## Displaying convoluted indices

##### Nov 16, 2021

I reviewed another batch of projects from Ray Vella's class at NYU. The following piece by Carlos Lasso made an impression on me. There are no pyrotechnics but he made one decision that added a lot of clarity to the graphic.

The underlying dataset gauges the income disparity of regions within nine countries. The richest and the poorest regions are selected for each country. Two time points are shown. Altogether, there are 9x2x2 = 36 numbers.

***

Let's take a deeper look at these numbers. Notice they are not in dollars, or any kind of currency, despite being about incomes. The numbers are index values, relative to 100. What does the reference level of 100 represent?

The value of 100 crosses every bar of the chart so that 100 has meaning in each country and each year. In fact, there are 18 definitions of 100 in this chart with 36 numbers, one for each country-year pair. The average national income is set to 100 for each country in each year. This is a highly convoluted indexing strategy.

The following chart is a re-visualization of the bottom part of Carlos' infographic.

I shifted the scale of the horizontal axis. The value of zero does not hold special meaning in Carlos' chart. I subtracted 100 from the relative regional income indices, thus all regions with income above the average have positive values while those below the national average have negative values. (There are other challenges with the ratio scale, which I'll skip over in this post. The minimum value is -100 while the maximum value can be very large.)

The rescaling is not really the point of this post. To see what Carlos did, we have to look at the example shown in class. The graphic which the students were asked to improve has the following structure:

This one-column structure places four bars beside each country, grouped by year. Carlos pulled the year dimension out, and showed the same dataset in two columns.

This small change makes a great difference in ease of comprehension. Carlos' version unpacks the two key types of comparisons one might want to make: trend within a given country (horizontal comparison) and contrast between countries in a given year (vertical comparison).

***

I always try to avoid convoluted indexing. The cost of using such indices is the big how-to-read-this box.

## The gift of small edits and subtraction

##### Oct 14, 2021

While making the chart on fertility rates (link), I came across a problem that pops up quite often, and is  ignored by most software programs.

Here is an earlier version of the chart I later discarded:

Compare this to the version I published in the blog post:

Aside from adding the chart title, there is one major change. I removed the empty plots from the grid. This is a visualization trick that should be called adding by subtracting. The empty scaffolding on the first chart increases our cognitive load without yielding any benefit. The whitespace brings out the message that only countries in Asia and Africa have fertility rates above 5.0.

This is a small edit. But small edits accumulate and deliver a big impact. Bear this in mind the next time you make a chart.

P.S.

(1) You'd have to use a lower-level coding language to execute this small edit. Most software programs are quite rigid when it comes to making small-multiples (facet) charts.

(2) If there is a next iteration, I'd reverse the Asia and Oceania rows.

##### Feb 03, 2020

In my just-published Long Read article at DataJournalism.com, I touched upon the subject of "How to Read this Chart".

Most data graphics do not come with directions of use because dataviz designers follow certain conventions. We do not need to tell you, for example, that time runs left to right on the horizontal axis (substitute right to left for those living in right-to-left countries). It's when we deviate from the norms that calls for a "How to Read this Chart" box.

***
A discussion over Twitter during the weekend on the following New York Times chart perfectly illustrates this issue. (The article is well worth reading to educate oneself on this red-hot public-health issue. I made some comments on the sister blog about the data a few days ago.)

Reading this chart, I quickly grasp that the horizontal axis is the speed of infection and the vertical axis represents the deadliness. Without being told, I used the axis labels (and some of you might notice the annotations with the arrows on the top right.) But most people will likely miss - at a glance - that the vertical axis utilizes a log scale while the horizontal axis is linear (regular).

The effect of a log scale is to pull the large numbers toward the average while spreading the smaller numbers apart - when compared to a linear scale. So when we look at the top of the coronavirus box, it appears that this virus could be as deadly as SARS.

The height of the pink box is 3.9, while the gap between the top edge of the box and the SARS dot is 6. Yet our eyes tell us the top edge is closer to the SARS dot than it is to the bottom edge!

There is nothing inaccurate about this chart - the log scale introduces such distortion. The designer has to make a choice.

Indeed, there were two camps on Twitter, arguing for and against the log scale.

***

I use log scales a lot in analyzing data, but tend not to use log scales in a graph. It's almost a given that using the log scale requires a "How to Read this Chart" message. And the NY Times crew delivers!

Right below the chart is a paragraph:

To make this even more interesting, the horizontal axis is a hidden "log" scale. That's because infections spread exponentially. Even though the scale is not labeled "log", think as if the large values have been pulled toward the middle.

Here is an over-simplified way to see this. A disease that spreads at a rate of fifteen people at a time is not 3 times worse than one that spreads five at a time. In the former case, the first sick person transmits it to 15, and then each of the 15 transmits the flu to 15 others, thus after two steps, 241 people have been infected (225 + 15 + 1). In latter case, it's 5x5 + 5 + 1 = 31 infections after two steps. So at this point, the number of infected is already 8 times worse, not 3 times. And the gap keeps widening with each step.

