visualization at the new york times

InfoVis 2007 kicked off with a keynote presentation from Matt Ericson, Deputy Graphics Director at the New York Times, titled “Visualizing Data for the Masses: Information Graphics at The New York Times.” This is obviously a topic near and dear to my heart, as a publication like the New York Times represents a space where the average “layperson” (as in visualization non-expert) is most likely to encounter information visualization.

Fernanda Viegas has blogged about the talk already on behalf of infosthetics, so I don’t want to repeat too much of what she had to say, but I did want to emphasize a few points that Matt made in his presentation (and afterwards).

As Fernanda mentions, one of Matt’s main points was that they approach infovis primarily as journalists, and that they see their work as “storytelling for the masses.” At the same time, he expressed one of their design principles as “show, don’t tell,” constantly asking themselves where they can show readers what’s happening rather than telling them in words. Through a bunch of examples, Matt described the design challenges they face in producing these pieces, including extremely short publication deadlines, readability issues (his point about scatterplots was really interesting -- apparently readers have a hard time understanding a two dimensional graph where time is not on the x (horizontal) axis), and issues of Tufte-style “honest portrayal,” where they must pay attention to whether the visualization gives the “right impression” of the data. His example of this latter point revolved around producing political maps (“red vs. blue” population maps, etc.) whose visual presentation reflects the actual statistics (for instance, he pointed to red vs. blue maps of the last presidential election results, by county, that show the US as primarily “red,” despite a much more even distribution in the popular vote).

Also interesting, despite their “show, don’t tell” mantra, was Matt’s emphasis on the usefulness of embedding textual descriptions within the visualizations themselves to help guide users, as well as the usefulness of combining different types of visualizations to reinforce the data (for instance, coupling a more “complex” image with a simpler one). Related to this was his suggestion that “presets” or “shortcuts” in to the data are particularly important to interactive visualizations, where users may be overwhelmed by the number of options on the interface. This directly reflects my experience developing educational visualization tools in the past.

So, after hearing all of this, I was really curious to know if Matt’s group, or the NYT in general does any analysis on the effectiveness (or even popularity) of their info-graphics. I got a chance to talk with him at length last night, where he indicated that while they would like to, they haven’t tried to collect that kind of information in a formal way. Being so restricted by deadlines, he described their operation as very much “by the seat of their pants,” where they mainly use their own editors at usability testers. He did suggest that they are trying to collect more statistics with their online interactive work, as it much easier to capture information about what their readers are spending time on. I would be really interested to see the results of that kind of survey. Either way, the principles their team employs seem to be quite effective, as they haven’t received majorly negative feedback about the graphics they produce. This surely speaks to the particular considerations of designing visualization “for the masses.”

Matt has published his presentation slides at his website, http://www.ericson.net/.

More InfoVis to report on later, but my internet connection is spotty at the hotel, so it may take a while.

infovis 2007!

I am off to InfoVis 2007! I'm very excited (and lucky) to attend, and I'm looking forward to a week of information visualization goodness, particularly with the conference focus on "information visualization for the masses" this year.

If anyone reading this is going to be there and wants to meet up, shoot me an email (it's down in the "about" section on the right side of the page). Otherwise, I'm hoping to experience a lot of blog-fodder while I'm there.

no love for financial visualization

The use of information visualization in the field of "business intelligence" is interesting to me, as it ostensibly represents an area where a large community of non-experts (with regard to visualization) are actively making use of information visualization, in the form of information dashboards, market maps, and other financial analysis tools. Somewhat curiously, despite their reliance on visualization systems, innovation in the visual design of these systems has evidently been lacking. Daniel Buenza has recently written about this in a blog post titled "Is there room for art in financial visualization?" I found this paragraph particularly telling:

[The lack of innovation] is surprising, because existing visualizations do not support profitable trading strategies. Indeed, most systems are based on timely news and time series of stock prices and volume. And yet, we know from basic financial economics that both past prices and news are a bad predictor of future stock prices. As for the ticker… the animated display of selected stock prices is a low-bandwidth visualization born in the era of the telegraph. That’s right: 19th C. Nowadays, information can travel much faster, and one does not need to wait for “my stock” to come up on the ticker.

