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.

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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

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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%.]

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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?

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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?

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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.


Who trades with Sweden

It's great that the UN is publishing dataviz but it can do better than this effort:

Untradestats_sweden

Certain problems are obvious. The country names turned sideways. The meaningless use of color. The inexplicable sequencing of the country/region.

Some problems are subtler. "Area, nes" - upon research - is a custom term used by UN Trade Statistics, meaning "not elsewhere specified".

The gridlines are debatable. Their function is to help readers figure out the data values if they care. The design omitted the top and bottom gridlines, which makes it hard to judge the values for USA (dark blue), Netherlands (orange), and Germany (gray).

See here, where I added the top gridline.

Redo_untradestats_sweden_gridline

Now, we can see this value is around 3.6, just over the halfway point between gridlines.

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A central feature of trading statistics is "balance". The following chart makes it clear that the positive numbers outweigh the negative numbers in the above chart.

Redo_untradestats_sweden

At the time I made the chart, I wasn't sure how to interpret the gap of 1.3%. Looking at the chart again, I think it's saying Sweden has a trade surplus equal to that amount.


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.