Deconstructing graphics as an analysis tool in dataviz

One of the useful exercises I like to do with charts is to "deconstruct" them. (This amounts to a deeper version of the self-sufficiency test.)

Here is a chart stripped down to just the main visual elements.

The game is to guess what is the structure of the data given these visual elements.

I guessed the following:

• The data has a top-level split into two groups
• Within each group, the data is further split into 3 parts, corresponding to the 3 columns
• With each part, there are a variable number of subparts, each of which is given a unique color
• The color legend suggests that each group's data are split into 7 subparts, so I'm guessing that the 7 subparts are aggregated into 3 parts
• The core chart form is a stacked column chart with absolute values so relative proportions within each column (part) is important
• Comparing across columns is not supported because each column has its own total value
• Comparing same-color blocks across the two groups is meaningful. It's easier to compare their absolute values but harder to compare the relative values (proportions of total)

If I knew that the two groups are time periods, I'd also guess that the group on the left is the earlier time period, and the one on the right is the later time period. In addition to the usual left-to-right convention for time series, the columns are getting taller going left to right. Many things (not all, obviously) grow over time.

The color choice is a bit confusing because if the subparts are what I think they are, then it makes more sense to use one color and different shades within each column.

***

The above guesses are a mixed bag. What one learns from the exercise is what cues readers are receiving from the visual structure.

Here is the same chart with key contextual information added back:

Now I see that the chart concerns revenues of a business over two years.

My guess on the direction of time was wrong. The more recent year is placed on the left, counter to convention. This entity therefore suffered a loss of revenues from 2017-8 to 2018-9.

The entity receives substantial government funding. In 2017-8, it has 1 dollar of government funds for every 2 dollars of revenues. In 2018-9, it's roughly 2 dollars of government funds per every 3 dollars of revenues. Thus, the ratio of government funding to revenues has increased.

On closer inspection, the 7 colors do not represent 7 components of this entity's funding. The categories listed in the color legend overlap.

It's rather confusing but I missed one very important feature of the chart in my first assessment: the three columns within each year group are nested. The second column breaks down revenues into 3 parts while the third column subdivides advertising revenues into two parts.

What we've found is that this design does not offer any visual cues to help readers understand how the three columns within a year-group relates to each other. Adding guiding lines or changing the color scheme helps.

***

Next, I add back the data labels:

The system of labeling can be described as: label everything that is not further broken down into parts on the chart.

Because of the nested structure, this means two of the column segments, which are the sums of subparts, are not labeled. This creates a very strange appearance: usually, the largest parts are split into subparts, so such a labeling system means the largest parts/subparts are not labeled while the smaller, less influential, subparts are labeled!

You may notice another oddity. The pink segment is well above $1 billion but it is roughly the size of the third column, which represents $250 million. Thus, these columns are not drawn to scale. What happened? Keep reading.

***

Here is the whole chart:

A twitter follower sent me this chart. Elon Musk has been feuding with the Canadian broadcaster CBC.

Notice the scale of the vertical axis. It has a discontinuity between $700 million and $1.7 billion. In other words, the two pink sections are artificially shortened. The erased section contains $1 billion (!) Notice that the erased section is larger than the visible section.

The focus of Musk's feud with CBC is on what proportion of the company's funds come from the government. On this chart, the only way to figure that out is to copy out the data and divide. It's roughly 1.2/1.7 = 70% approx.

***

The exercise of deconstructing graphics helps us understand what parts are doing what, and it also reveals what cues certain parts send to readers.

In better dataviz, every part of the chart is doing something useful, it's free of redundant parts that take up processing time for no reason, and the cues to readers move them towards the intended message, not away from it.

***

A couple of additional comments:

I'm not sure why old data was cited because in the most recent accounting report, the proportion of government funding was around 65%.

Source of funding is not a useful measure of pro- or anti-government bias, especially in a democracy where different parties lead the government at different times. There are plenty of mouthpiece media that do not apparently receive government funding.

