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CHAPTER 7

Data Visualization

T HROUGHOUT MUCH OF THE UNITED States, civilians have a legal right to kill an assailant when
they are threatened—or even feel that they may be threatened—with serious bodily harm.
According to “Stand Your Ground” laws, a person has no duty to retreat in the face of a violent
threat. Rather, he or she is permitted to use whatever degree of force is necessary to defuse
the situation, even if it means killing the assailant. Florida’s statutes on the justifiable use of
force, for example, mandate that the use of deadly force is permissible to deter a threat of
death, great bodily harm, or even the commission of a forcible felony such as robbery or
burglary.

Critics of Stand Your Ground laws point to racial disparities in application of these laws,
and express concerns that they make it too easy for shooters to claim self-defense. Supporters
counter that Stand Your Ground laws protect the rights of crime victims over those of
criminals, and serve to deter violent crime more generally. But it is not clear that Stand Your
Ground laws have this effect. Studies have looked at violent crime data within and across the
states and return mixed results. Some find decreases in property crimes such as burglary after
such laws are enacted, but others observe significant increases in homicides.

It was in the context of this debate that the news agency Reuters published a data
visualization much like the one shown on the following page. The graph illustrates the
number of homicides in the state of Florida over a period of twenty-two years.

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At first glance, this graph conveys the impression that Florida’s 2005 Stand Your Ground
law worked wonders. Firearm murders appear to rise until the late 1990s, then plateau, and
then drop precipitously once the Stand Your Ground law is adopted in 2005. But that’s not
what is happening. Look at the vertical axis on the graph above. It has been inverted! Zero is
at the top of the graph, not the bottom. Points lower down correspond to higher numbers of
murders. What seems to be a sharp drop in murders after 2005 is actually a rapid rise.
Displayed in conventional form, the graph would look more like this:

In Florida, Stand Your Ground was followed by a large increase in the number of gun
murders. (As we know from chapter 4, this does not mean that the law caused this increase.)
With a bit of time, most readers might catch on and draw the right conclusions about the
graph. But the point of data graphics is often to provide a quick and intuitive glimpse into a
complex data set. All too often we simply glance at a figure like this one. Perhaps we don’t
have time to read it carefully as we scroll through our news feeds. We assume we know what it
means, and move on.

In the United States, there is a heated debate between advocates and opponents of gun
control. When we share this graph with US audiences, most people assume that this figure is
deliberately deceptive. They take it for a duplicitous attempt by the pro-gun lobby to obscure
the rise in murders following the 2005 Florida legislation. Not so. The graph has a more
subtle and, in our view, more interesting backstory.

After critics decried the graph as misleading, the graphic designer explained her thought
process in choosing an inverted vertical axis: “I prefer to show deaths in negative terms
(inverted).”

Moreover, she added, her inspiration came from a forceful data graphic from the South
China Morning Post that depicted casualties from the Iraq War. That graph also inverted the
vertical axis, but it created the impression of dripping blood and was less prone to
misinterpretation.

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Contrary to what everyone assumes, the Florida Stand Your Ground graphic was not
intended to mislead. It was just poorly designed. This highlights one of the principles for
calling bullshit that we espouse. Never assume malice or mendacity when incompetence is a
sufficient explanation, and never assume incompetence when a reasonable mistake can
explain things.

How can you avoid being taken in by data on a graph? In this chapter, we look at the ways
in the which graphs and other forms of data visualization can distract, confuse, and mislead
readers. We will show you how to spot these forms of graphical bullshit, and explain how the
same data could be better presented.

THE DAWN OF DATAVIZ

C omputers are good at processing large quantitative data sets. Humans are not. We have a
hard time understanding the pattern and structure of data when they are presented in raw
form or even summarized in tables. We need to find ways to simplify information while
highlighting important ideas. Data visualizations can help.

Researchers in the sciences have been using graphs to explore and communicate scientific
and demographic data since the eighteenth century. During that period, the demographer
William Playfair pioneered the forms of data visualization that Microsoft Excel now churns
out by default: bar charts, line graphs, and pie charts. Around the same time, physical

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scientist Johann Heinrich Lambert published sophisticated scientific graphics of the sort we
still use today. His graphics plots are almost indistinguishable from the hand-drawn figures
presented in scientific journals up through the 1980s.

