Speedometer charts: love or hate

Pie chart hate is tired. In this post, I explain my speedometer hate. (Also called gauges,  dials)

Next to pie charts, speedometers are perhaps the second most beloved chart species found on business dashboards. Here is a typical example:

Speedometers_example

 

For this post, I found one on Reuters about natural gas in Europe. (Thanks to long-time contributor Antonio R. for the tip.)

Eugas_speedometer

The reason for my dislike is the inefficiency of this chart form. In classic Tufte-speak, the speedometer chart has a very poor data-to-ink ratio. The entire chart above contains just one datum (73%). Most of the ink are spilled over non-data things.

This single number has a large entourage:

- the curved axis
- ticks on the axis
- labels on the scale
- the dial
- the color segments
- the reference level "EU target"

These are not mere decorations. Taking these elements away makes it harder to understand what's on the chart.

Here is the chart without the curved axis:

Redo_eugas_noaxis

Here is the chart without axis labels:

Redo_eugas_noaxislabels

Here is the chart without ticks:

Redo_eugas_notickmarks

When the tick labels are present, the chart still functions.

Here is the chart without the dial:

Redo_eugas_nodial

The datum is redundantly encoded in the color segments of the "axis".

Here is the chart without the dial or the color segments:

Redo_eugas_nodialnosegments

If you find yourself stealing a peek at the chart title below, you're not alone.

All versions except one increases our cognitive load. This means the entourage is largely necessary if one encodes the single number in a speedometer chart.

The problem with the entourage is that readers may resort to reading the text rather than the chart.

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The following is a minimalist version of the Reuters chart:

Redo_eugas_onedial

I removed the axis labels and the color segments. The number 73% is shown using the dial angle.

The next chart adds back the secondary message about the EU target, as an axis label, and uses color segments to show the 73% number.

Redo_eugas_nodialjustsegments

Like pie charts, there are limited situations in which speedometer charts are acceptable. But most of the ones we see out there are just not right.

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One acceptable situation is to illustrate percentages or proportions, which is what the EU gas chart does. Of course, in that situation, one can alo use a pie chart without shame.

For illustrating proportions, I prefer to use a full semicircle, instead of the circular sector of arbitrary angle as Reuters did. The semicircle lends itself to easy marks of 25%, 50%, 75%, etc, eliminating the need to print those tick labels.

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One use case to avoid is numeric data.

Take the regional sales chart pulled randomly from a Web search above:

Speedometers_example

These charts are completely useless without the axis labels.

Besides, because the span of the axis isn't 0% to 100%, every tick mark must be labelled with the numeric value. That's a lot of extra ink used to display a single value!


Superb tile map offering multiple avenues for exploration

Here's a beauty by WSJ Graphics:

Wsj_powerproduction

The article is here.

This data graphic illustrates the power of the visual medium. The underlying dataset is complex: power production by type of source by state by month by year. That's more than 90,000 numbers. They all reside on this graphic.

Readers amazingly make sense of all these numbers without much effort.

It starts with the summary chart on top.

Wsj_powerproduction_us_summary

The designer made decisions. The data are presented in relative terms, as proportion of total power production. Only the first and last years are labeled, thus drawing our attention to the long-term trend. The order of the color blocks is carefully selected so that the cleaner sources are listed at the top and the dirtier sources at the bottom. The order of the legend labels mirrors the color blocks in the area chart.

It takes only a few seconds to learn that U.S. power production has largely shifted away from coal with most of it substituted by natural gas. Other than wind, the green sources of power have not gained much ground during these years - in a relative sense.

This summary chart serves as a reading guide for the rest of the chart, which is a tile map of all fifty states. Embedded in the tile map is a small-multiples arrangement.

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The map offers multiple avenues for exploration.

Some readers may look at specific states. For example, California.

Wsj_powerproduction_california

Currently, about half of the power production in California come from natural gas. Notably, there is no coal at all in any of these years. In addition to wind, solar energy has also gained. All of these insights come without the need for any labels or gridlines!

