Who we are
The Arizona Voter Project (AVP) is an initiative supported by the University of Arizona's School of Government and Public Policy, University of Arizona's Research Innovation and Impact, and the University of Arizona's College of Social and Behavioral Sciences. The goal of the AVP is to advance our collective understanding Arizona politics and society. The AVP consists of a team team of experts who build interactive data visualization tools and provide broader context for politics and trends in Arizona.
We believe these tools are useful for several reasons. Arizona is a rapidly changing state. When we vote, the State records whether we voted (but never who we voted for). These data are public and can provide us with valuable insights when joined with other data sources, such as U.S. Census Data, Federal Election Data, and high-quality public opinion data. The Arizona Voter Project's mission is to package this "big data" in an understandable and easy-to-digest manner, by creating tools and visualizations to learn more about politics and policy. The Arizona Voter Project is committed to providing free and accessible tools to interact with various data sources, so that we can make informed, evidence-based political decisions.
Director
Christopher Weber
Christopher Weber is the Director of the Arizona Voter Project. Dr. Weber received his PhD from Stony Brook University and specializes in American politics, political behavior, and political psychology.
Contact Chris
Key Staff
Mike Ryckman, Technical Lead
Mike Ryckman serves as the Technical Lead for the voter project, leading efforts in data management and engineering. He also holds a PhD in Political Science from the University of Arizona.
Fatih Erol, Research Scientist
Fatih Erol is Research Scientist at the Arizona Policy Lab in the School of Government and Public Policy at the University of Arizona. Dr. Erol is a collaborator on the Arizona Voter Project. Dr. Erol received his PhD from Koç University and specializes in psychology, survey research, experimental analysis, and text-as-data methods.