Fall

This course is for qualified students working on an individual basis with professors who have agreed to supervise such work. Graduate students doing independent work which cannot be classified as actual research will register for credit under course number 699.

MS students can take up to 3 units of GIST 699 only with advanced approval from a GIST advisor.

The GIST Capstone Workshop includes a formal report and presentation submitted in lieu of a Master’s Thesis and reflects what a student has learned from the MS-GIST program. This course focuses on addressing normative and/or scientific geographic problems, data capture, compilation and manipulation, and formulating methods and analysis to address the problem in a given timeline. 

MS students take 3 units of GIST 698

P-GIST students can take 698 only with advanced approval from a GIST advisor

This course is an introduction to the fundamentals of statistical analysis for Geographic Information Systems (GIS) and spatial data. Students will be taught elements, methods and theories of quantitative and statistical techniques used in spatial analysis and cartography, with emphasis on how to apply these tools to solve geographical, urban, and regional planning problems. This course emphasizes hands-on learning through practical lab exercises and statistical modeling projects that develop both technical proficiency and analytical thinking skills essential for spatial data analysis.

This course introduces students to the theory of geovisualization (GV) and presents a range of case study applications.  GV is intended to support spatio-temporal decision making across a range of domains, from urban planning to environmental management and climate analysis.  Students integrate their theoretical learning into the assigned Lab protocol to design, deliver and test a range of GV products.  A range of softwares are used including the ArcGIS suite and Tableau Public.

This course provides an introduction to the scientific principles and practices of remote sensing.  Topics that will be covered in this course include issues of spatial resolutions, the electromagnetic spectrum, remotely sensed sensors, spectral characteristics, digital and digitalization issues, multispectral and LiDAR image processing and enhancement, and land-use and land-cover classifications (LULC) and change detection. The course also emphasizes integration issues and analysis techniques that arise when merging remotely sensed data with geographic information systems (GIS).