卫生：1）开发神经网络以创建BE数据库 从图像数据中提取道路基础设施的特征，并从这些特征中创建邻域类型; 2)评估社区级BE特征和类型与行人碰撞之间的关联 3）评估社区社会环境与交通事故的关联性 行人碰撞和死亡的特征、感知以及BE特征和类型学。我是 寻求额外的培训：1）发展应用于公共卫生的深度学习方法的能力; 2） 创建健康和建筑环境的邻里指标和类型学; 3）应用贝叶斯 时空模型，以了解邻里特征和类型如何影响健康; 4） 发展多国合作，赠款写作和监督LMIC研究项目的技能。

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.