**Chapter Two: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints**
*by Catherine D'Ignazio and Lauren Klein*
Sociologist Patricia Hill Collins describes an ideal knowledge situation as
one in which "neither ethics nor emotions are subordinated to reason." So
why has emotion been so systematically excluded from data visualization? What
happens when we bring back emotion and embodiment?
> This chapter is a draft. The final version of
> *Data Feminism*
> will be published by the MIT Press in 2019. Please email
> [Catherine](mailto:catherine_dignazio@emerson.edu) and/or
> [Lauren](mailto:lauren.klein@lmc.gatech.edu)
> for permission to cite this manuscript draft.
In 2012, twenty kindergarten children and six adults were shot and
killed at an elementary school in Sandy Hook, CT. In the wake of this
tragedy, and the weight of others like it, the design firm Periscopic
started a new project -- to visualize gun deaths in the United States.
While there is no shortage of prior work in the form of bar charts or
line graphs of deaths per year, Periscopic, a company whose tagline is
"do good with data", took a different approach. When you load
[the webpage](http://guns.periscopic.com/?year=2013), you see a single,
arcing line that reaches out over time. Then, the color abruptly shifts
from orange to white. A small dot drops down, and you see the phrase,
"Alexander Lipkins, killed at 29". The arc continues to stretch across
the screen, coming to rest on the x-axis, where you see a second phrase,
"could have lived to be 93." Then, a second arc appears, displaying
another arcing life. The animation speeds up over time, and the arcing
lines increase, along with a counter that displays how many years of
life have been "stolen" from these gun victims. After a couple of
(long) minutes, the visualization moves through all of the year 2013,
arriving at 11,419 people killed and 502,025 stolen years.
What is different about Periscopic's visualization than a more
conventional bar chart of similar information such as "The era of
'active shooters'" from the Washington Post?
The Post's graphic has a proposition - that active shooter incidents
are on the rise - and demonstrates visual evidence to that effect. But
Periscopic's work is framed around a singular emotion: loss. People are
dying, their remaining time on earth has been stolen from them. These
people have names and ages. We presume they have parents and partners
and children who also suffer from that loss. The data scientists who
worked on the project used rigorous statistical methods and demographic
information in order to infer how long that person would have lived,
which are documented in their notes. But in spite of the statistical
rigor, and its undeniable emotional impact, "U.S. Gun Deaths" drew mixed
responses from the visualization community. They couldn't decide: *should a visualization evoke emotion?*
The received wisdom in technical communication circles is, emphatically,
"NO." In the recent book, "A Unified Theory of Information Design,"
the authors state: "The plain style normally recommended for technical visuals
is directed toward a deliberately neutral emotional field, a blank page in effect,
upon which viewers are more free to choose their own response to the information."
Here, plainness is equated with the absence of design, and thus greater
freedom on the part of the viewer to interpret the results for
themselves. Things like colors and icons, it is implied, work only to
stir up emotions and cloud the viewer's rational mind. In fact, in the
field of data visualization, any kind of ornament has historically been
viewed as suspect. Why? Well, as historian of science Theodore Porter
puts it, "quantification is a technology of distance" and distance was
historically imagined to serve objectivity by producing knowledge
independently of the people that make it. This echoes nineteenth-century
statistician Karl Pearson's exhortation for people to set aside their
own feelings and emotions when it came to statistics. The more
seemingly neutral, the more rational, the more true, the better. At a
data visualization master class in 2013, workshop leaders from the
Guardian newspaper called spreadsheet data -- those endless columns and
rows-- "clarity without persuasion."
Back in the olden days of visualization, before
the rise of the web elevated the visual display of data into a prized
(and increasingly pervasive) artform, Edward Tufte, statistician and
statistical graphics expert, invented a metric for measuring the
superfluous information included in a chart-- what he called the
"data-ink" ratio. In his view, a visualization
designer should strive to use ink only to display the data. Any ink
devoted to something other than the data itself -- such as background
color, iconography, or embellishment -- should be immediately erased
and, he all but says, the designer spat upon. Visual minimalism,
according to this logic, appeals to reason first. ("Just the facts,
ma'am" says Joe Friday to every female character on Dragnet).
