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Courses : Syllabi : 683/683L

Geography 683 Advanced Concepts in GIS

Instructor

Dr. Piotr Jankowski

Course Description

This graduate level course is aimed at students who already have a foundation in GIS concepts, techniques, and applications, and are interested in expending their knowledge of application development with GIS components. The course focuses on geoprocessing and scripting techniques as foundational tools for automating geoprocessing tasks and developing customized geoprocessing tools and workflow. The course material is organized into three modules: (1) learning Python scripting, (2) geoprocessing with Python scripting, and (3) Bayesian Theorem in spatial modeling.

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

Prerequisites

Geography 484 and Computer Science 108

Grading

Your grade in the 683 course will be based on the following elements:

Lab completion is for credit/noncredit only. To earn the lab credit students must achieve the total lab score of 70% or better. Students will submit a proof of completing each lab assignment. There will be three wrap-up labs concluding each course module:

  1. Introduction to Python for spatial-analysis and modeling
  2. Geoprocessing with Python
  3. Bayesian modeling

Books and Materials

Lecture PowerPoint files and lab notes available on Blackboard.

Weekly Topics

Week Topic
Week One Python 1: Basic Concepts
Week Two Python 2: Sequences, Modules and Files in Python
Week Three Python 3: Object-oriented Programming in Python
Week Four Python 4: Numeric Python
Week Five Geoprocessing 1: Model Builder
Week Six Geoprocessing 2: Introduction to Geoprocessor Object
Week Seven Geoprocessing 3: The Geoprocessor Programming Model and its Objects
Week Eight Geoprocessing 4: Debugging and Error Handling
Week Nine Geoprocessing 5: Developing Geoprocessing Applications
Week Ten Introduction to Bayes' Theorem
Week Eleven Bayes' Theorem in Spatial Modeling
Week Twelve Student Project Consultations
Week Thirteen Student Project Presentations
Week Fourteen Student Project Presentations

The statements found on this page/site are for informational purposes only. While every effort is made to ensure that this information is up to date and accurate, official information can be found in the university publications.