Here's a radar chart that works, sort of

In the same Reuters article that featured the speedometer chart which I discussed in this blog post (link), the author also deployed a small multiples of radar charts.

These radar charts are supposed to illustrate the article's theme that "European countries are racing to fill natural gas storage sites ahead of winter."

Here's the aggregate chart that shows all countries:

Reuters_gastorage_radar_details

In general, I am not a fan of radar charts. When I first looked at this chart, I also disliked it. But keep reading because I eventually decided that this usage is an exception. One just needs to figure out how to read it.

One reason why I dislike radar charts is that they always come with a lot of non-data-ink baggage. We notice that the months of the year are plotted in a circle starting at the top. They marked off the start of the war on Feb 24, 2022 in red. Then, they place the dotted circle, which represents the 80% target gas storage amount.

The trick is to avoid interpreting the areas, or the shapes of the blue and gray patches. I know, they look cool and grab our attention but in the context of conveying data, they are meaningless.

Redo_reuters_eugasradarall_1Instead of areas, focus on the boundaries of those patches. Don't follow one boundary around the circle. Pick a point in time, corresponding to a line between the center of the circle and the outermost circle, and look at the gap between the two lines. In the diagram shown right, I marked off the two relevant points on the day of the start of the war.

From this, we observe that across Europe, the gas storage was far less than the 80% target (recently set).

By comparing two other points (the blue and gray boundaries), we see that during February, Redo_reuters_eugasradarall_2gas storage is at a seasonal low, and in 2022, it is on the low side of the 5-year average. 

However, the visual does not match well with the theme of the article! While the gap between the blue and gray boundaries decreased since the start of the war, the blue boundary does not exceed the historical average, and does not get close to 80% until August, a month in which gas storage reaches 80% in a typical year.

This is example of a chart in which there is a misalignment between the Q and the V corners of the Trifecta Checkup (link).

_trifectacheckup_image

The question/message is that Europeans are reacting to the war by increasing their gas storage beyond normal. The visual actually says that they are increasing the gas storage as per normal.

***

As I noted before, when read in a particular way, these radar charts serve their purpose, which is more than can be said for most radar charts.

The designer made several wise choices:

Instead of drawing one ring for each year of data, the designer averaged the past 5 years and turned that into one single ring (patch). You can imagine what this radar chart would look like if the prior data were not averaged: hoola hoop mania!

Marawa-bgt

Simplifying the data in this way also makes the small multiples work. The designer uses the aggregate chart as a legend/how to read this. And in a further section below, the designer plots individual countries, without the non-data-ink baggage:

Reuters_gastorage_mosttofill

Thanks againto longtime reader Antonio R. who submitted this chart.

Happy Labor Day weekend for those in the U.S.!

 

 

 


Four numbers, not as easy as it seems

Longtime reader Aleksander B. wasn't convinced by the following chart shown at the bottom of AFP's infographic about gun control.

Afp_guns_bottom

He said:

Finally I was able to figure who got some support from NRA. But as a non-US citizen it was hard to get why 86% of republican tag points to huge red part. Then I figured out that smaller value of alpha channel codes the rest of republicans. I think this could be presented in some better way (pie charts are bad in presenting percentages of some subparts of the same pie chart - but adding a tag for 86% while skipping the tag for remaining 14% is cruel).

It's an example of how a simple chart with just four numbers is so hard to understand.

***

Here is a different view of the same data, using a similar structure as the form I chose for this recent chart on Swedish trade balance (link).

Redo_junkcharts_afpguns


Deficient deficit depiction

A twitter user alerted me to this chart put out by the Biden adminstration trumpeting a reduction in the budget deficit from 2020 to 2021:

Omb_deficitreduction

This column chart embodies a form that is popular in many presentations, including in scientific journals. It's deficient in so many ways it's a marvel how it continues to live.

There are just two numbers: -3132 and -2772. Their difference is $360 billion, which is less than just over 10 percent of the earlier number. It's not clear what any data graphic can add.

Indeed, the chart does not do much. It obscures the actual data. What is the budget deficit in 2020? Readers must look at the axis labels, and judge that it's about a quarter of the way between 3000 and 3500. Five hundred quartered is 125. So it's roughly $3.125 trillion. Similarly, the 2021 number is slightly above the halfway point between 2,500 and 3,000.

These numbers are upside down. Taller columns are bad! Shortening the columns is good. It's all counter intuitive.

Column charts encode data in the heights of the columns. The designer apparently wants readers to believe the deficit has been cut by about a third.

As usual, this deception is achieved by cutting the column chart off at its knees. Removing equal sections of each column destroys the propotionality of the heights.

