This is one of the rare cases when visualization produces stunning images that you just cannot avoid to admire. Last week I met Jarke Van Wijk at the Dagstuhl Seminar on Information Visualization and he showed to me these beautiful pictures of Myriahedral Projections.

Humans have struggled for ages to find appropriate methods to project the globe to a 2D surface but any technique results in quite some large distortions (Mercator’s projection for instance emphasizes the size of Europe and America). Jarke came up with a simple yet fascinating idea that reduces distortion to the minimum:

“Why not just take a map of a small part of the earth, which is almost perfect, glue neighboring maps to it, and repeat this until the whole earth is shown? Of course you get interrupts, but does this matter? What does such a map look like? To check this out, we developed myriahedral projections.“

I also like this (found from his interview with the New Scientist):

“Consider peeling an orange and trying to flatten it out, … The surface has to distort or crack.”

The video from the New Scientist makes it all clear:

One of the most fascinating aspects of the techniques is that the algorithm can take into account several criteria to decide where to perform the cuts.

Cutting along parallels or meridians creates somewhat familiar results like these:

But more sophisticated cuts can be done in a way to create more dramatic results:

As usual Jarke has this fantastic blend between technical sophistication and beauty. Myriahedral Projections go directly into my personal list of favorite projects of all times.

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Link. New Scientist’s article: Clever folds in a globe give new perspectives on Earth

It happens to me all the time. I start discussing about a visualization design with one or more colleagues and we end up gesturing like crazy, moving our fingers on the air, and expressing crazy concepts with words. No, no, no. It doesn’t work. Visualization is visual and words just don’t work.

So I started thinking about all the little mistakes we do (or at least I do) when we try to design a visualization with others and came up with this list of 8 rules.

1-Don’t speak. Draw.
It’s easy to fall into the trap of trying to explain the visual picture you have in mind verbally. As I said, it doesn’t work. Normally it goes like that: a bright ideas comes into your mind and words start coming off of my mouth. The person in front of me keeps saying yes, yes … and there’s no connection, no way to check we are talking about the same thing. Normally, the two pictures diverge a lot. Drawing doesn’t fix the problem completely but it puts some major constrains and makes the all thing run lot smoother.

2-Use low-fi tools.
The whiteboard is my favorite tool (checking that good pens are always around), but paper works perfectly fine if it’s a one-to-one game. But just make sure both of you have pen and space to draw! Sketching is a “power game” and the person who holds the pen has the power (thanks Andrews for this idea). Keep this in mind. Digital stuff just doesn’t work here, unless you want to show some mock-ups designed beforehand.  And yet, creativity might be heavily impaired because drawing several alternatives or variations on top of it might be really difficult.

3-Let everybody speak and don’t interrupt.
It’s too easy to start talking and forgetting that the best ideas often come from exchanging ideas. If you find your mates keeping their mouth shut there might be two problems: either you are not giving enough space to the other person or this person has nothing to say. In the first case, stop speaking. In the second, stop the meeting and find a better person to talk to.

4-Listen carefully.
I said carefully. This is very much related to the previous one. Letting others speak is important but it’s not enough. Since this is an intense exchange of ideas aiming at boosting creativity, you have to make sure your mind is not only absorbed by what YOU think. Your brain is excited and it takes some effort to focus on the other and not on yourself.

5-Think constructively.
Well … once you succeed in keeping your mouth shut (but not forever!) and listen to the others, you also have to find the right way to criticize what you listen to. Don’t think “this is bad” too early. Actually don’t think “this is bad” at all. The best approach is to ask yourself: “how can we make this thing look better together? How can this improve my idea? And how can I add something to this idea to make it better.”

6-Be a flea and an elephant.
Generating lots of alternatives is very important, especially at the beginning, but from time to time it’s important to stop and  dig into a specific idea. Both are important and there’s no specific rule and when to do what. Use your intuition and just remember to be a flea and sometimes also an elephant (thanks to Alan for the metaphor).

7-Take pictures. Save it.
You don’t want to lose those ideas you have been generating for 1 hour or so. It’s easy to walk away and think you will remember what you have done, but it’s not true. Normally, after few days many details fade away, if not the whole thing, and details often matter a lot. To be sure, take a picture. It takes few seconds and it doesn’t cost a thing.