Data visualizations saw limited use until the mid- to late nineteenth century. But by the
turn of the twentieth century, natural and social scientists alike regularly employed such
techniques to report their data and illustrate their theories. The popular press did not follow
immediately. Throughout much of the twentieth century, newspapers and magazines would
print the occasional map, pie chart, or bar chart, but even simple charts like these were
uncommon.*1 Below is a map published in The New York Times, and on this page is a
redrawing of a pie chart published in a 1920 Cyclopedia of Fraternities.

For much of the twentieth century, data visualizations in popular media either showed
only a single variable, as in a pie chart, or showed how a variable changed over time. A graph
might have shown how the price of wheat changed across the 1930s. But it would not have
illustrated how the price of wheat changed as a function of rainfall in the Grain Belt. In 1982,
statistician and data visualization guru Edward Tufte tabulated the fraction of graphs that did
show more complex relationships, for a range of news sources. One in every two hundred data
visualizations published in The New York Times illustrated relationships among multiple
variables (other than time). None of the data visualizations in The Washington Post or The
Wall Street Journal did so.

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In the 1980s, digital plotting software became readily available and newspapers started to
publish more charts and data graphics than they had in the past.

As charts proliferated, so did their sophistication. Today, newspapers such as The New
York Times employ sizable teams of data visualization experts. Many of the data graphics they
create are interactive visualizations that allow readers to explore multiple facets of complex
data sets and observe patterns in the relationships among multiple variables. Well-designed
data graphics provide readers with deeper and more nuanced perspectives, while promoting
the use of quantitative information in understanding the world and making decisions.

But there is a downside. Our educational system has not caught up. Readers may have
little training in how to interpret data graphics. A recent Pew Research Center study found
that only about half of Americans surveyed could correctly interpret a simple scatter plot.*2 In
particular, individuals without a college degree were substantially less likely to be able to

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draw correct conclusions from the graph. This is a problem in a world where data graphics are
commonplace.

Another problem is that while data visualizations may appear to be objective, the designer
has a great deal of control over the message a graphic conveys. Even using accurate data, a
designer can manipulate how those data make us feel. She can create the illusion of a
correlation where none exists, or make a small difference between groups look big. Again, our
educational system lags behind. Few people are taught how to spot these manipulations, or
even taught to appreciate the power a designer has to shape the story that the data tell. We
may be taught how to spot logical fallacies and how to verify claims from questionable
sources. But we are rarely taught anything about the ways in which data graphics can be
designed to mislead us.

One of our primary aims in this chapter is to provide you with these skills. Before we do
that, we want to look at the way that good old-fashioned bullshit (rather than deliberate
deception or misdirection) slips into data visualization.

DUCK!

I f you drive along the main road through the small hamlet of Flanders, on New York’s Long
Island, you will come across a tall statue of a white duck with a huge yellow bill and eyes made
from the taillights of a Model T Ford. If you stop and look more closely, you will see that the
Big Duck, as it is known locally, is not actually a tall statue but rather a small building. A
single door is recessed into the duck’s breast and leads into a small and windowless room
hollowed out from the duck’s body.

The Big Duck was erected in 1931 by a duck farmer to serve as a storefront for selling his
birds and their eggs. While ducks are no longer sold from within, the building has become a
beloved symbol of Flanders and is one of the glorious roadside attractions that once delighted
travelers on the pre-interstate highways of the United States.

The Big Duck is not particularly functional as a building, however. In architectural theory
it has become an icon of what happens when form is put ahead of function, a metaphor for
larger failings in the modernist movement.*3 In architecture, the term “duck” refers to any

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building where ornament overwhelms purpose, though it is particularly common in reference
to buildings that look like the products they sell. The headquarters of the Longaberger basket-
making corporation looks like a giant picnic basket. A shaved ice stand that we visited in
Santa Fe is shaped like the cylindrical blocks of ice from which their desserts are fashioned.