All about Connecticut

This dataviz project by CT Mirror is excellent. The project walks through key statistics of the state of Connecticut.

Here are a few charts I enjoyed.

The first one shows the industries employing the most CT residents. The left and right arrows are perfect, much better than the usual dot plots.

The industries are sorted by decreasing size from top to bottom, based on employment in 2019. The chosen scale is absolute, showing the number of employees. The relative change is shown next to the arrow heads in percentages.

The inclusion of both absolute and relative scales may be a source of confusion as the lengths of the arrows encode the absolute differences, not the relative differences indicated by the data labels. This type of decision is always difficult for the designer. Selecting one of the two scales may improve clarity but induce loss aversion.

***

The next example is a bumps chart showing the growth in residents with at least a bachelor's degree.

This is more like a slopegraph as it appears to draw straight lines between two time points 9 years apart, omitting the intervening years. Each line represents a state. Connecticut's line is shown in red. The message is clear. Connecticut is among the most highly educated out of the 50 states. It maintained this advantage throughout the period.

I'd prefer to use solid lines for the background states, and the axis labels can be sparser.

It's a little odd that pretty much every line has the same slope. I'm suspecting that the numbers came out of a regression model, with varying slopes by state, but the inter-state variance is low.

In the online presentation, one can click on each line to see the values.

***

The final example is a two-sided bar chart:

This shows migration in and out of the state. The red bars represent the number of people who moved out, while the green bars represent those who moved into the state. The states are arranged from the most number of in-migrants to the least.

I have clipped the bottom of the chart as it extends to 50 states, and the bottom half is barely visible since the absolute numbers are so small.

I'd suggest showing the top 10 states. Then group the rest of the states by region, and plot them as regions. This change makes the chart more compact, as well as more useful.

***

There are many other charts, and I encourage you to visit and support this data journalism.

 

 

 

Lay off bubbles

Wall Street Journal says that the scale of layoffs in the tech industry recently is worse than those caused by the pandemic lockdown. Here is the chart:

It's the dreaded bubble chart, complete with overlapping circles. Each bubble represents the total number of employees laid off in the U.S. in a given month.

The above isn't really the chart you find in the Journal. I have removed the two data labels from the chart. Look at the highlighted months of April 2020 and November 2022. Can you guess how much larger is the number of laid-off employees in November 2022 relative to April 2020?

***

If you guessed it's 100% - that the larger bubble is twice the size of the smaller one, then you're much better than I at reading bubble charts. Here is the published chart with the data labels:

I like to run this exercise - removing data labels - in order to reveal whether the graphical elements on the page are sufficient to convey the underlying data. Bubbles are typically not great at this. (This is what I call the self-sufficiency test.)

***

Another problem with bubble charts is that the sizes of the bubbles are arbitrary. This allows the designer to convey different messages with the same data.

Take a look at these two bubble charts:

The first one has huge bubbles, and lots of overlapping while the second one is roughly the same as the WSJ chart (I pulled a different dataset so the numbers may not be exactly the same).

Both charts are made from exactly the same data! In the second chart, the smallest bubbles are made very small while in the first chart, the smallest bubbles are still quite large.

Think twice before you make a bubble chart.

 

Getting simple charts right

Ian K. submitted this chart on Twitter:

The chart comes from a video embedded in this report (link) about Chicago cops leaving their jobs.

Let's start with the basics. This is an example of a simple line chart illustrating a time series of five observations. The vertical axis starts at 10,000 instead of 0. With this choice, the designer wants to focus on the point-to-point change in values, rather than its relation to the initial value.

Every graph has add-ons that assist cognition. On this chart, we have axis labels, gridlines and data labels. Every add-on increases reading time so we should be sparing.

First consider the gridlines. In the following chart, I conduct a self-sufficiency test by removing the data labels from the chart:

You can see that the last three values present no problems. The first two, especially the first value, are hard to read - because the top gridline is missing! The next chart restores the bounding gridline, so you can see the difference that one small detail can make:

***

Next, let's compare the following versions of the chart. The left one contains data labels without gridlines and axis labels. The right one has the gridlines and axis labels but no data labels.

