Two thousand five hundred ways to say the same thing

Wallethub published a credit card debt study, which includes the following map:


Let's describe what's going on here.

The map plots cities (N = 2,562) in the U.S. Each city is represented by a bubble. The color of the bubble ranges from purple to green, encoding the percentile ranking based on the amount of credit card debt that was paid down by consumers. Purple represents 1st percentile, the lowest amount of paydown while green represents 99th percentile, the highest amount of paydown.

The bubble size is encoding exactly the same data, apparently in a coarser gradation. The more purple the color, the smaller the bubble. The more green the color, the larger the bubble.


The design decisions are baffling.

Purple is more noticeable than the green, but signifies the less important cities, with the lesser paydowns.

With over 2,500 bubbles crowding onto the map, over-plotting is inevitable. The purple bubbles are printed last, dominating the attention but those are the least important cities (1st percentile). The green bubbles, despite being larger, lie underneath the smaller, purple bubbles.

What might be the message of this chart? Our best guess is: the map explores the regional variation in the paydown rate of credit card debt.

The analyst provides all the data beneath the map. 


From this table, we learn that the ranking is not based on total amount of debt paydown, but the amount of paydown per household in each city (last column). That makes sense.

Shouldn't it be ranked by the paydown rate instead of the per-household number? Divide the "Total Credit Card Paydown by City" by "Total Credit Card Debt Q1 2018" should yield the paydown rate. Surprise! This formula yields a column entirely consisting of 4.16%.

What does this mean? They applied the national paydown rate of 4.16% to every one of 2,562 cities in the country. If they had plotted the paydown rate, every city would attain the same color. To create "variability," they plotted the per-household debt paydown amount. Said differently, the color scale encodes not credit card paydown as asserted but amount of credit card debt per household by city.

Here is a scatter plot of the credit card amount against the paydown amount.


A perfect alignment!

This credit card debt paydown map is an example of a QDV chart, in which there isn't a clear question, there is almost no data, and the visual contains several flaws. (See our Trifecta checkup guide.) We are presented 2,562 ways of saying the same thing: 4.16%.


P.S. [6/22/2018] Added scatter plot, and cleaned up some language.




Lines, gridlines, reference lines, regression lines, the works

This post is part 2 of an appreciation of the chart project by Google Newslab, advised by Alberto Cairo, on the gender and racial diversity of the newsroom. Part 1 can be read here.

In the previous discussion, I left out the following scatter bubble plot.


This plot is available in two versions, one for gender and one for race. The key question being asked is whether the leadership in the newsroom is more or less diverse than the rest of the staff.

The story appears to be a happy one: in many newsrooms, the leadership roughly reflects the staff in terms of gender distribution (even though both parts of the whole compare disfavorably to the gender ratio in the neighborhoods, as we saw in the previous post.)


Unfortunately, there are a few execution problems with this scatter plot.

First, take a look at the vertical axis labels on the right side. The labels inform the leadership axis. The mid-point showing 50-50 (parity) is emphasized with the gray band. Around the mid-point, the labels seem out of place. Typically, when the chart contains gridlines, we expect the labels to sit right around each gridline, either on top or just below the line. Here the labels occupy the middle of the space between successive gridlines. On closer inspection, the labels are correctly affixed, and the gridlines  drawn where they are supposed to be. The designer chose to show irregularly spaced labels: from the midpoint, it's a 15% jump on either side, then a 10% jump.

I find this decision confounding. It also seems as if two people have worked on these labels, as there exists two patterns: the first is "X% Leaders are Women", and second is "Y% Female." (Actually, the top and bottom labels are also inconsistent, one using "women" and the other "female".)

The horizontal axis? They left out the labels. Without labels, it is not possible to interpret the chart. Inspecting several conveniently placed data points, I figured that the labels on the six vertical gridlines should be 25%, 35%, ..., 65%, 75%, in essence the same scale as the vertical axis.

