An opinion piece in the New York Times last week got a lot of attention in visualization circles for its use of a spiral chart as its opener. While the choice of chart and color scheme can be debated, I want to discuss the fact that the spiral is disconcertingly off-center.
Criticizing visualizations is a cottage industry of sorts, and an activity I have indulged in in the past as well. Redesigning those charts is also not uncommon, though it's usually other people's charts, and that isn't always welcome. Sarah Leo of The Economist has redesigned some of the charts made by that publication, and not only do her redesigns work better, her thoughts around some of the design issues are also very insightful.
It is my great honor and pleasure to announce the winner of the Worst User Interface Award 2016: it goes to the new chip-enabled credit card terminals introduced in the U.S. this year. My congratulations, as it is very well deserved.
Rankings are a common way of talking about data: who made the most money, who won the most medals, etc. But they hide issues in the underlying data. Is the difference between first and second meaningful or just noise? Here is a data video that nicely demonstrates the problem.
Presentations can be dreadful. Badly thought-out slides, boring structure, poorly delivered. I once told a colleague after a practice talk to please shoot me before she’d ever make me sit through such a talk again (to be fair, she had called the talk boring herself before she even began).
Stephen Few has written a long and scathing piece tearing down a paper presented at VIS earlier this year. While some of his criticism is justified, it is too focused on one of the authors, and it comes from an idea of visualization research that is far too limited.
Kaiser Fung doesn't like this graphic that accompanied a recent story about the bird flu in the Wall Street Journal. His redesign shows a lot less overlap and a lot more detail; so much, in fact, that it obscures the point of the chart.
In his piece Disinformation Visualization: How to lie with datavis, Mushon Zer-Aviv makes some interesting points about how framing the same data differently in visualization can make a big difference. Using the example of the abortion debate, he shows the usual chart tricks, cherry-picking, subsetting, etc., that is done to make the data support a particular story.
Fernanda Viégas and Martin Wattenberg have written a wonderful piece titled Design and Redesign in Data Visualization about criticism in data visualization. They thoughtfully analyze the practice and point out some of the issues when people create redesigns, including intellectual honesty and perfect hindsight.
The paper Becksploitation: The Over-Use of a Cartographic Icon by Kenneth Field and William Cartwright in The Cartographic Journal describes Harry Beck's famous map of the London Underground and what makes it great. It also offers a collection of misuses of the superficial structure, and critiques them. I wish we'd had papers (and titles!) like this in visualization.
Andy Kriebel's Data Viz Done Right is a remarkable little website. He collects good examples of data visualization and talks about what works and what doesn't. He does have bits of criticism sometimes, but he always has more positive than negative things to say about his picks. Good stuff.
Picturing the Uncertain World by Howard Wainer is a book about statistics and statistical thinking, aided by visual depictions of data. Each article in the collection starts by stating a question or phenomenon, which is then investigated further using some clever statistics.
Trees. They’re everywhere. And not just in the physical world, but in data visualization and knowledge representation as well. This is not a new phenomenon, it goes back thousands of years. Manuel Lima’s new book, The Book of Trees, gives an overview.
The mirrored line chart is a pet peeve of mine. It's very common close to elections when there are two parties or candidates: one's gains are at the other's expense. But it becomes even more egregious when there are two categories that have to sum up to 100% by their very definition.
I have complained, repeatedly, about the lack of good online resources for visualization; in particular, when it comes to discussion and critical reflection. Also, where can you go to get help with a visualization project? A few recent websites are tackling these issues in different ways.
A clever chart redesign last week got a lot of people talking about which one is “right.” What is more interesting to me is not which one is (supposedly) the better representation of the truth, but which purpose each one serves.
When I’m asked for a good book about visualization, I usually try to change the subject. There is no book I really love, they all have their issues. But thanks to Isabel Meirelles, I can now give a straight answer: Design for Information.
In February of last year, I wrote a posting based on some data I had scraped from Many Eyes, and criticizing where I thought it was going (or not going). Here is an update, eighteen months later, of some of the things that have happened in the meantime, and some new data.
Once you’ve seen one visualization book, you’ve seen them all. They tend to all look similar, use the same examples, and don’t provide much depth. Is it too easy to write a book when you can use such compelling images?
Inattentional and change blindness are two fascinating phenomena that more people should be aware of. The Invisible Gorilla describes them as well as some other interesting and surprising psychological research.
Maximizing the data-ink ratio sounds like a good idea, but when actually followed to the letter produces terrible and nonsensical results. Here is a more reasonable definition of chart junk that does away with the pretense of a mathematical formula and puts some common sense back into the question of good chart design.