The left chart prints the entire dataset onto the chart. The reader in essence is reading the raw data. That appears to be the intention of the chart designer as the data labels are in large size, placed inside shiny white boxes. The level of the boxes determines the reader's perception as those catch more of our attention than the dots that actually represent the data.

The right chart highlights the dots and the lines between them. The gridlines are way too thick and heavy so as to distract rather than abet. This chart presumes that the reader isn't that interested in the precise numbers as she is in the trend.

***

As Ian pointed out, one of the biggest problems with this chart is the appearance of even time intervals when all except one of the date values are January. This seemingly innocent detail destroys the chart. The line segments of the chart encodes the pre-post change in the staffing numbers. For most of the line segments, the metric is year-on-year change but the last two line segments on the right show something else: a 19-month change, followed by a 5-month change.

I did the following analysis to understand how big of a staffing problem CPD faces.

First I restored the January 2022 time value, while shifting the Aug 2022 value to its rightful place on the time axis. Next, I added the dashed brown line, which represents a linear extension of the trend seen between January 2020-2021, before the sudden dip. We don't know what the true January 2022 value is but the projected value based on past trend is around 12,200. By August, the projected value is around 11,923, about 300 above the actual value of 11,611. By January 2023, the projected value is almost exactly the same as the actual value.

This linear trending analysis is likely too simplistic but it offers a baseline to start thinking about what the story is. The long-term trend is still down but the apparent dip in 2022 may not be meaningful.

 

 

Achieving symmetry and obscurity

The following diagram found in an article on a logistics problem absorbed me for the larger part of an hour:

I haven't seen this chart form before, and it looks cute.

Quickly, I realize this to be one of those charts that require a big box "How to read me". The only hint comes in the chart title: the chart concerns combinations of planning problems. The planning problems are listed on the left. If you want to give it a go, try now before continuing with this blog post. 

***

It took me and a coworker together to unpack this chart. Here's one way to read it:

Assume I want to know what other problems the problem of "workforce allocation" is associated with. I'd go to the workforce allocation row, then scan both up and down the diagonals. Going up, I see that the authors found one (1) paper that discusses workforce allocation together with workforce level, two (2) papers that feature workforce allocation together with storage location assignment, etc. while going down, I see that workforce allocation is paired with batching in two papers and with order consolidation & sorting in one paper.

You may recognize the underlying data as a type of correlation matrix, which is commonly shown as an upper or lower triangular matrix. Indeed, the same data can be found in a different presentation in the same paper:

All the numbers are the same. What happened was the designer transformed the upper triangular matrix into an inverted (isoceles) triangle, then turned it aside. The row labels are preserved, while the column labels are dropped. Then, the row labels are snapped to cover the space which was formerly the empty lower triangular matrix.

A gain in symmetry, a loss in clarity.

***

Why is this cute, symmetric arrangement so much harder to read? It's out of step with the reader's cognitive path. The reader first picks a planning problem, then scans up and down looking for the correct pair.

Compare this to the matrix view: the reader picks a pair of problems, then finds the single cell that gives the number of articles.

One could borrow the reading strategy from the matrix, and proceed like this:

The reason why this cognition path doesn't come naturally is that there is only one set of labels on this triangular chart, compared to two sets in the common matrix format. It's unusual to have to pick out two items simultaneously from a single axis.

***

In the end, even though I like the idea of inducing symmetry, I am not convinced by the result.

***

The color scheme for the cells is also baffling. According to the legend, the dark color indicates research that solves a pair of problems in an integrated way while the light color is used when the researchers only analyze the interactions between the two problems.

What's odd is that each cell (pair of problems) is designated a single color. Since we expect researchers to take the different approaches to solving a given pair of problems, we deduce that the designated color represents the most frequent approach. What then does the number inside each cell represent? It can be the number of papers applying the color-coded solution approach, or it can be the total number of papers regardless of the solution approach.

 

P.S. [12-18-2022] See comments below for other examples of the triangular chart.