As the rest of the post describes, there is a conflict between the design of new visualization strategies and the reluctance of their users to let go of what "works well enough." While this is obviously an issue with any type of software design (or just an issue with "change" in general), it is particularly surprising to see in an area that so clearly relies on visual displays to do business.

I think this emphasizes two important points related to the design of "popular" visualization: First, obviously, we should pay special attention to that "final step" of getting new systems deployed in the field. While designers can produce useful systems based on theory (or even user-centered testing), the tools are only useful if they can be successfully be delivered to a user "in the wild." While it might be a logistical issue not related to the quality of the product, this final step may require special consideration at the design level.

Second (although I already gave it away), we need to be aware of the specific usage patterns of the people using (or not using) visualization tools. A design that looks good (haha) on paper may simply not integrate well within a users existing "workflow." Also not a particularly profound point, but one that often gets ignored in visualization design (even in "casual" systems).

As far as business intelligence goes, I'm excited to hear Stephen Few speak at InfoVis in a few days, as he is well known for his analysis of visualization in that field. Again, it strikes me as an interesting test-bed for popular visualization design (also, I am particularly intrigued by the use of symbology and semiotics in dashboard design, but that's a topic for another post), so I'm anxious to learn about the theoretical and methodological design strategies employed by designers in that particular sector.

casual (as in casual) visualization

Right after my last post on casual games and casual visualization, I remembered seeing this paper referenced on the conference program page for the upcoming InfoVis 2007. Zachary Pousman, John Stasko, and Michael Mateas stake out a design space for Casual Infovis:

Casual Infovis is the use of computer mediated tools to depict personally meaningful information in visual ways that support everyday users in both everyday work and non-work situations.

It is a great read that focuses on the visualization needs and usage patterns of non-experts. They keenly identify characteristics of infovis that are meaningful to this group (including the personalization of infovis systems that I was trying to gesture at in my thoughts about the smartvote.ch system), and suggest ways to emphasize a type of information interaction that "exists outside focused episodes of work." I was particularly interested in their discussion of "utilitarianism" versus "usefulness," as well as their re-consideration of evaluation methods for "casual" infovis systems. Being entrenched in the world of media studies, I am in complete agreement with an ethnographic approach to visualization evaluation.

My only criticism of their argument is that they seem to distance the various types of (valuable) insight gained through the use of casual systems from the analytic insights ostensibly achieved with "traditional" infovis systems. Ideally, casual use and analytic insight need not be mutually exclusive processes. While a casual system should necessarily avoid the levels of complexity characteristic of deeply analytical ("work focused") visualization tools, they should still (I would hope) encourage an appreciation for analysis in non-expert users while emphasizing the types of approachability described in this paper.

Anyways, I am really looking forward to hearing them present this paper at InfoVis 2007, as well as the whole "Visualization for the Masses" panel. It's very exciting to see visualization being thought about in this way.

casual (as in sex or Friday) visualization

Last week I was discussing the Halo3 visualizations with Nick Montfort, and the conversation turned (as it occasionally does) to the context in which people encounter information visualization. In particular, we were thinking about the differences between "casual" visualization use, where the user is not necessarily deeply invested in the information being represented (visualization of social networks, or "fun" visualizations being possible examples that promote this type of interaction), and more "serious" use, where precise understanding of the data is imperative ("business intelligence" tools, scientific visualization, etc.). How, if at all, should the design of popular visualization tools take context in to consideration?

Today Nick pointed me to this article by Ian Bogost that asks a similar question with regard to game design, with particular focus on casual games. Ian maps out the casual game space in terms of the player's level of involvement:

Applied to games, casual as informality characterizes the notions of pick-up play common in casual games while still calling for repetition and mastery. This is why casual games can value both short session duration and high replayability or addictiveness. Casual games may allow short session play time, but they demand high total playtime, and therefore high total time commitment on the part of the player. Low commitment represents the primary unexplored design space in the casual games market.

