Category Archives: Reviews

11 (Papers + Talks) Highlights from IEEE VIS’16

vis-webbanner2016

Hey, it took me a while to create this list! But better later than never. Here is my personal list of 11 highlight from the IEEE VIS’16 Conference.

If you did not have a chance to attend the conference you can start from here and then look into the following links:

Papers

Surprise! Bayesian Weighting for De-Biasing Thematic Maps.
Michael Correll, Jeffrey Heer.
https://github.com/uwdata/bayesian-surprise

Did you ever stumble into one of those choropleth maps in which the distribution of a given quantity is shown, (say, number of cars from a given manufacturer) but the only signal you can see is actually population density? This is the kind of problem Surprise! addresses. It deals with situations in which the quantity one wants to depict is confounded by another variable. To solve this problem Surprise! uses an underlying Bayesian model of how the quantity should be distributed and visualizes deviations from the model rather than quantity (hence the name Surprise!).

I think this is a brilliant idea which addresses a super common problem. I have seen people stumble into this problem countless times and I am glad we finally have a paper that explains the phenomenon and proposes a solution. The only issue is that visualizing surprise is not as natural as visualizing the actually quantity; which is normally what people would expect. One open challenge then is how to communicate both values at the same time.

Vega-Lite: A Grammar of Interactive Graphics.
Arvind Satyanarayan, Dominik Moritz, Kanit “Ham” Wongsuphasawat, Jeffrey Heer.
https://github.com/vega/vega-lite

The IDL team has done over the years and astounding job at developing an ecosystem of frameworks and tools to make the development of advanced visualizations easier and faster. Vega-Lite builds on top of Vega, which they presented last year, and proposes a much simpler language and extremely powerful functions to generate interactive graphics (with linked views, selections, filters, etc.). Arvind and Dominik gave a live demo and I have to say I am really impressed. While most existing frameworks focus on the representation part of visualization, this one focuses on interaction and as such it covers a really big gap. I am curious to see what people will manage to build using Vega-Lite. If you built some interactive visualizations in the past you certainly know that the interaction part is by far the hardest and messiest one. Vega-Lite seems to make it much simpler and straightforward than it used to be. I am looking forward to trying it out!

PROACT: Iterative Design of a Patient-Centered Visualization for Effective Prostate Cancer Health Risk Communication.
Anzu Hakone, Lane Harrison, Alvitta Ottley, Nathan Winters, Caitlin Guthiel, Paul KJ Han, Remco Chang.
http://web.cs.wpi.edu/~ltharrison/files/hakone2016proact.pdf

PROACT is a simple visualization dashboard that helps patients with prostate cancer understand their disease and make informed decisions about choosing between a conservative solution or surgery. The paper does a great job at describing the context and the challenges associated with such a delicate kind of situation and how visualization systems can be used by doctors and patients to enhance communication.

I consider this paper super relevant. While if you look into the images you won’t be impressed by fancy colorful views and interactions, the system has been demonstrated to be really effective in a very important and critical setting. It also raises awareness about issues we rarely discuss in visualization; especially how to deal with emotions and how to design systems that inform while being careful with the impact such knowledge may have on the viewers.

TextTile: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text.
Cristian Felix, Anshul Pandey, Enrico Bertini.
http://texttile.io

This is the latest product coming out of my lab. I plan to write a separate blog post on it later on. TextTile stems from multiple interactions we had with journalists and data analysts who need to look into data sets containing textual data together with tabular data (e.g., product reviews and surveys). In TextTile we propose a model that describes systematically how one can interactively query data starting from text and reflecting the results on the data table and vice-versa. The tool realizes this model in an interactive visual user interface with a mechanism similar to what is found in Tableau: the user creates queries and plots by dragging data fields to a predefined set of operations. I suggest you to try it on your own! You can find a demo here: http://texttile.io/.