 

 

The blue mist

The New York Times printed several charts about Twitter "blue checks," and they aren't one of their best efforts (link).

Blue checks used to be credentials given to legitimate accounts, typically associated with media outlets, celebrities, brands, professors, etc. They are free but must be approved by Twitter. Since Elon Musk acquired Twitter, he turned blue checks into a revenue generator. Yet another subscription service (but you're buying "freedom"!). Anyone can get a blue check for US$8 per month.

[The charts shown here are scanned from the printed edition.]

The first chart is a scatter plot showing the day of joining Twitter and the total number of followers the account has as of early November, 2022. Those are very strange things to pair up on a scatter plot but I get it: the designer could only work with the data that can be pulled down from Twitter's API.

What's wrong with the data? It would seem the interesting question is whether blue checks are associated with number of followers. The chart shows only Twitter Blue users so there is nothing to compare to. The day of joining Twitter is not the day of becoming "Twitter Blue", almost surely not for any user (Nevetheless, the former is not a standard data element released by Twitter). The chart has a built-in time bias since the longer an account exists, one would assume the higher the number of followers (assuming all else equal). Some kind of follower rate (e.g. number of followers per year of existence) might be more informative.

Still, it's hard to know what the chart is saying. That most Blue accounts have fewer than 5,000 followers? I also suspect that they chopped off the top of the chart (outliers) and forgot to mention it. Surely, some of the celebrity accounts have way over 150,000 followers. Another sign that the top of the chart was removed is that an expected funnel effect is not seen. Given the follower count is cumulative from the day of registration, we'd expect the accounts that started in the last few months should have markedly lower counts than those created years ago. (This is even more true if there is a survivorship bias - less successful accounts are more likely to be deleted over time.)

The designer arbitrarily labelled six specific accounts ("Crypto influencer", "HBO fan", etc.) but this feature risks sending readers the wrong message. There might be one HBO fan account that quickly grew to 150,000 followers in just a few months but does the data label suggest to readers that HBO fan accounts as a group tend to quickly attain high number of followers?

***

The second chart, which is an inset of the first, attempts to quantify the effect of the Musk acquisition on the number of "registrations and subscriptions". In the first chart, the story was described as "Elon Musk buys Twitter sparking waves of new users who later sign up for Twitter Blue".

The second chart confuses me. I was trying to figure out what is counted in the vertical axis. This was before I noticed the inset in the first chart, easy to miss as it is tucked into the lower right corner. I had presumed that the axis would be the same as in the first chart since there weren't any specific labels. In that case, I am looking at accounts with 0 to 500 followers, pretty inconsequential accounts. Then, the chart title uses the words "registrations and subscriptions." If the blue dots on this chart also refer to blue-check accounts as in the first chart, then I fail to see how this chart conveys any information about registrations (wbich presumably would include free accounts). As before, new accounts that aren't blue checks won't appear.

Further, to the extent that this chart shows a surge in subscriptions, we are restricted to accounts with fewer than 500 followers, and it's really unclear what proportion of total subscribers is depicted. Nor is it possible to estimate the magnitude of this surge.

Besides, I'm seeing similar densities of the dots across the entire time window between October 2021 and 2022. Perhaps the entire surge is hidden behind the black lines indicating the specific days when Musk announced and completed the acquisition, respectively. If the surge is hiding behind the black vertical lines, then this design manages to block the precise spots readers are supposed to notice.

Here is where we can use the self-sufficiency test. Imagine the same chart without the text. What story would you have learned from the graphical elements themselves? Not much, in my view.

***

The third chart isn't more insightful. This chart purportedly shows suspended accounts, only among blue-check accounts.

From what I could gather (and what I know about Twitter's API), the chart shows any Twitter Blue account that got suspended at any time. For example, all the black open circles occurring prior to October 27, 2022 represent suspensions by the previous management, and presumably have nothing to do with Elon Musk, or his decision to turn blue checks into a subscription product.

