Course Description
Models and theories in spatial data, including geostatistics, lattice data, spatial point patterns, and space-time data. The course will focus on random field theory, various spatial regression models, model fitting, inferences and spatial prediction, with applications to agriculture, environmental sciences, forestry, and public health.
Athena Title
SPATIAL STATISTICS
Prerequisite
STAT 4520/6520 and STAT 8260
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
Students will gain an understanding of the basic theories and methodologies of spatial statistics, and will learn to analyze spatial data using standard software.
Topical Outline
Topics to be covered include geostatistics, variograms, spatial prediction and kriging, lattice data, spatial autoregressive models, Markov random fields, image analysis, spatial point patterns, Poisson processes, Cox processes, marked point processes, space-time modeling. Additional topics may be presented at the discretion of the instructor.
Syllabus
Public CV