Whither the youth vote

The youth turnout is something that politicians and pundits bring up constantly when talking about the current U.S. presidential primaries. So I decided to look for the data. I found some data at the United States Election Project, a site maintained by Dr. Michael McDonald. The key chart is this one:

Electproject_voterturnoutbyage

This is classic Excel.

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Here is a quick fix:

Redo_electprojects_voterturnout

The key to the fix is to recognize the structure of the data.

The sawtooth pattern displayed in the original chart does not convey any real trends - it's an artifact that many people only turn out for presidential elections. (As a result, the turnout during presidential election years is driven by the general election turnout.)

The age groups have an order so instead of four different colors, use a progressive color scheme. This is one of the unspoken rules about color usage in data visualization, featured in my Long Read article.

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What do I learn from this turnout by age group chart?

Younger voters are much more invested in presidential elections than off-year elections. The youth turnout for presidential elections is double that for other years.

Participation increased markedly in the 2018 mid-term elections across all four age groups, reflecting the passion for or against President Donald Trump. This was highly unusual - and in fact, the turnout for that off-year is closer to the turnout of a presidential year election. Whether the turnout will stay at this elevated level is a big question for 2022!

For presidential elections, turnout has been creeping up over time for all age groups. But the increase in 2016 (Hillary Clinton vs Donald Trump) was mild. The growth in participation is more noticable in the younger age groups, including in 2016.

Let's look at the relative jumps in 2018 (right side of the left chart). The younger the age group, the larger the jump. Turnout in the 18-29 group doubled to 32 percent. Turnout in the oldest age group increased by 20%, nothing to sneeze at but less impressive than in the younger age groups.

Why this is the case should be obvious. The 60+ age group has a ceiling. It's already at 60-70%; how much higher can it go? People at that age have many years to develop their preference for voting in elections. It would be hard to convince the holdouts (hideouts?) to vote.

The younger age groups are further from the ceiling. If you're an organizer, will you focus your energy on the 60% non-voting 18-29-years-old, or the 30% non-voting 60+ years-old? [This is the same question any business faces: do you win incremental sales from your more loyal customers, hoping they would spend even more, or your less loyal customers?]

For Democratic candidates, the loss in 2016 is hanging over them. Getting the same people to vote in 2020 as in 2016 is a losing hand. So, they need to expand the base somehow.

If you're a candidate like Joe Biden who relies on the 60+ year old bloc, it's hard to see where he can expand the base. Your advantage is that the core voter bloc is reliable. Your problem is that you don't have appeal to the younger age groups. So a viable path to winning in the general election has to involve flipping older Trump voters. The incremental ex-Trump voters have to offset the potential loss in turnout from younger voters.

If you're a candidate like Bernie Sanders who relies on the youth vote, you'd want to launch a get-out-the-vote effort aimed at younger voters. A viable path can be created by expanding the base through lifting the turnout rate of younger voters. The incremental young voters have to offset the fraction of the 60+ year old bloc who flip to Trump.

 

 

 

 

 

 


Bad data leave chart hanging by the thread

IGNITE National put out a press release saying that Gen Z white men are different from all other race-gender groups because they are more likely to be or lean Republican. The evidence is in this chart:

Genz_survey

Or is it?

Following our Trifecta Checkup framework (link), let's first look at the data. White men is the bottom left group. Democratic = 42%, Independent = 28%, Republican = 48%. That's a total of 118%. Unfortunately, this chart construction error erases the message. We don't know which of the three columns were incorrectly sized, or perhaps the data were incorrectly weighted so that the error is spread out between the three columns.

But the story of the graphic is hanging by the thread - the gap between Democratic and Republican lean amongst white men is 6 percent, which is smaller than the data error of 10 percent. I sent them a tweet asking for a correction. Will post the corrected version if they respond.

Update: The thread didn't break. They replied quickly and issued the following corrected chart:

Genz_corrected

Now, the data for white men are: Democratic = 35%, Independent = 22%; Republican = 40%. Roughly 7% shift for each party affilitation so they may have just started the baseline at the wrong level when inverting the columns.

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The Visual design also has some problems. I am not a fan of inverting columns. In fact, column inversion may be the root of the error above.

Genz_whitemenLet me zoom in on the white men columns. (see right)

Without looking at the legend, can you guess which color is Democratic, Independent or Republican? Go ahead and take your best guess.

For me, I think red is Republican (by convention), then white is Independent (a neutral color) which means yellow is Democratic.

Here is the legend:

Genz-legend

So I got the yellow and white reversed. And that is another problem with the visual design. For a chart that shows two-party politics in the U.S., there is really no good reason to deviate from the red-blue convention. The color for Independents doesn't matter since it would be understood that the third color would represent them.

If the red-blue convention were followed, readers do not need to consult the legend.

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In my Long Read article at DataJournalism.com, I included an "unspoken rule" about color selection: use the natural color mapping whenever possible. Go here to read about this and other rules.

The chart breaks another one of the unspoken conventions. When making a legend, place it near the top of the chart. Readers need to know the color mapping before they can understand the chart.