Edward Tufte pointed out that an analogous problem is common in data visualization.
While aesthetics are important, data graphics should be about the data, not about eye-
catching decoration. Graphs that violate this principle are called “ducks.”

USA Today was among the pioneers of the dataviz duck. Its Daily Snapshots feature
presents generally unimportant information in the form of simple graphs. Each Daily
Snapshots graph is designed according to a loose connection to the topic at hand. Tubes of
lipstick stand in as the bars of a chart about how much women spend on cosmetics. A ball of
ice cream atop a cone becomes a pie chart in a graphic about popular ice cream brands. The
line of sight from a man’s face to a television screen zigs and zags to form a line graph of
Olympic Games viewership over the years. It’s hard to say any one example is dramatically
worse than any other, but the image on the previous page is representative of the USA Today
style.

USA Today has no monopoly on the form. In the graph below, modeled after one
published by Mint.com, tines of two forks serve as the bars in a bar chart. What is so bad
about this? Many things. The bars themselves—the information-carrying part of the graph—
use only a small fraction of the total space occupied by the graphic. The slanted angle is
challenging as well; we are not used to interpreting bar graphs angled in that way. Worse still,
the way that the forks are arranged side by side results in a baseline on the left fork that sits
well above the baseline of the right fork. That makes comparison between the two forks even
more difficult. Fortunately, the numerical values are written out. But if one has to rely on
them to interpret the figure, the graphic elements are basically superfluous and the
information could have been presented in a table.

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Ducks are usually a pathology of the popular press, but lately they have crept into the
scientific literature. We have to give the authors of the figure below some points for creativity,
but twisting a pie chart into a ram’s horn only reduces the viewer’s ability to make visual
comparisons among quantities.

We have described bullshit as being intended to persuade or impress by distracting,
overwhelming, or intimidating an audience with a blatant disregard for truth and logical
coherence. Data visualization ducks may not be full-on bullshit, but they shade in that
direction. Ducks are like clickbait for the mind; instead of generating a mouse click, they are
trying to capture a few seconds of your attention. Whereas a bar graph or line chart may seem
dry and perhaps complicated, a colorful illustration may seem fun enough and eye-catching
enough to draw you in.

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What is so wrong with that? What bothers us about ducks is that the attempt to be cute
makes it harder for the reader to understand the underlying data.

GLASS SLIPPERS AND UGLY STEPSISTERS

M ost people know the basic plot of Cinderella: A girl is adopted by an evil stepmother,
forced to cook and clean for her stepmother and stepsisters, and doesn’t get invited to the
grand ball where the prince is seeking a bride. Her fairy godmother appears and turns her
rags into a beautiful dress, her sandals into glass slippers, and a pumpkin into a glittering
coach; she attends the ball and captures the prince’s heart; knowing that the spell will wear off
at midnight, she flees as the clock begins to strike twelve. The prince, aided by a glass slipper
that Cinderella left behind in her flight, is determined to find this mystery woman who
captured his heart. In a sort of reverse Cochran defense,*4 the slipper fits no one but
Cinderella, the prince asks for her hand in marriage, and they live happily ever after. What
may be less familiar is that in the original Grimm brothers’ version of the tale, the evil
stepsisters make desperate attempts to fit into the glass slipper. They slice off their toes and
heels in an effort to fit their feet into the tiny and unyielding shoe.

If a data visualization duck shades toward bullshit, a class of visualizations that we call
glass slippers is the real deal. Glass slippers take one type of data and shoehorn it into a
visual form designed to display another. In doing so, they trade on the authority of good
visualizations to appear authoritative themselves. They are to data visualization what
mathiness is to mathematical equations.

The chemist Dmitri Mendeleev developed the periodic table in the second half of the
nineteenth century. His efforts were a triumph of data visualization as a tool for organizing
patterns and generating predictions in science. The periodic table is an arrangement of the
chemical elements from lightest to heaviest. The left-to-right positions reflect what we now
understand to be the fundamental atomic structure of each element, and predict the chemical
interactions of those elements. The particular blocky structure of the periodic table reflects
the way in which electrons fill the electron subshells around the atomic nucleus. By laying out
the known elements in a way that captured the patterns among them, Mendeleev was able to
predict the existence and properties of chemical elements that had not yet been discovered. In

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short, the periodic table is a highly specific form of data visualization, with a structure that
reflects the logic of atomic chemistry.

