Using ACS Multi-Year Estimates for Neighborhood Analyses (2014)

This session, featuring a discussion from Timothy Bray, PhD, from the Institute for Urban Policy Research at the University of Texas at Dallas, will explore the methodological issues surrounding the application of the American Community Survey (ACS) data to policy and planning problems. The presentation will include both tabularized / aggregate data, as well as data analyses with the Public Use Micro Sample (PUMS) data. 

Presenter: Dr. Timothy Bray is the Director of the Institute for Urban Policy Research at the University of Texas at Dallas. He is on the faculty of the School of Economic, Political, and Policy Sciences, where he teaches in the Public Policy, Political Economy, Public Affairs, and Criminology programs. Dr. Bray received his Ph.D. in Criminology from the University of Missouri-St. Louis. Prior to earning his doctorate, Dr. Bray served as an Assistant Bureau Chief with the Illinois State Police, where he headed the strategic and operational research units. Upon leaving the State Police, Dr. Bray was awarded the Achievement Medal for his innovative approach to solving contemporary and traditional policing issues. Before heading the research operations for the Illinois State Police, Dr. Bray worked in areas of strategic planning and performance measurement, then in an advisory capacity to the department’s Deputy Director for the Information and Technology Command. In addition to state government experience, Dr. Bray has worked in city and county agencies.

Dr. Bray's current research focuses on the development of multidimensional indicators for quality of life and disparities in quality of life, increasing the collective efficacy of private-public community-based partnerships, and tools to bring vast “big data” sources to strategically direct the efforts of community-based organizations toward greater impact. In addition, Dr. Bray’s research includes the development and application of hierarchical models to explain variation in neighborhood levels of social dislocations, including assessing and controlling for spatial dependence in the data.