Colin Ware is a well-known researcher in Information Visualization (InfoVis), and I consider him the one with the most scientific approach in the field. His book Information Visualization: Perception for Design is widely read and used in visualization courses, and is without doubt the most thorough treatment of the perceptual and cognitive psychology foundations of visualization (and design, for that matter). He was also kind enough to contribute a list of influences to this site, where he mentioned the book he was working on at the time.

(Why) Is This An InfoVis Book?

My interest in this book comes from several terms mentioned in the title and subtitle. I think that visual thinking is marginalized in visualization, where we often present particular data rather than provide the visual means for solving problems. Design and design skills are also still underappreciated in InfoVis. Visual gist, narrative, etc. are things people think and talk about, but very little of that translates into the work being done in InfoVis.

The Book

This is a well-designed book by somebody who knows exactly what he wants. In the preface, Ware talks about how he placed the images in the text so they would appear where they are needed, without the need for "See Figure x" to send the reader hunting for the right image. He uses that to great effect to set up little experiments where the reader has to read the instructions at the bottom of one page and only sees the image when turning the page. Most figures really are were they belong, but some are not, and that is a lot more apparent when so much emphasis is put on figure placement.

The writing is vivid and very readable. This is a book for an audience with a vast range of backgrounds, and Ware does not assume much previous knowledge or a great tolerance for jargon.

However, the book also feels superficial in places. A lot of the usual basics are only skimmed over, and that helps make the book manageable and get to the important parts, but some readers will want to know more and will not be provided with many pointers where to look.

Chapter By Chapter

Here is a brief summary of every chapter, with some thoughts on each of them. I am only mentioning topics that I found particularly interesting and/or relevant, more things are certainly covered in the book.

Chapter 1 dives right into cognition and change blindness to discuss visual memory and introduces the concept of the world as its own memory, which we constantly query as needed instead of keeping a model in our heads. Ware compares the eye to a digital camera, which is generally a bad idea, but he makes it work by introducing the concept of the brain pixel. The mixed bottom-up and top-down aspects of visual processing are also discussed and used to present a first overall model of vision.

Chapter 2 describes the parallel processing that takes place in the brain, the different pathways involved and how eye movement planning works. This is the basis for a discussion of features that "pop out," i.e., pre-attentive features and some of the mechanisms behind them (Ware points out that pre-attentive is really a misnomer, but it is probably too late to change the term now). A thorough model of visual search is constructed from the underlying mechanisms described in the chapter. This is also the first chapter that has a section on concrete examples of applying the presented information to design questions in InfoVis and visual design more generally.

Chapter 3 starts out discussing spatial organization of information, then drills down into a lowest-level description of edge detection on the neuron level to quickly bounce back to high-level tasks like texture detection, pattern learning, and all the way to visual metaphors. That is a lot of ground to cover, and it feels a little superficial. This chapter in particular seems to call for more details or at least more pointers to further reading, which are missing (see below).

Chapter 4 deals with some of the perceptual effects of color and explains, among other things, why we can see more detail in greyscale than color images. There are a lot of useful figures that illustrate the described phenomena. The interesting connection between names of colors in most languages and the number of colors that can be easily recognized and distinguished is worth pointing out, and provides a good rationale for a limit on the number of colors used.

Chapter 5 goes into depth perception by discussing depth cues and the role of motion in depth perception. The fact that we really only perceive a 2.5D world (or even less, Ware argues it's closer to 2.05D) is discussed and why 2.5D design makes sense. This chapter also talks about how depth perception is only really necessary if we make use of it for movements like grasping, which Ware argues is the reason for many 3D technologies like CAVEs and 3D movies failing (or at least not being the revolution everybody thought they would be).

Chapter 6 is about 3D objects, geons, as well as short-term (working) and long-term visual memory. Here like in most other chapters, Ware offers design tips related to the content, which are not just tacked on the discussion of the underlying mechanisms. One thing that confused me about this chapter was the assertion that most of what we see is already in our heads - how does that work with what was said in chapter 1, that the world is its own memory? A bit more discussion would have been useful here.