Here is the same chart with improved axis labels:


Re-labeling serves up a new issue. The key reference line on this chart isn't the horizontal parity line: it is the 45-degree line, showing that the leadership has the same proprotion of females as the rest of the staff. In the following plot (right side), I added in the 45-degree line. Note that it is positioned awkwardly on top of the grid system. The culprit is the incompatible gridlines.


The solution, as shown below, is to shift the vertical gridlines by 5% so that the 45-degree line bisects every grid cell it touches.



Now that we dealt with the purely visual issues, let me get to a statistical issue that's been troubling me. It's about that yellow line. It's supposed to be a regression line that runs through the points.

Does it appear biased downwards to you? It just seems that there are too many dots above and not enough below. The distance of the furthest points above also appears to be larger than that of the distant points below.

How do we know the line is not correct? Notice that the green 45-degree line goes through the point labeled "AVERAGE." That is the "average" newsroom with the average proportion of female staff and the average proportion of leadership staff. Interestingly, the average falls right on the 45-degree line.

In general, the average does not need to hit the 45-degree line. The average, however, does need to hit the regression line! (For a mathematical explanation, see here.)

Note the corresponding chart for racial diversity has it right. The yellow line does pass through the average point here:



In practice, how do problems seep into dataviz projects? It's the fact that you don't get to the last chart via a clean, streamlined process but that you pass through a cycle of explore-retrench-synthesize, frequently bouncing ideas between several people, and it's challenging to keep consistency!

And let me repeat my original comment about this project - the key learning here is how they took a complex dataset with many variables, broke it down into multiple parts addressing specific problems, and applied the layering principle to make each part of the project digestible.



Well-structured, interactive graphic about newsrooms

Today, I take a detailed look at one of the pieces that came out of an amazing collaboration between Alberto Cairo, and Google's News Lab. The work on diversity in U.S. newsrooms is published here. Alberto's introduction to this piece is here.

The project addresses two questions: (a) gender diversity (representation of women) in U.S. newsrooms and (b) racial diversity (representation of white vs. non-white) in U.S. newsrooms.

One of the key strengths of the project is how the complex structure of the underlying data is displayed. The design incorporates the layering principle everywhere to clarify that structure.

At the top level, the gender and race data are presented separately through the two tabs on the top left corner. Additionally, newsrooms are classified into three tiers: brand-names (illustrated with logos), "top" newsrooms, and the rest.


The brand-name newsrooms are shown with logos while the reader has to click on individual bubbles to see the other newsrooms. (Presumably, the size of the bubble is the size of each newsroom.)

The horizontal scale is the proportion of males (or females), with equality positioned in the middle. The higher the proportion of male staff, the deeper is the blue. The higher the proportion of female staff, the deeper is the red. The colors are coordinated between the bubbles and the horizontal axis, which is a nice touch.

I am not feeling this color choice. The key reference level on this chart is the 50/50 split (parity), which is given the pale gray. So the attention is drawn to the edges of the chart, to those newsrooms that are the most gender-biased. I'd rather highlight the middle, celebrating those organizations with the best gender balance.


The red-blue color scheme unfortunately re-appeared in a subsequent chart, with a different encoding.


Now, blue means a move towards parity while red indicates a move away from parity between 2001 and 2017. Gray now denotes lack of change. The horizontal scale remains the same, which is why this can cause some confusion.

Despite the colors, I like the above chart. The arrows symbolize trends. The chart delivers an insight. On average, these newsrooms are roughly 60% male with negligible improvement over 16 years.


Back to layering. The following chart shows that "top" newsrooms include more than just the brand-name ones.


The dot plot is undervalued for showing simple trends like this. This is a good example of this use case.

While I typically recommend showing balanced axis for bipolar scale, this chart may be an exception. Moving to the right side is progress but the target sits in the middle; the goal isn't to get the dots to the far right so much of the right panel is wasted space.