Decorative elements, on the other hand, are associated with messy
feelings-- or, worse, represent stealthy (and, of course, unscientific)
attempts at emotional persuasion. Data
visualization has even be classified as the
"unempathetic" art, in the words of designer Mushon Zer-Aviv, because
of its emphatic rejection of emotion.
The gendered dimension of this thinking should be as clear as when
President Richard Nixon, in a now-infamous statement, declared that no
"woman should be in any government job\... because they are erratic. And
emotional." (He subsequently admitted that "men are erratic and
emotional, too," but insisted that "a woman is more likely to be.") We
saw this same sentiment play out in the presidential debates of 2016,
between Hillary Clinton and Donald Trump, but it's not limited to
politics alone. The truth is that most Anglo-Western cultures have long
prized reason over emotion for its supposedly greater neutrality and
universality. And the belief that women are more emotional than men
(and, by contrast, that men are more reasoned than women) is one of the
most persistent stereotypes in the world today. Indeed,
psychologists have called it the "master
stereotype," and puzzled over how it endures even when certain
emotions-- even extreme ones, like anger and pride-- are simultaneously
coded as male. One need only compare any number of ["Hulk smash!"
GIFs](https://giphy.com/search/hulk-smash) to the equal number of
crazy-lady [PMS
memes](https://www.google.com/search?q=pms+memes&tbm=isch&tbo=u&sourc%0Ae=univ&sa=X&ved=0ahUKEwiSqfaknvLTAhUrsFQKHU6cBKAQsAQIJQ&biw=1152&%0A;bih=621&dpr=1.25)
floating around the Internet to prove, first of all, that everyone is
crazy; and second, that the stereotype of the unstable, irrational woman
persists.
But what happens if we let go of the binary logic for a minute and posit
two questions to challenge this master stereotype. First, is visual
minimalism really more neutral? And second, how might activating
emotion** --
leveraging it, rather than resisting emotion in data visualization --
help us learn, remember, and communicate with data?
Until recently, data visualization was a rather specialized form of
communication, more common in scientific papers and earnings reports
than on the front page of *The New York
Times*. Because of this
history, the field's theorists and practitioners have come from technical disciplines aligned with engineering and
computer science, and have not been trained in the most fundamental of
all Western communication theories: *rhetoric*. In the ancient Greek
treatise of the same name, Aristotle defines rhetoric as "the faculty
of observing in any given case the available means of persuasion."
Rhetoric does not (only) consist of political speeches made by men in
robes on ancient stages. Any communicating object that makes choices
about the selection and representation of reality is a rhetorical
object. Whether or not it is rhetorical (it is)
has nothing to do with whether or not it is "true" (it may or may not
be).
Why does the question of rhetoric matter? Well, because "a rhetorical
dimension is present in every design," as Jessica Hullman, a researcher
at the University of Washington, says of data visualization. This
includes visualizations that do not deliberately intend to persuade
people of a certain message. We would say that it *especially and
definitively* includes those so-called "neutral" visualizations that do
not appear to have an editorial hand. In fact, those might even be the
most perniciously persuasive visualizations of all!
Hullman and co-author Nicholas Diakopoulous wrote an influential
paper in 2011 introducing concepts of rhetoric
to the information visualization community.
Their main argument is that visualizing data involves editorial choices
-- some things are necessarily highlighted, while others are necessarily
obscured. When designers make these choices, they carry along with them
"framing effects," which is to say they have an impact on how people
interpret the graphics and what they take away from them. For example,
it is standard practice to cite the source of one's data. This
functions on a practical level -- so that a reader may go out and
download the data themselves. But this choice also functions as what
Hullman and Diakopoulous call *provenance rhetoric* designed to signal
the transparency and trustworthiness of the presentation source to
end-users. This trust between the designers and their audience, in turn,
increases the likelihood that viewers will believe what they
see.