Yet designers create periodic tables of everything under the sun. We’ve seen periodic
tables of cloud computing, cybersecurity, typefaces, cryptocurrencies, data science, tech
investing, Adobe Illustrator shortcuts, bibliometrics, and more. Some, such as the periodic
table of swearing, the periodic table of elephants, and the periodic table of hot dogs, are
almost certainly tongue in cheek. Others seem painfully serious: the periodic table of content
marketing, the periodic table of digital marketing, the periodic table of commerce marketing,
the periodic table of email marketing, the periodic table of online marketing, the periodic
table of marketing attribution, the periodic table of marketing signals, the periodic table of
marketing strategies, and let’s not forget the periodic table of b2b digital marketing metrics.
Don’t even get us started on the dozens of periodic tables of SEO—search engine optimization.
Having a hard time keeping track of all this? Fortunately, someone has created a periodic
table of periodic tables.

These faux periodic tables adopt a structure that doesn’t match the information being
classified. Mendeleev’s original periodic tables had a strong enough theoretical basis that he
was able to include gaps for elements yet to be discovered. By contrast, entries in mock
periodic tables are rarely exhaustive, and criteria for inclusion are often unclear. There are no
gaps in the periodic table of data visualization reproduced above. Does anyone really believe
we’ve discovered all the possible techniques for visualizing data? The majority of these other
periodic tables take pains to retain the structure of Mendeleev’s periodic table of elements.
Typically, each entry is assigned a number in ascending , but rarely if ever do these
numbers have anything like the fundamental importance of the atomic numbers listed on
Mendeleev’s table. These copycat tables hope to convey the illusion of systematic
classification, but they disregard logical coherence by aping the structure of Mendeleev’s table
instead of finding a more natural scheme for their members. All of them are bullshit.

In its ordinary use, the subway map is an exemplary form of visualization. Subway maps
take a large amount of complex geographic information and compress it. They discard all
irrelevant detail in to highlight the information a commuter needs to navigate the
subway system. The result is a simple map that is easy to read. The subway map has just a few
elements: subway stops arrayed in two dimensions, subway lines linking these stops in linear
(or circular) , and transfer stations where two lines join.

Unfortunately, designers find the subway irresistible—even when displaying content that
has none of the features of a subway system. We have seen subway maps of scientists,
websites, national parks, moral philosophy, Shakespearean plays, the books of the Bible, the
plot of James Joyce’s Ulysses, the Agile development and management framework, data
science skills, and more.

Some instantiations of the subway map metaphor do a better job than others. The Rock ’n’
Roll Metro Map uses the subway lines to represent genres: heavy metal, punk, alternative,
etc., where each station along the line is a band. The sequential structure of each “line” is
meaningful in this map. Lines proceed from the earliest to the most recent bands. Transfer
stations represent bands that span genres. But the physical positions of the bands on the page
don’t correspond to anything analogous to the positions of subway stations within a city.

The Underskin map of the human body uses different subway lines to represent different
bodily systems: the nervous system, the digestive system, the skeletal system, the lymphatic

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system, etc. Each stop is an organ or structure. Transfer stations represent involvement in
multiple systems. Physical position on the page corresponds to physical position within the
body. Subway maps of river systems and the Milky Way galaxy make similarly appropriate use
of two spatial dimensions. We concede that the components of a traditional subway map are
put to meaningful use in these cases, but these maps still strike us as gimmicks. More
appropriate visualization—anatomical diagrams, river charts, star maps—are already
commonplace.

Subway maps are so misused that, like periodic tables, they have provoked meta-level
commentary in the form of a Subway Map of Maps that Use Subway Maps as Metaphor.

Some sort of prize for perversity should be awarded to the Underground Map of the
Elements.*5

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Periodic tables and subway maps are highly specific forms of visualization. But even very
general visualization methods can be glass slippers. Venn diagrams, the overlapping ovals
used to represent group membership for items that may belong to multiple groups, are
popular glass slippers.