A gem among the snowpack of Olympics data journalism

It's not often I come across a piece of data journalism that pleases me so much. Here it is, the "Happy 700" article by Washington Post is amazing.



When data journalism and dataviz are done right, the designers have made good decisions. Here are some of the key elements that make this article work:

(1) Unique

The topic is timely but timeliness heightens both the demand and supply of articles, which means only the unique and relevant pieces get the readers' attention.

(2) Fun

The tone is light-hearted. It's a fun read. A little bit informative - when they describe the towns that few have heard of. The notion is slightly silly but the reader won't care.

(3) Data

It's always a challenge to make data come alive, and these authors succeeded. Most of the data work involves finding, collecting and processing the data. There isn't any sophisticated analysis. But a powerful demonstration that complex analysis is not always necessary.

(4) Organization

The structure of the data is three criteria (elevation, population, and terrain) by cities. A typical way of showing such data might be an annotated table, or a Bumps-type chart, grouped columns, and so on. All these formats try to stuff the entire dataset onto one chart. The designers chose to highlight one variable at a time, cumulatively, on three separate maps. This presentation fits perfectly with the flow of the writing. 

(5) Details

The execution involves some smart choices. I am a big fan of legend/axis labels that are informative, for example, note that the legend doesn't say "Elevation in Meters":


The color scheme across all three maps shows a keen awareness of background/foreground concerns. 

A pretty good chart ruined by some naive analysis

The following chart showing wage gaps by gender among U.S. physicians was sent to me via Twitter:


The original chart was published by the Stat News website (link).

I am most curious about the source of the data. It apparently came from a website called Doximity, which collects data from physicians. Here is a link to the PR release related to this compensation dataset. However, the data is not freely available. There is a claim that this data come from self reports by 36,000 physicians.

I am not sure whether I trust this data. For example:


Do I believe that physicians in North Dakota earn the highest salaries on average in the nation? And not only that, they earn almost 30% more than the average physician in New York. Does the average physician in ND really earn over $400K a year? If you are wondering, the second highest salary number comes from South Dakota. And then Idaho.  Also, these high-salary states are correlated with the lowest gender wage gaps.

I suspect that sample size is an issue. They do not report sample size at the level of their analyses. They apparently published statistics at the level of MSAs. There are roughly 400 MSAs in the U.S. so at that level, on average, they have only 90 samples per MSA. When split by gender, the average sample size is less than 50. Then, they are comparing differences, so we should see the standard errors. And finally, they are making hundreds of such comparisons, for which some kind of multiple-comparisons correction is needed.

I am pretty sure some of you are doctors, or work in health care. Do those salary numbers make sense? Are you moving to North/South Dakota?


Turning to the Visual corner of the Trifecta Checkup (link), I have a mixed verdict. The hover-over effect showing the precise values at either axes is a nice idea, well executed.

I don't see the point of drawing the circle inside a circle.  The wage gap is already on the vertical axis, and the redundant representation in dual circles adds nothing to it. Because of this construct, the size of the bubbles is now encoding the male average salary, taking attention away from the gender gap which is the point of the chart.

I also don't think the regional analysis (conveyed by the colors of the bubbles) is producing a story line.


This is another instance of a dubious analysis in this "big data" era. The analyst makes no attempt to correct for self-reporting bias, and works as if the dataset is complete. There is no indication of any concern about sample sizes, after the analyst drills down to finer areas of the dataset. While there are other variables available, such as specialty, and other variables that can be merged in, such as income levels, all of which may explain at least a portion of the gender wage gap, no attempt has been made to incorporate other factors. We are stuck with a bivariate analysis that does not control for any other factors.

Last but not least, the analyst draws a bold conclusion from the overly simplistic analysis. Here, we are told: "If you want that big money, you can't be a woman." (link)


P.S. The Stat News article reports that the researchers at Doximity claimed that they controlled for "hours worked and other factors that might explain the wage gap." However, in Doximity's own report, there is no language confirming how they included the controls.


