Assistant Director for Urban Informatics, Center for Spatial Data Science

Before pursuing his doctorate, Kevin worked as a long-range planner for two years in Manhattan, Kansas, serving as the staff liaison to the Historic Resources Board. This experience influences Kevin’s focus on research questions that can be directly applied to pressing urban problems, and on developing analyses that point to solutions that can be readily implemented by practicing planners.

Methodologically, he focuses primarily on quantitative econometric and spatial analysis approaches, including quasi-experimental regression methods, hierarchical linear modeling, spatial collocation and clustering techniques, spatial econometrics, and even some remote sensing approaches (geographic object-based image analysis).

Website: https://kevincredit.wordpress.com/

Twitter: @KevinCredit