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Intelligence. This blog provides me (and others on occasion) with a venue for ideas and opinions
that are either too urgent to wait for a full-blown article or too
limited in length, scope, or development to require the larger venue.
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December 12th, 2013
This blog entry was written by Bryan Pierce of Perceptual Edge.
If you’re interested in attending one of Stephen Few’s Visual Business Intelligence Workshops, he’s going to teach eleven public workshops in 2014, one in each of the following cities:
- Helsinki, Finland on March 11 - 13
- Stockholm, Sweden on March 18 - 20
- Minneapolis, Minnesota on April 8 - 10
- London, U.K. on April 22 - 24
- Oslo, Norway on May 6 - 8
- Utrecht, Netherlands on May 13 - 15
- Johannesburg, South Africa on June 3 - 5
- Portsmouth, Virginia on June 24 - 26
- Portland, Oregon on September 23 - 25
- Auckland, New Zealand in mid November
- Melbourne, Australia in late November
These workshops are a great way to learn the data visualization principles that Stephen teaches in his books.
November 20th, 2013
Errors in scientific research are all too common, and the problem has been getting worse. We’ve been led to believe that the methods of science are self-correcting, which is true, but only if they’re understood and followed, which is seldom the case. Ignorance of robust scientific methodology varies among disciplines, but it’s hard to imagine that any discipline can do worse than the errors that I’ve encountered in the field of information visualization.
An alarming article, “Trouble at the Lab,” in the October 19, 2013 edition of The Economist provides keen insight into the breadth, depth, and causes of this problem in academic research as a whole.
Academic scientists readily acknowledge that they often get things wrong. But they also hold fast to the idea that these errors get corrected over time as other scientists try to take the work further. Evidence that many more dodgy results are published than are subsequently corrected or withdrawn calls that much-vaunted capacity for self-correction into question. There are errors in a lot more of the scientific papers being published, written about and acted on than anyone would normally suppose, or like to think.
Various factors contribute to the problem. Statistical mistakes are widespread. The peer reviewers who evaluate papers before journals commit to publishing them are much worse at spotting mistakes than they or others appreciate. Professional pressure, competition and ambition push scientists to publish more quickly than would be wise. A career structure which lays great stress on publishing copious papers exacerbates all these problems. “There is no cost to getting things wrong,” says Brian Nosek, a psychologist at the University of Virginia who has taken an interest in his discipline’s persistent errors. “The cost is not getting them published.”
Graduate students are strongly encouraged by professors to get published, in part because the professor’s name will appear on the published study, even if they’ve contributed little, and professors don’t remain employed without long and continually growing lists of publications. In the field of information visualization, most of the students who do these studies have never been trained in research methodology, and it appears that most of their professors have skipped this training as well. It might surprise you to hear that most of these students and many of the professors also lack training in the fundamental principles and practices of information visualization, which leads to naïve mistakes. This is because most information visualization programs reside in computer science departments, and most of what’s done in computer science regarding information visualization, however useful, does not qualify as scientific research and does not involve scientific methods. There are exceptions, of course, but overall the current state of information visualization research is dismal.
The peer review system is not working. Most reviewers aren’t qualified to spot the flaws that typically plague information visualization research papers. Those who are qualified are often unwilling to expose errors because they want to be liked, and definitely don’t want to set themselves up as a target for a tit-for-tat response against their own work. On several occasions when I’ve written negative reviews of published papers, friends of mine in the academic community have written to thank me privately, but have never been willing to air their concerns publicly—not once. Without a culture of constructive critique, bad research will continue to dominate our field.
Papers with fundamental flaws often live on. Some may develop a bad reputation among those in the know, who will warn colleagues. But to outsiders they will appear part of the scientific canon.
Some of the worst information visualization papers published in the last few years have become some of the most cited. If you say something (or cite something) often enough, it becomes truth. We’ve all heard how people only use 10% of their brains. This is common knowledge, but it is pure drivel. Once the media latched onto this absurd notion, the voices of concerned neuroscientists couldn’t cut through the confusion.
How do we fix this? Here are a few suggestions:
- Researchers must be trained in scientific research methods. This goes for their professors as well. Central to scientific method is a diligent attempt to disprove one’s hypotheses. Skepticism of this type is rarely practiced in information visualization research.
- Researchers must be trained in statistics. Learning to get their software to spit out a p-value is not enough. Learning what a p-value means and when it should be used is more important than learning to produce one.
- Rigid standards must be established and enforced for publication. The respected scientific journal Nature has recently established an 18-point guideline for authors. Most of the guidelines that exist for information visualization papers are meager and in many cases counter-productive. For example, giving high scores for innovation encourages researchers to prioritize novelty over usefulness and effectiveness.
