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.]


How does the U.K. vote in the U.N.?

Through my twitter feed, I found my way to this chart, made by jamie_bio.

Jamie_bio_un_votes25032021

This is produced using R code even though it looks like a slide.

The underlying dataset concerns votes at the United Nations on various topics. Someone has already classified these topics. Jamie looked at voting blocs, specifically, countries whose votes agree most often or least often with the U.K.

If you look at his Github, this is one in a series of works he produced to hone his dataviz skills. Ultimately, I think this effort can benefit from some re-thinking. However, I also appreciate the work he has put into this.

Let's start with the things I enjoyed.

Given the dataset, I imagine the first visual one might come up with is a heatmap that shows countries in rows and topics in columns. That would work ok, as any standard chart form would but it would be a data dump that doesn't tell a story. There are almost 200 countries in the entire dataset. The countries can only be ordered in one way so if it's ordered for All Votes, it's not ordered for any of the other columns.

What Jamie attempts here is story-telling. The design leads the reader through a narrative. We start by reading the how-to-read-this box on the top left. This tells us that he's using a lunar eclipse metaphor. A full circle in blue indicates 0% agreement while a full circle in white indicates 100% agreement. The five circles signal that he's binning the agreement percentages into five discrete buckets, which helps simplify our understanding of the data.

Then, our eyes go to the circle of circles, labelled "All votes". This is roughly split in half, with the left side showing mostly blue and the right showing mostly white. That's because he's extracting the top 5 and bottom 5 countries, measured by their vote alignment with the U.K. The countries names are clearly labelled.

Next, we see the votes broken up by topics. I'm assuming not all topics are covered but six key topics are highlighted on the right half of the page.

What I appreciate about this effort is the thought process behind how to deliver a message to the audience. Selecting a specific subset that addresses a specific question. Thinning the materials in a way that doesn't throw the kitchen sink at the reader. Concocting the circular layout that presents a pleasing way of consuming the data.

***

Now, let me talk about the things that need more work.

I'm not convinced that he got his message across. What is the visual telling us? Half of the cricle are aligned with the U.K. while half aren't so the U.K. sits on the fence on every issue? But this isn't the message. It's a bit of a mirage because the designer picked out the top 5 and bottom 5 countries. The top 5 are surely going to be voting almost 100% with the U.K. while the bottom 5 are surely going to be disagreeing with the U.K. a lot.

I did a quick sketch to understand the whole distribution:

Redo_junkcharts_ukvotes_overview_2

This is not intended as a show-and-tell graphic, just a useful way of exploring the dataset. You can see that Arms Race/Disarmament and Economic Development are "average" issues that have the same form as the "All issues" line. There are a small number of countries that are extremely aligned with the UK, and then about 50 countries that are aligned over 50% of the time, then the other 150 countries are within the 30 to 50% aligned. On human rights, there is less alignment. On Palestine, there is more alignment.

What the above chart shows is that the top 5 and bottom 5 countries both represent thin slithers of this distribution, which is why in the circular diagrams, there is little differentiation. The two subgroups are very far apart but within each subgroup, there is almost no variation.

Another issue is the lunar eclipse metaphor. It's hard to wrap my head around a full white circle indicating 100% agreement while a full blue circle shows 0% agreement.

In the diagrams for individual topics, the two-letter acronyms for countries are used instead of the country names. A decoder needs to be provided, or just print the full names.

 

 

 

 

 

 


To explain or to eliminate, that is the question

Today, I take a look at another project from Ray Vella's class at NYU.

Rich Get Richer Assigment 2 top

(The above image is a honeypot for "smart" algorithms that don't know how to handle image dimensions which don't fit their shadow "requirement". Human beings should proceed to the full image below.)

As explained in this post, the students visualized data about regional average incomes in a selection of countries. It turns out that remarkable differences persist in regional income disparity between countries, almost all of which are more advanced economies.

Rich Get Richer Assigment 2 Danielle Curran_1

The graphic is by Danielle Curran.

I noticed two smart decisions.

First, she came up with a different main metric for gauging regional disparity, landing on a metric that is simple to grasp.