Why hold back? Here's a version of the chart showing the deficit was cut by half:

Junkcharts_redo_ombbudgetdeficit

The relative percent reduction depends on where the baseline is placed. The only defensible baseline is the zero baseline. That's the only setting under which the relative percent reduction is accurately represented visually.

***

This same problem presents itself subtly in Covid-19 vaccine studies. I explain in this post, which I rate as one of my best Covid-19 posts. Check it out!

 

 


Type D charts

A twitter follower sent the following chart:

China_military_spending

It's odd to place the focus on China when the U.S. line is much higher, and the growth in spending in the last few years in the U.S. is much higher than the growth rate in China.

_trifectacheckup_imageIn the Trifecta Checkup, this chart is Type D (link): the data are at odds with the message of the chart. The intended message likely is China is building up its military in an alarming way. This dataset does not support such a conclusion.

The visual design of the chart can't be faulted though. It's clean, and restrained. It even places line labels at the end of each line. Also, the topic of the chart - the arms race - is unambiguous.

One fix is to change the message to bring it in line with the data. If the question being addressed is which country spends the most on the military, or which country has been raising spending at the fastest rate, then the above chart is appropriate.

If the question is about spending in China, then a different measure such as average annual spending increase may work.

Neither solution requires changing the visual form. That's why data visualization excellence is more than just selecting the right chart form.


Getting to first before going to second

Happy holidays to all my readers! A special shutout to those who've been around for over 15 years.

***

The following enhanced data table appeared in Significance magazine (August 2021) under an article titled "Winning an election, not a popularity contest" (link, paywalled)

Sig_electoralcollege-smIt's surprising hard to read and there are many reasons contributing to this.

First is the antiquated style guide of academic journals, in which they turn legends into text, and insert the text into a caption. This is one of the worst journalistic practices that continue to be followed.

The table shows 50 states plus District of Columbia. The authors are interested in the extreme case in which a hypothetical U.S. presidential candidate wins the electoral college with the lowest possible popular vote margin. If you've been following U.S. presidential politics, you'd know that the electoral college effectively deflates the value of big-city votes so that the electoral vote margin can be a lot larger than the popular vote margin.

The two sub-tables show two different scenarios: Scenario A is a configuration computed by NPR in one of their reports. Scenario B is a configuration created by the authors (Leinwand, et. al.).

The table cells are given one of four colors: green = needed in the winning configuration; white = not needed; yellow = state needed in Scenario B but not in Scenario A; grey = state needed in Scenario A but not in Scenario B.

***

The second problem is that the above description of the color legend is not quite correct. Green, it turns out, is only correctly explained for Scenario A. Green for Scenario B encodes those states that are needed for the candidate to win the electoral college in Scenario B minus those states that are needed in Scenario B but not in Scenario A (shown in yellow). There is a similar problem with interpreting the white color in the table for Scenario B.

To fix this problem, start with the Q corner of the Trifecta Checkup.

_trifectacheckup_image

The designer wants to convey an interlocking pair of insights: the winning configuration of states for each of the two scenarios; and the difference between those two configurations.

The problem with the current design is that it elevates the second insight over the first. However, the second insight is a derivative of the first so it's hard to get to the second spot without reaching the first.

The following revision addresses this problem:

Redo_sig_electoralcollege_corrected

[12/30/2021: Replaced chart and corrected the blue arrow for NJ.]

 

 


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.

 

 

 

 

 

 


Charts that ask questions about the German election

In the prior post about Canadian elections, I suggested that designers expand beyond plots of one variable at a time. Today, I look at a project by DataWrapper on the German elections which happened this week. Thanks to long-time blog supporter Antonio for submitting the chart.

The following is the centerpiece of Lisa's work:

Datawrapper_germanelections_cducsu

CDU/CSU is Angela Merkel's party, represented by the black color. The chart answers one question only: did polls correctly predict election results?

The time period from 1994 to 2021 covers eight consecutive elections (counting the one this week). There are eight vertical blocks on the chart representing each administration. The right vertical edge of each block coincides with an election. The chart is best understood as the superposition of two time series.

You can trace the first time series by following a step function - let your eyes follow the flat lines between elections. This dataset shows the popular vote won by the party at each election, with the value updated after each election. The last vertical block represents an election that has not yet happened when this chart was created. As explained in the footnote, Lisa took the average poll result for the last month leading up to the 2021 election - in the context of this chart, she made the assumption that this cycle of polls will be 100% accurate.

The second time series corresponds to the ragged edges of the gray and black areas. If you ignore the colors, and the flat lines, you'll discover that the ragged edges form a contiguous data series. This line encodes the average popularity of the CDU/CSU party according to election polls.