P.S. See also my post on the sister blog that digs deeper into the metrics.

## The unspoken rules of visualization

##### Jan 31, 2020

My latest is at DataJournalism.com.

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).

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:

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.

## What is a bad chart?

##### Jul 22, 2019

In the recent issue of Madolyn Smith’s Conversations with Data newsletter hosted by DataJournalism.com, she discusses “bad charts,” featuring submissions from several dataviz bloggers, including myself.

What is a “bad chart”? Based on this collection of curated "bad charts", it is not easy to nail down “bad-ness”. The common theme is the mismatch between the message intended by the designer and the message received by the reader, a classic error of communication. How such mismatch arises depends on the specific example. I am able to divide the “bad charts” into two groups: charts that are misinterpreted, and charts that are misleading.

Charts that are misinterpreted

The Causes of Death entry, submitted by Alberto Cairo, is a “well-designed” chart that requires “reading the story where it is inserted and the numerous caveats.” So readers may misinterpret the chart if they do not also partake the story at Our World in Data which runs over 1,500 words not including the appendix.

The map of Canada, submitted by Highsoft, highlights in green the provinces where the majority of residents are members of the First Nations. The “bad” is that readers may incorrectly “infer that a sizable part of the Canadian population is First Nations.”

In these two examples, the graphic is considered adequate and yet the reader fails to glean the message intended by the designer.

Two fellow bloggers, Cole Knaflic and Jon Schwabish, offer the advice to start bars at zero (here's my take on this rule). The “bad” is the distortion introduced when encoding the data into the visual elements.

The Color-blindness pictogram, submitted by Severino Ribecca, commits a similar faux pas. To compare the rates among men and women, the pictograms should use the same baseline.

In these examples, readers who correctly read the charts nonetheless leave with the wrong message. (We assume the designer does not intend to distort the data.) The readers misinterpret the data without misinterpreting the graphics.

Using the Trifecta Checkup

The visual design of the Causes of Death chart is not under question, and the intended message of the author is clearly articulated in the text. Our concern is that the reader must go outside the graphic to learn the full message. This suggests a problem related to the syncing between the visual design and the message (the QV edge).

By contrast, in the Color Blindness graphic, the data are not under question, nor is the use of pictograms. Our concern is how the data got turned into figurines. This suggests a problem related to the syncing between the data and the visual (the DV edge).

***

When you complain about a misleading chart, or a chart being misinterpreted, what do you really mean? Is it a visual design problem? a data problem? Or is it a syncing problem between two components?

## Book Preview: How Charts Lie, by Alberto Cairo

##### Mar 18, 2019

If you’re like me, your first exposure to data visualization was as a consumer. You may have run across a pie chart, or a bar chart, perhaps in a newspaper or a textbook. Thanks to the power of the visual language, you got the message quickly, and moved on. Few of us learned how to create charts from first principles. No one taught us about axes, tick marks, gridlines, or color coding in science or math class. There is a famous book in our field called The Grammar of Graphics, by Leland Wilkinson, but it’s not a For Dummies book. This void is now filled by Alberto Cairo’s soon-to-appear new book, titled How Charts Lie: Getting Smarter about Visual Information.

As a long-time fan of Cairo’s work, I was given a preview of the book, and I thoroughly enjoyed it and recommend it as an entry point to our vibrant discipline.

In the first few chapters of the book, Cairo describes how to read a chart. Some may feel that there is not much to it but if you’re here at Junk Charts, you probably agree with Cairo’s goal. Indeed, it is easy to mis-read a chart. It’s also easy to miss the subtle and brilliant design decisions when one doesn’t pay close attention. These early chapters cover all the fundamentals to become a wiser consumer of data graphics.

***

How Charts Lie will open your eyes to how everyone uses visuals to push agendas. The book is an offshoot of a lecture tour Cairo took during the last year or so, which has drawn large crowds. He collected plenty of examples of politicians and others playing fast and loose with their visual designs. After reading this book, you can’t look at charts with a straight face!

***

In the second half of his book, Cairo moves beyond purely visual matters into analytical substance. In particular, I like the example on movie box office from Chapter 4, titled “How Charts Lie by Displaying Insufficient Data”. Visual analytics of box office receipts seems to be a perennial favorite of job-seekers in data-related fields.

The movie data is a great demonstration of why one needs to statistically adjust data. Cairo explains why Marvel’s Blank Panther is not the third highest-grossing film of all time in the U.S., as reported in the media. That is because gross receipts should be inflation-adjusted. A ticket worth \$15 today cost \$5 some time ago.

This discussion features a nice-looking graphic, which is a staircase chart showing how much time a #1 movie has stayed in the top position until it is replaced by the next higher grossing film.

Cairo’s discussion went further, exploring the number of theaters as a “lurking” variable. For example, Jaws opened in about 400 theaters while Star Wars: The Force Awakens debuted in 10 times as many. A chart showing per-screen inflation-adjusted gross receipts looks much differently from the original chart shown above.