So if plain, "unemotional" visualizations are not neutral, but are
actually extremely persuasive, then what does
this mean for the concept of neutrality in general? Scientists and
journalists are just some of the people that get nervous and defensive
when questions about neutrality and objectivity come up. Auditors and
accountants get nervous, too. They often assume that the only
alternative to objectivity is a retreat into complete relativism, and a
world in which everyone gets a medal for having an opinion. But
feminists would beg to differ. (Feminists, generally speaking, do not
like alternative facts any more than scientists do). Rather than
valorizing the "neutrality ideal," feminist thinkers like Sandra
Harding would posit a different kind of objectivity that strives for
truth *at the same time* that it considers-- and discloses-- the standpoint of the
designer. This has come to be called "standpoint
theory." It is defined by what Harding calls "strong objectivity"
which acknowledges that regular-grade, vanilla objectivity is mainly
made by mostly rich white guys in power and does not include the
experiences of women and other marginalized groups.
This myopia inherent in traditional "objectivity" is what provoked
renowned cardiologist Dr. Nieca Goldberg to title her book "Women Are
Not Small Men," because she found that heart disease in women unfolds
in a fundamentally different way than in men. The vast majority of
scientific studies-- not just of heart disease, but of most medical
conditions-- are conducted on male subjects, with women viewed as
varying from this "norm" only by their smaller size. Harding and her
followers would say that the key to fixing this
issue is to acknowledge that all science, and indeed all work in the
world, is undertaken by individuals, each with a particular standpoint -- gender, race, culture, heritage, life experience, and so on. Rather
than viewing these standpoints as threats that might
*bias* our work -- for, after all,
even the standpoint of a rich white guy in power is a standpoint -- we
should embrace each of our standpoints as valuable perspectives that can
*frame* our
work. Our diverse standpoints can generate
creative and wholly new research questions. We discuss standpoint theory
further in *Unicorns, Janitors, Ninjas, Wizards
and Rock Stars*, where we assert that a participatory process that
centers the standpoints of those most
marginalized is what makes for strong objectivity.
Along with this embrace of our various standpoints goes the rebalancing
of the false binary between reason and emotion.[^f1]
Since the early 2000s, there has been an explosion of research about
"affect"-- the term that academics use to refer to emotions and other
subjective feelings-- from fields as diverse as neuroscience, geography,
and philosophy. This work challenges the thinking, inherited from
Descartes, which casts emotion as irrational and illegitimate, even as
it undeniably influences all of the social and political processes of
our world. Feminist thinkers have long believed that emotion, and other
forms of subjective experiences, are legitimate ways of knowing and
producing knowledge about the world. Evelyn Fox Keller, a
physicist-turned-philosopher, famously employed the Nobel-prize-winning
research of geneticist Barbara McClintock, in order to show how even the
most profound of scientific discoveries are generated from a combination
of experiment and insight, reason and emotion. And sociologist Patricia
Hill Collins describes an ideal knowledge situation as one in which
"neither ethics nor emotions are subordinated to reason."
Once we embrace the idea of leveraging emotion in data visualization, we
can truly appreciate what sets Periscopic's Gun Deaths apart from the
*Washington Post* graphic, or any number of other gun death charts that
have appeared in newspapers and policy documents. The *Washington Post*
graphic represents death counts as blue ticks on a generic bar chart.
If we didn't read the caption, we wouldn't know whether we were
counting gun deaths in the U.S., or haystacks in Kansas, or exports from
Malaysia, or any other semi-remote statistics of passing
interest.
But the *Periscopic* visualization leads
with loss, grief, and mourning. It provides a
visual language for representing the years that could have been--
numbers that are accurate, but not technically facts. It uses pacing
and animation to help us appreciate the scale of one life, and then
compounds that scale 11,419-fold. The magnitude
of the loss, especially when viewed in aggregate, is a staggering and
profound truth-- and the visualization helps recognize it as such
through our own emotions. The generic WaPo bar chart cannot do the work
of communicating this truth.