Chapter 7 tackles visual and verbal narrative. It starts out by questioning the old adage that a picture is worth a thousand words. Of course, a thousand words (or even just a handful) can express many things a picture simply cannot, or not easily. So deciding when which should be used is an important question - at least in design, but perhaps also in visualization. But I felt that this chapter didn't quite live up to my expectations because it just stays too superficial. It talks about film directing and work done on assembly instructions for furniture by Tversky and others, but leaves the connection with more abstract kinds of representation open. I also found the treatment of sign language too superficial. While Ware talks about how it uses visual abstractions, it seems a strange choice for this chapter (it uses the same key parts of the brain as spoken language, despite its different modality), and there are many fascinating things about it (like the use of location for pronouns) that might inspire some new ideas in design.

Chapter 8 calls creative visual thinking "meta-seeing," and essentially switches the book's point of view from the recipient to the designer. Random scribbling is discussed as a way to find starting points, as well as recognizing figures by adding little "props," like beaks and eyes. It also talks about some fairly high-level design topics like spiral design and critique. This chapter in particular provides good insights into the design world for non-designers like InfoVis researchers.

Chapter 9, "The Dance of Meaning," is a review of the previous chapters, which I did not find necessary for a book of this length, even though it adds new examples and other information. There is an interesting example of using static representation vs. animation in a study of the behavior of humpback whales, which I felt could have been discussed in more detail in the main part of the book. Ware is a bit too modest in talking about his own work and misses some opportunities to illustrate and develop further some of the topics in the book because of that.

Further Thoughts

There are quite a few typos and other small errors in the book. Ware consistently misspells Anne Treisman's name, which is especially problematic because he uses it to showcase how a difference in orientation pops out (interestingly, he also misspelled her name in his previous book's text, but got it right in the references). I also found almost two dozen errata on my first reading.

There is also a lack of references, especially given the introductory nature of the book. While I like the sparse use of marginal notes and lack of footnotes (for the same reason figures should be where they are referenced), additional references in the back would not have hurt. Image credits could also be improved. For one of the images, the visible man is mentioned, but no explanation is given what that is or where it can be found. The same is true for TubeGuru, a planning system for the London Underground that does not live at an obvious URL (and there are many other sites of that name that do other things, like let users share videos). For an image sequence from the Powers of Ten movie, Ware mentions the names of the people behind the images, but not its title.

What is great are the connections to design throughout book. They really connect with the content of each chapter, and appear quite useful (to this non-designer). This is probably not a typical design book, but one every serious designer should read carefully to understand his/her profession in a much more profound way.

This is also an inspiring book. I consider it a good sign when I catch myself thinking the things described further rather than reading on, and that happened in every chapter of this book. I almost feel that every single chapter could be the synopsis for an entire book, with more details and ideas. That is all the more reason why there should be a lot more references in the book.

The book is listed as having 256 pages on Amazon, but it's really only about 185 (not counting the preface, index, etc.). I consider this a good thing, there are too many half-read books on my bookshelf that I will likely never finish. A manageable size makes a book much more practical and useful, though again based on the assumption that there will be plenty of pointers.

Is This A Visualization Book?

As an introductory text that covers a lot of ground on perception and cognition, and that draws connections with practical design issues, this book is hard to beat. If you have not read Ware's previous book, I would strongly recommend this one as a starter to wet your appetite. Perception for Design goes into a lot more detail on many of the topics covered (but does not cover all of them), but is also a lot more technical.

In addition to Ware's background, what makes this book relevant for InfoVis are the examples and the fact that most (if not all) the covered topics are directly relevant and applicable to visualization. In addition to the perceptual and cognitive topics, that is also true for the design issues that are mentioned. This book will provide a lot of thought-provoking and useful material, and many starting points for future research.

My colleague Celine Latulipe has made a nice website about her Dance.Draw project. In what she calls Exquisite Interaction, three dancers wield inertial mice and thus control shapes on a projection behind them. The result is interesting and beautiful.

Celine works in human-computer interaction, specifically on two-handed interaction for one or more people. What that means is that her programs are controlled by up to six mice – two per person, up to three people at the same time. She took that a step further in this collaboration with Sybil Huskey from the Dance Department at UNC Charlotte.