8-Take your time, it’s a process.
Finally, don’t pretend to solve everything in one shot. Designing a visualization, as any other kind of design activity, it’s a process. It takes time and it’s highly dependent on the state of your creativity. Stop when you see the spark of your creativity is fading, take your time and come back when the spark is back.

So, does it happen to you too? Any other idea? This is very much related to how I experience the whole thing but you might have other experiences as well. I’ll be more than happy to hear.

The term visual analytics is really funny. Many people use it and yet it has so many different interpretations. For many, especially on the web, it is just a sort of synonim for information or data visualization. For me it is not.

Visual analytics is the science that studies visual analytics problems. And a visual analytics problem is a data analysis problem where neither the computer nor the human alone can solve it in an effective way.

This is basically the same definition Daniel Keim (by the way my boss since last October 2010) gave in his keynote speech at AVI 2010 few days ago. We wrote together an invited paper for this speech and here I want to re-propose a specific slide of his talk as it summarizes many of the points I do care about.

Start with an interesting and important application. That’s the premise of visual analytics: it must solve a real problem, and one that is relevant. Too often we observe little visualizations with no value. They are fun and I have nothing against them, but we need to help people solve the problems they have. That’s the real value of visual analytics and I don’t see a better way to promote it than demonstrating that it helps people solving real problems.

Try to understand the data and the task. The first part is almost obvious: try to understand the data (even if it’s too easy to think the whole problem is contained and described in the data itself). The second is the the one I like: data is useless without tasks (or call it goal if you prefer). Too often people take some data and throws and handful of colored dots on the screen. We visualize data for a purpose: helping people achieve a goal. Also, understanding tasks is extremely hard: it takes understanding what is in the mind of the others and it takes special skills to talk with people working in other domains. Two causes I am familiar with: (1) people speak different (specialized) languages in different domains (ever tried to talk with biologists?); (2) while people think they know what they want they often can’t imagine what wonders a technology could do for them.

Examine the limits of current (automatic and visual) solutions. A basic understanding of what others did to solve a problems is a basic strategy that one just cannot avoid in any field. What’s special here is that visual analytics often tries to overcome limits found in purely visual or automatic solution. Both cases can happen. An automatic solution exists but computer algorithms just cannot do the job, it’s too hard, the human is needed. A visual solution exists but it just cannot scale. It doesn’t matter if you use one pixel per data item in a big powerwall, the human alone cannot cope with it.

Develop solutions (techniques and systems) integrating the most appropriate automated and interactive visual techniques. That’s the core part of visual analytics: finding a solution to a problem that neither the machine nor the human alone can solve. Finding the best synergy between the two. I still believe that the integration of automatic and interactive techniques is the way to go and I expect to see much more research and solutions in this direction in the near future. By the way, this doesn’t mean that every problems should be addressed in this way! Solving a problem with a pure computational solution is always preferred if it exists. Human involvement costs a lot and it’s foolish to employ humans for tasks that machines can perform well enough.

Evaluate their problem solving capability (compared to the fully automated or purely visual techniques). I am a big fan of evaluation (as you can see from my obsession with the BELIV workshop) and evaluation in visual analytics can be summarized by an enormous question mark. How do we evaluate visual analytics? While some research is on the way, visual analytics evaluation has several gaps. The major one is: how do we measure the capability of a system in terms of problem solving? When or how does a system help people solve a problem better than others? The main issue here is that a visual analytic solution has to be evaluated from an holistic perspective that takes into account a system in its whole environment, while current methodologies are optimized to dissect specific factors in controlled environments. Another big problem is: how do evaluate a system in terms of both its interactive visual part and its automatic computational component? The two things influence each other but how do we take them into account?

If you want to know more about this vision on visual analytics and see some examples of visual analytics solutions you can read the invited paper I wrote with my colleagues for AVI 2010. The paper contains many more details on what visual analytics is and how to integrate the computational and visual sides of it.

A couple of weeks ago I attended CHI 2010 in Atlanta, the premier conference on human-computer interaction. The conference is huge with many (tiring) parallel sessions, but it’s a great opportunity to meet friends, and of course, like every year, to attend a bunch of interesting infovis-related presentations. Here are some few papers I have selected from this year’s program.

It’s my great pleasure to announce here BELIV’10: BEyond time and errors: novel evaLuation methods for Information Visualization, a workshop on infovis evaluation I am organizing together with Adam Perer from IBM and Heidi Lam from Google.