Based on hints given on the chart, I surmised that Danielle computed the change in per-capita income in the richest and poorest regions separately for each country between 2000 and 2015. These regional income growth values are expressed in currency, not indiced. Then, she computed the ratio of these growth rates, for each country. The end result is a simple metric for each country that describes how fast income has been growing in the richest region relative to the poorest region.

One of the challenges of this dataset is the complex indexing scheme (discussed here). Carlos' solution keeps the indices but uses design to facilitate comparisons. Danielle avoids the indices altogether.

The reader is relieved of the need to make comparisons, and so can focus on differences in magnitude. We see clearly that regional disparity is by far the highest in the U.K.

***

The second smart decision Danielle made is organizing the countries into clusters. She took advantage of the horizontal axis which does not encode any data. The branching structure places different clusters of countries along the axis, making it simple to navigate. The locations of these clusters are cleverly aligned to the map below.

***

Danielle's effort is stronger on communications while Carlos' effort provides more information. The key is to understand who your readers are. What proportion of your readers would want to know the values for each country, each region and each year?

***

A couple of suggestions

a) The reference line should be set at 1, not 0, for a ratio scale. The value of 1 happens when the richest region and the poorest region have identical per-capita incomes.

b) The vertical scale should be fixed.


Speaking to the choir

A friend found the following chart about the "carbon cycle", and sent me an exasperated note, having given up on figuring it out. The chart came from a report, and was reprinted in Ars Technica (link).

Gcp_s09_2021_global_perturbation-800x371

The problem with the chart is that the designer is speaking to the choir. One must know a lot about the carbon cycle already to make sense of everything that's going on.

We see big and small arrows pointing up or down. Each arrow has a number attached to it, plus a range inside brackets. These numbers have no units, and it's not obvious what they are measuring.

The arrows come in a variety of colors. The colors are explained by labels but the labels dexcribe apparently unrelated concepts (e.g. fossil CO2 and land-use change).

Interspersed with the arrows is a singular dot. The dot also has a number attached to it. The number wears a plus sign, which signals it's being treated differently than the quantities with up arrows.

The singular dot is an outcast, ostracized from the community of dots in the bottom part of the chart. These dots have labels but no numbers. They come in different sizes but no scale is provided.

The background is divided into three parts, showing the atmosphere, the land mass, and the ocean. The placement of the arrows and dots suggests each measured quantity concerns one of these three parts. Well... except the dot labeled "surface sediments" that sit on the boundary of the land mass and the ocean.

The three-way classification is only one layer of the chart. A different classification is embedded in the color scheme. The gray, light green, and aquamarine arrows in the sky find their counterparts in the dots of the land mass, and the ocean.

What's more, the boundaries between land and sky, and between land and ocean are also painted with those colors. These boundary segments have been given different colors so that the lengths of these segments seem to contain data but we aren't sure what.

At this point, I noticed thin arrows which appear to depict back and forth flows. There may be two types of such exchanges, one indicated by a cycle, the other by two straight arrows in opposite directions. The cycles have no numbers while each pair of straight thin arrows gets two numbers, always identical.

At the bottom of the chart is a annotation in red: "Budget imbalance = -1.0". Presumably some formula ties the numbers shown above to this -1.0 result. We still don't know the units, and it's unclear if -1.0 is a bad number. A negative number shown in red typically indicates a bad number but how bad is it?

Finally, on the top right corner, I found a legend. It's not obvious at first because the legend symbols (arrows and dots) are shown in gray, a color not used elsewhere on the chart. It appears as if it represents another color category. The legend labels do little for me. What is an "anthropogenic flux"? What does the unit of "GtCO2" stand for? Other jargon includes "carbon cycling" and "stocks". The entire diagram is titled "carbon cycle" while the "carbon cycling" thin arrows are only a small part of the diagram.

The bottom line is I have no idea what this chart is saying to me, other than that the earth is a complex system, and that the designer has tried valiantly to impregnate the diagram with lots of information. If I am well read in environmental science, my experience is likely different.

 

 

 

 

 


Two commendable student projects, showing different standards of beauty

A few weeks ago, I did a guest lecture for Ray Vella's dataviz class at NYU, and discussed a particularly hairy dataset that he assigns to students.