There appears to be a cluster of suspensions since Musk took over. I am not sure what that means. Certainly, it says he's not about "total freedom". Most of these suspended accounts have fewer than 50 followers, and only been around for a few weeks. And as before, I'm not sure why the analyst decided to focus on accounts with fewer than 500 followers.

What could have been? Given the number of suspended accounts are relatively small, an interesting analysis would be to form clusters of suspended accounts, and report on the change in what types of accounts got suspended before and after the change of management.

***

The online article (link) is longer, filling in some details missing from the printed edition.

There is one view that shows the larger accounts:

While more complete, this view isn't very helpful as the biggest accounts are located in the sparsest area of the chart. The data labels again pick out strange accounts like those of adult film stars and an Arabic news site. It's not clear if the designer is trying to tell us that most of Twitter Blue accounts belong to those categories.

***
See here for commentary on other New York Times graphics.

 

 

 

 

Energy efficiency deserves visual efficiency

Long-time contributor Aleksander B. found a good one, in the World Energy Outlook Report, published by IEA (International Energy Agency).

The use of balloons is unusual, although after five minutes, I decided I must do some research to have any hope of understanding this data visualization.

A lot is going on. Below, I trace my own journey through this chart.

The text on the top left explains that the chart concerns emissions and temperature change. The first set of balloons (the grey ones) includes helpful annotations. The left-right position of the balloons indicates time points, in 10-year intervals except for the first.

The trapezoid that sits below the four balloons is more mysterious. It's labelled "median temperature rise in 2100". I debate two possibilities: (a) this trapezoid may serve as the fifth balloon, extending the time series from 2050 to 2100. This interpretation raises a couple of questions: why does the symbol change from balloon to trapezoid? why is the left-right time scale broken? (b) this trapezoid may represent something unrelated to the balloons. This interpretation also raises questions: its position on the horizontal axis still breaks the time series; and  if the new variable is "median temperature rise", then what determines its location on the chart?

That last question is answered if I move my glance all the way to the right edge of the chart where there are vertical axis labels. This axis is untitled but the labels shown in degree Celsius units are appropriate for "median temperature rise".

Turning to the balloons, I wonder what the scale is for the encoded emissions data. This is also puzzling because only a few balloons wear data labels, and a scale is nowhere to be found.

The gridlines suggests that the vertical location of the balloons is meaningful. Tracing those gridlines to the right edge leads me back to the Celsius scale, which seems unrelated to emissions. The amount of emissions is probably encoded in the sizes of the balloons although none of these four balloons have any data labels so I'm rather flustered. My attention shifts to the colored balloons, a few of which are labelled. This confirms that the size of the balloons indeed measures the amount of emissions. Nevertheless, it is still impossible to gauge the change in emissions for the 10-year periods.

The colored balloons rising above, way above, the gridlines is an indication that the gridlines may lack a relationship with the balloons. But in some charts, the designer may deliberately use this device to draw attention to outlier values.

Next, I attempt to divine the informational content of the balloon strings. Presumably, the chart is concerned with drawing the correlation between emissions and temperature rise. Here I'm also stumped.

I start to look at the colored balloons. I've figured out that the amount of emissions is shown by the balloon size but I am still unclear about the elevation of the balloons. The vertical locations of these balloons change over time, hinting that they are data-driven. Yet, there is no axis, gridline, or data label that provides a key to its meaning.

Now I focus my attention on the trapezoids. I notice the labels "NZE", "APS", etc. The red section says "Pre-Paris Agreement" which would indicate these sections denote periods of time. However, I also understand the left-right positions of same-color balloons to indicate time progression. I'm completely lost. Understanding these labels is crucial to understanding the color scheme. Clearly, I have to read the report itself to decipher these acronyms.

The research reveals that NZE means "net zero emissions", which is a forecasting scenario - an utterly unrealistic one - in which every country is assumed to fulfil fully its obligations, a sort of best-case scenario but an unattainable optimum. APS and STEPS embed different assumptions about the level of effort countries would spend on reducing emissions and tackling global warming.

