Achieving symmetry and obscurity

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

Table7_orderpicking_pyramiddiagram

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:

Fig7_howtoread

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:

Table6_orderpicking

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.

Junkcharts_vangil_transform

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.

Fig7_howtoread_2

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

Fig7andfig6_cognition

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

Fig7_howtoread_3

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.]

Nyt_twitterblue_chart1

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".

Nyt_twitterblue_chart2

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.

Nyt_twitterblue_chart3

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:

Nyt_twitterblue_largestaccounts

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).

Iea_balloonchart_emissions

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.

Iea_balloonchart_emissions_legend

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:

Iea_balloonchart_emissions_green

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.

Washingtonpost_aftercollege

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.

Junkcharts_redo_washpost_postgraddestinations_1

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:

Junkcharts_redo_washpost_postgraddestinations_2

 


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:

Speedometers_example

 

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

Eugas_speedometer

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:

Redo_eugas_noaxis

Here is the chart without axis labels:

Redo_eugas_noaxislabels

Here is the chart without ticks:

Redo_eugas_notickmarks

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

Here is the chart without the dial:

Redo_eugas_nodial

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

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

Redo_eugas_nodialnosegments

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:

Redo_eugas_onedial

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.

Redo_eugas_nodialjustsegments

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:

Speedometers_example

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:

Twitter_financialpyramid

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:

Financialpyramid_data

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!

Redo_financialpyramid

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

 


Visualizing the impossible

Note [July 6, 2022]: Typepad's image loader is broken yet again. There is no way for me to fix the images right now. They are not showing despite being loaded properly yesterday. I also cannot load new images. Apologies!

Note 2: Manually worked around the automated image loader.

Note 3: Thanks Glenn for letting me about the image loading problem. It turns out the comment approval function is also broken, so I am not able to approve the comment.

***

A twitter user sent me this chart:

twitter_greatreplacement

It's, hmm, mystifying. It performs magic, as I explain below.

What's the purpose of the gridlines and axis labels? Even if there is a rationale for printing those numbers, they make it harder, not easier, for readers to understand the chart!

I think the following chart shows the main message of this poll result. Democrats are much more likely to think of immigration as a positive compared to Republicans, with Independents situated in between.

Redo_greatreplacement

***

The axis title gives a hint as to what the chart designer was aiming for with the unconventional axis. It reads "Overall Percentage for All Participants". It appears that the total length of the stacked bar is the weighted aggregate response rate. Roughly 17% of Americans thought this development to be "very positive" which include 8% of Republicans, 27% of Democrats and 12% of Independents. Since the three segments are not equal in size, 17% is a weighted average of the three proportions.

Within each of the three political affiliations, the data labels add to 100%. These numbers therefore are unweighted response rates for each segment. (If weighted, they should add up to the proportion of each segment.)

This sets up an impossible math problem. The three segments within each bar then represent the sum of three proportions, each unweighted within its segment. Adding these unweighted proportions does not yield the desired weighted average response rate. To get the weighted average response rate, we need to sum the weighted segment response rates instead.

This impossible math problem somehow got resolved visually. We can see that each bar segment faithfully represent the unweighted response rates shown in the respective data labels. Summing them would not yield the aggregate response rates as shown on the axis title. The difference is not a simple multiplicative constant because each segment must be weighted by a different multiplier. So, your guess is as good as mine: what is the magic that makes the impossible possible?

[P.S. Another way to see this inconsistency. The sum of all the data labels is 300% because the proportions of each segment add up to 100%. At the same time, the axis title implies that the sum of the lengths of all five bars should be 100%. So, the chart asserts that 300% = 100%.]

***

This poll question is a perfect classroom fodder to discuss how wording of poll questions affects responses (something called "response bias"). Look at the following variants of the same questions. Are we likely to get answers consistent with the above question?

As you know, the demographic makeup of America is changing and becoming more diverse, while the U.S. Census estimates that white people will still be the largest race in approximately 25 years. Generally speaking, do you find these changes to be very positive, somewhat positive, somewhat negative or very negative?

***

As you know, the demographic makeup of America is changing and becoming more diverse, with the U.S. Census estimating that black people will still be a minority in approximately 25 years. Generally speaking, do you find these changes to be very positive, somewhat positive, somewhat negative or very negative?

***

As you know, the demographic makeup of America is changing and becoming more diverse, with the U.S. Census estimating that Hispanic, black, Asian and other non-white people together will be a majority in approximately 25 years. Generally speaking, do you find these changes to be very positive, somewhat positive, somewhat negative or very negative?

What is also amusing is that in the world described by the pollster in 25 years, every race will qualify as a "minority". There will be no longer majority since no race will constitute at least 50% of the U.S. population. So at that time, the word "minority" will  have lost meaning.


A German obstacle course

Tagesschau_originalA twitter user sent me this chart from Germany.