[...] If Casual Friday is the metaphor that drives casual games as we know them now, then Casual Sex might offer a metaphor to summarize the field’s unexplored territory. If casual games (as in Friday) focus on simplicity and short individual play sessions that contribute to long-term mastery and repetition, then casual games (as in sex) focus on simplicity and short play that might not ever be repeated—or even remembered.

While I'm not sure how well these conceptions of casual games literally map back to the world of information visualization (although the "Casual Friday" model sounds like the sort of happy relationship with visualization tools that I could support), the terms of the analysis undoubtedly do. Just as casual games took gaming mainstream, a "pick up and play" paradigm will have to define popular encounters with information visualization tools. And while the "easy to learn, difficult to master" casual game design philosophy already echoes Ben Schneiderman's long-standing information visualization mantra of "Overview, zoom & filter, details-on-demand," there's certainly more insight to be gleaned from the sort of contextual consideration Ian presents here.

Actually, given their similarity in terms of user interaction, popular information visualization design might do well to learn from the successes of the casual games sector!

visualizing... Halo 3!

(Disclaimer: I am a Halo fanboy.)

The PGA Tour has been visualizing golf stats for a few years now with their ShotLink-powered TOURCast tools, so why not try it with one of the most popular video games around?

With the recent release of Halo 3 on the Xbox 360, I've been getting in to tracking my stats on Bungie.net, the very robust web platform associated with the game. Maintained by Bungie (developers of the Halo games) and Microsoft, Bungie.net provides not only standard community-minded features like forums and game development updates (as well as hosting a wide variety of user generated content that can be uploaded from your Xbox 360 to the website), but also tracks every single statistic of every single game of Halo 3 being played on Xbox Live every single day. We're talking millions of games a day. After playing a few matches online, you can head over to Bungie.net to review the results, with detailed statistics for every player involved (number of kills, favorite weapons, medals earned, etc.), just like a golf match!

Recently, much to my delight, Bungie has begun experimenting with visualizing some of this data. Their more high-profile example is the one pictured above (click for larger version), a geographic map of the (real) world that greets you upon logging in to Xbox Live in Halo3 (or visiting Bungie.net) indicating the locations of everyone in the world currently playing Halo. It's pretty simple but effective, and watching the global distribution of Halo players change over the course of the day can be mildly entertaining (and informative).

Their second foray into visualization, which is unfortunately less developed, is the Halo3 Game Viewer. When reviewing the statistics for any Halo3 game (ever!!!) on Bungie.net, you have an option of bringing up several images (from different angles) of the map on which the game took place, which is then overlaid with player icons indicating where each player was standing when they scored a kill (or were killed), with arrows pointing towards their victims. In principle, you can follow a player's movement through the map as they played, perhaps learning the patterns and tricks of a skilled fragger!

Unfortunately, in practice the Game Viewer is not particularly usable. First it is a static, non-interactive image, but it tries to encode time-based data. Second, the player icons are so small that it is difficult to identify who is who. It does allow you to filter down to only a single player, but even then it's hard to relate the temporality of the game to the spatial distinctions on the map. Above is an original Game Viewer image of my most recent game. The icons represent players (I am the dark-blueish-on-gray "cube" icon), the arrows point from killer to victim, and the bar along the bottom is a rough timeline of events (top row indicates killers, bottom row victims). Here is a marked-up version that tries to explain what's going on (click for larger version). Either way, it ain't obvious:

This is something I would love to see Bungie develop in to a more fully featured tool. As I mentioned, the PGA Tour has been using ShotLink to virtually re-create the activity on the golf course for a while now; a similarly functional tool for Halo would be great for going over your strategies and learning from the best players! Visualization of all the other recorded stats would be fantastic too. There is a built-in audience here for useful public visualization. So hook it up, Bungie!

