Theory

2010 is The Year of Information Visualization Theory. Here's why.

In January, my Ph.D. student Caroline Ziemkiewicz told me about an interesting observation she had made: in different papers comparing tree visualizations, treemaps came out as best, worst, or somewhere in the middle. One difference she noticed was how the questions were worded: when a levels metaphor was used, treemaps did badly; a containment metaphor, on the other hand, seemed to favor treemaps. So we decided to investigate – the result will be presented at InfoVis on Monday, October 20.

What is a visualization? The word is problematic, and there have been very few definitions that try to define this field we are working in. More importantly: what is not a visualization? It is easy to argue that anything visual is a visualization in some way – but does that mean anything? Here is a definition of visualization and a few examples to illustrate the different criteria.

While reading Jarke van Wijk's Views on Visualization^(ref), I could not help but notice the negative references he makes to the subjectivity of visualization. A visualization science on par with statistics would certainly require the elimination of any and all subjectivity. I do not think that visualization is such a science, or that it being that is even all that desirable.

The term visualization is used to mean different things in different contexts, and even visualization that is based on data can be done for different reasons and with different goals. Mixing up these different types of visualization leads to misunderstandings and confusion. Here is an attempt at teasing apart the two major types of data-based visualization, and understanding the differences.

According to one definition^(ref), engineering is making things based on scientific principles – as opposed to the intuitive making that defines a craft. Information visualization (InfoVis) is practiced like a craft today, based mostly on practical examples, but not on theoretical basics. Here is a sketch of not only InfoVis as an engineering field, but InfoVis as a science.

When peanuts are bombs, clown-shaped cake ornaments are muzzle fires, and young guys are skateboards, we are talking about representation. We take it for granted that words can refer to things or abstract concepts, and colored spots on a piece of paper can depict data. Representation is really quite remarkable, and a better understanding of it will make a big difference in how we build visualizations.