Skilled data artists and designers know these things already, and are
pushing the boundaries for what affective and embodied data
visualization could look like. In 2010, Kelly Dobson founded the Data
Visceralization research group at the Rhode Island School of Design
(RISD) Digital + Media Graduate program. The goal for this group was not
to visualize data but to *visceralize* it. Visual things are for the
eyes, but visceralizations are data that the whole body can experience--
emotionally, as well as physically.
The reasons for doing visceralizing data have
to do with more than simply creative experimentation. How do visually impaired people access charts and
dashboards? According to the World Health Organization, 253 million
people globally live with some form of visual impairment. This might
include cataracts, glaucoma and complete blindness. Creators in the
visceralization mode have crafted haptic data
visualizations,[^f2]
data walks, data quilts, musical scores from scientific data, wearable
objects that capture your breaths and play them back later, and data
performances. These types of objects and events are more likely to be
found in the context of galleries and museums and research labs, but
there are many lessons to be learned from them for those of us who make
visualizations in more everyday settings.
For example, in the project "[A Sort of Joy (Thousands of Exhausted
Things)](https://vimeo.com/133815147)", a theater troupe joined with a
data visualization firm to craft a live
performance based on metadata about the artworks held by New York's
Museum of Modern Art. With 123,951 works in its collection, MoMA's
metadata consists of the names of artists, the titles of artworks, their
media formats, and their time periods. But how does an artwork make it
into the museum collection to begin with? Major art museums and their
collection policies have long been the focus of feminist critique
because the question of whose work gets collected translates into the
question of whose work is counted in the annals of history-- and, as you
might guess, this history has mostly consisted of a parade of white male
"masters."
In 1989, for example, the Guerrilla Girls, an anonymous collective of
female artists, published what we would today call an infographic: [Do
women have to be naked to get into the Met.
Museum?](http://www.tate.org.uk/art/artworks/guerrilla-girls-do-women-have-to-be-na%0Aked-to-get-into-the-met-museum-p78793)
The graphic was designed to be displayed on a billboard. However, it was
rejected by the sign company because it "wasn't clear enough." (If
you ask us, it's pretty clear). The Guerrilla Girls then paid for it to
be printed on posters which were displayed throughout the New York City
bus system, until the bus company cancelled their contract, stating that
the figure "seemed to have more than a fan in her hand." (It is
definitely more than a fan). The figure is certainly provocative, but
the poster also makes a data-driven argument by tabulating gender
statistics for artists included in the Met collection, and comparing
them to the gender stats for the *subjects* of art included in the
collection. As per the poster, the Met readily collects paintings in
which women are the subjects, but not those in which women are the
artists themselves.
*A Sort of Joy* tackles a similar subject, but with wholly different
tactics. The performance starts with a group of white men standing in a
circle in the center of the room. They face out towards the audience,
which stands around them. The men are dressed like stereotypical museum
visitors: collared shirts, slacks, etc. Each wears headphones and holds
an iPad on which the names of artists in the collection scroll by.
"John," the men say together. We see the iPads scrolling through all
of the names of artists in the MOMA collection whose first name is John:
John Baldessari, John Cage, John Lennon, John Waters, and so on. Three
female performers, also wearing headphones and carrying iPads with
scrolling names, pace around the circle of men,. "Robert," the men say
together, and the names scroll through the Roberts alphabetically. The
women are silent and keep walking. "David," the men say together. It
soon becomes apparent that the artists are sorted by first name, and
then ordered by which first name has the most works in the collection.
Thus, the Johns and Roberts and Davids come first, because they have the
most works in the collection. But Marys have fewer works, and Mohameds
and Camilas are barely in the register. Several minutes later, after the
men say "Michael", "James", "George", "Jean", "Hans",
"Thomas", "Walter", "Edward", "Yan", "Joseph", "Martin",
"Mark", "José", "Louis", "Frank", "Otto", "Max", "Steven",
"Jack", "Henry", "Henri", "Alfred", "Alexander", "Carl",
"Andre", "Harry", "Roger" and "Pierre", "Mary" finally gets
her due. It's spoken by the female performers; the first sound they've
made.