- Peer reviewers must be carefully vetted to confirm that they possess the required expertise.
- Rigid guidelines must be established for the peer review process.
- Peer review should not be done anonymously. I no longer review papers for most publications because they require reviewers to remain anonymous, which I refuse to do. No one whose judgment affects the work of others should be allowed to remain anonymous. Also, anyone who accepts poorly done research for publication should be held responsible for that flawed judgment.
- Researchers should be encouraged to publish their work even when it fails to establish what they expected. The only failure in research is research done poorly. Findings that conflict with expectations are still valuable findings. Even poorly done research is valuable if the authors admit their mistakes and learn from them.
- Researchers should be encouraged to replicate the studies of others. Even in the “hard sciences,” most published research cannot be successfully replicated. One of the primary self-correcting practices of science is replication. How many information visualization papers that attempt to replicate research done by others have you seen? I’ve seen none.
I’m sure that other suggestions belong on this list, but these of the ones that come to mind immediately. Many leaders in the information visualization community have for years discussed the question, “Is data visualization science?” My position is that it could be and it should be, but it won’t be until we begin to enforce scientific standards. It isn’t easy to whip a sloppy, fledgling discipline into shape and you won’t win a popularity contest by trying, but the potential of information visualization is too great to waste.
October 23rd, 2013
This blog entry was written by Bryan Pierce of Perceptual Edge.
A cycle plot is a type of line graph that is useful for displaying cyclical patterns across time. Cycle plots were first created in 1978 by William Cleveland and his colleagues at Bell Labs. We published an article about them in 2008, written by Naomi Robbins, titled Introduction to Cycle Plots. Here is an example of a cycle plot that displays monthly patterns across five years:
(Click to enlarge.)
In this cycle plot, the gray lines each represent the values for a particular month across the five-year period from 2009 through 2013. For instance, the first gray line from the left represents January. Looking at it we can see that little changed in January between 2009 and 2010, then values dipped in 2011 and then increased again in 2012 and 2013. The overlapping horizontal blue line represents the mean for the five years of January values.
The strength of the cycle plot is that it allows us to see a cyclical pattern (in this case the pattern formed by the means across the months of a year) and how the individual values from which that pattern was derived have changed during the entire period. For instance, by comparing the blue horizontal lines, we can see that June is the month with the second highest values on average, following December. We can also see that the values steadily trended upwards from January through June before dropping off in July. This much we could also see by looking at a line graph of the monthly means. However, using the cycle plot, we can also see how the values for individual months have changed across the years by looking at the gray lines. If you look at the gray line for June, you can see that we’ve had a steady decline from one June to the next across all five years, to the point that the values for May have surpassed the values for June in the last two years. Unless something changes, this steady decline could mean that June will no longer have the second highest average in the future. The decline in June is not something that we could easily spot if we were looking at this data in another way.
Despite their usefulness, one of the reasons I think we don’t see cycle plots more often is that they’re not supported directly by Excel. They can be made in Excel, but it’s a nuisance. To help with this problem, we’ve put together an Excel template for creating cycle plots using a method that we learned about from Ulrik Willemoes, who attended one of Stephen’s public workshops. It contains a cycle plot for displaying months and years, as shown above, and also a cycle plot for displaying days and weeks. All you need to do is plug in your own data and make some minor changes if you want to display a different number of years or weeks. Step-by-step instructions are included in the Excel file. Enjoy!
October 18th, 2013
Review of the Research Study “What Makes a Visualization Memorable?”
Michelle Borkin, et. al. (Harvard School of Engineering and Applied Sciences and MIT)
No topic within the field of data visualization has created more heated debate over the years than that of “chart junk.” This is perhaps because, when Edward Tufte first introduced the concept, he did so provocatively, inviting a heated response. Ever since, this debate has not only flourished without signs of cessation, but it has generated some of the least substantive and defensible claims in the field. I’ve contributed to this debate many times, always trying to rein it back into the realm of science. Whenever a research study that appears to defend the usefulness of chart junk is published, the Web immediately comes alive with silly chatter, consisting mostly of chest thumping: “Ha, ha! Take that!” The latest study of this ilk was presented this week at the annual IEEE VisWeek Conference by Michelle Borkin, et. al. (students and faculty at Harvard and MIT), titled “What Makes a Visualization Memorable?” Yeah, you guessed it, apparently it’s chart junk.
