Data Management in GIS

CPSC 695

Course Announcements:

Welcome to CPSC 695 !




bulletContact information
bulletTextbook and recommended reading
bulletCourse information
bulletCourse outline
bulletCourse evaluation
bulletUseful links

Course Textbook

 Recommended reading:

1. Spatial Databases: With Application to GIS
Philippe Rigaux, Michel O. Scholl,
Morgan Kaufmann Publishers, 2001

2. Principles of Geographical Information Systems
P. Burough R. McDonnell 1998 Oxford University Press

3. Fundamentals of Geographical Information Systems 
M.N. DeMers, et al; John Wiley & Sons, 2nd edition, 1999

4. Data Visualization in the GeoSciences
 James Carr Prentice Hall 2002

5. Statistical Analysis of Geographic Information
with Arcview GIS and ArcGIS

David Wong and Jay Lee, Wiley, 2005


Course Description

Advanced geometric algorithms for representation, analysis and visualization of GIS and other applied models Data structures such as progressive mesh, ROAM, multidimensional Delaunay triangulation, quadtree, trapezoidalization, and convex partitioning. Basic techniques such as incremental, divide-and-conquer, sweep-plane, and dimension reduction. Algorithms for surface simplification, culling, quality measurement and error metrics. 

Computational problems of computing an overlay of two subdivisions, triangulating a polygon, computing line segment intersection, and geometric database querying will be discussed. The impact of GIS characteristics on the computability of these problems will be addressed. Selected techniques for parallel algorithm implementation will also be discussed. Applications to other areas, such as modeling of complex dynamic systems, optimization and visualization will be covered. New areas such as web-vbased GIS and sensor networks will be also reviewed.

The course is strongly recommended for graduate students who perform research in computer graphics, visualization, GIS, algorithms, modeling and computational geometry areas.

Objectives and Outcomes

The objective in offering this course is to study advanced algorithms in the area of applied sciences, computer graphics and GIS. The course introduces geometric data structures used for efficient representation and analysis of terrain models. Algorithms for surface simplification, culling, quality measurement and error metrics are discussed in details. The course also reviews some known techniques for algorithm development, and includes a discussion of some parallel algorithms. 

The methods and techniques learned in the course find a variety of applications in the areas of computer graphics, modeling, and spatial databases. Some applications to simulation of complex dynamic systems, optimization and visualization will also be discussed. The assignment component of the course will involve an in-depth analysis of a selected method, writing a research paper, and making a presentation on a subject.

Ideally, on successful completion of this course, a student will be familiar with a variety of methods in representation, optimization and visualization in a variety of applications, will have a working knowledge of advanced data structures and techniques, and be prepared to apply this knowledge in related disciplines.

The course is suitable for graduate student enrolled in M. Sc or Ph. D program in CPSC Department, as well as students enrolled in graduate degree granting programs in other departments, including MGIS students. It exposes students to a variety of issues related to both theoretical foundations of GIS and a variety of industrial applications. 


Course Outline


Week 1 Sept 12-15


Meet and Greet. Course Outline.  GIS Introduction   


Algorithmic foundations of GIS. Text 1: Ch 2. What is GIS?

Week 2 Sept 19-22


Data models and representation in GIS. Text 1: Ch 2; Text 2; Text 3: Ch 2-3
 Lecture on data models 1


Advanced geometric data structures (progressive mesh, ROAM, Voronoi diagram, multidimensional Delaunay triangulation, quadtree) Text 1: Ch 5, Text 2: Ch. 9,10,11. Lecture on data models 2

Week 3 Sept 20-29


GIS Open Data Source Introduction

This lecture focuses on very useful and accessible Open GIS data sources

In addition, Open Earthquake Data also being briefly introduced.

Introduction of Google Map and Google Earth

The basics of the Google Maps interface, the fundamentals of the Google Maps API, and how to organize and translate existing information into a format that can successfully be used within Google Maps and Google Earth applications.

Week 4 Oct 6

Invited lecture

Week 6 Oct 17-20


 Algorithm design techniques (incremental, divide-and-conquer, sweep-plane, dimension reduction) Text 1: Ch5, Text 2: Ch 9,10,11.
Lecture on data structures


 Algorithm design techniques. Lecture on data models 1 Lecture on data models 2 Lecture on geometric algorithms

Week 7 Oct 24-27

bulletAlgorithmic techniques for dealing with large data sets. Text book Ch 5, Ch 7.Lecture on operations on spatial databases
bulletLectures on data quality and errors.

