CSC 472/672 Data Visualization
Course Outline
Fall, 1996
G. Scott Owen

1 Definitions and Goals of Visualization

For any visualization course it is important to discuss background, definitions, and goals in order to provide a common understanding of visualization. We will discuss the following subtopics in the tutorial:

  1. History of (scientific) visualization
  2. Definitions of visualization
  3. Goals of visualization

2 Abstract Visualization Concepts

It is necessary to establish a framework for the use of visualization. Students should learn how to make use of concepts and paradigms, specifically of the ones they are not yet familiar with (e.g., paradigms from Fine Arts for Computer Science students). We will discuss the following subtopics in the course:

  1. General visualization models and taxonomy
  2. Examples of specific visualization models and paradigms

3 Scientific Methods and Concepts

This theme explains the relationship between the 'real world' and the 'models' we have available in order to understand the real world and the 'empirical (data) measurements' we have of the real world. Non-science students have usually little approach to models, data concepts and reality. We will discuss the following subtopics in the tutorial:

  1. Scientific concepts: what is a model; model vs. acquiring; going from macro-to micro worlds
  2. Modeling concepts: mathematical methods to represent reality; mathematical concepts; computational models
  3. Data concepts: how to represent reality; data collections; errors

4 Aspects of Data

Various aspects of data, such as acquisition, classification, storage and retrieval of data, will be discussed. Appropriate subtopics are

  1. Acquisition of data (Simulation vs. measuring devices)
  2. Discipline-independent classification of information sources
  3. Data base issues
  4. Query languages
  5. Reliability of data

5 Visualization Techniques

This section provides tutorial participants with a wealth of ideas for visual representations and teaches them how to apply appropriate tools. We will discussthe following:

  1. Scalar and point techniques
  2. Vector techniques visualization techniques
  3. Multidimensional techniques, e.g. glyphs
  4. Volume Visualization (Rendering)
  5. Attribute Mapping

6 Human Perception and Cognition Concepts

This section will enhance the understanding of how to use graphics tools to support human perception in order to gain insight into phenomena that we seek to interpret. We will discuss the following subtopics in the tutorial:

  1. The human visual system (biological, psychophysical and cognitive issues, visual phenomena, texture and color perception)
  2. Perception theories
  3. Presenting complex information to the human visual system (e.g. data exploration, natural computing, integrated displays, using senses additional to vision)
  4. Practical considerations (e.g. expressiveness, effectiveness, interactivity, annotations, avoiding pitfalls)
  5. Evaluation methods

7 Interaction Issues

Interaction techniques are fundamental to the design and use of visualization systems. We will discuss interaction from the view point of ergonometry, HCI and hardware techniques.

8 Existing Visualization Systems/Tools

Available visualization systems will be discussed, primarily focusing on IRIS Explorer 3.0

9 Aesthetics in Visualization

The following subtopics will be discussed:

  1. Aspects of successful visualizations
  2. Comparison of good and bad visual representations

10 Related Topics

A visualization course might include fundamental aspects of mathematics and computer science. The presentation of appropriate subtopics depends on the objectives of the course and the background of the students. Appropriate subtopics may be:

  1. Information Visualization
  2. Sonification techniques
  3. Virtual Environments
  4. World Wide Web issues
  5. Data, image, and animation compression
  6. Mathematical techniques (e.g. vectors, matrices, interpolation approximation, transformations for 2- and 3-d, parametric versus implicit versus explicit representations, curves, surfaces, fractals);
  7. Computer graphics (e.g. 2-d drawing, clipping, filling; 3-d modeling, rendering, lighting; transparency, translucency; raytracing, radiosity, volume rendering; graphics standards and libraries)
  8. General computer science (e.g. user interface design; computational geometry; computer hardware architectures, input/output technologies; data structures, data models, data formats, data transfer; programming languages)