When I last attended VisWeek in 2011, my favorite research study was presented by this same researcher, Michelle Borkin. Her study produced a brilliant, life-saving visualization of the coronary arteries that could be used by medical doctors to diagnose plaque build-up that indicates heart disease. It was elegant in its simplicity and clarity. Borkin’s latest study, however, does not resemble her previous work in the least. Here’s the paper’s abstract in full:
An ongoing debate in the Visualization community concerns the role that visualization types play in data understanding. In human cognition, understanding and memorability are intertwined. As a first step towards being able to ask questions about impact and effectiveness, here we ask: “What makes a visualization memorable?” We ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon’s Mechanical Turk, we collected memorability scores for hundreds of these visualizations, and discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types). Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards how to design effective visualizations.
The authors collected a large set of data visualizations from the Web. Each visualization was coded by the research team for various characteristics (type of visualization, number of colors, data-ink ratio, the presence of pictograms, etc.) During a test session, subjects were shown one data visualization at a time for one second each, followed by a 1.4 second period of blank screen before the next visualization would appear. Each session displayed approximately 120 visualizations. The test was set up as a game with the objective of clicking whenever a visualization that appeared previously appeared a second time. A particular visualization never appeared more than twice. Hits (the subject indicted correctly that the visualization had appeared previously) and false hits (the subject incorrectly indicated that a visualization had previously appeared when it hadn’t) were both scored, but misses were not. The study’s objective was to determine which of the characteristics that were coded caused visualizations to be most memorable.
Any form of presentation, be it a book, speech, lecture, infographic, news story, or research paper, to name but a few, should be judged on how well it achieves the author’s objectives and the degree to which those objectives are worthwhile. A research paper in particular should be judged by how well it does what the authors claim and how useful its findings are to the field of study. This study does not actually do what it claims. What it actually demonstrates is quite different from the authors’ claims and does not qualify as new information.
The title of this study, “What Makes a Visualization Memorable?,” is misleading. It doesn’t demonstrate what makes a visualization memorable. A more accurate title might be: “When visualizations are presented for one second each in a long series, what visual elements or attributes most enable people to remember that they’ve seen it if it appears a second time?” That’s a mouthful and not a particularly great title, but it accurately describes what the study was actually designed to test. The study did not determine what makes a visualization memorable, but what visual elements or attributes included in the visualization would be noticed when viewed for only a second and then recognized when seen again. A data visualization contains content. Its purpose is to communicate that content. A visualization is not memorable unless its content is memorable. Merely knowing that you saw something a minute or two ago does not contribute in any obvious way to data visualization. And, more fundamentally, remembering something about the design of a visualization is nothing but a distraction. Ultimately, only the content matters; the design should disappear.
When an image appears before your eyes for only a second and then disappears, what actually goes on in your brain perceptually and cognitively? When the image is a visualization, you don’t have time to even begin making sense of it. At best, what happens in that brief moment is that something catches your eye that can be stored as a distinct memory. When the task that is being tested is your ability to recall if you’ve seen the image before when it’s flashed in front of your eyes a second time, then it’s necessary that the memory differentiates the image from the others that are being presented. If a clean and simple bar graph appears, there is nothing unique, no differentiator, from which to form a distinct memory. At best in that single second that you view it the concept “bar graph” forms in your brain, but you’re seeing many bar graphs and nothing about them is being recorded to differentiate them. If you see something with a profusion of colors, that colorful image is imprinted, which can serve as a distinct memory for near-term recall. If you see a novel form of display, a representation of that novelty can be retained. If you see a diagram that forms a distinct shape, it can be temporarily retained. What I’m describing is sometimes called stickiness. Something sticks because something about it stood out as memorable. That something rarely has anything to do with the content of the visualization.
Visualizations cannot be read and understood in a second. Flashing a graph in front of someone’s eyes for a second tells us nothing useful about the graphical communication, with one possible exception: the ability to grab attention. Knowing this can be useful when you are displaying information in a context that requires that you first catch viewers’ eyes to get them to look, such as in a newspaper or on a public-facing website. This potential use of immediate stickiness, however, was not mentioned in the study.
So, when the authors of this study made the following claim, they were mistaken:
Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards determining how to design effective visualizations.
Whether the assertion is true or not, this study did not test it. They went on to say:
Clearly, a more memorable visualization is not necessarily a more comprehensible one. However, knowing what makes a visualization memorable is a step towards answering higher level questions like “What makes a visualization engaging?” or “What makes a visualization effective?”.
Although the first sentence is true, what follows is pure conjecture. The authors seemed to wake up toward the end of the paper when they stated:
We do not want just any part of the visualization to stick (e.g., chart junk), but rather we want the most important relevant aspects of the data or trend the author is trying to convey to stick.