At this stage, I come upon another discovery. The grey section is missing any acronym labels. It's actually the legend of the chart. The balloon sizes, elevations, and left-right positions in the grey section are all arbitrary, and do not represent any real data! Surprisingly, this legend does not contain any numbers so it does not satisfy one of the traditional functions of a legend, which is to provide a scale.

There is still one final itch. Take a look at the green section:

What is this, hmm, caret symbol? It's labeled "Net Zero". Based on what I have been able to learn so far, I associate "net zero" to no "emissions" (this suggests they are talking about net emissions not gross emissions). For some reason, I also want to associate it with zero temperature rise. But this is not to be. The "net zero" line pins the balloon strings to a level of roughly 2.5 Celsius rise in temperature.

Wait, that's a misreading of the chart because the projected net temperature increase is found inside the trapezoid, meaning at "net zero", the scientists expect an increase in 1.5 degrees Celsius. If I accept this, I come face to face with the problem raised above: what is the meaning of the vertical positioning of the balloons? There must be a reason why the balloon strings are pinned at 2.5 degrees. I just have no idea why.

I'm also stealthily presuming that the top and bottom edges of the trapezoids represent confidence intervals around the median temperature rise values. The height of each trapezoid appears identical so I'm not sure.

I have just learned something else about this chart. The green "caret" must have been conceived as a fully deflated balloon since it represents the value zero. Its existence exposes two limitations imposed by the chosen visual design. Bubbles/circles should not be used when the value of zero holds significance. Besides, the use of balloon strings to indicate four discrete time points breaks down when there is a scenario which involves only three buoyant balloons.

***

The underlying dataset has five values (four emissions, one temperature rise) for four forecasting scenarios. It's taken a lot more time to explain the data visualization than to just show readers those 20 numbers. That's not good!

I'm sure the designer did not set out to confuse. I think what happened might be that the design wasn't shown to potential readers for feedback. Perhaps they were shown only to insiders who bring their domain knowledge. Insiders most likely would not have as much difficulty with reading this chart as did I.

This is an important lesson for using data visualization as a means of communications to the public. It's easy for specialists to assume knowledge that readers won't have.

For the IEA chart, here is a list of things not found explicitly on the chart that readers have to know in order to understand it.

• Readers have to know about the various forecasting scenarios, and their acronyms (APS, NZE, etc.). This allows them to interpret the colors and section titles on the chart, and to decide whether the grey section is missing a scenario label, or is a legend.
• Since the legend does not contain any scale information, neither for the balloon sizes nor for the temperatures, readers have to figure out the scales on their own. For temperature, they first learn from the legend that the temperature rise information is encoded in the trapezoid, then find the vertical axis on the right edge, notice that this axis has degree Celsius units, and recognize that the Celsius scale is appropriate for measuring median temperature rise.
• For the balloon size scale, readers must resist the distracting gridlines around the grey balloons in the legend, notice the several data labels attached to the colored balloons, and accept that the designer has opted not to provide a proper size scale.

Finally, I still have several unresolved questions:

• The horizontal axis may have no meaning at all, or it may only have meaning for emissions data but not for temperature
• The vertical positioning of balloons probably has significance, or maybe it doesn't
• The height of the trapezoids probably has significance, or maybe it doesn't

 

 

Where have the graduates gone?

Someone submitted this chart on Twitter as an example of good dataviz.

The chart shows the surprising leverage colleges have on where students live after graduation.

The primary virtue of this chart is conservation of space. If our main line of inquiry is the destination states of college graduations - by state, then it's hard to beat this chart's efficiency at delivering this information. For each state, it's easy to see what proportion of graduates leave the state after graduation, and then within those who leave, the reader can learn which are the most popular destination states, and their relative importance.

The colors link the most popular destination states (e.g. Texas in orange) but they are not enough because the designer uses state labels also. A next set of states are labeled without being differentiated by color. In particular, New York and Massachusetts share shades of blue, which also is the dominant color on the left side.

***

The following is a draft of a concept I have in my head.