***

Another highlight is Cairo’s analysis of the “cone of uncertainty” chart frequently referenced in anticipation of impending hurricanes in Florida.

Cairo and his colleagues have found that “nearly everybody who sees this map reads it wrongly.” The casual reader interprets the “cone” as a sphere of influence, showing which parts of the country will suffer damage from the impending hurricane. In other words, every part of the shaded cone will be impacted to a larger or smaller extent.

That isn’t the designer’s intention! The cone embodies uncertainty, showing which parts of the country has what chance of being hit by the impending hurricane. In the aftermath, the hurricane would have traced one specific path, and that path would have run through the cone if the predictive models were accurate. Most of the shaded cone would have escaped damage.

Even experienced data analysts are likely to mis-read this chart: as Cairo explained, the cone has a “confidence level” of 68% not 95% which is more conventional. Areas outside the cone still has a chance of being hit.

This map clinches the case for why you need to learn how to read charts. And Alberto Cairo, who is a master visual designer himself, is a sure-handed guide for the start of this rewarding journey.

***

Here is Alberto introducing his book.

## The state of the art of interactive graphics

##### Mar 10, 2016

Scott Klein's team at Propublica published a worthy news application, called "Hell and High Water" (link) I took some time taking in the experience. It's a project that needs room to breathe.

The setting is Houston Texas, and the subject is what happens when the next big hurricane hits the region. The reference point was Hurricane Ike and Galveston in 2008.

This image shows the depth of flooding at the height of the disaster in 2008.

The app takes readers through multiple scenarios. This next image depicts what would happen (according to simulations) if something similar to Ike plus 15 percent stronger winds hits Galveston.

One can also speculate about what might happen if the so-called "Mid Bay" solution is implemented:

This solution is estimated to cost about \$3 billion.

***

I am drawn to this project because the designers liberally use some things I praised in my summer talk at the Data Meets Viz conference in Germany.

Here is an example of hover-overs used to annotate text. (My mouse is on the words "Nassau Bay" at the bottom of the paragraph. Much of the Bay would be submerged at the height of this scenario.)

The design has a keen awareness of foreground/background issues. The map uses sparse static labels, indicating the most important landmarks. All other labels are hidden unless the reader hovers over specific words in the text.

I think plotting population density would have been more impactful. With the current set of labels, the perspective is focused on business and institutional impact. I think there is a missed opportunity to highlight the human impact. This can be achieved by coding population density into the map colors. I believe the colors on the map currently represent terrain.

***

This is a successful interactive project. The technical feats are impressive (read more about them here). A lot of research went into the articles; huge amounts of details are included in the maps. A narrative flow was carefully constructed, and the linkage between the text and the graphics is among the best I've seen.

## Fixing the visual versus fixing the story

##### Feb 13, 2015

It's great for me when my friend Alberto Cairo lent a helping hand (link). Here is the original chart showing deaths in African and Middle East countries due to recent unrest:

This is Cairo's redesign:

There is no doubt the new version brings out the data more clearly. I like the cropping of the continent. I'd color-code the countries using the same legend as above.

I'm troubled by the concept of the original chart. I struggle to find any interesting correlation of deaths, whether with time, with government reaction, or with geography. Of the three, I think geography is the most correlated so a good design should bring that out. (Of course, geographical bias is expected and thus rather boring.)

If the intention of the chart is to answer the question of what factors affect deaths, then the wrong variables are being utilized.

So, as regards the Trifecta Checkup, Cairo solved the V problem while the D problem remains.

## The snow made me do it - California, here I come

##### Jan 29, 2015

California readers: here's a chance to come meet me. I am giving talks in San Diego (Feb 3) and San Mateo (Feb 5) next week, courtesy of JMP. Free registration is here

These talks are related to two ongoing projects of mine: the first project is to create a theory of data visualization criticism. How can we use precise language to describe our reactions - good and bad - to data visualization work? The second project is surrounding how to find stories from a mass of data.

I'd love to meet some of you on the West Coast who are fans of the blog. Please also forward this announcement to your friends or colleagues who might be interested.

## Losing sleep over schedules

##### Jan 05, 2015

Fan of the blog, John H., made a JunkCharts-style post about a chart that has been picked as a "Best of" for 2014 by Fast Company (link). I agree with him. It seems more fit to be on the "Worst of" list. Here it is:

As John pointed out, the outside yellow arc (Beethoven) and the inside green arc (Simenon) present, shockingly, the same exact sleep schedule (10 pm to 6 am).

John unrolled the arcs and used R to make this version:

Go here to read John's entire post.

***

Another improvement is to add a "control". One way to understand how unusual these sleep patterns are is to compare them to the average person.

I'm also a little dubious as to the reliability of this data. How do we know their sleep schedules? And how variant were their schedules?

If I rate this via the Trifecta Checkup, I'd classify this as Type DV.