Michael Bales and his associates at Cornell are working on a new visual tool for citations data. This is an area that is ripe for some innovation. There is a lot of data available but it seems difficult to gain insights from them. The prototypical question is how authoritative is a particular researcher or research group, judging from his or her or their publications.
A proxy for "quality" is the number of times the paper is cited by others. More sophisticated metrics take into account the quality of the researchers who cite one's work. There are various summary statistics e.g. h-index that attempts to capture the data distribution but reducing to a single number may remove too much context.
Contextual information is very important for interpretation: certain disciplines might enjoy higher average numbers of citations because researchers tend to list more references, or that papers typically have large numbers of co-authors; individual researchers may have a few influential papers, or a lot of rarely-cited papers or anything in between.
A good tool should be able to address a number of such problems.
Michael was a former student who attended the Data Visualization workshop at NYU (syllabus here), and the class spent some time discussing his citations impact tool. He contacted me to let me know that what we did during the workshop has now reached the research conferences.
Here is a wireframe of the visual form we developed:
This particular chart shows the evolution in citations data over three time periods for a specific sub-field of study. The vertical scale is a percentile ranking based on some standard used in the citations industry. We grouped the data into deciles (and within each deciles, into thirds) to facilitate understanding. The median rank is highlighted - we can see that in this sub-field, the publications have both increased in quantity but also in quality with the median rank showing improvement over the three periods of time. Because "review articles" are interpreted differently by some, those are highlighted in purple.
One of the key strengths of this design is the filter mechanism shown on the right. The citations researcher can customize comparisons. This is really important because the citations data are meaningless by themselves; they only attain meaning when compared to peer groups.
Here is an even rougher sketch of the design:
For a single researcher, this view will list all of his or her papers, ordered by each paper's percentile rank, with review papers given a purple color.
The entire VIVO dashboard project by Weill Cornell Medicine has a github page, but the citation impact tool does not seem to be there at the moment. Michael tells me the citation impact tool is found here.
My readers are nailing it when it comes to finding charts that deserve close study. On Twitter, the conversation revolved around the inversion of the horizontal axis. Favorability is associated with positive numbers, and unfavorability with negative numbers, and so, it seems the natural ordering should be to place Favorable on the right and Unfavorable on the left.
Ordinarily, I'd have a problem with the inversion but here, the designer used the red-orange color scheme to overcome the potential misconception. It's hard to imagine that orange would be the color of disapproval, and red, of approval!
I am more concerned about a different source of confusion. Take a look at the following excerpt:
If you had to guess, what are the four levels of favorability? Using the same positive-negative scale discussed above, most of us will assume that going left to right, we are looking at Strongly Favorable, Favorable, Unfavorable, Strongly Unfavorable. The people in the middle are neutrals and the people on the edages are extremists.
But we'd be mistaken. The order going left to right is Favorable, Strongly Favorable, Strongly Unfavorable, Unfavorable. The designer again used tints and shades to counter our pre-conception. This is less successful because the order defies logic. It is a double inversion.
The other part of the chart I'd draw attention to is the column of data printed on the right. Each such column is an act of giving up - the designer admits he or she couldn't find a way to incorporate that data into the chart itself. It's like a footnote in a book. The problem arises because such a column frequently contains very important information. On this chart, the data are "net favorable" ratings, the proportion of Favorables minus the proportion of Unfavorables, or visually, the length of the orange bar minus the length of the red bar.
The net rating is a succinct way to summarize the average sentiment of the population. But it's been banished to a footnote.
Anyone who follows American politics a little in recent years recognizes the worsening polarization of opinions. A chart showing the population average is thus rather meaningless. I'd like to see the above chart broken up by party affiliation (Republican, Independent, Democrat).
This led me to the original source of the chart. It turns out that the data came from a Fox News poll but the chart was not produced by Fox News - it accompanied this Washington Postarticle. Further, the article contains three other charts, broken out by party affiliation, as I hoped. The headline of the article was "Bernie Sanders remains one of the most popular politicians..."
But reading three charts, printed vertically, is not the simplest matter. One way to make it easier is to gift the chart a purpose. It turns out there are no surprises among the Republican and Democratic voters - they are as polarized as one can imagine. So the real interesting question in this data is the orientation of the Independent voters - are they more likely to side with Democrats or Republicans?
