Kevin Credit

Kevin is the Assistant Director for Urban Informatics at the Center for Spatial Data Science at the University of Chicago, as well as an Assistant Instructional Professor for GIScience there. His research covers a variety of topics related to economic development, transportation, spatial data science, and location optimization, including using predictive machine learning algorithms to better understand the economic impact of new transit lines and analyzing the spatial patterns in the recent retail “apocalypse”. Before receiving his PhD in Geography at Michigan State University, Kevin worked as a long-range planner for the City of Manhattan, Kansas, and obtained his Master’s in Regional and Community Planning from Kansas State University.

Courses taught by Kevin Credit

Area-Based Location Optimization: Urban Green Space Selection

By the end of this course you will understand the basic principles of area-based location optimization and be able to solve the knapsack, threshold, and shape problems using LINGO software. The course also shows how to map the results of these skills in QGIS.

48 Mins
AICP CM

Classical Location Theory

This course traces the key theories and conceptual models that have been developed to explain why economic activities tend to locate where they do.

169 Mins
AICP CM

Location Optimization

This course introduces the basic principles of location optimization models and provides a hands-on tutorial on point-based location optimization using QGIS and LINGO.

70 Mins
AICP CM
SACPLAN CPD

Suitability Analysis and Linear Optimization: Siting a New Transit Line

This course applies suitability analysis techniques and least-cost path analysis—which optimizes routes on linear features—to planning for and siting a new transit line.

92 Mins
AICP CM