In addition, you want the reader's eyes to read the legend in the same way they read the columns. The columns goes left to right from Democratic to Independent to Republican. The legend should do the same!

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Here is a quick re-do that fixes the visual issues (except the data error). It's an Excel chart but it doesn't have to be bad.

Redo_genzsurvey

 


Pie chart conventions

I came across this pie chart from a presentation at an industry meeting some weeks ago:

Mediaconversations_orig

This example breaks a number of the unspoken conventions on making pie charts and so it is harder to read than usual.

Notice that the biggest slice starts around 8 o'clock, and the slices are ordered alphabetically by the label, rather than numerically by size of the slice.

The following is the same chart ordered in a more conventional way. The largest slice is placed along the top vertical, and the other slices are arranged in a clock-wise manner from larger to smaller.

Redo_junkcharts_mediaconversations1

This version is easier to read because the reader does not need to think about the order of the slices. The expectation of decreasing size is met.

The above pie chart, though, reveals breaking of another convention. The colors on this chart signify nothing! The general rule is color differences should encode data differences. Here, the colors should go from deepest to lightest. (One can even argue that different tinges is redundant.)

Redo_junkcharts_mediaconversations2

You see how this version is even better. In the previous version, the colors are distracting. You're wondering what they mean, and then you realize they signify nothing.

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As designers of graphics, we follow a bunch of conventions silently. When a design deviates from it, it's harder to understand.

Recently, I wrote a long article for DataJournalism.com, setting out many of these unspoken conventions. Read it here.

 


Too many colors on a chart is bad, but why?

The following chart is bad, but how so?

Junkcharts_colors_columnchart

The chart is annoying because of the misuse of colors.

What is the purpose of the multiple colors used in this chart? It's not encoding any data. Colors are used here to differentiate one bar from its two neighbors. Or perhaps to make the chart more "appealing".

The reason why the coloring scheme backfires is that readers may look for meaning in the colors. What's common between Iceland, United States and Germany for them to be assigned green? What about Japan, New Zealand, Spain and France, all of which shown yellow?

The readers' instinct is driven by a set of unspoken rules that govern the production of data visualization. Specifically, the rule here is: color differences reflect data differences. When such a rule is violated, the reader is misled and confused.

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For more about this rule, other rules related to making bar charts, and other other rules for making data graphics, please read my Long Read article, here.

 


The unspoken rules of visualization

My latest is at DataJournalism.com.

Ejc_unspokenrulesbanner

It's an essay on the following observation:

The efficiency and multidimensionality of the visual medium arise from a set of conventions and rules, which regularises the communications between producers of data visualisation and its consumers. These conventions and rules are often unspoken: it's the visual equivalent of saying ’it goes without saying’ .

There are lots of little things visualization designers do in their sleep that don't get mentioned. When a visual design deviates from these rules, the readers may get confused.

Here is one example I discussed in the article (hat tip to Xan Gregg).

Fig04_piechart_diverging

This pie chart is not easy to read beyond the obvious point that English is the most popular. The following pie chart is much easier on the readers:

Fig03_piechart_conforming

Why?

The designer follows some common conventions, such as placing the first slice at the top vertical, sorting the slices from largest to smallest (excepting the "other"), and introducing multiple colors only to encode data differences.

These rules are silently applied, and are not announced to the reader. There is a network effect: the more practitioners use these rules, the stronger they stick.

My essay attempts to outline some of the most important unspoken rules of visualizaiton. For more, see here.


Gazing at petals

Reader Murphy pointed me to the following infographic developed by Altmetric to explain their analytics of citations of journal papers. These metrics are alternative in that they arise from non-academic media sources, such as news outlets, blogs, twitter, and reddit.

The key graphic is the petal diagram with a number in the middle.

Altmetric_tetanus

I have a hard time thinking of this object as “data visualization”. Data visualization should visualize the data. Here, the connection between the data and the visual design is tenuous.

There are eight petals arranged around the circle. The legend below the diagram maps the color of each petal to a source of data. Red, for example, represents mentions in news outlets, and green represents mentions in videos.

Each petal is the same size, even though the counts given below differ. So, the petals are like a duplicative legend.

The order of the colors around the circle does not align with its order in the table below, for a mysterious reason.

Then comes another puzzle. The bluish-gray petal appears three times in the diagram. This color is mapped to tweets. Does the number of petals represent the much higher counts of tweets compared to other mentions?

To confirm, I pulled up the graphic for a different paper.

Altmetric_worldwidedeclineofentomofauna

Here, each petal has a different color. Eight petals, eight colors. The count of tweets is still much larger than the frequencies of the other sources. So, the rule of construction appears to be one petal for each relevant data source, and if the total number of data sources fall below eight, then let Twitter claim all the unclaimed petals.

A third sample paper confirms this rule:

Altmetric_dnananodevices

None of the places we were hoping to find data – size of petals, color of petals, number of petals – actually contain any data. Anything the reader wants to learn can be directly read. The “score” that reflects the aggregate “importance” of the corresponding paper is found at the center of the circle. The legend provides the raw data.