Evaluating the Impact of Binning 2D Scalar Fields.
Lace Padilla, P. Samuel Quinan, Miriah Meyer, and Sarah H. Creem-Regehr.
https://www.cs.utah.edu/~miriah/publications/binning-study.pdf

I chose to include this paper because I found its message extremely inspiring. In visualization research we often cite a principle (proposed by Jock MacKinlay) called the “expressiveness principle“. The principle  states that “a visual encoding should express all of the relationships in the data, and only the relationships in the data“. This paper shows that this principle may actually not always hold. The paper describes experiments in which performance improves when a continuous value is presented with discrete color steps rather than continuous; a solution that breaks the expressiveness principle.  This may seem a minor detail but I believe it demonstrates a much bigger idea: there is lots of conventional wisdom ready to be debunked and it is up to us to hunt for this kind of research. Every single scientific endeavor is a loop of construction and destruction of past theories and idea. This paper is a great example of the destruction part of the cycle. We need more papers like this one!

VizItCards: A Card-Based Toolkit for Infovis Design Education.
Shiqing He and Eytan Adar.
http://www.cond.org/vizitcards.pdf

What a lovely lovely project! If you have ever tried to teach visualization you know how hard it is. Students just don’t get it if you give lectures and lots of theory. Visualization needs to be learned by doing. But organizing a course on doing in a systematic way is hard. Damn hard! Shiqing and Eytan have done an amazing job at making this process systematic and easy to adopt. They developed a toolkit and a set of cards instructors can use to guide students during a series of design workshops. One aspect I like a lot, other than the cards idea, is that many exercises have been ideated starting from an existing data visualization project and “retrofitted” to their original “amorphous” status of having a bunch of data and a vague goal. This is what the students are shown at the beginning and at the end of the process they can compare their results with the results developed in the original project. You can find the toolkit here: http://vizitcards.cond.org/supp/index.html. I am planning to adopt some of it myself next time I’ll teach my course (too late for this semester).

Colorgorical: Creating discriminable and preferable color palettes for information visualization.
Connor C. Gramazio, David H. Laidlaw, Karen B. Schloss.
http://vrl.cs.brown.edu/color

Creating categorical color palettes is a hard task and if you want to do it manually it’s even harder. Colorgorical is a new color selection tool that enables you to build new categorical color palettes using a lot of useful and interesting parameters, including: perceptual difference, name difference, pair preference, and name uniqueness. An internal algorithm tries to optimize all the desired parameters and generates a new color palette for you. You can also add starting colors to make sure some colors you want to have are actually present in the final color palette. I strongly suggest you to play with it! They have a nice web site explaining all the parameters and a simple interface to generate new palettes.

Talks

An Empire Built On Sand: Reexamining What We Think We Know About Visualization.
Robert Kosara.
https://eagereyes.org/papers/an-empire-built-on-sand

Robert’s talk was more of a performance than a talk. I really really enjoyed it. His talk at BELIV was all focused on the idea that we in vis regard some ideas as truth and keep repeating them even if evidence for them is actually weak or nonexistent. Robert kept repeating, in a wonderfully coordinated sequence, “how do we know that?” … “how do we know that?” … “how do we know that?“. I loved it. Too bad the talk was not recorded. But you can find the accompanying paper here. Kudos to Robert for assuming the role of contrarian at vis. We really need people like him who do not hold back, speak with candor, and are ready to yell the “the emperor has no clothes”.

We Should Never Stop BELIVing: Reflections on 10 Years of Workshops on the Esoteric Art of Evaluating Information Visualization.
Enrico Bertini.
http://bit.ly/beliv-keynote

Here is another one from yours truly. I started the BELIV workshop on evaluation in vis in 2006 with Giuseppe Santucci (my PhD advisor) and Catherine Plaisant and the organizers kindly asked me to give a keynote for the 10 years anniversary. If you click on the URL above you can watch the entire talk. I tried to be funny and also to give a sense of how much progress we have made and what may come next. Evaluation in visualization is a continuously evolving endeavor and there is much to learn and perfect. The vis community has been receptive to new ideas on how to conduct empirical research and I predict we will see a lot of innovation in coming years. Let me know what you think if you watch the video!

