Geography 683 – Advanced gis lecture




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GEOGRAPHY 683 – Advanced GIS lecture
Prof. Piotr Jankowski, office: SH322

E-mail: piotr@geography.sdsu.edu

Office hours: M, 11:00 – 12:00 PM, T, 2:00 – 3:00 PM
GEOGRAPHY 683L – Advanced GIS lab

Ph.D. Associate, Arika Ligmann-Zielinska, office: SH322A

E-mail: arikalz@gmail.com

Office hours: M 12:00-1:00PM or by appointment

COURSE OBJECTIVE
This graduate level course is aimed at students who have a foundation in basic GIS techniques and applications, and are interested in expending their knowledge into the area of modeling with GIS. The course objective is to introduce geoprocessing and scripting techniques as foundational tools for developing spatial process models. Specific modeling approaches featured in the course include probabilistic modeling using Bayesian and Dempster-Shafer theorems.

STUDENTS RESPONSIBILITIES

Students are expected to actively participate in class, read assigned articles and develop a project of their own choice. The exercises accompanying and elaborating the material presented during lectures will be introduced in the lab. They provide the stepping-stones to master modeling techniques discussed during lectures. The final grade for Geog 683 will be based on the quality of project, due at the end of the course. The final grade for Geog 683L will be based on the quality of lab work.


The project should present an implementation of modeling concepts and procedures learned in the course. Students are encouraged to select project topics that reflect their respective areas of interest/research. Both individual and two-person team projects are welcome. Students must present a formal (typed) project proposal by November 6.

Course Resources

  • Lecture PPoint files and lab notes available on-line at: http://geography.sdsu.edu/People/Pages/jankowski/public_html/web683/index683.htm

  • Software: ArcGIS 9.1, Python 2.1, Agent Analyst for ArcGIS, and IDRISI Kilimanjaro are available in SH 338

  • A reading packet for the course available in the bookstore. Other readings will be provided as needed.


Course Schedule


Class meeting

Topic

Aug. 28 - PJ

Geoprocessing in ArcGIS 9.x

Sep. 11 – ALZ

Introduction to Python, Part1

Sep. 18 - ALZ

Introduction to Python, Part 2

Sep. 25 - ALZ

Introduction to Python, Part 3

Oct. 2 - PJ

Geoprocessing with Python 1

Oct. 9 - PJ

Geoprocessing with Python 2

Oct. 16 - ALZ

Geoprocessing with Python 3

Oct. 23 - PJ

Introduction to Bayes' Theorem in Spatial Modeling

Oct. 30 - PJ

Bayes' Theorem in Spatial Predictive Modeling

Nov. 6 – PJ

Application of Bayes' and Dempster-Shafer Theorems for Modeling of Landslides, Students Project Proposals due

Nov. 13 – ALZ

Agent-Based Modeling in GIS 1

Nov. 20 – ALZ

Agent-Based Modeling in GIS 2

Nov. 27 – PJ

TBA

Dec. 4 – PJ

Student Project Presentations

Dec. 11 – PJ

Student Project Presentations

Grading for Geog 683


Final grade will be based on the course project including:

25% for the originality of the project

50% for the demonstrated mastery of modeling method(s) selected for the project

15% for the quality of the project paper, and

10% for the quality of the project presentation.
Grading for Geog 683L

All labs should be completed. Students must submit a proof that they completed the lab (the type of proof will be decided every time). Lab completion is for credit/noncredit only.



There will be a total of 4 wrap-up graded labs after each course module:


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