FNR/STAT 598Z Applied Spatial Statistics
TTh
10:30-11:45, BRNG B261
Spring, 2008
Instructor: Professor
http://www.stat.purdue.edu/people/faculty/zhanghao
Office Hours: Monday, Tuesday and Thursday, 1:30-2:30pm
Prerequisite: A graduate course in statistics or
probability.
Primary Audience: Students who are interested
in analyzing spatial data.
Description:
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 arise in many scientific disciplines
such as agronomy, plant pathology, forestry and natural resources,
environmental and health studies, climatology, geology, biosecurity,
etc. Spatial statistics is currently one of the most active research areas in
statistics and there has been a tremendous advancement in methodological and
computational research in spatial statistics that enables us to analyze massive
spatial data today. 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. One objective is for students to be able to identify appropriate
methods and analyze spatial data in their research.
Topics: This course covers statistical methods for georeferenced data (such as ozone measurements from
different monitoring stations), spatial point patterns (such as incidents of
plant/human disease), and areal data (such as county
statistics in the
·
Stationarity and variogram models
·
Fitting a variogram model
·
Kriging or best
linear unbiased prediction (simple kriging, ordinary kriging, and universal
kriging).
·
Kriging with
large datasets
·
Non-stationary
models
·
Spatio-temporal models
·
Multivariate
methods (direct and cross covariograms, cokriging)
·
Conditional
autoregressive models
·
Spatial point
patterns
·
Complete
randomness and Poisson processes
·
Cluster processes
and inhibition processes
·
K-function
·
Intensity
function and inhomogenous Poisson processes
·
Scan statistics
·
Simulation of
spatial processes and spatial point patterns
Grading: Final grades will be based on
homework, project and/or exams. Students
are allowed and indeed encouraged to study as a group. However, each one has to
turn in an independent work in his/her own words. For
example, although it is allowed to study together for programming, a student
needs to turn in his/her own work to show that the student is able to carry out
the programming.
The instructor reserves the right to make any changes
that he deems academically advisable.