visual search engines

The visual search engine KartOO came up in a conversation I had recently, so I have been thinking a bit about the design of these kinds of tools. Obviously, as the internets get bigger and bigger, search tools are going to become even more important than they are now, both in terms of their effectiveness and their usability, so what better place for visualization to work its magic than to help people navigate this frightening morass of information? There is arguably a limit to the effectiveness of the Google-style "list of hits" method for sifting through search results, particularly if we want to understand the connectedness of concepts across the web, so the logical thing to do is begin presenting search results visually as network graphs and the like. KartOO and TouchGraph are the two most compelling attempts at this that I'm aware of on the web right now.

Briefly looking at each tool individually (searching on "information visualization"), I like KartOO's related semantic categories listed on the left, but I don't find the visual element particularly useful. Some features are confusing or counter-intuitive: What is the significance of the colored "heat map" in the background? What is it registering? It suggests that the area between nodes is meaningful, but I'm fairly certain it isn't (unless very abstractly). Also, if it is some kind of density map, its distribution should be weighted by the size of the nodes it is referring to... again, this doesn't seem to be the case. Adding to the confusion, the lines connecting nodes are only visible when you mouse-over them. Finally, KartOO's approach is to present several "maps" per search, but it isn't at all clear how those maps are related (which they presumably should be, given that they are all built off the same search terms).

On the plus side, I do like some of their use of animation, and their inclusion of symbols indicating the type of file being referenced (HMTL, pdf, word document, etc... see their help page for a list), as this can be quite useful. Unfortunately the effectiveness of the latter is undermined by the addition of page thumbnails on the icons as well, which I think does a complete disservice to the visualization. The thumbnails obscure the file type icons while simultaneously being too small to be of any value on their own.

Visually, I like TouchGraph better. The interface is much cleaner, as is the relationship between nodes. It also handles much more data on the same map, which is both a blessing and a curse. While it allows you to see much more structure, the maps can quickly get too dense to read. There are some simple filtering mechanisms provided, but some more robust options would be nice (for example, using transparency to (de)emphasize elements of the graph would be nice here, so that you could focus on certain areas while still retaining some perception of the larger whole. As it stands now, you can only make nodes visible or invisible, which can be misleading in some cases.). One very nice feature is that the logo of a website is attached to its node, so that if you know your logos, you can immediately identify various groups of pages (for instance, Blogger pages are identified by the red "B" logo, and can often be seen grouped together).

My only other criticisms are on the "search" side of things: First, "expanding" nodes only adds ten new connections at a time, but new connections don't appear to be sorted by relevancy. So, for example, the first ten connections to a given node may be largely irrelevant, but the next ten might be not be. But you won't see the second ten unless you explicitly ask for them, which you might not bother to do if the initial connected nodes are not compelling enough. This may be a technical limitation. Second, while the ability to expand nodes is nicely conducive to "exploring" your search results, connected nodes beyond the first ten don't necessarily seem to heed your original search parameters. In my case, expanding a node associated with information visualization quickly lead me to a group of pages on Indian blogging, which had nothing to do with information visualization at all. This may be by design (it does make for interesting exploration), but it seems that if I'm searching for hits related to "information visualization" I should only get results related to that term.

So, in conclusion, there are features I like and dislike about both these tools, and they are both interesting to use. But I think it's important to point out that the documentation for both is pretty weak. Neither one even documents all of their respective features. KartOO in particular fails to explain non-intuitive elements of the visual display - doesn't that defeat the purpose of a visual browser? If you want a visualization tool to be widely used, the single most important feature (besides making the tool as intuitive as possible) is good documentation. As they stand now, I get the impression that these tools are just a little too complicated or unintuitive to persuade the average user to switch from something like Google.

Am I wrong? Are there folks out there that are using tools like this instead of Google? If so, what do you like about them? What makes them more useful?