For audience members, the experience starts as one of slight confusion.
Why are there men in a circle? Why do they randomly speak someone's
name? And what are those women walking around so intently? But "Mary"
becomes a kind of a-ha moment-- the same that researcher Robert Kosara
says that data visualization is so good at producing -- when the highly gendered nature of the collection is
revealed. From that point on, audience members start to listen
differently, eagerly awaiting the next female name. It takes more than
three minutes for "Mary" to be spoken, and the next female name,
"Joan," doesn't come for a full minute longer. "Barbara" follows
immediately after that, and then the men return to reading, "Werner",
"Tony", "Marcel", "Jonathan".
From a data analysis perspective, "A Sort of Joy" consists of simple
operations: only counting and grouping. The results could easily have
been represented by a bar chart or a tree map of first names. But
rendering the dataset as a time-based
experience makes the audience wait
and listen. It also contradicts long-held
wisdom in visualization design, as expressed by Ben Shneiderman in the
mid-1990s: "Overview first, zoom and filter, then
details-on-demand." Instead, in this
data performance, we do not see "the whole picture". We hear and see
and experience each datapoint one at a time. The different gender
expressions, body movements, and verbal tones of the performers draw our
collective attention to the issue of gender in the MoMA collection.
We start to anticipate when the next female name
will arise, and begin to speculate on whether it will be a Rosa, a
Rhonda, or another name entirely. We *feel* the gender differential,
rather than *see* it. This feeling is affect. It comprises the emotions
that arise when experiencing the performance and the physiological
reactions to to the sounds and movements made
by the performers, as well as the desires and drives that result-- even
if that drive is to walk into another room because the performance is
disconcerting or just plain long.
Designing data visceralizations requires a much more holistic conception
of the viewer. The viewer isn't just a pair of eyes attached to a brain.
They are a whole body-- a complex, feeling one. Theirs is a body located
in space, with a history and a future. This notion of the visceral
viewer, and of the additional knowledge that can be conveyed when
designing with visceralization in mind, can be found in many current
projects, even if they don't always describe their work in those terms.
For example, Catherine (one of the authors of this book) and artist
Andi Sutton led walking tours of the future coastline of Boston based on
sea level rise. And Lauren (the other author)
and her team of Georgia Tech undergrads recreated Elizabeth Peabody's
living-room-rug-sized charts from the 19th century using touch sensors
and individually addressable LEDs.
Mikhail Mansion made a leaning, bobbing chair that animatronically shifts based
on real-time shifts in river currents. Teri Rueb staged "sound
encounters" between the geologic layers of a landscape and the human
body that is affected by them. Simon Elvins drew a giant paper map of
silence in London that you can actually listen to. While these projects may seem to be speaking to another
part of brain than your standard Sankey diagrams or network maps, there
is something to be learned from the opportunities opened up by
visceralizing data. In fact, scientists are now proving by
experiment what designers and artists have long
known through practice: activating emotion, leveraging embodiment, and
creating novel presentation forms help people learn more from
data-driven arguments, and remember them more fully.
It turns out that visceralizing data may also help designers solve a
pernicious problem in the visualization community: how to represent
uncertainty in a medium that's become rhetorically synonymous with the
truth. Its "truthiness" is both a feature and a bug. One of the best things about data visualization is that it
*does* look so certain, so factual, and so authoritative. But why? After
doing a sociological analysis, Helen Kennedy determined that four
conventions of data visualization reinforce people's perceptions of its
factual basis: 1) two-dimensional viewpoints, 2) clean layouts, 3)
geometric shapes and lines, and 4) the inclusion of data sources at the
bottom. These conventions contribute to the perception of data
visualization as objective, scientific and neutral.
But even if you use these conventions with the best and most pure
intentions, it's something of a problem for feminist design, because
feminist theory maintains that there is no such thing as a purely
objective view of the world. Knowledge is always partial, as Sandra
Harding has shown us, and these conventions would seem to contradict
that basic philosophical tenet. Haraway's "God trick," which we
discuss in *Bring Back the Bodies*, is exactly that: a trick to make you
believe that you can see everything, all at once, from an imaginary and
impossible standpoint.