Wsj_powerproduction_westernstatesBrowsing around California, readers find different patterns in other Western states like Oregon and Washington.

Hydroelectric energy is the dominant source in those two states, with wind gradually taking share.

At this point, readers realize that the summary chart up top hides remarkable state-level variations.

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There are other paths through the map.

Some readers may scan the whole map, seeking patterns that pop out.

One such pattern is the cluster of states that use coal. In most of these states, the proportion of coal has declined.

Yet another path exists for those interested in specific sources of power.

For example, the trend in nuclear power usage is easily followed by tracking the purple. South Carolina, Illinois and New Hampshire are three states that rely on nuclear for more than half of its power.

Wsj_powerproduction_vermontI wonder what happened in Vermont about 8 years ago.

The chart says they renounced nuclear energy. Here is some history. This one-time event caused a disruption in the time series, unique on the entire map.

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This work is wonderful. Enjoy it!


Funnel is just for fun

This is part 2 of a review of a recent video released by NASA. Part 1 is here.

The NASA video that starts with the spiral chart showing changes in average global temperature takes a long time (about 1 minute) to run through 14 decades of data, and for those who are patient, the chart then undergoes a dramatic transformation.

With a sleight of hand, the chart went from a set of circles to a funnel. Here is a look:

Nasa_climatespiral_funnel

What happens is the reintroduction of a time dimension. Imagine pushing the center of the spiral down into the screen to create a third dimension.

Our question as always is - what does this chart tell readers?

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The chart seems to say that the variability of temperature has increased over time (based on the width of the funnel). The red/blue color says the temperature is getting hotter especially in the last 20-40 years.

When the reader looks beneath the surface, the chart starts to lose sense.

The width of the funnel is really a diameter of the spiral chart in the given year. But, if you recall, the diameter of the spiral (polar) chart isn't the same between any pairs of months.

Nasa_climatespiral_fullperiod

In the particular rendering of this video, the width of the funnel is the diameter linking the April and October values.

Remember the polar gridlines behind the spiral:

Nasa_spiral_gridlines

Notice the hole in the middle. This hole has arbitrary diameter. It can be as big or as small as the designer makes it. Thus, the width of the funnel is as big or as small as the designer wants it. But the first thing that caught our attention is the width of the funnel.

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The entire section between -1 and + 1 is, in fact, meaningless. In the following chart, I removed the core of the funnel, adding back the -1 degree line. Doing so exposes an incompatibility between the spiral and funnel views. The middle of the polar grid is negative infinity, a black hole.

Junkcharts_nasafunnel_arbitrarygap

For a moment, the two sides of the funnel look like they are mirror images. That's not correct, either. Each width of the funnel represents a year, and the extreme values represent April and October values. The line between those two values does not signify anything real.

Let's take a pair of values to see what I mean.

Junkcharts_nasafunnel_lines

I selected two values for October 2021 and October 1899 such that the first value appears as a line double the length of the second. The underlying values are +0.99C and -0.04C, roughly speaking, +1 and 0, so the first value is definitely not twice the size of the second.

The funnel chart can be interpreted, in an obtuse way, as a pair of dot plots. As shown below, if we take dot plots for Aprils and Octobers of every year, turn the chart around, and then connect the corresponding dots, we arrive at the funnel chart.

Junkcharts_nasafunnel_fromdotplots

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This NASA effort illustrates a central problem in visual communications: attention (what Andrew Gelman calls "grabbiness") and information integrity. On the one hand, what's the point of an accurate chart when no one is paying attention? On the other hand, what's the point of a grabby chart when anyone who pays attention gets the wrong information? It's not easy to find that happy medium.


What do I think about spirals?

A twitter user asked how I feel about this latest effort (from NASA) to illustrate global warming. To see the entire video, go to their website.

Nasa_climatespiral_fullperiod

This video hides the lede so be patient or jump ahead to 0:56 and watch till the end.

Let's first describe what we are seeing.