I recently came across this book that claims to collect the 100 most important diagrams in the history of mankind. It’s a good collection, with many wonderful examples, though it has its flaws.
A while ago, Kaiser Fung criticized a chart for its uselessness because it only showed four numbers. The chart appeared on the smart web comic Abstruse Goose (which, as of this writing, is down for a site reorganization).
Data visualization is often used to just display data, with little thought put into supporting visual thinking. Giving people tools to do some visual math is a good idea; the visual properties need to be picked carefully however, to make this work.
After posting my response to Gelman and Unwin's article on visualization and statistical graphics recently, I have now collected links to all the three other responses as well: Stephen Few, Paul Murrell, and Hadley Wickham.
This is how scholarly exchanges used to work: Scientist A publishes a result, Scientist B then writes an angry letter saying that Scientist A is full of it, to which A responds with more insults, etc., and all that published in a fine scholarly journal. I was recently asked to respond to a piece Andrew Gelman and Anthony Unwin had written about visualization for the Journal of Computational and Graphical Statistics, which had some issues.
The visual representation of data has gone through a number of phases, with its goals switching back and forth between analysis and presentation over time. Many introductions to visualization tend to portray historical examples as all being done for the same purpose. That, I argue in this short, incomplete, and cherry-picked history, is not true.
When Alberto Cairo first told me about the book he was writing, called The Functional Art, he warned me that only a small part of it was going to be about visualization. I have no idea what he was talking about, the book I read was a visualization book from start to finish. It is one of the most interesting and insightful books on the topic I have read in a while.
Last Monday, I got to attend Edward Tufte’s one-day course. I was looking forward to a day of interesting examples, ideas, and discussions, but was disappointed by the amount of rambling and largely historical examples, with little connection to real, current visualization (or presentation) work.
Moritz Stefaner and Stephan Thiel have created a visualization of sensor data acquired by a floating mill on the river Tyne in Newcastle upon Tyne, Great Britain. Their choice of a flow metaphor for non-flow data creates a cognitive dissonance that makes the visualization difficult to understand.
Gregor Aisch recently wrote a posting about gauges, and how he finds them inspiring and beautiful in their simplicity, even though they are generally disliked in visualization. His posting highlights a common misconception about visualization, and a conflation of different uses of data display, that is worth exploring.
Criticism in visualization can be harsh, pedantic, and stupid. But it is also a useful tool that shows the thinking behind the seemingly simple graphical shapes we use, and teaches people things they might not be aware of. While I largely agree with Andy Kirk's criticism of visualization criticism and the danger of scaring people away from visualization, his "grown-up criticism" argument cuts both ways: grown-ups can argue a point without getting upset.
The Obama administration released a chart a while ago that shows job losses during the last year of the Bush administration and the first year after Obama took office. The chart is simple yet effective in the way it communicates a message. It also has some very subtle design elements that communicate a much more negative undertone than is immediately obvious.
Remember when visualization for the masses was all the rage, back in 2007? We were so young and hopeful. Many Eyes and Swivel were the harbingers of a new age of data literacy and well-informed debate. Visualization was going to be social and change the world. Alas, it was not to last. Swivel is gone, and Many Eyes clearly seen its best days. This is despite the fact that interest in visualization is growing, and it turns out that Many Eyes is as busy now as never before. I have scraped some data from the site that shows that despite the lack of updates and new features, people's use of it is still increasing. The data also gives some interesting insights into what people use it for.
Andrew Gelman recently wrote a blog posting in which he draws an interesting comparison between writing styles and graphics styles. I think he's on to something, and the comparison can be taken a bit further to illustrate some common misunderstandings around visualization.
I was asked to write a review of two recent visualization books for Science: Nathan Yau's Visualize This and Manuel Lima's Visual Complexity. The piece appeared in the last issue of 2011, right before Christmas. Below is a link and some additional comments on the review and the two books.
Heatmaps and 3D pie charts are often criticized, and for good reason. But they're not always a bad choice, and can work for simple data presentation. Context is important when criticizing visualizations, especially when there are no obvious improvements that can be made.
We criticize flashy infographics and bad visualizations, but we also want to attract viewer's attention. We strive for accuracy and efficiency, but we also want to tell stories. We dislike chart junk, but we like beautiful charts. We need to find a middle ground.
Pie charts don't get much respect. They're almost always considered the wrong choice by those supposedly in the know. But how do we know that this is true? What evidence do we have to support this? The truth is, not much. And when we start digging for proof, it turns out that pie charts are much better than we want to admit.
