The downside of discouraging pie charts

It's no secret most dataviz experts do not like pie charts.

Our disdain for pie charts causes people to look for alternatives.

Sometimes, the alternative is worse. Witness:

This chart comes from the Spring 2018 issue of On Investing, the magazine for Charles Schwab customers.

It's not a pie chart.

I'm forced to say the pie chart is preferred.

The original chart fails the self-sufficiency test. Here is the 2007 chart with the data removed.

It's very hard to figure out how large are those pieces, so any reader trying to understand this chart will resort to reading the data, which means the visual representation does no work!

Or, you can use a dot plot.

This version emphasizes the change over time.

 

Is the chart answering your question? Excavating the excremental growth map

San Franciscans are fed up with excremental growth. Understandably.

Here is how the Economist sees it - geographically speaking.

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In the Trifecta Checkup analysis, one of the questions to ask is "What does the visual say?" and with respect to the question being asked.

The question is how much has the problem of human waste in SF grew from 2011 to 2017.

What does the visual say?

The number of complaints about human waste has increased from 2011 to 2014 to 2017.

The areas where there are complaints about human waste expanded.

The worst areas are around downtown, and that has not changed during this period of time.

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Now, what does the visual not say?

Let's make a list:

• How many complaints are there in total in any year?
• How many complaints are there in each neighborhood in any year?
• What's the growth rate in number of complaints, absolute or relative?
• What proportion of complaints are found in the worst neighborhoods?
• What proportion of the area is covered by the green dots on each map?
• What's the growth in terms of proportion of areas covered by the green dots?
• Does the density of green dots reflect density of human waste or density of human beings?
• Does no green dot indicate no complaints or below the threshold of the color scale?

There's more:

• Is the growth in complaints a result of more reporting or more human waste?
• Is each complainant unique? Or do some people complain multiple times?
• Does each piece of human waste lead to one and only one complaint? In other words, what is the relationship between the count of complaints and the count of human waste?
• Is it easy to distinguish between human waste and animal waste?

And more:

• Are all complaints about human waste valid? Does anyone verify complaints?
• Are the plotted locations describing where the human waste is or where the complaint was made?
• Can all complaints be treated identically as a count of one?
• What is the per-capita rate of complaints?

In other words, the set of maps provides almost all no information about the excrement problem in San Francisco.

After you finish working, go back and ask what the visual is saying about the question you're trying to address!

 

As a reference, I found this map of the population density in San Francisco (link):

 

Two thousand five hundred ways to say the same thing

Wallethub published a credit card debt study, which includes the following map:

Let's describe what's going on here.

The map plots cities (N = 2,562) in the U.S. Each city is represented by a bubble. The color of the bubble ranges from purple to green, encoding the percentile ranking based on the amount of credit card debt that was paid down by consumers. Purple represents 1st percentile, the lowest amount of paydown while green represents 99th percentile, the highest amount of paydown.

The bubble size is encoding exactly the same data, apparently in a coarser gradation. The more purple the color, the smaller the bubble. The more green the color, the larger the bubble.

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The design decisions are baffling.

Purple is more noticeable than the green, but signifies the less important cities, with the lesser paydowns.

With over 2,500 bubbles crowding onto the map, over-plotting is inevitable. The purple bubbles are printed last, dominating the attention but those are the least important cities (1st percentile). The green bubbles, despite being larger, lie underneath the smaller, purple bubbles.

What might be the message of this chart? Our best guess is: the map explores the regional variation in the paydown rate of credit card debt.

The analyst provides all the data beneath the map. 

From this table, we learn that the ranking is not based on total amount of debt paydown, but the amount of paydown per household in each city (last column). That makes sense.

Shouldn't it be ranked by the paydown rate instead of the per-household number? Divide the "Total Credit Card Paydown by City" by "Total Credit Card Debt Q1 2018" should yield the paydown rate. Surprise! This formula yields a column entirely consisting of 4.16%.

What does this mean? They applied the national paydown rate of 4.16% to every one of 2,562 cities in the country. If they had plotted the paydown rate, every city would attain the same color. To create "variability," they plotted the per-household debt paydown amount. Said differently, the color scale encodes not credit card paydown as asserted but amount of credit card debt per household by city.

Here is a scatter plot of the credit card amount against the paydown amount.

A perfect alignment!

This credit card debt paydown map is an example of a QDV chart, in which there isn't a clear question, there is almost no data, and the visual contains several flaws. (See our Trifecta checkup guide.) We are presented 2,562 ways of saying the same thing: 4.16%.

 

P.S. [6/22/2018] Added scatter plot, and cleaned up some language.

 

 

 

Digital revolution in China: two visual takes

The following map accompanied an article in the Economist about China's drive to create a "digital silkroad," roughly defined as making a Silicon Valley. 

The two variables plotted are the wealth of each province (measured by GDP per capita) and the level of Internet penetration. The designer made the following choices:

• GDP per capita is presented with less precision than Internet penetration. The former is grouped into five large categories while the latter is given as a percentage to one decimal place.
• The visual design favors GDP per capita which is encoded as the shade of color of each province. The Internet penetration data appeared added on as an afterthought.

If we apply the self-sufficiency test (i.e. by removing the printed data from the chart), it's immediately clear that the visual elements convey zero information about Internet penetration. This is a serious problem for a chart about the "digital silkroad"!

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If those two variables are chosen, it would seem appropriate to convey to readers the correlation between the two variables. The following sketch is focused on surfacing the correlation.

(Click on the image to see it in full.) Here is the top of the graphic:

The individual maps are not strictly necessary. Just placing provincial names onto the grid is enough, because regional pattern isn't salient here.

The Internet penetration data were grouped into five categories as well, putting it on equal footing as GDP per capita.

 

Foodies say, add dataviz spice please

This Buzzfeed article proves that foodies love their food served with dataviz (tip: Chris P.). Menus are an undertapped resource when it comes to data visualization.

There are several examples worth discussing.

Venn diagrams are not easy to read, people.

Plus they are hard to construct well... note the asymmetric areas.

Here is one without circles:

Then, I pared it down to its essence:

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This beer map is pretty great:

Some of its virtues:

• The spacious layout utilizing two dimensions, instead of a one-dimensional list of dense text
• Ordering using two dimensions relevant to the decision problem (assuming those two dimensions are the most important for their clients)
• Unconventional, attention-grabbing
• More equitable: different readers will read the chart in different orders. I'll hypothesize that they will end up with a more even distribution of drink orders than with a list in which everyone reads top to bottom

Potential problems:

• Not enough space to explain the drinks. Don't the clients want to know what's in them?
• I wonder how they measured the degree of "classic"-ness.

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This next menu contains an error:

When the drink comes in one size, only one price is listed. If it comes in two sizes, two prices should be listed.

Is the cafe owner shading Americans as not good at math?