Tu Thur 3-4:15, EE 115
This course covers a wide range of statistical models and methods for data that are collected at different spatial locations and perhaps at different times. These data are called spatial or spatio-temporal data, which are prevalent in many scientific disciplines such as agronomy, plant pathology, forestry and natural resources, environmental and health studies, climatology, geology, biosecurity, etc. Due to the advance in technology, massive spatial data are collected in various disciplines, which do require novel methods to process and analyze. Consequently, spatial statistics is currently one of the most active research areas in statistics. This course will introduce the classical methods as well as some newly developed ones, and will provide ample hands-on activities. The programming language R and a few packages for analyzing spatial data will be introduced. The primary objective is for students to be able to identify appropriate methods and analyze spatial data in their research.