Each dancer has a mouse in each hand. The mice track motion (similar to a Wii-mote), and thus provide some data about the movements of the dancers. A typical question after such a performance is if she wants to use the mice to track the movements more precisely, but I fail to see the point in doing this. If the goal were motion tracking or capture, there are ways of doing this that are much better than simple mice. Adding more data is really of little use, what is interesting here is the abstraction from the movements to the screen.

What is being visualized is data from the mice, which represents the dance, which in turn is an interpretation of the music. So in a sense, we are seeing a visualization of the music. Of course, the goal is not a readable visualization, but a more abstract, artistic representation.

Be sure to watch the video, though the visualization is a bit washed out. The performance usually takes place in an almost entirely dark room, where the visuals are much more dominant. The music is also quite catchy.

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.

The idea behind this article was to discuss the background of vis criticism a bit, talk about practical experiences, and offer a glimpse into a possible future. David and Fritz share their experiences teaching a class of computer science and design students, where critiquing played a big role. Lars responds to my criticism with a critique of critiquing. And I claim that criticism is something we're already doing (paper reviews) and need to do more to further develop the theory in our field.

Of course, this reminds of my own plans for not just a local, but a world visualization day. That one is on hold for the moment, but I hope to have some news in the near future. I have been asked about it by several people recently, and I am still convinced that there is a need for such an event. More to come soon ...

Treemaps are the single most used 'real' InfoVis technique there is. Interestingly, they have proven to be even more useful for unstructured data than for the hierarchies which they were originally developed for. Here is a brief history, discussion of current practical uses, and of the importance of treemaps for the adoption and understanding of information visualization.

Describing treemaps in words is a guarantee for blank stares from my students. The concept behind treemaps, while visually simple and compelling, is actually quite bizarre: recursively slice and dice a rectangle in different directions to represent a (potentially large) hierarchy.

From Maps to Squares to Cushions

Depending on the data, the rectangles can have very different aspect ratios, making them hard to compare: a thin long rectangle of the same area as an almost square one looks very different. Bruls, Huizing, and van Wijk therefore developed Squarified Treemaps^(ref), which optimize the placement of nodes within a level to make them as square as possible. While that is a great idea to make static treemaps more readable, it causes problems when treemaps are used to show developments over time. Ordered Treemap Layouts^(ref), developed by Shneiderman and Wattenberg, solve this problem by conserving the ordering of elements while seeking to keep nodes as square as possible, and thus produce very stable layouts. While it is fairly uncommon to compare trees over time, their most interesting application is an interactive map of the market, which shows the stock market in an entirely new way. That map is available at SmartMoney.com (in the menu on the left, click Tools and Maps, and then select Map of the Market from the submenu that appears).

The power of this map is the way it shows a lot of stocks at the same time, grouped by sector. This makes it possible to see current trends much more clearly than showing lots of individual graphs, and companies that behave differently than their competitors are easy to spot. The size of the nodes in the image above shows each company's market capitalization, and the color indicates the difference of the current price from the closing price of the day before (green is up, red is down). The little Ns indicate news items, and these are particularly interesting for those companies that beat the rest of their sector (or lose when everybody else is winning).

Treemaps with lots of items tend to be hard to use because of the high spatial frequencies from the many lines enclosing small nodes. Cushion Treemaps^(ref) by van Wijk and van de Wetering solve that problem by applying a texture to the rectangles that make them appear like little, shiny cushions. Because of the perceived discontinuity in texture between nodes, lines are no longer necessary to separate nodes, so more of the space can be used for the actual node display, and much smaller nodes can be shown than in a flat treemap. The image shows a part of my Documents folder, with the color indicating the file type and the size the file size.

There are a number of programs that use cushion treemaps to show the contents of a hard disk to find out how space is used by different types of files, like SequioaView (Windows), Disk Inventory X (Mac OSX), and KDirStat (Linux).

There are too many variations of treemaps to show here (see Shneiderman's page for a very exhaustive list), but one that keeps being shown in discussions of treemaps are Voronoi Treemaps^(ref). These generalize the treemap from the rectangle to arbitrary shapes by using a Voronoi tessellation of the space.

While these certainly look beautiful and interesting, they are of questionable utility. The authors claim that the fact that lines separating levels are not parallel makes it easier to see the tree structure, but comparing sizes seems difficult and there are more effective ways to show tree structure. I am not aware of any studies comparing Voronoi treemaps to other visualizations, but I highly doubt that they would be particularly useful.

A Hierarchical Visualization Method for Flat Data

The big success treemaps have had in research and certainly commercially has not been from their use in visualizing hierarchies, but rather unstructured (or flat) data sets. Hierarchies are clearly overshadowed in importance and data volume by relational data in databases, or data tables. There is no inherent structure in such tables, and the order of entries does not generally play a role. So how and why are treemaps used for such data?

A lot of that data is categorical, that means that many dimensions only contain a very limited number of different values, and these values generally mean something other than the numbers with which they are often encoded. A data set about all the people on the Titanic may contain data about the class passengers were in (four different values: crew, first, second, third class), their sex (two values), and their survival (two values). This kind of data is usually understood in terms of subsets: show me the number of people in all classes by sex and survival. In terms of subsets, this means one number for every class, sex, and survival status. In database language, this would be the cross product between these dimensions, and in terms of OLAP, this data could be retrieved using a cube or rollup query.

The hierarchy comes from repeatedly splitting up the data into subsets according to the dimensions. In the above example, we may split the data by the class first, then every class is split up into two subsets for both sexes, and each of these subsets is finally split into two for survivors and non-survivors.

Now we have a tree that can be visualized using a treemap or other hierarchy visualization. And since the size of the nodes is the most interesting here, and the structure of the tree is generally not the focus, treemaps are an excellent choice. In addition to showing the values at the leaves of the tree, they also show the aggregate sizes at the intermediate levels, thus offering a quick way to do aggregates. Of course, the order of dimensions determines the tree, and it must be possible to reorder the dimensions, add or remove dimensions, etc.

A simple, non-squarified treemap of the above data could look like this:

Beyond Treemaps: Bar and Pie Charts!

Vliegen and van Wijk published a very interesting paper at InfoVis 2006 on Visualizing Business Data with Generalized Treemaps^(ref), in which they took the elements of the treemap to construct bar and pie charts. In the illustration below, orange elements mean cases for which a notary firm made a loss, green are cases that provided revenue. The different kinds of visualization provide for easier access for people not familiar with treemaps, and make it possible to compare different criteria.

The paper also describes some interesting layout strategies for treemaps when dealing with large numbers of nodes, allowing the user to pick a "vanishing point" where the smallest (or largest) nodes will be placed. Voronoi treemaps, generalized treemaps, etc. show that there is a lot of room to explore new ideas with treemaps.

The TreeMap Industry

There is a variety of applications of treemaps to different kinds of data, hierarchical and otherwise. The news map, shows which news stories are popular, a usenet visualization offers a glimpse into the past of the newsgroup hierarchy, a visualization of slashdot stories allows the user to explore tags and keywords, a visualization of items in an exhibit provides an unusual view on a few hundred items, and there are a lot of other examples.

Treemaps have become the bigger brother of bar charts, and have been widely adopted especially for data analysis that requires more detailed views on large numbers of individual items (that cannot simply be shown as aggregate bars). SmartMoney was the first widely available treemap implementation for non-hierarchical data, and since then, a whole industry has been built around treemaps. I don't have numbers, but treemaps must be a multi-million dollar industry by now. MagnaView, The Hive Group, LabEscape, ProClarity (recently acquired by Microsoft), etc. are either exclusively or also selling implementations of treemaps for different uses. It's only a question of time until treemaps (and perhaps histograms, hint, hint) will make it into Excel and other real consumer tools.

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 subjectivity argument stands out especially in a paper titled Views on Visualization, which is more of a position statement than a traditional technical paper. While van Wijk presents ideas that are common in visualization (or at least acceptable once presented), he stresses the personal nature of his views in the introduction, and uses the first person deliberately when talking about his experiences and anecdotal evidence.

Subjectivity as a Problem

Clearly, subjectivity is something negative:

In the ideal case, one would hope that extraction of knowledge from data is an objective process, in the sense that the outcome does not depend on who performs it, and that the analysis can be repeated afterward by others, with the same outcome. Statistics aims at this, a typical pattern is the use of statistical tests to validate hypotheses on the data. Such tests make assumptions on the data (such as a normal distribution) and have free parameters (like the confidence level), but, furthermore, they do meet the criteria for objectiveness.
Unfortunately, visualization often does not meet this aim. 

He then goes on to argue that it's not all bad, but gets very defensive: This does not mean that visualization is useless. And why would it?

It appears to me that we have the wrong ideas about visualization, and this is limiting our abilitiy to make visualization more than a manual alternative to data mining. Subjectivity is only bad if we suspect the user to twist the visualization to an extent where the visual result is not actually reflected in the data (which is undoubtably possible in many cases). But there are also other ways of looking at subjectivity, one of them is commonly known as the task. Statistics and data mining in all their objective glory cannot capture the intent of the user: what is the goal? What do I want to get out of the data? What does the data mean to me? There is of course an infinite number of objective truths about the any particular data set, and all but a very few are entirely meaningless to the user. Any actual analysis involves picking a particular subset of those truths to come to a conclusion, or to make a point.

Statistics is an obvious standard for comparison, especially for InfoVis (the paper is general enough to actually cover both Information and Scientific Visualization, though). Many techniques in InfoVis are based on statistical measures if not statistical graphics, and Martin Theus even claims that visualization is applied statistics – van Wijk seems to have a similar point of view. But why compete with statistics? And how? It's obvious that visualization will never have the accuracy a purely mathematical method has, or offer the thorough data digestion of data mining. Why go back to slide rules when we have pocket calculators?

Visualization: Careless Conduit or Complex Communication Conveyor?

It is easy to see that there is not just one right visualization for any given data set and task. And any visualization fulfills many more purposes than its designer foresees. We visualization designers are not simply applying our vast knowledge of visual representation, perception, and cognition to pipe data into the brain as efficiently as possible, but rather engage a complex and unpredictable mind. Our choices of parameters and interactions influence what the user sees and can do with a visualization. Our choice of data set to demonstrate a visualization technique prejudices the user's impression of how powerful the visualization can be. And the user's experiences, tasks, and goals.

A visualization is not simply a conduit for pumping information from the computer's hard disk into the user's brain. Such a view of visualization would only work if we were machines without the proper interfaces to directly plug into the data cloud. The power and beauty of visualization goes much further, and provides many interesting challenges along the way.

Objective Visualization: How?

Let's try a little gedankenexperiment to see how viable an objective visualization would be. In addition to the perfect visualization method we would need the proper conditions: a calibrated display to get proper color reproduction, controlled ambient light that is not distracting and not influencing the user's perception, an environment that is free from shapes that could prejudice the user's mind by being recognized in the visualization, etc. Further, the user would need to be standardized: properly trained in visualization as well as the particular data set, with no pre-existing ideas about the data, no interest in a specific outcome of the analysis, and clearly no color-blindness, color deficiencies, less-than-perfect vision, or other perceptual or cognitive deviations from the norm.

Even if all that was possible, would that be desirable? What would be the point? Just as visualization can be and is used in subjective ways, objectivity of statistics is only skin-deep: choosing the right measures and parameters is subjective and task-dependent! The entire point of letting humans deal with data sets is that there is value in subjectivity, experience, and opinion – plus the fact that the the consumers of an analysis are human, too!

Visualization is Communication

This is what is commonly known as expression and communication. Assuming there has been no downright lying, we have to accept any interpretation of the data as valid, just as we have to accept any statistical measure. But by emphasizing different aspects of the data, by picking subsets, or by fiddling with settings, we can present different points of view. That may make things more complex, but also more interesting.

Does that mean that anything goes? Any visualization is good and useful? Of course not, but we need to broaden our ideas and criteria for good visualizations. There is never only one truth about a data set, regardless of how it is analyzed or presented. This was a major point of discussion at the Social Data Visualization Panel, and perhaps the crucial one. Academics can't control visualization, nobody will ask us to sanction every visualization that is produced. We need to learn to let go.

Conclusions

I don't disagree with van Wijk's points about subjectivity, but I think we are doing ourselves a disservice by trying to eliminate, control, or even just downplay it. Subjectivity is a great strength that may be daunting to the technically minded person, but that makes things like photography, email, language, etc. so powerful. Just like there is no central authority for which truths can be expressed in English, there is no central authority for visualization. Why would we want things to be different?

2007 was the year of social visualization. With any luck, 2008 will be when people will start embracing the subjective nature of visualization and realize that visualization is communication.

Update: Jorge Camoes has posted an interesting discussion of Minard's Napoleon's March that provides food for thought along similar lines. 

Image credits: Left half taken from the cited paper; right part from illustration for the Wikipedia entry on communication by Luis Javier Rodriguez Lopez.

The InfoVis conference this year had a theme that was not planned, but that made it even more impressive. That theme was InfoVis for the Masses, or Visualization for the People, and it was present throughout the keynote, many paper presentations, the panel, the World Visualization Day BOF, and the capstone. This is the beginning of a new era in visualization, and it is exciting to watch it happen.

Keynote: Matthew Ericson, Visualizing Data for the Masses

Matthew Ericson, deputy graphics director at the New York Times, spoke about the trade-offs between communicating complex data and packaging it in ways that a large number of people will be able to read. He showed a number of examples where they had tried quite complex visualizations, and others where they added hints to scatterplots to show what the different structures meant. Perhaps the most prevalent visualization are still maps, and Ericson gave a few examples of good and problematic uses of maps, and what they did to make them better (Enrico Bertini discusses Ericson's keynote and this particular issue on his blog).

What strikes me as interesting is that readers of the New York Times – and more generally people who still read newspapers, for that matter – should be more inclined to learn about new things like visualization. By raising the bar a bit in a paper like the New York Times, the overall visual literacy could be raised quite effectively.

Ericson summarized his main take-away message "InfoVis for the Masses, In Short": Find, Explain, Annotate, Design, Edit. And these were exactly the points that were discussed in presentations and discussions throughout the conference.

Paper Session: Visualization for the Masses

Ben Shneiderman chaired the session titled Visualization for the Masses, which contained four very interesting papers: one on Many Eyes, one on scented widgets for additional context in user interfaces, one on automatic configuration of visualization in Tableau, and one on casual uses of information visualization (as opposed to experts analyzing very specific data). Ben not only did the usual session chair things, but framed the session with additional remarks on the importance of the presented papers and how they related to other work (even fooling someone into thinking it was an actual panel).

Panel: Impact of Social Data Visualization

The panel The Impact of Social Data Visualization I organized continued in that vein. Fernanda Viégas (who also designed the brilliant image at the top of this article) wrote a posting on infosthetics where she described the feeling of a revolution in the room. Brent Fitzgerald of Swivel (who, like Fernanda, was one of the panelists) did not quite agree, and was also surprised by the discussion about the proper use of complex visualization tools by the unwashed masses.

This highlighted an interesting problem: academics are generally not used to seeing their work widely used, and so imagining that happen creates anxiety and even outright rejection. Ola Rosling (also on the panel) made a great comparison with photography: the tools for taking pictures are widely available, and people take lots of pictures. That doesn't make everybody a photographer, but the general lack of skill and knowledge does not mean that people shouldn't be taking pictures or post them on Flickr – the really good photos still stand out. Martin Wattenberg spoke about vernacular visualization, which does not have to follow the rules for "proper use" of visualization methods, but is still a valid way of looking at one's data and can broaden our view of what visualization methods can be used for (perhaps similar to the way treemaps are now used more for categorical data than for hierarchies).

My attempts at stirring up controversy by making very political statements about and with visualization (using, among other things, my Presidential Demographics example) did not lead to the heated discussion I had hoped for, but it was a great discussion nonetheless with many interesting points: how do we know if we can trust the data, how do we get visualization tools into the hands of people with little or no access to technology, how do we create a repository of visualization techniques, etc. The panel was clearly a success, I spoke to a number of people who told me they had been inspired by it and were rethinking what their work could do for a much broader audience than they had originally thought about.

Mike Danziger has written a great account of the panel with some excellent observations.

Birds-of-a-Feather: World Visualization Day

This meeting was not publicized too well (other than here, of course), and the room was also a bit out of the way. Still, about 20 people made it there, and there was a good discussion on how to do it, who the target audience should be, etc. Stephen Few agreed to say a few opening words, and he made a good case for the importance of a broader understanding of visualization by the general public.