The dataset consists of monthly average global temperature "anomalies" from 1880 to 2021 - an "anomaly" is the deviation of the average temperature that month from a reference level (seems like this is fixed at the average temperatures by month between 1951 and 1980).

A simple visualization of the dataset is this:

Junkcharts_redo_nasasprials_longline

We see a gradual rise in temperature from the 1980s to today. The front half of this curve is harder to interpret. The negative values suggest that the average temperatures prior to 1951 are generally lower than the temperature in the reference period. Other than 1880-1910, temperatures have generally been rising.

Now imagine chopping up the above chart into yearly increments, 12 months per year. Then wrap each year's line into a circle, and place all these lines onto the following polar grid system.

Junkcharts_redo_nasaspiral_linesandcircles

Close but not quite there. The circles in the NASA video look much smoother. Two possibilities here. First is the aspect ratio. Note that the polar grid stretches the time axis to the full circle while the vertical axis is squashed. Not enough to explain the smoothness, as seen below.

Junkcharts_redo_nasaspirals_unsmoothedwide

The second possibility is additional smoothing between months.

Junkcharts_redo_nasaspirals_smoothedlines

The end result is certainly pretty:

Nasa_climatespiral_fullperiod

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Is it a good piece of scientific communications?

What is the chart saying?

I see red rings on the outside, white rings in the middle, and blue rings near the center. Red presumably means hotter, blue cooler.

The gridlines are painted over. The 0 degree (green) line is printed over again and again.

The biggest red circles are just beyond the 1 degree line with the excess happening in the January-March months. In making that statement, I'm inferring meaning to excess above 1 degree. This inference is purely based on where the 1-degree line is placed.

I also see in the months of December and January, there may have been "cooling", as the blue circles edge toward the -1 degree gridline. Drawing this inference actually refutes my previous claim. I had said that the bulge beyond the +1 degree line is informative because the designer placed the +1 degree line there. If I applied the same logic, then the location of the -1 degree line implies that only values more negative than -1 matter, which excludes the blue bulge!

Now what years are represented by these circles? Test your intuition. Are you tempted to think that the red lines are the most recent years, and the blue lines are the oldest years? If you think so, like I do, then we fall into a trap. We have now imputed two meanings to color -- temperature and recency, when the color coding can only hold one.

The only way to find out for sure is to rewind the tape and watch from the start. The year dimension is pushed to the background in this spiral chart. Instead, the month dimension takes precedence. Recall that at the start, the circles are white. The bluer circles appear in the middle of the date range.

This dimensional flip flop is a key difference between the spiral chart and the line chart (shown again for comparison).

Junkcharts_redo_nasasprials_longline

In the line chart, the year dimension is primary while the month dimension is pushed to the background.

Now, we have to decide what the message of the chart should be. For me, the key message is that on a time scale of decades, the world has experienced a significant warming to the tune of about 1.5 degrees Celsius (35 F2.7 F). The warming has been more pronounced in the last 40 years. The warming is observed in all twelve months of the year.

Because the spiral chart hides the year dimension, it does not convey the above messages.

The spiral chart shares the same weakness as the energy demand chart discussed recently (link). Our eyes tend to focus on the outer and inner envelopes of these circles, which by definition are extreme values. Those values do not necessarily represent the bulk of the data. The spiral chart in fact tells us that there is not much to learn from grouping the data by month. 

The appeal of a spiral chart for periodic data is similar to a map for spatial data. I don't recommend using maps unless the spatial dimension is where the signal lies. Similarly, the spiral chart is appropriate if there are important deviations from a seasonal pattern.

 

 


Speaking to the choir

A friend found the following chart about the "carbon cycle", and sent me an exasperated note, having given up on figuring it out. The chart came from a report, and was reprinted in Ars Technica (link).

Gcp_s09_2021_global_perturbation-800x371

The problem with the chart is that the designer is speaking to the choir. One must know a lot about the carbon cycle already to make sense of everything that's going on.

We see big and small arrows pointing up or down. Each arrow has a number attached to it, plus a range inside brackets. These numbers have no units, and it's not obvious what they are measuring.