The following diagram purports to illustrate the fraction of Canadians who have used
marijuana.

With its shaded overlapping circles, the image screams “Venn diagram.” But think about
it. The 44.8 percent and 11 percent circles barely overlap. If this were a Venn diagram, that
would mean that most of the people who “met criteria for pot abuse or dependency in their
lifetime” had not “used pot at least once in their lifetime.” Instead, each circle simply indicates
the size of the group in question. The overlaps do not convey any meaning.

Hillary Clinton posted a graph like the following to Twitter. Again, this looks like a Venn
diagram, but the labeling doesn’t make sense. Instead, each region seems to be nothing more
than a slot in which to place some text. The figure is just a confusing way of saying the text
enclosed: “90% of Americans, and 83% of gun owners, support background checks.”

We see something similar in this figure from a scientific paper about the use of Twitter
data for studying public engagement with scientific papers. While the figure below looks like a
Venn diagram, the nested ovals are purely an ornamental backdrop for three numbers and
five words.

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In addition to diagrams that look like Venn diagrams but are not, we often see Venn
diagrams that mostly serve as a way to list various desired attributes. The example on the next
page is emblematic of the genre. Product excellence, effective branding, and promotional
focus all seem like good things. And at their intersection, another good thing: profit. But look
at the other entries. Why is demand generation at the intersection of effective branding and
promotional focus, to the exclusion of product excellence? Why does revenue growth exclude
effective branding? Why does industry leadership exclude promotional focus? Nobody seems
to have thought these things through. It seems more like a series of self-congratulatory
phrases were dropped into the diagram at random in the hope that no one would think too
carefully about their placement.

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And then of course there is the risk of invoking the Venn diagram metaphor accidentally.
One prominent informatics company produced posters that looked something like the
following. While intended to be visually attractive fluff, the implication this makes to anyone
who has seen a Venn diagram is that the company’s values mostly exclude trust, partnership,
innovation, and performance.

Another popular form of diagram, particularly in fields such as engineering and anatomy,
is the labeled schematic. Below, examples of each.

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This is a classic form of data visualization, and such diagrams provide an efficient way to
label the parts of a complex image. But more and more we see these diagrams being co-opted
in some sort of loose metaphorical fashion. Take the unicorn on this page, used to advertise a
business analytics award program.

The labels on this diagram make no sense. What do forelegs have to do with machine
learning and visualization? Is there any reason that R programming is associated with a hind
leg instead? Why doesn’t the right hind leg have an attribute? Why does the head “analytical
thinker” refer to a kind of person, whereas the other body parts refer to skills? Why does
“business acumen” correspond to the tail? (We don’t think the designers meant to suggest
that it’s the closest of the categories to a horse’s ass.) This is just a list of terms that the
designer thinks are important, made to look like a labeled diagram.

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This pencil has the same problem. We are not sure how the parts of the pencil correspond
to their labels, or even what information we are supposed to take away from this figure.
Perhaps that business development erases the mark of happiness?

We conclude with an example of a metaphor taken so far over the top that it becomes self-
parody.

The figure on the next page has something to do with learning and education, but we have
no idea what.

Ducks decorate or obscure the meaningful data in a graphic by aiming to be cute. Glass
slippers create a false sense of rigor by shoehorning one type of data into a wholly unsuitable
data visualization.

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AN AXIS OF EVIL

D ata visualizations can also mislead, either by intention or by accident. Fortunately, most of
these deceptions are easy to spot if you know what you are looking for.

Many data graphics, including bar charts and scatter plots, display information along
axes. These are the horizontal and vertical scales framing the plot of numeric values. Always
look at the axes when you see a data graphic that includes them.

Designers have a number of tricks for manipulating the axes of a graph. In 2016,
columnist and professor Andrew Potter created a furor with a commentary in the Canadian
news magazine Maclean’s. In that piece, he argued that many of Quebec’s problems could be
traced to the fact that “compared to the rest of the country, Quebec is an almost pathologically
alienated and low-trust society, deficient in many of the most basic forms of social capital that
other Canadians take for granted.” In an effort to support Potter’s argument, the magazine
subsequently published the following data graphic.

At a glance, this graph …