Dual axes: a favorite of tricksters

Twitter readers directed me to this abomination from the St. Louis Fed (link).

This chart is designed to paint the picture that China is this grave threat because it's been ramping up military expenditure so much so that it exceeded U.S. spending since the 2000s.

Sadly, this is not what the data are suggesting at all! This story is constructed by manipulating the dual axes. Someone has already fixed it. Here's the same data plotted with a single axis:

(There are two set of axis labels but they have the same scale and both start at zero, so there is only one axis.)

Certainly, China has been ramping up military spending. Nevertheless, China's current level of spending is about one-third of America's. Also, imagine the cumulative spending excess over the 30 years shown on the chart.

Note also, the growth line of U.S. military spending in this period is actually similarly steep as China's.

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Apparently, the St. Louis Fed is intent on misleading its readers. Even though on Twitter, they acknowledged people's feedback, they decided not to alter the chart.

If you click through to the article, you'll find the same flawed chart as before so I'm not sure how they "listened". I went to Wayback Machine to check the first version of this page, and I notice no difference.

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If one must make a dual axes chart, it is the responsibility of the chart designer to make it clear to readers that different lines on the chart use different axes. In this case, since the only line that uses the right hand side axis is the U.S. line, which is blue, they should have colored the right hand axis blue. Doing that does not solve the visualization problem; it merely reduces the chance of not noticing the dual axes.

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I have written about dual axes a lot in the past. Here's a McKinsey chart from 2006 that offends.

Visual cues affect how data are perceived

Here's a recent NYT graphic showing California's water situation at different time scales (link to article).

It's a small multiples display, showing the spatial distribution of the precipitation amounts in California. The two panels show, respectively, the short-term view (past month) and the longer-term view (3 years). Precipitation is measured in relative terms,  so what is plotted is the relative ratio of precipitation in the reference period, with 100 being the 30-year average.

Green is much wetter than average while brown is much drier than average.

The key to making this chart work is a common color scheme across the two panels.

Also, the placement of major cities provides anchor points for our eyes to move back and forth between the two panels.

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The NYT graphic is technically well executed. I'm a bit unhappy with the headline: "Recent rains haven't erased California's long-term drought".

At the surface, the conclusion seems sensible. Look, there is a lot of green, even deep green, on the left panel, which means the state got lots more rain than usual in the past month. Now, on the right panel, we find patches of brown, and very little green.

But pay attention to the scale. The light brown color, which covers the largest area, has value 70 to 90, thus, these regions have gotten 10-30% less precipitation than average in the past three years relative to the 30-year average.

Here's the question: what does it mean by "erasing California's long-term drought"? Does the 3-year average have to equal or exceed the 30-year average? Why should that be the case?

If we took all 3-year windows within those 30 years, we're definitely not going to find that each such 3-year average falls at or above the 30-year average. To illustrate this, I pulled annual rainfall data for San Francisco. Here is a histogram of 3-year averages for the 30-year period 1991-2020.

For example, the first value is the average rainfall for years 1989, 1990 and 1991, the next value is the average of 1990, 1991, and 1992, and so on. Each value is a relative value relative to the overall average in the 30-year window. There are two more values beyond 2020 that is not shown in the histogram. These are 57%, and 61%, so against the 30-year average, those two 3-year averages were drier than usual.

The above shows the underlying variability of the 3-year averages inside the reference time window. We have to first define "normal", and that might be a value between 70% and 130%.

In the same way, we can establish the "normal" range for the entire state of California. If it's also 70% to 130%, then the last 3 years as shown in the map above should be considered normal.

 

 

Accounting app advertises that it doesn't understand fractions

I captured the following image of an ad at the airport at the wrong moment, so you can only see the dataviz but not the text that came with it. The dataviz is animated with blue section circling around and then coming to a halt.

The text read something like "75% of the people who saw this ad subsequently purchased something". I think the advertiser was TripActions. It is an app for accounting. Too bad their numbers people don't know 75% is three-quarters and their donut chart showed a number larger than 75%.

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Browsing around the TripActions website, I also found this pie chart.

The radius of successive sectors is decreasing as the size of the proportions shrinks. As a result, the same two sectors labeled 12% at the bottom have differently-sized areas. The only way this dataviz can work is if the reader decodes the angles sustained at the center, and ignores the areas of the sectors. However, the visual cues all point readers to the areas rather than the angles.

In this sense, the weakness of this pie chart is the same as that of the racetrack chart, discussed recently here.

In addition, the color dimension is not used wisely. Color can be used to group the expenses into categories, or it can be used to group them by proportion of total (20%+, 10-19%, 5-9%, 1-4%, <1%).

 

 

Longest life, shortest length

Racetrack charts refuse to die. For old time's sake, here is a blog post from 2005 in which I explain why they don't make good dataviz.

Our latest example comes from Visual Capitalist (link), which publishes a fair share of nice dataviz. In this infographics, they feature a racetrack chart, just because the topic is the lifespan of cars.

The whole infographic has four parts, each a racetrack chart. I'll focus on the first racetrack chart (shown above), which deals with the product category of sedans and hatchbacks.

The first thing I noticed is the reference value of 100,000 miles, which is described as the expected lifespan of a typical car made in the 1970s. This is of dubious value since the top of the page informs us the current relevant reference value is 200,000 miles, which is unlabeled. We surmise that 200,000 miles is indicated by the end of the grey sections of the racetrack. (This is eventually confirmed in the next racettrack chart for SUVs in the second sectiotn of the infographic.)

Now let's zoom in on the brown section of the track. Each of the four sections illustrates the same datum = 100,000 miles and yet they exhibit different lengths. From this, we learn that the data are not encoded in the lengths of these tracks -- but rather the data are to be found in the angle sustained at the centre of the concentric circles. The problem with racetrack charts is that readers are drawn to the lengths of the tracks rather than the angles at the center, which are not explicitly represented.

The Avalon model has the longest life span on this chart, and yet it is shown as the shortest curve.

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The most baffling part of this chart is not the visual but the analysis methodology.

I quote:

iSeeCars analyzed over 2M used cars on the road between Jan. and Oct. 2022. Rankings are based on the mileage that the top 1% of cars within each model obtained.

According to this blurb, the 245,710 miles number for Avalon is the average mileage found in the top 1% of Avalons within the iSeeCars sample of 2M used cars.

The word "lifespan" strikes me as incorporating a date of death, and yet nothing in the above text indicates that any of the sampled cars are at end of life. The cars they really need are not found in their sample at all.

I suppose taking the top 1% is meant to exclude younger cars but why 1%? Also, this sample completely misses the cars that prematurely died, e.g. the cars that failed after 100,000 miles but before 200,000 miles. This filtering also ensures that newer models are excluded from the sample.

In the Trifecta Checkup, this qualifies as Type DV. The dataset does not answer the question of concern while the visual form distorts the data.