Week 8 Nov 1-3

bulletStatistical Analysis in GIS. Text 6. Lectures on Statistical Analysis in GIS.
bulletLecture on statistical analysis 1
bulletLecture on statistical analysis 2

Week 9 Nov 8 (Nov 10-13 reading break)

bulletClustering, statistical analysis and autocorrelation in GIS Lecture on statistical analysis and autocorrelation
bulletVariance demo


Week 10 Nov 15-22

bulletGIS hardware. Lecture on GIS Visualization Software and Hardware
bulletLecture on Multiresolution and rendering


Week 11 Nov 22-27

bulletLecture on Data mining
bulletLecture on databases, interpolation and networks.


Week 12 Nov 29-Dec 1

bulletFuture directions

Week 13 Dec 6

bulletFinal project presentations

Course Evaluation

Three components are included in the determination of the course grade.

Component Component Weight
Project 40%
Assignments/Quiz(es) 40%
Presentations 20%

Assignments/Quizzes can be in the form of a written test, a research paper analysis, a presentation on the subject, or a problem to solve over some period of time. There are two presentations in this course (each weighted 10%). First one includes a 45 min presentation on an assigned  subject, a 30 min presentation at the end of the term is related to the project. 

Assignment 1 - Term paper (10%) and Presentation (10%)

Due: Sept 27, 2016, 9:00 am

Assignment 1  can be downloaded here.

List of topics:

-GIS software/GIS hardware

-GIS databases

-Geometric data structures in GIS

-Algorithms in GIS

-GIS visualization

-Web-based GIS

-Sensor-based GIS

-Remote sensing and GIS

-GIS for Virtual Reality

-GIS and biometrics

-Security in data representation

Note: a 30 minute presentation on the chosen subject will be scheduled in October


Quiz (20%)

Sample review questions

Assignment 3 - Practical Assignment  (10%)

Due: November 21th, Monday, 9:00 am by e-mail to your instructor

Excel data file can be downloaded.


Useful GIS Software links:

TIBCO software:

GrassGIS is an open source project:

A trial version (60 days) of ArcGIS is available at :

For data analysis software, Acastat (and others) can be found at

S-plus trial version statistical analysis software S-PLUS Trial Edition 6.2 Download

As a course policy, Assignments received after the due date will not be accepted. However, due to extenuating circumstances, the assignment can be accepted up to 3 days late with a deduction of 10% for each late day.



Students are encouraged to discuss the assignments and methods of solution with other students. All work that is handed in as a solution to the assignment must be original work of the student. Any student found copying all or part of an assignment from another student or another source without proper acknowledgement of the copyright will face consequences outlined in Plagiarism/Cheating/Other Academic Misconduct sections of the University calendar.


Quizzes are essential part of this course and intended to help students to develop valuable research skills, such as ability to solve problems using knowledge obtained from the course, ability to critically analyze methods and techniques, ability to compare and contrast different solutions, ability to write a critique on a paper or a scientific article, and ability to compare different approaches to the same problem.


Individual research project is an essential component of this course. Students are expected to select a topic of research project from the course outline, which can include theoretical research in the area or applied studies (including a programming component in OpenGL, ArcView, Geo/SQL and Visual_Data).

Project requirements can be found here.

Useful links

bullet Optical Illusion Site - Green Dragon
bulletOptical Illusions
bulletVirtual Words research
bulletNew task environment research
bulletAlgorithms animation
bulletFortune sweep-plane animation
bulletHistory of Mathematics
bulletDictionary of algorithms, data structures and problems
bullet ArcView Tutorial
bulletArcGIS Tutorial
bulletGIS Software - analysis
bulletGIS Software - rendering, viewing
bulletSERI web site
bulletOpenGL Documentation
bulletOpenGL Tutorial
bulletGEO/SQL Software
bullet GE Smallworld Software
bullet Oracle Spatial Software
bullet GIS On-Line data sources
bullet AutoDesk Map
bulletDigital Atmosphere
bulletCAVE Visualization
bulletWeek1 - GIS demo
bullet Fortune sweep-line demo
bulletProf. H. Leung Guest Lecture
bulletInvited VisGRA
bulletTerrain models: visualization, reconstruction and simplification. Text 3, Ch10. Lecture on terrain rendering1.  Lecture on rendering and multi-resolution2. 
bulletTerrain models: visualization, reconstruction and simplification. Text 3, Ch10 Lecture on real-time rendering3. 
bulletLecture on real-time rendering4 




This page was last updated on 01/06/06

Department of Computer Science
University of Calgary
2500 University Dr. N.W.
Calgary, AB, T2N1N4
Office: MS 269
Phone: (403) 220-5105
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