It came with a translation:

"Explanation: The chart says how many car drivers plan to purchase a new state-sponsored ticket for public transport. And of those who do, how many plan to use their car less often."

Because visual language should be universal, we shouldn't be deterred by not knowing German.

The structure of the data can be readily understood: we expect three values that add up to 100% from the pie chart. The largest category accounts for 58% of the data, followed by the blue category (40%). The last and smallest category therefore has 2% of the data.

The blue category is of the most interest, and the designer breaks that up into four sub-groups, three of which are roughly similarly popular.

The puzzle is the identities of these categories.

The sub-categories are directly labeled so these are easy for German speakers. From a handy online translator, these labels mean "definitely", "probably", "rather not", "definitely not". Well, that's not too helpful when we don't know what the survey question is.

According to our correspondent, the question should be "of those who plan to buy the new ticket, how many plan to use their car less often?"

I suppose the question is found above the column chart under the car icon. The translator dutifully outputs "Thus rarer (i.e. less) car use". There is no visual cue to let readers know we are supposed to read the right hand side as a single column. In fact, for this reader, I was reading horizontally from top to bottom.

Now, the two icons on the left and the middle of the top row should map to not buying and buying the ticket. The check mark and cross convey that message. But... what do these icons map to on the chart below? We get no clue.

In fact, the will-buy ticket group is the 40% blue category while the will-not group is the 58% light gray category.

What about the dark gray thin sector? Well, one needs to read the fine print. The footnote says "I don't know/ no response".

Since this group is small and uninformative, it's fine to push it into the footnote. However, the choice of a dark color, and placing it at the 12-o'clock angle of the pie chart run counter to de-emphasizing this category!

Another twitter user visually depicts the journey we take to understand this chart:

Tagesschau_reply

The structure of the data is revealed better with something like this:

Redo_tagesschau_newticket

The chart doesn't need this many colors but why not? It's summer.

 

 

 

 


Variance is a friend of dataviz

Seven years ago, I wrote a post about "invariance" in data visualization, which is something we should avoid (link). Yesterday, Business Insider published the following chart in an article about rising gas prices (link):

Businessinsider_gasprices_prices

The map shows the average prices at the pump in seven regions of the United States. 

This chart is succeeded by the following map:

Businessinsider_gasprices_pricechange

This second map shows the change in average gas prices in the same seven regions.

This design is invariant to the data! While the data change, the visualization looks identical. That's because the data are not encoded to any visual element - they are just printed as labels.

 


Multicultural, multicolor, manufactured outrage

Twitter users were incensed by this chart:

Twitter_worstpiechart

It's being slammed as one of the most outrageous charts ever.

Mollywhite_twitter_outrageous

***

An image search reveals this chart form has international appeal.

In Kazakh:

Eurasianbank_piechart_kazakh

In Turkish:

Medirevogrupperformans_piechart_turkey

In Arabic, but the image source is a Spanish company:

Socialpubli_piechart_spain

In English, from an Indian source:

Panipatinstitute_piechart_india

In Russian:

Russian_piechart

***

Some people are calling this a pie chart.

But it isn't a pie chart since the slices clearly add up to more than one full circle.

It may be a graph template from an infographics website. You see people are applying data labels without changing the sizes or orientation or even colors of the slices. So the chart form is used as a container for data, rather than an encoder.

***

The Twitter user who called this "outrageous" appears to want to protect the designer, as the words have been deliberately snipped from the chart.

Mollywhite_twitter_outrageous_tweet

Nevertheless, Molly White coughed up the source in a subsequent tweet.

Mollywhite_twitter_outrageous_source

A bit strange, if you stop and think a little. Why would Molly shame the designer 20 hours later after she decided not to?

 

 

According to Molly, the chart appeared on the website of an NFT company. [P.S. See note below]

Here's the top of the page that Molly White linked to:

Mollywhite_twitter_outrageous_web3isgoinggreat

Notice the author of this page. That's "Molly White",  who is the owner of this NFT company! [See note below: she's the owner of a satire website who was calling out the owner of this company.]

Who's more outrageous?

Someone creating the most outrageous chart in order to get clout from outraged Twitter users and drive traffic to her new NFT venture? Or someone creating the template for the outrageous chart form, spawning an international collection?

 

[P.S. 3/17/2022 The answer is provided by other Twitter users, and the commentors. The people spreading this chart form is more ourageous. I now realized that Molly runs a sarcastic site. When she linked to the "source", she linked to her own website, which I interpreted as the source of the image. The page did contain that image, which added to the confusion. I must also add her work looks valuable, as it assesses some of the wild claims in Web3 land.

Mollywhite_site
]

[P.S. 3/17/2022 Molly also pointed out that her second tweet about the source came around 45 minutes after the first tweet. Twitter showed "20 hours" because it was 20 hours from the time I read the tweet.]