ST733-001:  
Applied Spatial Statistics
Spring Session, 2005



Course:

ST733-001 Applied Spatial Statistics

Time:

TH from 2:35 to 3:50 p.m.

Place:

3216 Broughton Hall

 

 

Instructor:

Sujit Ghosh

Email:

sghosh@stat.ncsu.edu

Telephone

515-1950

Office:

203A Patterson Hall

Office hours:

Tuesday and Thursday 11:10 - 12:10 or by appointment

 

TA:

Jun Yoshizaki

Email:

jyoshiz@stat.ncsu.edu

Telephone:

513-4833

Office:

Statistics Tutorial Center, 009 Patterson Hall

Office hours:

Monday 10:15 - 12:15 or by appointment


Class links: Lectures & Assignments| Ask a question | Private tutors

Course prerequisite:  ST512

Optional text: Schabenberger, O. and Gotway, C. A. (2005).   Statistical Methods for Spatial Data Analysis. CRC Press.  (ISBN:1584883227)

Homework: Homework will normally be assigned (as indicated on the homework page) at the end of class each Thursday (due on Tuesday). Unexcused late homework will not be accepted. The final homework average will be computed after dropping the two lowest grades.

Examinations: Examinations will be closed book and closed notes.   However students will be permitted to bring one 8½ by 11 inch sheet of notes (both sides) to the midterm exam and two to the final exam. The final exam will be cumulative, but weighted towards the materials covered after the midterm.  

Practice Problems: [Practice Problems - I | Practice Problems - II]


Exam schedule:
Midterm exam
Tuessday, March 15
2:35-3:50 p.m.
In-class
Syllabus: Lectures 1-14
Final exam
Tuesday, May 3
1:00-4:00 p.m.
Take Home
Syllabus: Lectures 1-30

Asking questions: If you have questions about lectures, homework assignments, exams, procedures or any other aspect of the course please log onto http://courses.ncsu.edu/ , follow the links to "ST" and "ST733" and click on "Message Board".  Then click on "Post New Topic", enter your question in the Message box, and click on "Submit Message".  You will receive a response from me or another student. Everyone in the class will be able see your question and the response.

Anonymous mail: If you wish to send me an anonymous suggestion or reminder, send email to st733-001-comments@wolfware.ncsu.edu. The system will remove mail headers, but you must remember to removes your signature and other identifying information.

Grading System:  Final grade will be based on:

Final Semester Score = (3xHW + 3xM + 4×F)/10

where HW is the homework average (out of 100) after dropping the two lowest scores and M and F are the scores (out of 100) on the midterm and the final exam.  Grades will be assigned on the ± scale.

Auditing:  Auditors are expected to attend class regularly and submit homework on the same schedule as the other students.  The final grade for auditors (AU or NR) will be based on their final homework average.   A homework score of 75 or better is required for an AU.

Policy on Academic Integrity:  The University policy on academic integrity is spelled out in Appendix L of the NCSU Code of Student Conduct. For a more though elaboration see the NCSU Office of Student Conduct website.  For this course group work on homework is encouraged.  However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own.

Students with Disabilities: Reasonable accommodations will be made for students with verifiable disabilities.  In order to take advantage of available accommodations, students must register with Disability Services for Students (DSS), 1900 Student Health Center, CB# 7509, 515-7653.

Reference material (Have requested these be on reserve at DH Hill Library):


Millard, S. P. and Neerchal, N. K. (2000). Environmental Statistics with S-PLUS. CRC Press

Kanevski, M. and Maignan, M. (2004). Analysis and Modelling of Spatial Environment Data. CRC Press


Course objectives:  

The objective of this course, ST733, is to develop a calculus-level understanding and working knowledge of spatial models. The course provides an introduction to the rudiments of statistical inference based on spatially correlated data. Mehtod of estimation and testing will be developed for geostatistical models based on variograms and spatial autogressive models. Concepts, methods and applications are emphasized, rather than theory.   Successful completion of this course will provide you with a foundation for understanding spatial statistical inference material presented in other courses.

Students taking the course will have completed both ST512.

Syllabus:  In ST733 we shall complete the following concepts.
  • Introduction to Spatial Data: Types of spatial data, autocorrelation functions, stationarity, isotropy.
  • Spatial Predictiction: Optimal prediction, ordinary and universal kriging, cokriging.
  • Autoregressive Models: Lattice data, Simultaneous and Conditional Models, Parameter Estimation.
  • Spatial Regression: Linear and generalized linear models with correlated data, Estimation and testing.
  • Spatial Point Patterns: Random, Aggregated and Regular Patterns, Complete Spatial Randomness, second order propperties.

Last updated on: February 25, 2005