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Some years ago, one of my NYU students worked on a project relating to paper citations. He eventually presented the work at a conference. I featured it previously.

Michaelbales_citationimpact

Notice how the visual design provides context for interpretation – by placing each paper/researcher among its peers, and by using a relative scale (percentiles).

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I’m ignoring the D corner of the Trifecta Checkup in this post. For any visualization to be meaningful, the data must be meaningful. The type of counting used by Altmetric treats every tweet, every mention, etc. as a tally, making everything worth the same. A mention on CNN counts as much as a mention by a pseudonymous redditor. A pan is the same as a rave. Let’s not forget the fake data menace (link), which  affects all performance metrics.


Taking small steps to bring out the message

Happy new year! Good luck and best wishes!

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We'll start 2020 with something lighter. On a recent flight, I saw a chart in The Economist that shows the proportion of operating income derived from overseas markets by major grocery chains - the headline said that some of these chains are withdrawing from international markets.

Econ_internationalgroceries_sm

The designer used one color for each grocery chain, and two shades within each color. The legend describes the shades as "total" and "of which: overseas". As with all stacked bar charts, it's a bit confusing where to find the data. The "total" is actually the entire bar, not just the darker shaded part. The darker shaded part is better labeled "home market" as shown below:

Redo_econgroceriesintl_1

The designer's instinct to bring out the importance of international markets to each company's income is well placed. A second small edit helps: plot the international income amounts first, so they line up with the vertical zero axis. Like this:

Redo_econgroceriesintl_2

This is essentially the same chart. The order of international and home market is reversed. I also reversed the shading, so that the international share of income is displayed darker. This shading draws the readers' attention to the key message of the chart.

A stacked bar chart of the absolute dollar amounts is not ideal for showing proportions, because each bar is a different length. Sometimes, plotting relative values summing to 100% for each company may work better.

As it stands, the chart above calls attention to a different message: that Walmart dwarfs the other three global chains. Just the international income of Walmart is larger than the total income of Costco.

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Please comment below or write me directly if you have ideas for this blog as we enter a new decade. What do you want to see more of? less of?


Marketers want millennials to know they're millennials

When I posted about the lack of a standard definition of "millennials", Dean Eckles tweeted about the arbitrary division of age into generational categories. His view is further reinforced by the following chart, courtesy of PewResearch by way of MarketingCharts.com.

PewResearch-Generational-Identification-Sept2015

Pew asked people what generation they belong to. The amount of people who fail to place themselves in the right category is remarkable. One way to interpret this finding is that these are marketing categories created by the marketing profession. We learned in my other post that even people who use the term millennial do not have a consensus definition of it. Perhaps the 8 percent of "millennials" who identify as "boomers" are handing in a protest vote!

The chart is best read row by row - the use of stacked bar charts provides a clue. Forty percent of millennials identified as millennials, which leaves sixty percent identifying as some other generation (with about 5 percent indicating "other" responses). 

While this chart is not pretty, and may confuse some readers, it actually shows a healthy degree of analytical thinking. Arranging for the row-first interpretation is a good start. The designer also realizes the importance of the diagonal entries - what proportion of each generation self-identify as a member of that generation. Dotted borders are deployed to draw eyes to the diagonal.

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The design doesn't do full justice for the analytical intelligence. Despite the use of the bar chart form, readers may be tempted to read column by column due to the color scheme. The chart doesn't have an easy column-by-column interpretation.

It's not obvious which axis has the true category and which, the self-identified category. The designer adds a hint in the sub-title to counteract this problem.

Finally, the dotted borders are no match for the differential colors. So a key message of the chart is buried.

Here is a revised chart, using a grouped bar chart format:

Redo_junkcharts_millennial_id

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In a Trifecta checkup (link), the original chart is a Type V chart. It addresses a popular, pertinent question, and it shows mature analytical thinking but the visual design does not do full justice to the data story.

 

 


Light entertainment: people of color

What colors do the "average" person like the most and the least? The following chart found here (Scott Design) tells you favorite and least favorite colors by age groups:

Color-preferences-by-age

(This is one of a series of charts. A total of 10 colors is covered by the survey. The same color can appear in both favorites and least favorites since these are aggregate proportions. Almost 40% of the respondents are under 18 and only one percent are over 70.)

Here's one item that has stumped me thus far: how are the colors ordered within each figurine?


The time of bird seeds and chart tuneups

The recent post about multi-national companies reminded me of an older post, in which I stepped through data table enhancements.

Here is a video of the process. You can use any tool to implement the steps; even Excel is good enough.

 

 

The video is part of a series called "Data science: the Missing Pieces". In these episodes, I cover the parts of data science that are between the cracks, the little things that textbooks and courses do not typically cover - the things that often block students from learning efficiently.

If you have encountered such things, please comment below to suggest future topics. What is something about visualizing data you wish you learned formally?

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P.S. Placed here to please the twitter-bot

DSTMP2_goodchart_thumb