The arrows come in a variety of colors. The colors are explained by labels but the labels dexcribe apparently unrelated concepts (e.g. fossil CO2 and land-use change).

Interspersed with the arrows is a singular dot. The dot also has a number attached to it. The number wears a plus sign, which signals it's being treated differently than the quantities with up arrows.

The singular dot is an outcast, ostracized from the community of dots in the bottom part of the chart. These dots have labels but no numbers. They come in different sizes but no scale is provided.

The background is divided into three parts, showing the atmosphere, the land mass, and the ocean. The placement of the arrows and dots suggests each measured quantity concerns one of these three parts. Well... except the dot labeled "surface sediments" that sit on the boundary of the land mass and the ocean.

The three-way classification is only one layer of the chart. A different classification is embedded in the color scheme. The gray, light green, and aquamarine arrows in the sky find their counterparts in the dots of the land mass, and the ocean.

What's more, the boundaries between land and sky, and between land and ocean are also painted with those colors. These boundary segments have been given different colors so that the lengths of these segments seem to contain data but we aren't sure what.

At this point, I noticed thin arrows which appear to depict back and forth flows. There may be two types of such exchanges, one indicated by a cycle, the other by two straight arrows in opposite directions. The cycles have no numbers while each pair of straight thin arrows gets two numbers, always identical.

At the bottom of the chart is a annotation in red: "Budget imbalance = -1.0". Presumably some formula ties the numbers shown above to this -1.0 result. We still don't know the units, and it's unclear if -1.0 is a bad number. A negative number shown in red typically indicates a bad number but how bad is it?

Finally, on the top right corner, I found a legend. It's not obvious at first because the legend symbols (arrows and dots) are shown in gray, a color not used elsewhere on the chart. It appears as if it represents another color category. The legend labels do little for me. What is an "anthropogenic flux"? What does the unit of "GtCO2" stand for? Other jargon includes "carbon cycling" and "stocks". The entire diagram is titled "carbon cycle" while the "carbon cycling" thin arrows are only a small part of the diagram.

The bottom line is I have no idea what this chart is saying to me, other than that the earth is a complex system, and that the designer has tried valiantly to impregnate the diagram with lots of information. If I am well read in environmental science, my experience is likely different.

 

 

 

 

 


Metaphors, maps, and communicating data

There are some data visualization that are obviously bad. But what makes them bad?

Here is an example of such an effort:

Carbon footprint 2021-02-15_0

This visualization of carbon emissions is not successful. There is precious little that a reader can learn from this chart without expensing a lot of effort. It's relatively easy to identify the largest emitters of carbon but since the data are not expressed per-capita, the chart mainly informs us which countries have the largest populations. 

The color of the bubbles informs readers which countries belong to which parts of the world. However, it distorts the location of countries within regions, and regions relative to regions, as the primary constraint is fitting the bubbles inside the shape of a foot.

The visualization gives a very rough estimate of the relative sizes of total emissions. The circles not being perfect circles don't help. 

It's relatively easy to list the top emitters in each region but it's hard to list the top 10 emitters in the world (try!) 

The small emitters stole all of the attention as they account for most of the labels - and they engender a huge web of guiding lines - an unsightly nuisance.

The diagram clings dearly to the "carbon footprint" metaphor. Does this metaphor help readers consume the emissions data? Conversely, does it slow them down?

A more conventional design uses a cartogram, a type of map in which the positioning of countries are roughly preserved while the geographical areas are coded to the data. Here's how it looks:

Carbonatlasthumb

I can't seem to source this effort. If any reader can find the original source, please comment below.

This cartogram is a rearrangement of the footprint illustration. The map construct eliminates the need to include a color legend which just tells people which country is in which continent. The details of smaller countries are pushed to the bottom. 

In the footprint visualization, I'd even consider getting rid of the legend completely. This means trusting that readers know South Africa is part of Africa, and China is part of Asia.