Capstone Talk: The three laws of communication.
Jean-luc Doumont.
http://www.principiae.be/

Wow! I had absolutely no idea who Jean-Luc was before I entered the room and started listening to his talk. This is by far one of the best capstone talks I have ever attended at VIS, if not the best. Jean-Luc gave a talk on how to convey messages effectively and organized it around a number of principles he developed through the years of his activity training people on effective communication. This guy know what he is talking about. His body language, the way he expresses his thoughts, the quality and density of information in what he says, the style of his slides, etc., everything is great. His work can inform any professional who needs to communicate information better, being it visual or verbal. He has a fantastic book which looks very much like Tufte’s but more on general communication. If you have never heard of him take a look at his work, he is amazing … and super fun!

Communicating Methods, Results, and Intentions in Empirical Research.
Jessica Hullman.
http://steveharoz.com/publications/vis2016-panel/improve-empirical-research.html

Jessica is doing some of the most interesting type of work in visualization. Her blend of core statistical concept and visualization is very much needed and one of the most interesting recent trend in vis: how to use vis to communicate statistics better and, at the same time, how to use statistics to do better vis research. In her short talk Jessica raised a number of important points on how we communicate research, not only to others but also to ourselves, and how we can introduce practices that may reduce the chances we are fooling ourselves. The world of experimental research and statistics is changing very fast and we are witnessing a wave of great self-criticism and reform. While this is true for science in general, the world of visualization research is also very receptive to what is happening and Jessica is one of the few vis people who is helping us make sense of it.

That’s all folks! I hope you’ll find these projects inspiring!

Book: Statistics as Principled Argument

stats-principledI just started reading Statistics as Principled Argument and I could not resist to start writing something about it because, simply stated, it’s awesome.

The reason why I am so excited is because this is probably the first stats book I found that focusses exclusively on the narrative and rhetorical side of statistics.

Abelson makes explicit what most people don’t seem to see, or be willing to admit: it does not matter how rigorous your data collection and analysis is (and by the way it’s very hard to be rigorous in the first place), every conclusion you draw out of data is is full of rhetoric. Continue reading

Review: OECD’s Better Life Index

Better Life Index LogoIf you are a regular reader of this blog you might have noticed how easily I fall into the trap of going into long rants against this and that. Not too big a problem, rants seem to work well in the blogging arena, but I realize that while I have some good criticism here and there, I almost never ever spend time praising someone else’s work. This is partly due to my not being too excited by the average visualizations I encounter on the web, but rest assured there is good stuff to talk about nonetheless. And here we are. I am very pleased to break this habit today and spend some time praising a recent project you might have noticed on the web: the OECD Better Life Index. Let me first explain what it is and then I’ll tell you why I think this great work.

The Better Life Index

The Better Life Index is a visualization developed by the OECD (Organisation for Economic Co-operation and Development ) under the supervision of Jerome Cukier and his colleagues and the help of two external designers, Moritz Stefaner and RauReif. You can find a very interesting description of the the process and the role of the designers in Jerome’s post mortem article.

The idea was to come up with some kind of Progress Index, which we could communicate once a year or something. Problem – this was exactly against the recommendations of the commission, which warned against an absolute, top-down ranking of countries.

Eventually, we came up with an idea. A ranking, yes, but not one definitive list established by experts. Rather, it would be a user’s index, where said user would get their own index, tailored to their preferences.

So, the OECD had a bunch of indicators summarizing aspects that concur to quality of life and happiness and wanted to find a way to represent this data in a way that makes people compare one country to another, but in a flexible and not totally pre-ordered way.

The final result, after a number of iterations is a interactive visualization based on icons (they call it flowers). The user can give a weight to the various factors building the index and see how the countries change their ranking. If you want to better understand how it works give it a try, it’s much easier than trying to figure out how it works from my description.

Here is the default result taking into account the whole set of factors.

better life index

Here is my own Better Life Index.

better life index

What I like and why

Why do I like this visualization so much? I have a number of reasons that I will list in a minute but in summary I like it because it’s a an example of how function and aesthetics can serve each other’s purpose.

There have been endless (and useless IMHO) discussions about form and function in visualization, where people are always a bit right and a bit wrong at the same time. My take at it is that when something works you can see it, no need to spend a thousand words on it. The Better Life Index, is beautiful and functional at the same time and this is the way to go.

