The Austin AI Housing Analysis is a Year 2 Good Systems Project that aims to build a predictive AI system that can test past and future regulatory scenarios and help inform affordable residential development policies in Austin. This talk will cover preliminary research to date into how affordable housing in Austin has changed over time and how developmental policies over the past several decades have both encouraged and discouraged affordable housing differently throughout the city.
Junfeng Jiao is an associate professor in the School of Architecture at The University of Texas at Austin. He is the chair of UT Good Systems Grand Challenge, chair of the Smart City Bridging Discipline Program, and director of the Urban Information Lab. Junfeng’s research focuses on urban informatics and machine learning, particularly how different technologies come together to create smart cities and how those affect people’s behaviors. He coined the term of “transit desert” and measured it in all major U.S. cities. His research has been supported by the National Institutes of Health, the NSF, the Department of Transportation, the Texas Department of Transportation, the Washington Department of Transportation, UT Austin, Intel, and Google and reported by popular national media outlets such as ABC, Associated Press, CNN, CityLab, Fox News, NBC, NPR, New York Times, and Wired. He received his doctorate in urban planning and design from the University of Washington.
Josh Conrad is a doctoral candidate in the School of Architecture studying new GIS methodologies for building and landscape research. He is currently a data researcher with the Urban Information Lab as well as with UT Libraries. His doctoral research studies the history and future of equity-oriented GIS data practices in historic preservation. Additionally, he works professionally as a data consultant with the historic preservation firm HHM & Associates in Austin.
Part of the Smart Cities Consortium.
Event Details
Date and Time
March 2, 2021, All Day