This partnership used daily criminal activity reports to analyze and identify spatial and temporal crime patterns across campus and municipal law enforcement jurisdictions; facilitated information exchange through the creation of a Geographic Information System (GIS); and improved collective crime-reduction measures through spatial and temporal analyses of the data. In order to enable practitioners to create their own maps of crime-incident locations, the researchers created automated models capable of extracting and geocoding crime incidents. This improves efficiency and promotes continued analysis after the formal project ends. Initial research findings indicate there are numerous spatial and temporal hot-spot areas in each police jurisdiction, as well as numerous hot spots that are cross-jurisdictional. Observed crime clusters have identifiable space-time relationships, enabling the development of proactive crime-reduction methods. Preliminary cumulative assessments suggest student victimization is random, based on crimes of opportunity and consistent with current environmental criminology theories. Campus and municipal crime rates of 10 priority offenses had a consistent decline during the 2010-2011 study period; however, crime rates of other offenses increased. This increase may be due to proactive measures that resulted in more offenses being reported or observed by law enforcement officers deployed to hot-spot areas. 11 tables, 10 figures, 116 references, and appended cartographic model to automate mapping processes, classification of West Virginia Code to NIBRS categories, 2010-2011 cross-jurisdiction kernel density maps, additional Knox Space-Time Interaction test results, and data archived at the National Archive of Criminal justice Data