This course is aimed at students who have a foundation in basic GIS techniques and applications and are interested in expanding their knowledge into their area of spatial decision making and visualization of the decision maps. We will start with the linkage between GIScience, spatial analysis, and decision support. We will then discuss different decision-making techniques and highlight the important distinction between conventional MCDA methods and spatially explicitly multicriteria approaches. An overview of handling spatial uncertainty as well as sensitivity analysis will be discussed.

Introduce the concepts and principles of GIS. Both theoretical and applied realms of GIS are emphasized in this course. Some important spatial analysis topics in GIS are also included.

Students in this course will learn about the pedagogy behind GIS curriculum and instruction through practice as lab assistants in an introductory GIS course. Alongside readings establishing evidence-based practices in GIS instruction, students will work to identify barriers and frustrations for GIS learners, and ways to overcome them.

This graduate-level course provides introductory exposure to the basic cartography skills used for digital map making, primarily using ArcGIS Pro and Adobe Illustrator to map physical environments as well as 3D scenes. Course learning goals include a fundamental understanding of map composure, as well as map elements such as north arrows, scale bars, and legends. General best practices for data management such as zipping and unzipping file archives and geodatabases will also be covered.

Earth surface processes and their relation to topography and landscape evolution. Focus on hillslope, fluvial, and other processes that shape Earth's surface. Field trips by arrangement.

Earth surface processes and their relation to topography and landscape evolution. Focus on hillslope, fluvial, and other processes that shape Earth's surface. Field trips by arrangement.

Not available at this time

Not available at this time

Not available at this time

Automated geography helps us to understand the complex geographic phenomena that are intractable to solve by conventional techniques. This class focuses on opportunities for taking a computational approach to the solution of complex spatial problems, often non-deterministic. Through introductory lab practices and foundational lectures, the course covers various computer-based models and techniques applicable to spatial science, including expert systems, cellular automata, agent-based modeling, genetic algorithms, visualization, and data mining.