Lines that delight, lines that blight

This WSJ graphic caught my eye. The accompanying article is here.


The article (judging from the sub-header) makes two separate points, one about the total amount of money raised in IPOs in a year, and the change in market value of those newly-public companies one year from the IPO date.

The first metric is shown by the size of the bubbles while the second metric is displayed as distances from the horizontal axis. (The second metric is further embedded, in a simplified, binary manner, in the colors of the bubbles.)

The designer has decided that the second metric - performance after IPO - to be more important. Therefore, it is much easier for readers to know how each annual cohort of IPOs has performed. The use of color to map to the second metric (and not the first) also helps to emphasize the second metric.

There are details on this chart that I admire. The general tidiness of it. The restraint on the gridlines, especially along the horizontal ones. The spatial balance. The annotation.

And ah, turning those bubbles into lollipops. Yummy! Those dotted lines allow readers to find the center of each bubble, which is where the values of the second metrics lie. Frequently, these bubble charts are presented without those guiding lines, and it is often hard to find the circles' anchors.

That leaves one inexplicable decision - why did they place two vertical gridlines in the middle of two arbitrary years?

Batmen not as interesting as it seems

When this post appears, I will be on my way to Seattle. Maybe I will meet some of you there. You can still register here.

I held onto this tip from a reader for a while. I think it came from Twitter:

20160326_woc432_1 batman

The Economist found a fun topic but what's up with the axis not starting at zero?

The height x weight gimmick seems cool but on second thought, weight is not the same as girth so it doesn't make much sense!

In the re-design, I use bubbles to indicate weight and vertical location to indicate height. The data aren't as interesting as one might think. All the actors pretty much stayed true to the comic-book ideal, with Adam West being the closest. I also changed the order of the actors.


I left out the Lego, as it creates a design challenge that does not justify the effort.



More chart drama, and data aggregation

Robert Kosara posted a response to my previous post.

He raises an important issue in data visualization - the need to aggregate data, and not plot raw data. I have no objection to that point.

What was shown in my original post are two extremes. The bubble chart is high drama at the expense of data integrity. Readers cannot learn any of the following from that chart:

  • the shape of the growth and subsequent decline of the flu epidemic
  • the beginning and ending date of the epidemic
  • the peak of the epidemic*

* The peak can be inferred from the data label, although there appears to be at least one other circle of approximately equal size, which isn't labeled.

The column chart is low drama but high data integrity. To retain some dramatic element, I encoded the data redundantly in the color scale. I also emulated the original chart in labeling specific spikes.

The designer then simply has to choose a position along these two extremes. This will involve some smoothing or aggregation of the data. Robert showed a column chart that has weekly aggregates, and in his view, his version is closer to the bubble chart.

Robert's version indeed strikes a balance between drama and data integrity, and I am in favor of it. Here is the idea (I am responsible for the added color).



Where I depart from Robert is how one reads a column chart such as the one I posted:


Robert thinks that readers will perceive each individual line separately, and in so doing, "details hide the story". When I look at a chart like this, I am drawn to the envelope of the columns. The lighter colors are chosen for the smaller spikes to push them into the background. What might be the problem are those data labels identifying specific spikes; they are a holdover from the original chart--I actually don't know why those specific dates are labeled.


In summary, the key takeaway is, as Robert puts it:

the point of this [dataset] is really not about individual days, it’s about the grand totals and the speed with which the outbreak happened.

We both agree that the weekly version is the best among these. I don't see how the reader can figure out grand totals and speed with which the outbreak happened by staring at those dramatic but overlapping bubbles.

Is it worth the drama?

Quite the eye-catching chart this:


The original accompanied this article in the Wall Street Journal about avian flu outbreaks in the U.S.

The point of the chart appears to be the peak in the flu season around May. The overlapping bubbles were probably used for drama.

A column chart, with appropriate colors, attains much of the drama but retains the ability to read the data.