(Stats 679, 1 credits)
Detect and quantify spatial patterns and learn to model in the presence of such patterns. Students will gain experience with spatial point patterns (testing nonrandomness, simulating and characterizing patterns); lattice data (spatial autocorrelation and regression); and geostatistics (variograms, ordinary and universal kriging, inference, assessing assumptions, and extensions). Concepts of managing big data will be integrated throughout the course. Lessons and activities are taught in the programming language, R.
This course is part of the curriculum for: