Reviews

Book Review: Visual Thinking for Design, by Colin Ware

2008-06-08T21:07:25-04:00

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.

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.

The book ends with an argument for the importance of design in the designed world we live in as well as touching on the fact that some of the basic human perceptual limitations and skills are changing because of exposure to new kinds of stimuli, like video games.

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 whet 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.

InfoVis 2007: InfoVis for the Masses

2007-11-24T22:35:39-05:00

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.

Review: Swivel vs. Many Eyes

2007-02-18T19:37:49-05:00

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.

The Need

While the tools for creating charts and basic visualizations from data are available to everybody (and for free too, thanks to projects like OpenOffice), there is still a lack of knowledge how to properly use them. Most people have created a basic bar or pie chart before, but few realize how much you can do with even the few simple tools in Excel.

What is more, very little data is freely available. If I want to put my sales data or the price of oil into context, where do I get the data from? The makers of Swivel make some good points about data being locked up, as does Hans Rosling (in a totally different context). How much more could we know if all that data was made available and easy to access?

So social data sharing and visualization websites could have an enormous impact on the world, by giving many people access to a lot of data that would otherwise be hard or impossible to obtain. And they can expose a lot of people to visualization that would not otherwise see good uses visualization techniques, and to learn from others how to make the most of them. Visualization also needs to break out of the academic world and do something for real people with real data and real questions. The existence of these sites, and the fact that money is being spent on them, means that visualization is growing up, and in a few years will hopefully be part of mainstream technology the way other electronic media are today.

Founders

Both websites are small ventures, and the differences in approach can be tracked back easily to the people who run them. Swivel is clearly a business, with people working full-time on the site, venture capital, and a plan on how to eventually make money. It was started by Dmitry Dimov and Brian Mulloy, two physics graduates, and is backed by venture capital from Cnet founder Halsey Minor. They have a total of seven employees, and three advisors: Minor and a colleagues of his, plus a university professor at Berkeley who is working in databases and networks. Swivel was founded in 2005 (around the same time as YouTube), had a private beta for several months, and launched its public "preview" version of the site on December 6, 2006.

When I first heard of about Swivel, I immediately looked at their About page to see if I recognized any names. I didn't, and I was quite disappointed. Visualization people are not exactly known for their business abilities, but I would have expected to see at least one familiar person to do some consulting for them. This is especially important in the case of Swivel, whose makers seem to be completely unaware of the work that is being done in information visualization.

Many Eyes can be seen as the answer from the visualization side. The site is run by IBM's new Visual Communication Lab, which includes three young but prolific visualization researchers: Martin Wattenberg, Fernanda Viégas, and Frank van Ham. All three of them have made an impact in the visualization world, and are actively publishing at the relevant conferences. The lab has a total of five people, who are not working full-time on the site, though. The group's history is not easy to track, but Wattenberg started at IBM in 2002, and Viégas and van Ham apparently joined him last year. Many Eyes was launched January 23, 2007.

Many Eyes exists in a much more academic setting than Swivel, with less resources and no discernible business plan. IBM can probably run the site for years without the need for profit, and they may even consider it more of a marketing effort and keep it like that. One important question for Many Eyes is therefore its longer-term sustainability, especially if it gets more popular and requires more resources to run.

Approach

Uploading a data set is quite similar in Swivel and Many Eyes, with Swivel offering more options in addition to pasting a tab-separated file (CSV upload and scraping off a website). Many Eyes is more interactive here, extracting the column headers and seemingly understanding what is going on. Swivel requires the user to input the data without too much help.

Once the data is uploaded the two sites start to differ. While Many Eyes offers a simple "visualize" button for the new dataset, Swivel creates a number of visualizations automatically. This mechanism is apparently central to Swivel's strategy: impress with numbers. Swivel's start page now boasts over one million graphs, and the number is growing quickly (around 15,000 per day). These charts are based on only around 2,400 data sets, which means that there is an average of 450 graphs per data set. Most of these are obviously useless, and the number of graphs is thus artificially inflated to an extent that is ridiculous. It does explain the description of Swivel as "YouTube for data" though, a slogan that was used for a short time when it was launched. But it also means that Swivel needs to be very careful if it wants to be taken seriously, since outrageous claims do not exactly build trust.

Many Eyes, in contrast, has fewer graphs than data sets, which seems much more plausible. There seem to be a lot of data sets that were uploaded as tests or needed to be redone, but which were never deleted. The signal-to-noise ratio is therefore much higher on Many Eyes.

Swivel's business model is to eventually sell subscriptions to its professional edition to users who want to use its capabilities on their own data and compare that data to all the public data on the site – but who do not want to share their data with the entire Internet. The question of course is whether these users will trust Swivel with their valuable proprietary data, and see enough value in Swivel's own tools to take that risk (cost probably won't be an issue, at least not in comparison). After all, they can always download the public data from Swivel and use it with their own tools.

Social Aspects

Both sites have interesting approaches to making data analysis more social. Both allow users not only to upload and visualize data, but also to download the data that exists on the sites. This is very useful, as it provides badly needed access to a huge variety of data.

Both sites also allow users to comment on graphs, though this is handled better in Many Eyes. Since you can interact with the visualization, you can include a snapshot of what you were looking at when you submitted the comment. That snapshot then becomes a link that will take another user to exactly the same configuration of the visualization, even including search terms (e.g., in a treemap).

There are also forums on Many Eyes, though they require a separate login name, and as of writing this they mostly contain spam. Well-integrated forums would do wonders for both of these sites, though they obviously require a lot of work. More structured/threaded comments would perhaps be a start, like the ones in Drupal.

Existing visualizations can be changed easily in Swivel. There is an Edit This button that allows you to change the style (colors, etc.) and graph types, and a Compare function that allows you to add other data. Both allow you to save the result as a new graph (or as the same one, if you made it yourself). I don't really understand why these are separate functions, since adding new data might require changing some of the visualization styles to make sense of it. But these are the functions that make it easy to create new things from existing graphs and data, and thus really take advantage of what has been done before. Many Eyes is missing such a function, and only partially makes up for it through interaction and snapshots. Adding data to an existing visualization requires the user to completely regenerate it from scratch first. To throw out another buzzword, that is not very enabling.

Swivel also has tags and graphs can be rated by users. Both are kind of obvious features of Web 2.0 sites, and their lack (at least of the tagging) is a definite loss for Many Eyes. Tags provide easy and democratic means of navigation, and are basically a must for a highly interactive website. Ratings are not nearly as useful or necessary, but they provide the mechanisms for the ever popular "highest rated" lists.

The interactivity and live snapshot ideas of Many Eyes combined with the editing and comparison features of Swivel plus a well-integrated forum would make for one killer of a data analysis website.

Technology

Swivel is built using AJAX, while Many Eyes uses Java. The technology is a key factor in these websites, for both ease of access for the user and development effort for the developers. Just looking at Swivel's graphs requires no more than a web browser that can display images, and that is clearly a plus. Interaction is done through JavaScript, which, despite its quirks and overall ugliness, has become the tool of choice for interactive websites. All images are generated on the server, which produces a potential bottleneck in the future (and apparently already has, the site was incredibly slow at the beginning of January, but is now very usable). But again, it works on most modern browsers without any effort on the part of the user.

Many Eyes' use of Java is understandable from an academic perspective, and also from the fact that this is done by IBM (which has done a lot more for Java than Sun). But it has also been called a "brave decision" by a user on the forums, and for a good reason. As great as Java is for programming, its use in browsers is becoming a nightmare. And while having Java installed could be taken for granted a few years ago, this is not longer the case. Microsoft has done a lot of work towards that end, and users have gotten used to seeing interactive stuff in Flash, or using AJAX, rather than Java. The makers of Many Eyes also mean it when they say that on the Mac, the site is "best viewed in Safari": the mouse coordinates are off by quite a bit in Firefox and Camino, which is odd (I haven't seen that before).

But of course Java has one great advantage: interaction. The interactive features of Many Eyes are wonderful, and would be a major nightmare to code in AJAX or Flash. There is also a lot of existing code in Java that its makers are undoubtedly using, and so they were able to launch the site much faster. Another aspect that should not be ignored is server load. Having the client draw the images means a lot less server power is necessary to keep the site going.

Still, I think that Many Eyes will need to consider using either AJAX or Flash or be limited to a fairly small (and shrinking) potential audience because of its use of Java. And that would be a real shame.

Swivel uses an open-source graphing library called Ploticus, which produces static graphics and also has the ability to generate image maps for interaction. They seem to be very keen on not coding their own graphics, which strikes me as a strange limitation. 2D InfoVis is not exactly rocket science, and there is a lot of good literature out there on visualization techniques that are described well enough for a competent programmer to implement in very short time. Swivel acknowledges that Many Eyes has a larger variety of graphical capabilities (they call it "Tufte on Steroids"), which seems to indicate that they will want to stick with line and bar charts.

Many Eyes' makers have a much broader horizon on visualization, and quite obviously do all the programming themselves. This leads not only to a much greater variety of visualization techniques and interaction (see below), but also to the prospect of an expanding set of visualizations, and the implementation of cutting-edge work.

Capabilities

Swivel offers a total of four visualization techniques: vertical and horizontal bar charts, scatterplots, and line charts. The data can be shown as absolute value, difference from the average (in the data set), and as a percentage of the largest value. The user can change the colors of the background and lines, which is a necessary feature to keep all the graphs from looking the same.

In contrast, Many Eyes offers more than a dozen visualization types, and that's not even counting bar charts twice. There are zoomable maps of the world and the US (for showing states); standard line graphs, stacked line graphs, and line graphs for categories (which can be hierarchical); bar charts, block histograms, and bubble charts; scatterplots and network diagrams; pie charts, treemaps and change treemaps (which are really just a different mapping of data onto treemaps). Many Eyes' classification of visualization options is simple but very useful, and a great guide to show users what to try out to answer specific questions.

All of Many Eyes' visualizations are interactive, allowing the user to query exact numbers, zoom, etc. This is especially useful (and necessary) in the case of maps and treemaps. The ability to create live snapshots for comments is also an excellent feature that is worth mentioning again.

The resulting graphs look much nicer on Many Eyes, and are also easier to read. The legends in Swivel's graphs are badly laid out (why not align them?), and the color spots next to the descriptions are too small too small to be clearly seen and matched with the colors of the lines.

In addition to the visualization, Swivel does some data analysis. It calculates a measure of correlation for all possible pairs of data dimensions present in the graph, and shows them as bar graphs. Like with the automatic creation of graphs from data, this feature creates a lot of unnecessary and often nonsensical information. If the correlations were displayed in a matrix rather than as individual bars (i.e., using some fairly straight-forward information visualization), the presentation would make more sense and use up a lot less space (for n data dimensions, they calculate correlations for all n*(n-1)/2 pairs, i.e., for seven curves this makes 21 correlations). Swivel warns the users that correlation does not equal causation, which is a good thing.

What Many Eyes is missing is mixing and matching (or "mashing up") of parts of different data sets, not just one. The navigation for this is a bit clumsy in Swivel, but the ability to put the data into a different context and compare it to other data makes the site a lot more interesting. This feature is a real requirement if users want to make more of the data they have uploaded than getting it visualized.

Both Swivel and Many Eyes handle the comparison of numbers surprisingly badly. When combining more than one data dimension, Swivel converts all numbers to percentages, and puts them on the same scale. This does not make sense in many cases, and makes it impossible to read the real numbers when an interesting development is found. Many Eyes does not convert the numbers, but can only handle one scale, which is also rather limiting. This is not some weird and unusual use case, but what real people do with real data all the time – and can easily do in Excel (and there are even clever stacking techniques). Both Swivel and Many Eyes should be able to handle two axes, and Many Eyes' interactive features should make it easy to use more than two.

What is also missing from both sites is the ability to mix different graph types. This is especially useful when showing not just absolute values, but also rates of change. There are good reasons to show the values as bar charts and the change as lines, for example. And who knows, perhaps I want to add some more data as points? This should be fairly easy to do for Many Eyes, but may be a limitation of the Ploticus library Swivel is using.

While both sites can handle whole numbers and floats, Swivel also understands dates and handles them well. Many Eyes does not seem to recognize dates, but can deal with hierarchical categories and graphs.

