Stat 5810, Section 001 - Applied Spatial Statistics (Fall 2000) - Syllabus

Instructors:

Primary Instructor:
Dr. Jürgen Symanzik
Office: Lund 325
Phone: 797-0696
e-mail: symanzik@sunfs.math.usu.edu
http://www.math.usu.edu/~symanzik/

Second Instructor:
Dr. Kevin Hestir
Office: Lund 220
Phone: 797-2826
e-mail: hestir@sunfs.math.usu.edu
http://www.math.usu.edu/people/hestir.html

http://www.math.usu.edu/~symanzik/teaching/2000_spatial/statspatial.html

Text:

T. C. Bailey, A. C. Gatrell (1995): Interactive Spatial Data Analysis, Longman/Wiley, New York.

Recommended for S+SpatialStats:
S. P . Kaluzny, S. C. Vega (1997): S+SpatialStats, Springer, New York.

Both books are available through the Web (e.g., bestbookbargains.com, bn.com or amazon.com) and should ship within a few days.

Classes:

Monday August 28, 2000 - Friday December 8, 2000.

The class will meet for 2 hours once per week in a regular classroom. The 3rd lecture each week will take place in a computer lab. Times and rooms will be finally scheduled after the first organizational meeting. It is to be expected that the class and lab sessions will take place late in the afternoons.

An organizational meeting will be held on Monday 8/28/00 at 4pm in room 315 in Lund Hall. If you cannot attend this first meeting but would like to take this class, please let us know by e-mail before 8/28/00 which days and times will work for you as regular meeting time.

If you want to register before the first meeting, the index number for this class is 19925.

Credits:

3 Credits.

Extra Fee:

There will be a $ 15 extra computer fee since we are using one of the computer labs from Natural Resources.

Course Objectives:

This class will provide a first insight into applied spatial statistics. We will use a lot of computer software and practical examples to provide a basic understanding of existing tools and solutions for spatial statistics.

We plan to discuss the following topics (among others):
- Introduction:
  - Spatial vs. non-spatial data analysis.
  - General concepts in spatial data analysis.
  - Computers, GIS, and visualization for spatial data analysis.
- Analysis of Spatial Point Patterns (SPP):
  - Visualizing SPP.
  - Exploring SPP (e.g., nearest neighbor distances, K function).
  - Modelling SPP (e.g., complete spatial randomness and other models).
- Analysis of Spatially Continuous Data (SCD):
  - Visualizing SCD.
  - Exploring SCD (e.g., spatial moving averages, variogram and covariogram).
  - Modelling SCD (e.g., trend surfaces, variogram models, kriging).
- Additional material from research articles and other books will be added in the course of the semester.
We will work with the following software:
- S+SpatialStats.
- The ArcView/XGobi/XploRe Environment.
This course should be of equal interest to advanced undergraduate students and graduate students - from majors such as Statistics, Natural Resources, Geology, Agriculture, Family Life, and everybody else who is working with spatial statistical data.

Course Requirements:

Prerequirements: Participants should have some basic statistical knowledge at the Stat 3000 level. Having some working experience with S-Plus (or any other statistical package) or ArcView (a Geographic Information System) would be beneficial.
Class Attendance: You are responsible for all material that has been assigned as required reading or presented in the lecture.
Grading: There will be some "regular" homework assignments (simple exercises, small computations, etc.) that make up 20% of your overall course grade.

Most of the time, students will be required to work in groups of 3 or 4 students on practical problems and a project. It is assumed that each group consists of students from different departments such that one person may be familiar with S-Plus, another person may be familiar with ArcView, and another student has advanced statistical knowledge but little experience with any software. Groups have to present their results in class. 60% of your grade will be given based on these presentations and underlying written reports.

Homeworks, solutions to practical problems, and projects will be collected in class on the due date that will be stated on each assignment. Homework and projects mailed in, sent by FAX, or sent by e--mail by the due date will be accepted as well. In general, late homework will not be accepted.

20% of your grade will be based on a comprehensive final (in the computer lab) that checks for a basic understanding of concepts and use of software in applied spatial statistics.
Disabilities: If a student has a disability that will likely require some accommodation by the instructor, the student must contact the instructor and document the disability through the Disability Resource Center, preferably during the first week of the course. Any requests for special considerations relating to attendance, pedagogy, taking of examinations, etc. must be discussed with and approved by the instructor. In cooperation with the Disability Resource Center, course materials may be provided in alternative formats - large print, audio, diskette or Braille.

Last Update August 28, 2000.