The idea of the workshop was born in 2006 almost by chance when me, my phd advisor Giuseppe (Beppe) Santucci, and Catherine Plaisant had the possibility to organize a workshop at AVI 2006 and felt that it was time to gather some people and talk about the problem of evaluation in infovis.
The first workshop was a real success with very interesting discussions and (highly cited) papers out of it. After this experience we thought that having a BELIV every two years could be a good idea and the right time frame. In fact we organized it again at CHI 2008 and now again at CHI 2010.
The goal of BELIV is to raise fundamental questions about evaluation. The main big question around the workshop, and the reason why we believe it is important, is that visualization still needs to explain why, when, and how it is useful and we don’t have the right tools yet to fully answer these questions.
So, if you are interested in participating there are two options: submit a position paper or a regular research paper. Position papers present a personal view on evaluation and are meant to introduce your point of view in the workshop discussion. Research papers are meant to provide substantial contributions to the research community with novel ideas.
A great plus of this edition is that it is two-day workshop and that it will be a lot more interactive than past editions. Despite its success many participants to past editions voiced the need to have less presentations and more productive discussions, and we strongly agreed with them.
So, BELIV 2010 will be based on short presentations on Day 1, with planned sessions to collect relevant issues for the next day. Day 2 will be all centered around the discussion of the collected issues.
Many more details can be found at the workshop website, where you can also find links to previous editions so that you can better understand what a BELIV workshop is.
For any questions please send me an email or post a comment here.

Data is largely available, no question. Everywhere we hear that the new big trend is data crunching and that the great thing of years 2000 is the large provision of freely available data sets. Just recently the US Government has released data.gov, and this event has been acclaimed by numerous people in visualization and in data analysis as a big step toward a better world. Ok, data is good. All of it is good. But I think we are getting overly excited about it.

I see a dangerous trend here: thinking that data is the only thing we need and that having large data at our hands will solve some problems. But, data per se has no real value if it is not related to problems and people! So, I see many new interesting web sites (and tools!) popping up on the Internet but I don’t see any guidance about what to do with them.

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I am sorry guys, I feel a strong need to share my frustration with you today. I have discovered yet another infovis library to create the most beautiful visualizations in the world and instead of being excited I am depressed. That’s great I really champion the effort of these good guys but a tough question keep hammering in my head: why so many libraries and so few tools? Libraries are great and really needed to speed up the development process but here I perceive a dangerous trend: there are a lot more libraries than real tools written with them!

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Shame on me, I didn’t keep my word on reporting from EuroVis. Anyway here is a very small selection of the remarkable things I have seen during the conference.

Pat Hanrahan’s talk was really deep and thoughtful. A lot of new basic material to think about visualization under a new lens.
Then I forced myself to select only 3 paper out of the long program. They are engaging and new in some sense. Obviously this is totally personal. And there were many other good ones worth reading.

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Hi there! I’m writing from Berlin where I came to attend the EuroVis 2009 conference. EuroVis is the premier conference on visualization in Europe and every year it hosts a mix of very interesting SciVis and InfoVis works.

The program is out and you can give it a look here. The program comprises also a keynote from Pat Hanrahan, which I’m really looking forward to see. It has the promising title: “Systems of Thought: When to Use Visual Representations in Problem Solving“.
This is just a short notice to tell you that I intend to post at least one post a day to wrap up on the things I see and to share my thoughts with you. I’ll also try to showcase the most interesting works.
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P.S. If any of you is also attending EuroVis’09 drop me a line, we might end up drinking a good German beer in one of the many wonderful places this city has

Yesterday in a meeting with our industrial partners I received yet another lesson. Simply put: though fancy and well-crafted visualization is useless if it doesn’t help people take actions.
Ok I must admit it, this is maybe only true in business sectors (is it?) but what I come to realize is that we infovis enthusiasts are too much focused on the never ending refrain that visualization is useful to explore data and that we need it to make sense of things.
This is certainly true but this is only part of the story. Take the million managers out there. Not trained to cope with complex stats or charting tools but desperately in need to take decisions based on data. What do they need? To explore and make sense of thing? Sure, to some extent … but ultimately to take complex decisions in a very constrained setting and tight time limits.

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