More needs to be posted on this topic, and more will be coming soon.

Capstone: Stephen Few, InfoVis As Seen By The World Out There

Finally, Stephen Few's capstone talk (which Fernanda also wrote about) on InfoVis as seen by the world out there rounded off the conference by covering the things people identify as visualization, and the ideas about visualization they get from the media and blogs. Examples include Few's favorite punch-bag, information dashboards, as well as articles that depict InfoVis as mostly shiny eye candy, and finally products like the ambient orb.

I felt that the examples he used could have been a bit closer to InfoVis, though. It was a bit too easy for the audience to point fingers and say "Look at those dashboards!" or "Look at those 3D charts!", and not understand that we are guilty of many of the same mistakes. Stephen did use some InfoVis examples in the tutorial he taught (which I wasn't able to attend, but the tutorial notes are on the conference DVD).  

Perhaps the closest thing to the conference he criticized was Swivel's "bling" feature, and I'm a bit torn on that issue. While the name is certainly unfortunate (and perhaps betrays their own opinion of the feature), and the background images don't add anything to the graphs, they are generally done in a way that is not obtrusive and that doesn't make it impossible to read the graphs. Sure, they could have spent their time better on including more types of visualization; but then again, they probably reach a lot more business people who can use blinged Swivel graphs in their presentations and who wouldn't want to use treemaps or other more complex visualization techniques, anyway.

Stephen has posted his thoughts as well as the slides from his talk, and it seems that he liked this year's conference quite a bit. After his scathing criticism last year (that got him quite a bit of attention in the InfoVis world), that is good to see. It just makes sense to have not just the academic InfoVis club at the conference, but people who do practical work like Stephen Few, and people who make software that is used by lots of people like Swivel (I also spoke to a lady from Microsoft's Excel group, and I'm sure other companies were represented).

Quo Vadis, InfoVis?

I believe that we are seeing a paradigm shift in InfoVIs, away from the specialized user (who may not exist, after all) to a more general audience. This won't be easy – paradigm shifts are always painful –, but it's a good thing. It will make us more aware of what we are doing and why, and will greatly increase the impact of our field. There is certainly a danger of "dumbing things down" for untrained users, and we have to be careful about that. But most user studies are done with users who are not familiar with whatever tool they're supposed to use, and often not even with the application area, so we may already be halfway there. And also for those applications that are specialized and that only apply to a certain field, it will be healthy and helpful to be aware of the context and the usage scenarios in a broader context. It's all about the user, after all.

Since my first posting about the need for a World Visualization Day, I have started a basic website, opened a Facebook group, and taken a first shot at designing a logo. I have refined my ideas on the next steps as well as the support needed. Please support this idea by joining the facebook group, commenting here and on the WVD website, and most of all, by attending the BOF meeting at Vis.

You may have noticed the little news feed below the bookmarks on the right. I will slowly move over discussions of this topic to the dedicated website, so I can again concentrate on other topics here.

I hope to get support from the vgtc, and for that your participation in the BOF meeting will be very important (see the "Business Plan" for details). The facebook group has 24 members now, only seven of whom I invited. I hope this will grow substantially over the next two weeks, because it gives us a way to gauge interest. Right now, it is very difficult to tell how many people are interested in this topic, and how many will be willing to actually organize and host activities. But we will know more in about a week!

The recipients of the Nobel Peace Prize 2007 are Al Gore and the UN Intergovernmental Panel on Climate Change. While the decision is undoubtedly a political one (not unlike this year's Nobel Literature Prize), Gore has made a huge impact with his documentary, An Inconvenient Truth. A large part of that comes from his use of graphs and charts.

The charts and the underlying data have of course been the subject of much scrutiny and ridicule, mostly from people who do not agree with the message of the movie. A thorough critique of these is certainly in order, but the fascinating thing is that these abstract charts help to further his story. Similar to Hans Rosling's talk (which I will keep linking to until everybody has watched it at least five times), the visual presentation of abstract data is what gets the message across - even if the representation is itself rather abstract.

Visualization and information graphics are excellent tools for presentation, and their impact is being felt in the world. Visualization has to step out of the ivory tower and become aware of its power. We need to get the message out!