Official Description: Lecture—2 hours; laboratory—6 hours. Class size limited to 25. Provides an overview of quantitative approaches in spatial data analysis. Overview of different approaches used for inference, modeling, and prediction. Also learn how to write computer programs to implement these methods.—III. (III.) Hijmans
Instructors: Robert J. Hijmans
Other info and comments: The syllabus is here
Also, Dr. Hijmans writes:
I teach “Quantitative Geography” (GEO200CN), a graduate course that is part of the core curriculum for the Geography grad group students. It is a survey course about spatial data analysis and modeling, not a “pure” statistics course. It including subjects such as point pattern analysis, kriging, inference, cellular automata and Markov chains. It has lectures, discussions, and a intensive lab (we use R).
[Non-geography] students can take the class (and they do) without taking other geo classes. The ideal preparation is intro to stats + regression, a Geographic Information Systems (GIS) class, and familiarity with R.
Dr. Hijman’s may in the future teach a course focused more on inference with spatial data, which was taught as a 298 by Robert Plant
Software used: R