Colin Ware's latest book Visual Thinking for Design has a promising subtitle: active vision, attention, visual queries, gist, visual skills, color, narrative, design. That's covering quite a bit of ground, and also a lot of things not usually considered in visualization. While this is a book about design, I was interested in what it could teach people in InfoVis, and I review it from that point of view.

A paper on visualization criticism just appeared in the Visualization Viewpoints section of this month's Computer Graphics and Applications (CG&A). Authors are yours truly, Fritz Drury, Lars Erik Holmquist, and David Laidlaw.

Kaiser at Junk Charts has posted an interesting challenge based on the question how to visualize an Autism diagnosis dataset in a better way (originally posted by Igor Carron at Nuit Blanche). I'm offering my own redesign of the data below, and discuss my different approach and what it tells us about the visualization of sets in general.

Another semester is ending, and another class being taught using criticism as a main component is winding down. This time, I had a good mix of computer science, design, architecture, and liberal studies students. All the comments I received regarding the critiques were very positive, and the students' progress in their visualization designs reinforces those.

Chernoff Faces are discussed in every information visualization course, and are referenced in many papers that talk about glyphs. Yet the only serious use of faces in visualization is for calibration, not for data display. Faces are so special that we better know their perceptual properties really well before we can use them, which we don't.

Social websites are all the rage right now, and are not just hyped by the media (MySpace and YouTube in particular), but there are also large amounts of money involved (again, MySpace and YouTube). But does the social model make sense for data analysis and visualization? And will users play and interact with data the way they do with other media? Two websites were launched recently to find out: Swivel and Many Eyes. Here is a first review, looking at the two sites in terms of their founders, approach, social aspects, technology, capabilities, broad appeal, and ethics.

Visualization people often talk about mapping. Mapping is the process that translates data into a visual representation, and the main challenge in the visualization of abstract data. A good mapping is one that leads to insights into the data, while a bad mapping does not. It is important, however, to keep in mind what the purpose of the depiction is, or one runs the risk of applying the wrong standards.