In terms of capabilities, Many Eyes wins hands down. Of course, Swivel's focus is a slightly different one, but they still need to provide some more useful techniques and cannot limit themselves to not even include pie charts. Treemaps are also making inroads into the world of business intelligence, so users who are familiar with data analysis tools will expect to see more than just the bare minimum in visualization. And besides, there are no data analysis capabilities other than the charts and the simple correlation calculation on Swivel yet.

The only big thing Many Eyes is missing is a way to combine data from different data sets into one visualization. This is a big one, though, that they will need to address soon.

Broad Appeal

Both sites seamlessly fit into the whole social bookmarking/blogging world. They provide HTML snippets to include graphs in blogs and on other websites, and Swivel also has a Digg It! link on every graph page.

Swivel's embedding function allows for a lot of customization, including a "Sparkline" version (which is just a scaled down version of the graph, and not very useful - true sparklines would be great, though). Interestingly (and slightly unnervingly), Swivel embedded images are "alive", i.e., the settings are not included in the code you paste into your page, but the images react to changes you make on the Swivel page – even after they have been embedded.

Many Eyes has a similar function that creates a very nice and compact thumbnail, but does not allow you to embed a larger snapshot of the visualization (at least not directly). The link simply points to the visualization, and does not take parameters or interactions into account (like the comment thumbnail directly on the page does). This seems to be the only category where Many Eyes wins in terms of simplicity, and where Swivel has a lot more options than are really needed.

Swivel also has topical charts, like one for Valentine's Day and one on taxes right now. These make for easy link targets for blogs, and undoubtedly create a lot of hits on the site. Swivel's style is also more like that of established social activity sites like Digg and Flickr than Many Eyes. In a word, Swivel is simply prettier, and looks more modern.

Ethics

I had not expected to be writing about ethics in this review, but Swivel is doing a few things that deserve to be discussed in a bit more depth.

Among the featured graphs on Swivel's page right now is the one below. It seems to show the number of people speaking a specific language at home, and their ability to speak English (in American households), and there is a strong correlation between the two. But while the blue bars show the percentage of people who think they speak English "very well", the red bars simply show the rest. The strong correlation between these two numbers is therefore not very surprising, since there is a simple mathematical dependency between them. This is simply chart junk, and while Swivel can't keep their users from producing meaningless charts, they are very unwise to promote this kind of nonsense on their front page.

The second issue I have are their inflated numbers. Their claim of over a million graphs may be technically correct, but only a tiny fraction of these were generated by users, and most of them make no sense at all. I understand that they are trying to create a buzz, and that they are fighting for media attention among such established hype targets as MySpace and YouTube. But doing this with such questionable means cannot be in their best interest, and will hurt them in the long run.

To be fair, Swivel also scores some positive points in the ethics department. They provide the means to include a data citation and URL where the data came from directly into the graph, thus adding both weight (if the data comes from a reputable source, and not just somebody's imagination) and credit to the original source(s). Many Eyes only shows that information on the graph's website, but it's missing when somebody posts a screenshot. Swivel also warns its users that correlation and causation are not the same thing.

Conclusions

Swivel needs to tone down the hype and grow up. They are not YouTube, and data visualization isn't nearly as sexy and mass marketable as video sharing. They can only live on the Digg crowd for so long, at some point they will need to appeal to more sophisticated customers that are actually willing to spend money. Those will see through their marketing buzz and will demand more capabilities. Playing with data is great, but it will only take them so far.

Many Eyes needs to rethink Java. They undoubtedly have good reasons to use it, and in a perfect world they would be right. In this world, however, Java is more a stumbling block than an enabling technology on the browser end today, and this will severely limit their reach. One of their challenges will be to provide cutting-edge visualization tools in a way that people can actually use. Easier editing and better interoperability between their already large number of visualizations should make the service much more attractive. They should also present an idea where they are trying to go with this, if the site will exist for a longer time, or just disappear once IBM stops believing that anything can come of it.

So yes, there is a lot to criticize, and I am not shy about doing that. Both websites are a great start, but have a long way to go. It is great to see that social websites are not just used for sharing videos of sleeping kittens any more, but for collaboratively making sense of our world.

Robert Kosara is an Assistant Professor of Computer Science at the University of North Carolina at Charlotte. His research is in information visualization, specifically interactive visualization of categorical data and understanding the perceptual and representational aspects of visualization. He also runs the website EagerEyes.org.