Carbonfootprint_part

Imagine: what if this chart comes without a color legend? Do we really need it?

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I'd like to try a word cloud visual for this dataset. Something that looks like this (obviously with the right data encoding):

Michaeltompsett_worldmapwords

(This map is by Michael Tompsett who sells it here.)

 


Reading an infographic about our climate crisis

Let's explore an infographic by SCMP, which draws attention to the alarming temperature recorded at Verkhoyansk in Russia on June 20, 2020. The original work was on the back page of the printed newspaper, referred to in this tweet.

This view of the globe brings out the two key pieces of evidence presented in the infographic: the rise in temperature in unexpected places, and the shrinkage of the Arctic ice.

Scmp_russianheat_1a

A notable design decision is to omit the color scale. On inspection, the scale is present - it was sewn into the graphic.

Scmp_russianheat_colorscale

I applaud this decision as it does not take the reader's eyes away from the graphic. Some information is lost as the scale isn't presented in full details but I doubt many readers need those details.

A key takeaway is that the temperature in Verkhoyansk, which is on the edge of the Arctic Circle, was the same as in New Delhi in India on that day. We can see how the red was encroaching upon the Arctic Circle.

***Scmp_russianheat_2a

Next, the rapid shrinkage of the Arctic ice is presented in two ways. First, a series of maps.

The annotations are pared to the minimum. The presentation is simple enough such that we can visually judge that the amount of ice cover has roughly halved from 1980 to 2009.

A numerical measure of the drop is provided on the side.

Then, a line chart reinforces this message.

The line chart emphasizes change over time while the series of maps reveals change over space.

Scmp_russianheat_3a

This chart suggests that the year 2020 may break the record for the smallest ice cover since 1980. The maps of Australia and India provide context to interpret the size of the Arctic ice cover.

I'd suggest reversing the pink and black colors so as to refer back to the blue and pink lines in the globe above.

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The final chart shows the average temperature worldwide and in the Arctic, relative to a reference period (1981-2000).

Scmp_russianheat_4

This one is tough. It looks like an area chart but it should be read as a line chart. The darker line is the anomaly of Arctic average temperature while the lighter line is the anomaly of the global average temperature. The two series are synced except for a brief period around 1940. Since 2000, the temperatures have been dramatically rising above that of the reference period.

If this is a stacked area chart, then we'd interpret the two data series as summable, with the sum of the data series signifying something interesting. For example, the market shares of different web browsers sum to the total size of the market.

But the chart above should not be read as a stacked area chart because the outside envelope isn't the sum of the two anomalies. The problem is revealed if we try to articulate what the color shades mean.

Scmp_russianheat_4_inset

On the far right, it seems like the dark shade is paired with the lighter line and represents global positive anomalies while the lighter shade shows Arctic's anomalies in excess of global. This interpretation only works if the Arctic line always sits above the global line. This pattern is broken in the late 1990s.

Around 1999, the Arctic's anomaly is negative while the global anomaly is positive. Here, the global anomaly gets the lighter shade while the Arctic one is blue.

One possible fix is to encode the size of the anomaly into the color of the line. The further away from zero, the darker the red/blue color.

 

 


Dreamy Hawaii

I really enjoyed this visual story by ProPublica and Honolulu Star-Advertiser about the plight of beaches in Hawaii (link).

The story begins with a beautiful invitation:

Propublica_hawaiibeachesfrontimage

This design reminds me of Vimeo's old home page. (It no longer looks like this today but this screenshot came from when I was the data guy there.) In both cases, the images are not static but moving.

Vimeo-homepage

The tour de force of this visual story is an annotated walk along the Lanikai Beach. Here is a snapshot at one of the stops:

Propublica_hawaiibeaches_1368MokuluaDr_small

This shows a particular homeowner who, according to documents, was permitted to rebuild a destroyed seawall even though officials were supposed to disallow reconstruction in order to protect beaches from eroding. The property is marked on the map above. The image inside the box is a gif showing waves smashing the seawall.