Finding the right chart for complex data is not an easy task. A reader pointed me to a presentation (PDF) by the New Hampshire Department of Education that illustrates some of the thinking behind choosing a new visual representation. The tool of choice here is the bubble chart.
Visualization may not be as precise as statistics, but it provides a unique view onto data that can make it much easier to discover interesting structures than numerical methods. Visualization also provides the context necessary to make better choices and to be more careful when fitting models. Anscombe's Quartet is a case in point, showing that four datasets that have identical statistical properties can indeed be very different.
Almost exactly 25 years ago, on January 28, 1986, Space Shuttle Challenger disintegrated seconds after lift-off. One of Edward Tufte's most famous examples of bad charts are the ones used by engineers who argued against the launch, and who failed to convince. It's a fascinating story, but it has one major fault: it is not true.
John Snow's map of the cholera dead after London's 1854 epidemic is often heralded as one of the earliest examples of graphical data analysis. Steven Johnson's The Ghost Map gives a lot of background about the London of the 1850s, Snow's work, and how central the map really was.
After my interview with Swivel founders Brian Mulloy and Dmitry Dimov on what happened to Swivel.com, I felt there were still many open questions. So I reached out to Halsey Minor, whose (cleverly-named) incubator Minor Ventures had funded Swivel, and who had made the decision to pull the plug. In this interview, he talks about his issues with Swivel, his priorities in developing products, and what it would take to bring Swivel back.
Earlier this summer, the visualization website Swivel.com disappeared from the internet. To find out what happened, I tracked down and interviewed Swivel's two founders, Brian Mulloy and Dmitry Dimov.
You all know what statistics is, right? I mean, everybody knows. But if you had to explain why it's useful, and what it's useful for, would you have an answer? Do you know how statistics makes a difference in all our lives, all the time? Even if you (think you) do, check out Kaiser Fung's book, Numbers Rule Your World.
There are thousands of visualizations on Many Eyes, but there is little in terms of further analysis and deeper discussion. There are dozens of visualization websites now that let you upload your data, but they all provide the same few visualization techniques and practically no analysis tools. While visualization for the masses may be here, we're not actually seeing much analysis from those same masses.
The first episode of season 4 of Mad Men opens with Don Draper being interviewed by a journalist. He doesn't tell him anything that's of interest and then dodges the question Who is Don Draper? by claiming that he was taught as a child not to talk about himself. Scientists do an equally terrible job at communication, and for many of the same reasons. Cornelia Dean's book Am I Making Myself Clear? offers fascinating insights into both journalism and science, and provides concrete ideas for how to do better.
Information graphics (infographics) have gotten a bad rep lately because of a sudden wave of badly designed, uninformative graphics. But when they are done right, infographics can be both highly informative and enjoyable to look at and discover. Here are a few recent examples to demonstrate that.
Is visualization art? Are video games art? Is programming art? Is art art? You can discuss these questions at length, but without concrete criteria, they end up being academic exercises rather than leading to some kind of conclusion. One criterion, which I believe to be suited especially well for visualization, is the sublime. Art is sublime, visualization is not. Hence, visualization is not art.
There is almost universal agreement that any extraneous elements in a chart or visualization, elements that do not represent numbers, are detrimental to understanding the data. A paper that was presented at CHI recently described a study to figure out just how bad all this chart junk really was. As it turns out, it's actually rather helpful.
Visualization is not a very clearly defined field. There are many variations, ways of doing it, and ideas around it. That is valuable, because it keeps the field moving and brings in fresh ideas. But it also brings with it people who like using visualization's tools and talk about visualization, but what they are doing is something else. We need to start calling these things what they are: a cargo cult of visualization.
Terrible charts seem to be in season. Rarely have I come across so many incredibly bad charts in such a short time: information graphics that don't actually depict data, distorted representations, useless color schemes, and the worst pie chart ever.
I recently criticized Ben Fry's visualization of health care cost data from GE and claimed that I knew how to do it better. While my analysis may not be as pretty and flashy as Fry's, it provides actual insight into the data. It also reveals an interesting issue: the data is really dull. So dull, in fact, that a visualization was needed to cover up that fact.
There are many terrible charts out there, whether visually ugly and cluttered, or pretty but empty or even misleading (like this beautiful pie chart example featured on Fox News recently). Andrew Vande Moere at infosthetics is hosting a competition to find the ugliest and most useless charts.
General Electric recently commissioned Ben Fry and Seed Media to visualize health data to communicate the costs of different kinds of diseases to the public. The result is pretty and colorful, but of little value if you actually want to learn something.
