University with graduate programs, including doctoral programs

less than 15

This is a 3 credit senior/graduate level course with pre-requisites in statistics, programming, and basic geographic information concepts. The course has a mandatory lab (homework and a final project).

Students will be able to:

• Formulate their own hypotheses on a variety of geographic problems and establish a spatial analysis plan to test multiple hypotheses for each problem.
• Synthesize various statistical methods (e.g. on point data, continuous data, area data) to analyze their hypotheses, critique results from various methods and refine hypotheses as appropriate.
• Apply the two aforementioned goals to geographic problems beyond their strict area of expertise (e.g. a biologist working on a transportation problem).

Students need to start by identifying a spatial problem. Then they need to collect appropriate datasets. They should examine available spatial analysis techniques taught in lectures and establish a plan of action. They should follow the triangle visualize-explore-model. Combinations of methods can be used leading to a variety of results. Students need to evaluate these results and possibly identify a new approach to test.

Examples from various domains will be included (forestry, biology) so students focus on methods rather than input data.

The final class project will help them put everything together.

Skills for students entering the course:

• Quantitative skills—statistics, calculus, exposure to GIS
• Writing
• Literature Reading and Evaluation
• Working in teams
• Critical Assessment

Skills developed/improved by students during the course:

• Quantitative—spatial statistics
• Programming—Matlab
• Critical thinking, ability to generalize and apply their knowledge in other domains
• Oral Presentation

Quantitative—spatial statistics: Weekly labs and final project will help students. Interaction with instructor and TA is crucial.
Programming—Matlab: same as above
Critical thinking, ability to generalize and apply their knowledge in other domains: Cases from various domains will be presented to and analyzed by the students. Critical thinking will be supported in lectures through discussion and real-time statistical model manipulations.
Oral Presentation: Each student will select a paper and present it in the classroom. I pass along evaluation forms for other students to grade the presenter. AFTER the presentations I provide them with material on how to present. I follow up with their project presentations which seem significantly improved.

Building their confidence in problem-solving skills though step-by-step critical thinking. No other significant attitudinal goals.

Assessment is done by:

• Two evaluations in the middle and the end of the semester.
• Homework every week that lets them apply acquired knowledge.
• Lengthy final project at the end of the semester.
• Use all the above to initiate discussions with students, especially during lab and office hours. Challenge them to do better while encouraging them on their progress.