As the reader scrolls down, the image window runs through a carousel of gifs of houses along the beach. The images are synchronized to the reader's progress along the shore. The narrative makes stops at specific houses at which point a text box pops up to provide color commentary.

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The erosion crisis is shown in this pair of maps.

Propublica_hawaiibeaches_oldnewshoreline-sm

There's some fancy work behind the scenes to patch together images, and estimate the boundaries of th beaches.

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The following map is notable for its simplicity. There are no unnecessary details and labels. We don't need to know the name of every street or a specific restaurant. Removing excess details makes readers focus on the informative parts. 

Propublica_hawaiibeaches_simplemap-sm

Clicking on the dots brings up more details.

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Enjoy the entire story here.


Locating the political center

I mentioned the September special edition of Bloomberg Businessweek on the election in this prior post. Today, I'm featuring another data visualization from the magazine.

Bloomberg_politicalcenter_print_sm

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Here are the rightmost two charts.

Bloomberg_politicalcenter_rightside Time runs from top to bottom, spanning four decades.

Each chart covers a political issue. These two charts concern abortion and marijuana.

The marijuana question (far right) has only two answers, legalize or don't legalize. The underlying data measure the proportions of people agreeing to each point of view. Roughly three-quarters of the population disagreed with legalization in 1980 while two-thirds agree with it in 2020.

Notice that there are no horizontal axis labels. This is a great editorial decision. Only coarse trends are of interest here. It's not hard to figure out the relative proportions. Adding labels would just clutter up the display.

By contrast, the abortion question has three answer choices. The middle option is "Sometimes," which is represented by a white color, with a dot pattern. This is an issue on which public opinion in aggregate has barely shifted over time.

The charts are organized in a small-multiples format. It's likely that readers are consuming each chart individually.

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What about the dashed line that splits each chart in half? Why is it there?

The vertical line assists our perception of the proportions. Think of it as a single gridline.

In fact, this line is underplayed. The headline of the article is "tracking the political center." Where is the center?

Until now, we've paid attention to the boundaries between the differently colored areas. But those boundaries do not locate the political center!

The vertical dashed line is the political center; it represents the view of the median American. In 1980, the line sat inside the gray section, meaning the median American opposed legalizing marijuana. But the prevalent view was losing support over time and by 2010, there wer more Americans wanting to legalize marijuana than not. This is when the vertical line crossed into the green zone.

The following charts draw attention to the middle line, instead of the color boundaries:

Junkcharts_redo_bloombergpoliticalcenterrightsideOn these charts, as you glance down the middle line, you can see that for abortion, the political center has never exited the middle category while for marijuana, the median American didn't want to legalize it until an inflection point was reached around 2010.

I highlight these inflection points with yellow dots.

***

The effect on readers is entirely changed. The original charts draw attention to the areas first while the new charts pull your eyes to the vertical line.

 


How many details to include in a chart

This graphic by Bloomberg provides the context for understanding the severity of the Atlantic storm season. (link)

Bloomberg_2020storms_vertical

At this point of the season, 2020 appears to be one of the most severe in history.

I was momentarily fascinated by a feature of modern browser-based data visualization: the death of the aspect ratio. When the browser window is stretched sufficiently wide, the chart above is transformed to this look:

Bloomberg_2020storms_horizontal

The chart designer has lost control of the aspect ratio.

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This Bloomberg chart is an example of the spaghetti-style plots that convey variability by displaying individual units of data (here, storm years). The envelope of the growth curves gives the range of historical counts while the density of curves roughly offers some sense of the most likely counts at different points of the season.

But these spaghetti-style plots are not precise at conveying the variability because the density is hard to gauge. That's where aggregating the individual units helps.

The following chart does not show individual storm years. It shows the counts for the median season at selected points in time, and also a band of variability (for example, you'd include say 90 or 95% of the seasons).

Redo_bloomberg_2020storms

I don't have the raw data so the aggregating is done by eyeballing the spaghetti.

I prefer this presentation even though it does not plot every single data point one has in the dataset.