Coming from the academic and computer science side of visualization, I always assumed that it would be self-evident to anybody that visualization is first and foremost useful, and only happens to also produce nice pictures. Alas, this is not actually the case. To most people, visualization means pretty pictures first, and maybe also a fact or two. We have to fight that or risk the trivialization and marginalization of visualization as an analytic tool.
Of all the sins committed against visualization on the Internet, the Periodic Table of Visualization Methods stands out as the most egregious. Its collection of actual visualization methods, structural diagrams, and feel-good business bullshit does not fit a structure that was devised to understand the world – and that is actually a very effective visualization in itself.
The New York Times uses some of the best information graphics and visualizations on its web site and in the printed paper. But there is also a strange undercurrent of bad graphics, many of which commissioned from other sources, and often published in the New York Times Magazine. It almost feels like between all the good graphs, they need an outlet for the crazy stuff.
SPSS recently released their new Viz Designer, a visualization engine built on Leland Wilkinson's work (The Grammar of Graphics and nViZn). The comparison with Tableau is unavoidable since both are based on the same underlying ideas. Right now, Viz Designer does not look good in that comparison.
What is a visualization? The word is problematic, and there have been very few definitions that try to define this field we are working in. More importantly: what is not a visualization? It is easy to argue that anything visual is a visualization in some way – but does that mean anything? Here is a definition of visualization and a few examples to illustrate the different criteria.
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.
A paper on visualization criticism just appeared in the Visualization Viewpoints section of this month's Computer Graphics and Applications (CG&A). Authors are yours truly, Fritz Drury, Lars Erik Holmquist, and David Laidlaw.
The term visualization is used to mean different things in different contexts, and even visualization that is based on data can be done for different reasons and with different goals. Mixing up these different types of visualization leads to misunderstandings and confusion. Here is an attempt at teasing apart the two major types of data-based visualization, and understanding the differences.
Kaiser at Junk Charts has posted an interesting challenge based on the question how to visualize an Autism diagnosis dataset in a better way (originally posted by Igor Carron at Nuit Blanche). I'm offering my own redesign of the data below, and discuss my different approach and what it tells us about the visualization of sets in general.
According to one definition(ref), engineering is making things based on scientific principles – as opposed to the intuitive making that defines a craft. Information visualization (InfoVis) is practiced like a craft today, based mostly on practical examples, but not on theoretical basics. Here is a sketch of not only InfoVis as an engineering field, but InfoVis as a science.
When peanuts are bombs, clown-shaped cake ornaments are muzzle fires, and young guys are skateboards, we are talking about representation. We take it for granted that words can refer to things or abstract concepts, and colored spots on a piece of paper can depict data. Representation is really quite remarkable, and a better understanding of it will make a big difference in how we build visualizations.
Another semester is ending, and another class being taught using criticism as a main component is winding down. This time, I had a good mix of computer science, design, architecture, and liberal studies students. All the comments I received regarding the critiques were very positive, and the students' progress in their visualization designs reinforces those.
Chernoff Faces are discussed in every information visualization course, and are referenced in many papers that talk about glyphs. Yet the only serious use of faces in visualization is for calibration, not for data display. Faces are so special that we better know their perceptual properties really well before we can use them, which we don't.
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.com (defunct as of late 2010) 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.
At a panel discussion at Vis 2006, we were blasted for raising the question, Is there Science in Visualization? A senior visualization researcher said that he was embarrassed that this question was being discussed, and that we were trying to push our way of doing things on the community. The panel was still a success, but this proved just how far we still have to go.
Making visualization more aesthetically pleasing is certainly an important goal. Another one is to make visualization a part of our everyday lives. Ambient information displays are a way of doing both, and they are often inspired by pieces of art. But what if the viewers think they are just looking at a picture, and don't realize that it presents information to them?
The main means of communication in science is the (printed) journal article or conference paper, which only contains text and static images. This limits the way we can illustrate change, interaction, and dynamics. We do not have the appropriate language to effectively describe our work not only in terms of what it shows, but how and why it works. We also lack a means of talking about our own and others' work in ways that critically reflect on what has been done. We need to learn from art criticism, where this is all possible.
Commenting on a diagram showing the flow of electricity from its production to its use (and waste), the author of the Junk Charts blog writes: As it stands, this chart murmurs but does not opine. Which is exactly what it should do.
The winner of the 2006 Science and Engineering Visualization Challenge (organized by Science and the National Science Foundation, NSF) shows "five well-known mathematical surfaces, rendered as glass objects in a highly realistic 'Still Life.'" Using reflection, colored lighting, and otherwise unstructured sufaces makes for an image that does not convey the actual shapes particularly well. But it sure is pretty.