Thus, the area between the step function and the ragged line measures the gap between polls and election day results. When the polls underestimate the actual outcome, the area is colored gray; when the polls are over-optimistic, the area is colored black. In the last completed election of 2017, Merkel's party underperformed relative to the polls. In fact, the polls in the entire period between the 2013 and 2017 uniformly painted a rosier picture for CDU/CSU than actually happened.

The last vertical block is interpreted a little differently. Since the reference level is the last month of polls (rather than the actual popular vote), the abundance of black indicates that Merkel's party has been suffering from declining poll numbers on the approach of this week's election.

***

The picture shown above seems to indicate that these polls are not particularly good. It appears they have limited ability to self-correct within each election cycle. Aside from the 1998-2002 period, the area colors seldom changed within each cycle. That means if the first polling average overestimated the party's popularity, then all subsequent polling averages were also optimistic. (The original post focused on a single pollster, which exacerbates this issue. Compare the following chart with the above, and you'll find even fewer color changes within cycle here:

Datawrapper_germanelections_cdu_singlepoll

Each pollster may be systematically biased but the poll aggregate is less so.)

 

Here's the chart for SDP, which is CDU/CSU's biggest opponent, and likely winner of this week's election:

Datawrapper_germanelections_spd

Overall, this chart has similar features as the CDU/CSU chart. The most recent polls seem to favor the SPD - the pink area indicates that the older polls of this cycle underestimates the last month's poll result.

Both these parties are in long-term decline, with popularity dropping from the 40% range in the 1990s to the 20% range in the 2020s.

One smaller party that seems to have gained followers is the Green party:

Datawrapper_germanelections_green

The excess of dark green, however, does not augur well for this election.

 

 

 

 

 


Ridings, polls, elections, O Canada

Stephen Taylor reached out to me about his work to visualize Canadian elections data. I took a look. I appreciate the labor of love behind this project.

He led with a streamgraph, which presents a quick overview of relative party strengths over time.

Stephentaylor_canadianelections_streamgraph

I am no Canadian election expert, and I did a bare minimum of research in writing this blog. From this chart, I learn that:

  • the Canadians have an irregular election schedule
  • Canada has a two party plus breadcrumbs system
  • The two dominant parties are Liberals and Conservatives. The Liberals currently hold just less than half of the seats. The Conservatives have more than half of the seats not held by Liberals
  • The Conservative party (maybe) rebranded as "progressive conservative" for several decades. The Reform/Alliance party was (maybe) a splinter movement within the Conservatives as well.
  • Since the "width" of the entire stream increased over time, I'm guessing the number of seats has expanded

That's quite a bit of information obtained at a glance. This shows the power of data visualization. Notice Stephen didn't even have to include a "how to read this" box.

The streamgraph form has its limitations.

The feature that makes it more attractive than an area chart is its middle anchoring, resulting in a form of symmetry. The same feature produces erroneous intuition - the red patch draws out a declining trend; the reader must fight the urge to interpret the lines and focus on the areas.

The breadcrumbs are well hidden. The legend below discloses that the Green Party holds 3 seats currently. The party has never held enough seats to appear on the streamgraph though.

The bars showing proportions in the legend is a very nice touch. (The numbers appear messed up - I have to ask Stephen whether the seats shown are current values, or some kind of historical average.) I am a big fan of informative legends.

***

The next featured chart is a dot plot of polling results since 2020.

Stephentaylor_canadianelections_streamgraph_polls_dotplot

One can see a three-tier system: the two main parties, then the NDP (yellow) is the clear majority of the minority, and finally you have a host of parties that don't poll over 10%.

It looks like the polls are favoring the Conservatives over the Liberals in this election but it may be an election-day toss-up.

The purple dots represent "PPC" which is a party not found elsewhere on the page.

This chart is clear as crystal because of the structure of the underlying data. It just amazes me that the polls are so highly correlated. For example, across all these polls, the NDP has never once polled better than either the Liberals or the Conservatives, and in addition, it has never polled worse than any of the small parties.

What I'd like to see is a chart that merges the two datasets, addressing the question of how well these polls predicted the actual election outcomes.

***

The project goes very deep as Stephen provides charts for individual "ridings" (perhaps similar to U.S. precincts).

Here we see population pyramids for Vancouver Center, versus British Columbia (Province), versus Canada.

Stephentaylor_canadianelections_riding_populationpyramids

This riding has a large surplus of younger people in their twenties and thirties. Be careful about the changing scales though. The relative difference in proportions are more drastic than visually displayed because the maximum values (5%) on the Province and Canada charts are half that on the Riding chart (10%). Imagine squashing the Province and Canada charts to half their widths.

