The primary objective is for students to be able to distinguish different types of spatial data (geostatistical, areal, point process), quantify appropriate spatial dependence/association, and apply the classical methods as well as advanced techniques to different spatial data.

This course covers a wide range of statistical models and methods for data that are collected at different spatial locations (and at different times).

These data are called spatial  (or spatio-temporal) data, which are prevalent in many disciplines such as forestry, climatology, geology, environmental and health sciences, and economy, etc.

Topics covered include the geostatistical techniques of kriging and variogram analysis, point process methods for spatial case control, and area-level analysis.

Modes of study

- participation in course work: lectures, exercises, exam