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.

PROJECT NARRATIVE Roads and neighborhoods with a built environment that support safe and active transportation are a major priority in low- and middle-income countries (LMICs) due to 90% of road traffic deaths occurring in these locations, especially to pedestrians and other vulnerable road users, yet data on key built environment features at a large scale are not always readily available in these settings. My career goal is to improve population health by examining the effects of the built environment and transportation on health through the adoption and use of methods that can leverage Big Data sources and answer complex, multilevel research questions by overcoming the lack of built environment data in LMICs. The proposed research uses deep learning and advanced statistical methods to create a citywide dataset of built and social environment features in Bogota, Colombia that will provide crucial data to answer questions of their impact on pedestrian injuries and deaths, as well as assessing the presence of health inequities in their distribution and that will lay the groundwork to expand these efforts to more cities in Latin America and other LMICs.