I imagine this to be a tile map. The underlying data are not public so I just copied down a bunch of interesting states. This view brings out the spatial information, as we expect graduates are moving to neighboring states (or the states with big cities).

The students in the Western states are more likely to stay in their own state, and if they move, they stay in the West Coast. The graduates in the Eastern states also tend to stay nearby, except for California.

I decided to use groups of color - blue for East, green for South, red for West. Color is a powerful device, if used well. If the reader wants to know which states send graduates to New York, I'm hoping the reader will see the chart this way:

 

Speedometer charts: love or hate

Pie chart hate is tired. In this post, I explain my speedometer hate. (Also called gauges,  dials)

Next to pie charts, speedometers are perhaps the second most beloved chart species found on business dashboards. Here is a typical example:

 

For this post, I found one on Reuters about natural gas in Europe. (Thanks to long-time contributor Antonio R. for the tip.)

The reason for my dislike is the inefficiency of this chart form. In classic Tufte-speak, the speedometer chart has a very poor data-to-ink ratio. The entire chart above contains just one datum (73%). Most of the ink are spilled over non-data things.

This single number has a large entourage:

- the curved axis
- ticks on the axis
- labels on the scale
- the dial
- the color segments
- the reference level "EU target"

These are not mere decorations. Taking these elements away makes it harder to understand what's on the chart.

Here is the chart without the curved axis:

Here is the chart without axis labels:

Here is the chart without ticks:

When the tick labels are present, the chart still functions.

Here is the chart without the dial:

The datum is redundantly encoded in the color segments of the "axis".

Here is the chart without the dial or the color segments:

If you find yourself stealing a peek at the chart title below, you're not alone.

All versions except one increases our cognitive load. This means the entourage is largely necessary if one encodes the single number in a speedometer chart.

The problem with the entourage is that readers may resort to reading the text rather than the chart.

***

The following is a minimalist version of the Reuters chart:

I removed the axis labels and the color segments. The number 73% is shown using the dial angle.

The next chart adds back the secondary message about the EU target, as an axis label, and uses color segments to show the 73% number.

Like pie charts, there are limited situations in which speedometer charts are acceptable. But most of the ones we see out there are just not right.

***

One acceptable situation is to illustrate percentages or proportions, which is what the EU gas chart does. Of course, in that situation, one can alo use a pie chart without shame.

For illustrating proportions, I prefer to use a full semicircle, instead of the circular sector of arbitrary angle as Reuters did. The semicircle lends itself to easy marks of 25%, 50%, 75%, etc, eliminating the need to print those tick labels.

***

One use case to avoid is numeric data.

Take the regional sales chart pulled randomly from a Web search above:

These charts are completely useless without the axis labels.

Besides, because the span of the axis isn't 0% to 100%, every tick mark must be labelled with the numeric value. That's a lot of extra ink used to display a single value!

Metaphors give and take

Another submission came in from Euro Twitter. The following chart is probably from Germany:

As JB noted, this chart explains a financial pyramid scheme. I believe the numbers on the left are participants while the numbers on the right are the potential ill-gotten gains per person. The longer the pyramid scheme lasts, the more people participate, the more money flows to the top.

The pyramid is a natural metaphor for visualizing pyramid schemes. The levels of the pyramid correspond to levels of a pyramid scheme - the newly recruited participants expand the base while passing revenues up the pyramid.

***

The chart fails because it's not really a dataviz. There are exactly three bars that are scaled according to data. Everything else is presented as data labels.

Let's look at the two data series separately:

Each series is exponentially growing (in opposite directions). [Some of the data labels for participants may be incorrect.]

Unfortunately, the triangle is not a good medium to display exponential growth. In fact, the triangular structure imposes a linear growth constraint. The length of the base is directly proportional to the height from the top. As one traverses downwards level by level, the width of the base grows linearly - not exponentially.

To illustrate exponential growth, the edge of the triangle cannot be a straight line - it has to be s steep curve!

While natural, the pyramid metaphor is also severely restricting. The choice of chart form has unexpected consequences.