The first and most important reason why I like the BLI is that it uses a technique I technically call “multi-dimensional icons”, probably the most neglected tool in the history of data visualization. The thing they call “flowers” it’s actually an icon (some core vis people would rather call it glyph) representing multi-dimensional data.

Every country is an icon, and each icon is made out of a number of “petals”. Each petal represents a data dimension (a better life factor in this case) and its length is proportional to the value the given country has on the corresponding factor. Plus, each factor is colored with a unique hue.

The same technique can be used every time you want to represent multiple dimensions of a series of objects at the same time. If you think about it carefully you will see that there are not many alternatives around. The only problem of this technique is that it doesn’t scale to a large number of objects, but this is not a problem here.

I also like the fact that color was used redundantly to represent the data dimensions. In principle each petal has a unique angle and position so it shouldn’t be a problem to identify which petal is which, but with color it obviously works much better. Plus, it works nicely as a cross-reference to the legend.

Using height as a way to convey the total score is a good choice. You can also change the ordering of the countries from alphabetical to by rank, which I actually prefer (see the gap in the middle now?)

better life index

 

Position is the most powerful visual primitive we have and it makes sense to use it to convey the most important information: how the countries score according to the selected factors.

Speaking of interaction, it is quite simple but effective. It’s easy to understand the function of the menu on the right hand side (see it on the web site) and as soon as you use it you get an understanding of how it works. The animation makes the whole thing smooth and calm, giving the feeling that it’s always possible to return on your owns steps and make several experiments.

To some extent it is possible to get the feeling of which country changes the most when a new set of factors are used, but it doesn’t really work too well, as following all the movements at the same time it’s not easy.

This is the only limitation I have found so far. I would like to have a way to better understand how the rank changes when a new set of parameters is chosen. But I cannot really blame anyone for it. This is the kind of design where the more you try to add the more complex and less appealing it gets. Adding such a functionality would require a lot of additional complexity and there’s no simple and elegant solution that comes into my mind right now.

Money doesn’t buy happiness

I have been playing with it for a while and if you didn’t do it yet I encourage you to try it out. It’s a lot of fun! Try to ask yourself some questions or see whether there is anything strange you notice. I have a number of interesting things I’ve noticed or learned but this one is the one that really stands out for me: the best countries score quite badly in the income domain.

Regardless the composition of factors you choose, there are always a bunch of countries that are leading the BLI, e.g., Norway, Sweden, New Zealand, Canada. Give a look to their flowers: even if they are almost always at the top, they score very badly in one domain: income.

Where should I live?

I don’t know how you interpret the data depicted in the BLI. Personally, the first reaction I had was: “ok then … so where should I live next?” And driven by this idea I started exploring several alternatives. But after a while I felt like two main things were missing.

The first one is information about weather. While in fact I give a great deal of importance to things like work-life balance, I often find myself thinking about weather when I think about a potential new country to live in. Maybe this is the heritage of my Italian origins, but it’s crazy to see how good weather is poorly correlated with the average well-being of a country. Isn’t it? I think some information about weather would be a great addition to the BLI. I would expect to see a lot of shift in the ranking and … well I am sure Italy would score much much better!

Another subtle but more important factor is that it’s really hard to judge quality of life in terms of whole countries. Take the USA, can you really consider these parameters descriptive for any of the main cities in the U.S.? I am sure there are cities that score a lot better and a lot worse than the average. And probably the same is true for any other country. I think it would be really fantastic to see the same data on a city level. Do you think this is even possible? Is the data out there? I would be much more interested to know how the BLI is in Rome, Berlin, Toronto, New York, etc … because in the end when you move to a new place you move more into a city than into a country.

Conclusion

In summary as I said I think the BLI is a very nice example of simple and effective visualization. It takes into account all the constrains posed by the project and comes up with a little nice tool that works just right for its purpose. Is it the best for everything? No, it is not of course. But it’s very well done for the goal people at OECD had. Everyone can learn from it.

My sincere compliments to Moritz and Jerome with whom I often interact on Twitter, and to all the other guys who worked on it. Very well done guys!

Did you like this review? Please do not forget to tweet about it and/or comment on it below. Thanks!