I'm happy to see the work of the students, and there are two pieces in particular that show promise.

The following dot plot by Christina Barretto shows the disparities between the richest and poorest nations increasing between 2000 and 2015.

BARRETTO  Christina - RIch Gets Richer Homework - 2021-04-14

The underlying dataset has the average GDP per capita for the richest and the poor regions in each of nine countries, for two years (2000 and 2015). With each year, the data are indiced to the national average income (100). In the U.K., the gap increased from around 800 to 1,100 in the 15 years. It's evidence that the richer regions are getting richer, and the poorer regions are getting poorer.

(For those into interpreting data, you should notice that I didn't say the rich getting richer. During the lecture, I explain how to interpret regional averages.)

Christina's chart reflects the tidy, minimalist style advocated by Tufte. The countries are sorted by the 2000-to-2015 difference, with Britain showing up as an extreme outlier.

***

The next chart by Adrienne Umali is more infographic than Tufte.

Adrienne Umali_v2

It's great story-telling. The top graphic explains the underlying data. It shows the four numbers and how the gap between the richest and poorest regions is computed. Then, it summarizes these four numbers into a single metric, "gap increase". She chooses to measure the change as a ratio while Christina's chart uses the difference, encoded as a vertical line.

Adrienne's chart is successful because she filters our attention to a single country - the U.S. It's much too hard to drink data from nine countries in one gulp.

This then sets her up for the second graphic. Now, she presents the other eight countries. Because of the work she did in the first graphic, the reader understands what those red and green arrows mean, without having to know the underlying index values.

Two small suggestions: a) order the countries from greatest to smallest change; b) leave off the decimals. These are minor flaws in a brilliant piece of work.

 

 


Metaphors, maps, and communicating data

There are some data visualization that are obviously bad. But what makes them bad?

Here is an example of such an effort:

Carbon footprint 2021-02-15_0

This visualization of carbon emissions is not successful. There is precious little that a reader can learn from this chart without expensing a lot of effort. It's relatively easy to identify the largest emitters of carbon but since the data are not expressed per-capita, the chart mainly informs us which countries have the largest populations. 

The color of the bubbles informs readers which countries belong to which parts of the world. However, it distorts the location of countries within regions, and regions relative to regions, as the primary constraint is fitting the bubbles inside the shape of a foot.

The visualization gives a very rough estimate of the relative sizes of total emissions. The circles not being perfect circles don't help. 

It's relatively easy to list the top emitters in each region but it's hard to list the top 10 emitters in the world (try!) 

The small emitters stole all of the attention as they account for most of the labels - and they engender a huge web of guiding lines - an unsightly nuisance.

The diagram clings dearly to the "carbon footprint" metaphor. Does this metaphor help readers consume the emissions data? Conversely, does it slow them down?

A more conventional design uses a cartogram, a type of map in which the positioning of countries are roughly preserved while the geographical areas are coded to the data. Here's how it looks:

Carbonatlasthumb

I can't seem to source this effort. If any reader can find the original source, please comment below.

This cartogram is a rearrangement of the footprint illustration. The map construct eliminates the need to include a color legend which just tells people which country is in which continent. The details of smaller countries are pushed to the bottom. 

In the footprint visualization, I'd even consider getting rid of the legend completely. This means trusting that readers know South Africa is part of Africa, and China is part of Asia.

Carbonfootprint_part

Imagine: what if this chart comes without a color legend? Do we really need it?

***

I'd like to try a word cloud visual for this dataset. Something that looks like this (obviously with the right data encoding):

Michaeltompsett_worldmapwords

(This map is by Michael Tompsett who sells it here.)

 


Making graphics last over time

Yesterday, I analyzed the data visualization by the White House showing the progress of U.S. Covid-19 vaccinations. Here is the chart.

Whgov_proportiongettingvaccinated

John who tweeted this at me, saying "please get a better data viz".

I'm happy to work with them or the CDC on better dataviz. Here's an example of what I do.

Junkcharts_redo_whgov_usvaccineprogress

Obviously, I'm using made-up data here and this is a sketch. I want to design a chart that can be updated continuously, as data accumulate. That's one of the shortcomings of that bubble format they used.

In earlier months, the chart can be clipped to just the lower left corner.

Junkcharts_redo_whgov_usvaccineprogress_2


Reading an infographic about our climate crisis

Let's explore an infographic by SCMP, which draws attention to the alarming temperature recorded at Verkhoyansk in Russia on June 20, 2020. The original work was on the back page of the printed newspaper, referred to in this tweet.

This view of the globe brings out the two key pieces of evidence presented in the infographic: the rise in temperature in unexpected places, and the shrinkage of the Arctic ice.

Scmp_russianheat_1a

A notable design decision is to omit the color scale. On inspection, the scale is present - it was sewn into the graphic.

Scmp_russianheat_colorscale

I applaud this decision as it does not take the reader's eyes away from the graphic. Some information is lost as the scale isn't presented in full details but I doubt many readers need those details.

A key takeaway is that the temperature in Verkhoyansk, which is on the edge of the Arctic Circle, was the same as in New Delhi in India on that day. We can see how the red was encroaching upon the Arctic Circle.

***Scmp_russianheat_2a

Next, the rapid shrinkage of the Arctic ice is presented in two ways. First, a series of maps.

The annotations are pared to the minimum. The presentation is simple enough such that we can visually judge that the amount of ice cover has roughly halved from 1980 to 2009.

A numerical measure of the drop is provided on the side.

Then, a line chart reinforces this message.

The line chart emphasizes change over time while the series of maps reveals change over space.

Scmp_russianheat_3a

This chart suggests that the year 2020 may break the record for the smallest ice cover since 1980. The maps of Australia and India provide context to interpret the size of the Arctic ice cover.

I'd suggest reversing the pink and black colors so as to refer back to the blue and pink lines in the globe above.

***

The final chart shows the average temperature worldwide and in the Arctic, relative to a reference period (1981-2000).

Scmp_russianheat_4

This one is tough. It looks like an area chart but it should be read as a line chart. The darker line is the anomaly of Arctic average temperature while the lighter line is the anomaly of the global average temperature. The two series are synced except for a brief period around 1940. Since 2000, the temperatures have been dramatically rising above that of the reference period.

If this is a stacked area chart, then we'd interpret the two data series as summable, with the sum of the data series signifying something interesting. For example, the market shares of different web browsers sum to the total size of the market.

But the chart above should not be read as a stacked area chart because the outside envelope isn't the sum of the two anomalies. The problem is revealed if we try to articulate what the color shades mean.

Scmp_russianheat_4_inset

On the far right, it seems like the dark shade is paired with the lighter line and represents global positive anomalies while the lighter shade shows Arctic's anomalies in excess of global. This interpretation only works if the Arctic line always sits above the global line. This pattern is broken in the late 1990s.

Around 1999, the Arctic's anomaly is negative while the global anomaly is positive. Here, the global anomaly gets the lighter shade while the Arctic one is blue.

One possible fix is to encode the size of the anomaly into the color of the line. The further away from zero, the darker the red/blue color.

 

 


A testing mess: one chart, four numbers, four colors, three titles, wrong units, wrong lengths, wrong data

Twitterstan wanted to vote the following infographic off the island:

Tes_Alevelsresults

(The publisher's website is here but I can't find a direct link to this graphic.)

The mishap is particularly galling given the controversy swirling around this year's A-Level results in the U.K. For U.S. readers, you can think of A-Levels as SAT Subject Tests, which in the U.K. are required of all university applicants, and represent the most important, if not the sole, determinant of admissions decisions. Please see the upcoming post on my book blog for coverage of the brouhaha surrounding the statistical adjustments (to be posted sometime this week, it's here.).

The first issue you may notice about the chart is that the bar lengths have no relationship with the numbers printed on them. Here is a scatter plot correlating the bar lengths and the data.

Junkcharts_redo_tes_alevels_scatter


As you can see, nothing.

Then, you may wonder what the numbers mean. The annotation at the bottom right says "Average number of A level qualifications per student". Wow, the British (in this case, English) education system is a genius factory - with the average student mastering close to three thousand subjects in secondary (high) school!

TES is the cool name for what used to be the Times Educational Supplement. I traced the data back to Ofqual, which is the British regulator for these examinations. This is the Ofqual version of the above chart:

Ofqual_threeAstar

The data match. You may see that the header of the data table reads "Number of students in England getting 3 x A*". This is a completely different metric than number of qualifications - in fact, this metric measures geniuses. "A*" is the U.K. equivalent of "A+". When I studied under the British system, there was no such grade. I guess grade inflation is happening all over the world. What used to be A is now A+, and what used to be B is now A. Scoring three A*s is tops - I wonder if this should say 3 or more because I recall that you can take as many subjects as you desire but most students max out at three (may have been four).

The number of students attaining the highest achievement has increased in the last two years compared to the two years before. We can't interpret these data unless we know if the number of students also grew at similar rates.

The units are students while the units we expect from the TES graphic should be subjects. The cutoff for the data defines top students while the TES graphic should connote minimum qualification, i.e. a passing grade.

***
Now, the next section of the Ofqual infographic resolves the mystery. Here is the chart:

Ofqual_Alevelquals

This dataset has the right units and measurement. There is almost no meaningful shift in the last four years. The average number of qualifications per student is only different at the second decimal place. Replacing the original data with this set removes the confusion.

Junkcharts_redo_tes_alevels_correctdata

While I was re-making this chart, I also cleaned out the headers and sub-headers. This is an example of software hegemony: the designer wouldn't have repeated the same information three times on a chart with four numbers if s/he wasn't prompted by software defaults.

***

The corrected chart violates one of the conventions I described in my tutorial for DataJournalism.com: color difference should reflect data difference.

In the following side-by-side comparison, you see that the use of multiple colors on the left chart signals different data - note especially the top and bottom bars which carry the same number, but our expectation is frustrated.

Junkcharts_redo_tes_alevels_sidebyside

***

[P.S. 8/25/2020. Dan V. pointed out another problem with these bar charts: the bars were truncated so that the bar lengths are not proportional to the data. The corrected chart is shown on the right below:

Junkcharts_redo_tes_alevels_barlengths

8/26/2020: added link to the related post on my book blog.]


Ask how you can give

A reader and colleague Georgette A was frustrated with the following graphic that appeared in the otherwise commendable article in National Geographic (link). The NatGeo article provides a history lesson on past pandemics that killed millions.

Natgeo_pandemichistory

What does the design want to convey to readers?

Our attention is drawn to the larger objects, the red triangle on the left or the green triangle on the right. Regarding the red triangle, we learn that the base is the duration of the pandemic while the height of the black bar represents the total deaths.

An immediate curiosity is why a green triangle is lodged in the middle of the red triangle. Answering this question requires figuring out the horizontal layout. Where we expect axis labels we find an unexpected series of numbers (0, 16, 48, 5, 2, 4, ...). These are durations that measure the widths of the triangular bases.

To solve this puzzle, imagine the chart with the triangles removed, leaving just the black columns. Now replace the durations with index numbers, 1 to 13, corresponding to the time order of the ending years of these epidemics. In other words, there is a time axis hidden behind the chart. [As Ken reminded me on Twitter, I forgot to mention that details of each pandemic are revealed by hovering over each triangle.]

This explains why the green triangle (Antonine Plague) is sitting inside the large red triangle (Plague of Justinian). The latter's duration is 3 times that of the former, and the Antonine Plague ended before the Plague of Justinian. In fact, the Antonine occurred during 165-180 while the Justinian happened during 541-588. The overlap is an invention of the design. To receive what the design gives, we have to think of time as a sequence, not of dates.

***

Now, compare the first and second red triangles. Their black columns both encode 50 million deaths. The Justinian Plague however was spread out over 48 years while the Black Death lasted just 5 years. This suggests that the Black Death was more fearsome than the Justinian Plague. And yet, the graphic presents the opposite imagery.

This is a pretty tough dataset to visualize. Here is a side-by-side bar chart that lets readers first compare deaths, and then compare durations.

Redo_natgeo_pandemichistory

In the meantime, I highly recommend the NatGeo article.