Analyses of income and rent/own status are also provided.

This part of the dashboard exhibits a problem common in most dashboards - they present each dimension of the data separately and miss out on the more interesting stuff: the correlation between dimensions. Do people in their twenties and thirties favor specific parties? Do richer people vote for certain parties?

***

The riding-level maps are the least polished part of the site. This is where I'm looking for a "how to read it" box.

Stephentaylor_canadianelections_ridingmaps_pollwinner

It took me a while to realize that the colors represent the parties. If I haven't come in from the front page, I'd have been totally lost.

Next, I got confused by the use of the word "poll". Clicking on any of the subdivisions bring up details of an actual race, with party colors, candidates and a donut chart showing proportions. The title gives a "poll id" and the name of the riding in parentheses. Since the poll id changes as I mouse over different subdivisions, I'm wondering whether a "poll" is the term for a subdivision of a riding. A quick wiki search indicates otherwise.

Stephentaylor_canadianelections_ridingmaps_donut

My best guess is the subdivisions are indicated by the numbers.

Back to the donut charts, I prefer a different sorting of the candidates. For this chart, the two most logical orderings are (a) order by overall popularity of the parties, fixed for all ridings and (b) order by popularity of the candidate, variable for each riding.

The map shown above gives the winner in each subdivision. This type of visualization dumps a lot of information. Stephen tackles this issue by offering a small multiples view of each party. Here is the Liberals in Vancouver.

Stephentaylor_canadianelections_ridingmaps_partystrength

Again, we encounter ambiguity about the color scheme. Liberals have been associated with a red color but we are faced with abundant yellow. After clicking on the other parties, you get the idea that he has switched to a divergent continuous color scale (red - yellow - green). Is red or green the higher value? (The answer is red.)

I'd suggest using a gray scale for these charts. The hardest decision is going to be the encoding between values and shading. Should each gray scale be different for each riding and each party?

If I were to take a guess, Stephen must have spent weeks if not months creating these maps (depending on whether he's full-time or part-time). What he has published here is a great start. Fine-tuning the issues I've mentioned may take more weeks or months more.

****

Stephen is brave and smart to send this project for review. For one thing, he's got some free consulting. More importantly, we should always send work around for feedback; other readers can tell us where our blind spots are.

To read more, start with this post by Stephen in which he introduces his project.


Reading this chart won't take as long as withdrawing troops from Afghanistan

Art sent me the following Economist chart, noting how hard it is to understand. I took a look, and agreed. It's an example of a visual representation that takes more time to comprehend than the underlying data.

Econ_theendisnear

The chart presents responses to 3 questions on a survey. For each question, the choices are Approve, Disapprove, and "Neither" (just picking a word since I haven't seen the actual survey question). The overall approval/disapproval rates are presented, and then broken into two subgroups (Democrats and Republicans).

The first hurdle is reading the scale. Because the section from 75% to 100% has been removed, we are left with labels 0, 25, 50, 75, which do not say percentages unless we've consumed the title and subtitle. The Economist style guide places the units of data in the subtitle instead of on
the axis itself.

Our attention is drawn to the thick lines, which represent the differences between approval and disapproval rates. These differences are signed: it matters whether the proportion approving is higher or lower than the proportion disapproving. This means the data are encoded in the order of the dots plus the length of the line segment between them.

The two bottom rows of the Afghanistan question demonstrates this mental challenge. Our brains have to process the following visual cues:

1) the two lines are about the same lengths

2) the Republican dots are shifted to the right by a little

3) the colors of the dots are flipped

What do they all mean?

Econ_theendofforever_subset

A chart runs in trouble when you need a paragraph to explain how to read it.

It's sometimes alright to make complicated data visualization that illustrates complicated concepts. What justifies it is the payoff. I wrote about the concept of return on effort in data visualization here.

The payoff for this chart escaped me. Take the Democratic response to troop withdrawal. About 3/4 of Democrats approve while 15% disapprove. The thick line says 60% more Democrats approve than disapprove.

***

Here, I show the full axis, and add a 50% reference line

Junkcharts_redo_econ_theendofforever_1

Small edits but they help visualize "half of", "three quarters of".

***

Next, I switch to the more conventional stacked bars.

Junkcharts_redo_econ_theendofforever_stackedbars

This format reveals some of the hidden data on the chart - the proportion answering neither approve/disapprove, and neither yes/no.

On the stacked bars visual, the proportions are counted from both ends while in the dot plot above, the proportions are measured from the left end only.

***

Read all my posts about Economist charts here