Good house-keeping means when you acquire stuff, you must remove other stuff. After adding the party dimension, it makes more sense to collapse the favorability dimension - precisely by using the net favorable rating column:
I sketched out this blog post right before the Superbowl - and was really worked up as I happened to be flying into Atlanta right after they won (well, according to any of our favorite "prediction engines," the Falcons had 95%+ chance of winning it all a minute from the end of the 4th quarter!) What I'd give to be in the SuperBowl-winning city the day after the victory!
Maybe next year. I didn't feel like publishing about SuperBowl graphics when the wound was so very raw. But now is the moment.
The following chart came from Orange County Register on the run-up to the Superbowl. (The bobble-head quarterbacks also came from OCR). The original article is here.
The choice of a set of dot plots is inspired. The dot plot is one of those under-utilized chart types - for comparing two or three objects along a series of metrics, it has to be one of the most effective charts.
To understand this type of design, readers have to collect three pieces of information: first is to recognize the dot symbols, which color or shape represents which object being compared; second is to understand the direction of the axis; third is to recognize that the distance between the paired dots encodes the amount of difference between the two objects.
The first task is easy enough here as red stands for Atlanta and blue for New England - those being the team colors.
The second task is deceptively simple. It appears that a ranking scale is used for all metrics with the top ("1st") shown on the left side and the bottom ("32nd") shown on the right. Thus, all 32 teams in the NFL are lined up left to right (i.e. best to worst).
Now, focus your attention on the "Interceptions Caught" metric, third row from the bottom. The designer indicated "Fewest" on the left and "Most" on the right. For those who don't know American football, an "interception caught" is a good defensive play; it means your defensive player grabs a ball thrown by the opposing team (usually their quarterback), causing a turnover. Therefore, the more interceptions caught, the better your defence is playing.
Glancing back at the chart, you learn that on the "Interceptions Caught" metric, the worst team is shown on the left while the best team is shown on the right. The same reversal happened with "Fumbles Lost" (fewest is best), "Penalties" (fewest is best), and "Points Allowed per Game" (fewest is best). For four of nine metrics, right is best while for the other five, left is best.
The third task is the most complicated. A ranking scale always has the weakness that a gap of one rank does not yield information on how important the gap is. It's a complicated decision to select what type of scale to use in a chart like this, and in this post, I shall ignore this issue, and focus on a visual makeover.
I find the nine arrays of 32 squares, essentially the grid system, much too insistent, elevating information that belongs to the background. So one of the first fixes is to soften the grid system, and the labeling of the axes.
In addition, given the meaningless nature of the rank number (as mentioned above), I removed those numbers and used team logos instead. The locations on the axes are sufficient to convey the relative ranks of the two teams against the field of 32.
Most importantly, the directions of all metrics are now oriented in such a way that moving left is always getting better.
While using logos for sports teams is natural, I ended up replacing those, as the size of the dots is such that the logos are illegible anyway.
The above makeover retains the original order of metrics. But to help readers address the key question of this chart - which team is better, the designer should arrange the metrics in a more helpful way. For example, in the following version, the metrics are subdivided into three sections: the ones for which New England is significantly better, the ones for which Atlanta is much better, and the rest for which both teams are competitive with each other.
In the Trifecta checkup (link), I speak of the need to align your visual choices with the question you are trying to address with the chart. This is a nice case study of strengthening that Q-V alignment.
Attendess of my Copenhagen seminar this week saw an example of a Type QV chart (description of Trifecta checkup here), where the biggest problem is a disconnect between the question being addressed and the visual form.
The visually arresting form makes the number 60 scream. It is a small puzzle to figure out what 60 stands for. The red color is the 9th worst level of corruption out of 10 given in the scale. There were 60 countries placed into this level.
It's all very meaningless. The chart itself is proof that the countries were divided into uneven - apparently arbitrarily sized - segments. We learn nothing about how this "corruption perceptions index" is constructed, and which or how many countries were rated in total.
And even if all those issues could be resolved, knowing the histogram of countries ranked by perceived corruption does not tell us anything about corruption in those countries - it only informs about a minor aspect of the ranking scheme.
Notice, also, the country labels provided on the left column include just 7 countries, those in the best and worst levels, thus missing all of the sixty countries that caught our attention.
The last baffling decision is to create an eleventh phantom level dressed in black.
To reconstruct this, one first has to decide on a worthwhile question to illustrate.