This first course presents the fields, and roughly follows Ward et al.'s first chapter.
Munzner is on the way to publish a book on Information Visualization. I had hoped the book would be ready earlier Munzner's book is structured around a new model of visualization that unifies design and evaluation issues in a single framework.
Visualization design is a complex problem that can be made easier by splitting concerns into four cascading levels, as shown in the left figure. The top level of problem characterization is to characterize the problems and data of a particular target domain. The next level of abstraction is to map those domain-specic problems and data into abstract and generic tasks and data types. The third level, encoding and interaction, is to design the visual encoding and interaction techniques that use the data to support those tasks. The innermost fourth level of algorithm is to create an algorithm to carry out that design efficiently.
Designing and building visualization is not just about creating nice images. Munzner again:
Computer-based visualization systems provide interactive visual representations of datasets intended to help people carry out some task more effectively. When we design a visualization, how do we gure out if we have succeeded? There are many criteria we might use. We could ask whether somebody using the system can do something better.
Threats and validation. When diving into the four levels while designing a visualization, design choice must be made. How are we sure these choices were the right ones to make? Munzner has an answer, and unfolds her four level model together with a list of threats and possible validation to these threats.
Information Visualization is a multi-disciplinary activity – and the model partly explains why. Designing a system or technique involves domain expertise, cognition, graphical design, human-computer interaction and dialog, and algorithmic efficiency. not to talk about multi-disciplinary issues that pops up when collaborating with experts from any specific domain (biology, social sciences, etc.).
Note (Munzner): This nested process model collapses algorithmic and implementation considerations together; a more detailed model could pull them apart into two separate levels.
Exercises / Assignments
This is a rather common way of looking at things shared by the InfoVis community. The good thing about it is that everybody agrees on it. Another good side of it is that the pipeline tends to conciliate both the user's perspective and the engineering perspective -how you build a viz system). The bad side of it is it focused on the automated side of InfoVis (processes and algorithmic) rather than on cognitive processes.
Here are a few picks collected on the net: