Urban Health Risk Mapping: Predicting and Mapping Neighborhood-Scale Health Outcomes

Guest speaker Junfeng Jiao, an associate professor and director of the Urban Information Lab at the University of Texas at Austin, will discuss his research involving a machine learning system that can measure the health effects of neighborhood environments in ten major U.S. cities using public data. This system provides insight into the relationship between different health outcomes (e.g., obesity, stroke, cancer, diabetes, etc) and surrounding build environments at the census tract level. There is also an interactive website that allows users to understand how changes to their neighborhoods can affect their health; for example, if a user changes neighborhood sidewalk density, the website will show the expected changes in health behavior and outcomes, such as decreased physical activity level and increased diabetes risk.