Yes, this statement is absolutely true. Unfortunately, this study does not address this aspect of stickiness at all. Sanity prevailed when they further stated:
We also hope to show in future work that memorability — i.e., treating visualizations as scenes — does not necessarily translate to an understanding of the visualizations themselves. Nor does excessive visual clutter aid comprehension of the actual information in the visualization (and may instead interfere with it).
If they do go on to show this in the future, they will have succeeded in exposing the uselessness of this paper. If only this realization had encouraged them to forego the publication of this study and quickly move on to the next.
If we reframed this study as potentially useful for immediately catching the reader’s eye and that alone, the following findings might have some use:
Not surprisingly, attributes such as color and the inclusion of a human recognizable object enhance memorability. And similar to previous studies we found that visualizations with low data-to-ink ratios and high visual densities (i.e., more chart junk and “clutter”) were more memorable than minimal, “clean” visualizations.
More surprisingly, we found that unique visualization types (pictoral [sic], grid/matrix, trees and networks, and diagrams) had significantly higher memorability scores than common graphs (circles, area, points, bars, and lines). It appears that novel and unexpected visualizations can be better remembered than the visualizations with limited variability that we are exposed to since elementary school.
As I mentioned in the beginning, however, these are not new findings. It’s interesting that finding described in the second paragraph above contradicted the authors’ expectations. They assumed that familiar visualizations, such as bar and line graphs, would be more memorable than novel visualizations. We’ve known for some time that novelty is sticky. The wonderful book by brothers Chip and Dan Heath, Made to Stick, made a big deal of this.
The one part of this study that I found most interesting and informative was a section that wasn’t actually relevant to the study. The authors quantified the number of times particular types of visualization appeared in four particular venues: scientific publications, infographics, all news media, and government and world organization. I found it interesting to note that news media of all types use bar and line graphs extensively, but infographics seldom include them. It was also interesting that tables supposedly appear much more often in infographics than in scientific publications, which doesn’t actually ring true to my experience.
A few other problems with the study are worth mentioning:
- The authors created a new taxonomy for categorizing visualizations that wasn’t actually useful for the task at hand. When revealed for only a second, there is nothing that we could reliably conclude about the comparative memorability the visualization types defined by their taxonomy. Because their taxonomy did not define visualization types as homogenous groups, comparisons made between them are meaningless. For example, grouping all graphs together that show distributions (histograms, box plots, frequency polygons, strip plots, tallies, stem-and-leaf plots, etc.) is not useful for determining the relative memorability of visualization types.
- They described bars (rectangles) and lines (contours) as “not natural,” but diagrams, radial plots, and heat maps as “more natural” and thus more memorable. From the perspective of visual perception, however, few shapes are more natural than rectangles and contours, which represent much of our world.
- I found it interesting that the racial mix of participants in the experiment (41.7% Caucasian, 37.5% South Asian, 4.2% African, 4.2% East Asian, 1.1% Hispanic, and 11.3% other/unreported) was considered by the authors to be “sampled fairly from the Mechanical Turk worker population.” When did Mechanical Turk become the population that matters? Wouldn’t it be more useful to have a fair sample of the general population? A 37.5% proportion of South Asians is not at all representative of the population in the United States in particular or the world in general, nor are 4.2% African and 1.1% Hispanic representative.
I’ve yet to see a useful study about chart junk in the last decade or so. Perhaps there’s something about the controversial nature of the debate and the provocative nature of claims that chart junk is useful (e.g., the possibility of knocking Tufte and Few down a notch or two) that shifts researchers from System 2 thinking (slow and rational) into System 1 (fast and emotional). Despite the flaws in this study, just like the others that have preceded it, dozens of future studies will cite it as credible and people will make outlandish claims based on it, which has already begun in the media.
September 30th, 2013
I suspect that one of the reasons why people are drawn to pie charts is the fact that these charts are familiar from elementary school instruction in the meaning and mathematical use of fractions. Based on this instruction, a pie chart is the image that becomes strongly associated with the parts-of-a-whole concept (a.k.a., fractions). But, just because this is how fractions have been traditionally taught in schools, should we assume that pie charts are the best visual representation for learning fractions? Although the metaphor is easy to grasp (the slices add up to an entire pie), we know that visual perception does a poor job of comparing the sizes of slices, which is essential for learning to compare fractions. Learning that one-fifth is larger than one-sixth, which is counter-intuitive in the beginning, becomes further complicated when the individual slices of two pies—one divided into five slices and other into six—look roughly the same. Might it makes more sense to use two lines divided into sections instead, which are quite easy to compare when placed near one another?
This not only makes sense based on our understanding of visual perception, but recent research has demonstrated that it in fact works better for learning. Take a moment to read the recent article about this by Sue Shellenbarger in The Wall Street Journal entitled “New Approaches to Teaching Fractions” (September 24, 2013).