This is the argument from philosophy. But representing uncertainty is
also a known problem in data journalism and visualization
research. In these realms, people may care a
tiny bit less about feminist epistemology but do care deeply about end
users' ability to accurately interpret graphic depictions of data and
use them to make decisions. To this end, designers have created a huge
array of charts and techniques for quantifying and representing
uncertainty. These include box-plots, violin plots, gradient plots, and
confidence intervals. Unfortunately, however, people are terrible at
recognizing uncertainty in data visualizations, even when they're
explicitly told that something is uncertain. According to work by Geoff
Cumming and colleagues, researchers themselves have a hard time
understanding confidence intervals. And even everyday weather forecasts
such as "There's a 30% chance of rain tomorrow" are generally
interpreted by the public to mean "It will rain 30% of the time" or
"It will rain in 30% of my area."[^f3]
Emotion is not only useful for communicating uncertainty. Let's return
to the question around visual minimalism: Are minimal charts really
"better," as Edward Tufte has claimed? Charts
like "Monstrous Costs," depicted above, have been historically
dismissed as "chart junk." In this
case, the data has to do with presidential
campaign spending from 1972 - 1982.
You will notice that there is a very low data to ink ratio-- recall
Tufte's other major pet peeve. For example, the
monster's eyes and body would be deemed as "not data" and thus
embellishment. Likewise, there is gratuitous spittle emanating from the
monster's mouth, and the tail forms an S-curve that is then turned into
a dollar sign with two utterly superfluous grey lines. The monster is also wearing a VOTE badge, which, again,
does not represent any aspect of the data at hand. Plus, monsters do not
vote-- so how irrational is that?
There are many popular blogs devoted to ridiculing this so-called chart
junk. But recently, researchers have challenged the notion that chart
junk is junk at all. A 2010 study by Scott Bateman and colleagues in
Computer Science at the University of Saskatchewan that found that
"embellished" charts do not hinder people's ability to accurately
read them, and in fact, they are actually better for memorability. When
polled 2 to 3 weeks later, people were much more likely to recall the
message of the embellished chart over a minimalist chart that displayed
the same data. People also thought the junk charts were more attractive,
and enjoyed them more (Duh. Who doesn't like monsters better than bar
charts?!) Likewise, in 2016, Michelle Borkin and colleagues showed that
visualizations that make use of novel presentation styles are more
memorable, and therefore more effective.
Relating the visual form to the topical content of a chart *works*. So,
as data journalist Mona Chalabi says, "If it's about farts, draw a butt
for god's sakes."
After all this, you might be wondering, "So why is it that people like
junk so much?" But that is not really the right question. The question
we should all be asking is, "How has the view from an imaginary and
impossible standpoint-- the 'whole picture,' the 'overview,' -- come
to be seen as rational and objective at all?"
The rational, scientific, objective viewpoint actually comes from a
mythical, imaginary, impossible standpoint. The view from no body. "The
god trick of seeing everything from nowhere," as Donna Haraway says
it.
But let's say it more simply, "How did we arrive at conventions in
data visualization that prioritize rationality, devalue emotion, and
completely ignore the human body except for two eye stalks attached to a
brain?" Any knowledge community inevitably places certain things at the
center and casts others out, in the same way that male bodies have been
taken as the norm in scientific study and female bodies imagined as
deviation from the norm, or that rationality has been valued as an
authoritative mode of communication and emotion cast out. But, following
feminist theorist Elizabeth Grosz, what is regarded as "excess" in any
given system might possibly be the most interesting thing to explore
because it tells us the most about what *and who* the system is trying
to exclude.
In the case of data visualization, this excess is emotion and affect,
embodiment and expression, embellishment and
decoration. These are the aspects of human experience coded "female,"
and thus devalued by the logic of our master stereotype. But how might
we intentionally flip this? All design fields, including visualization,
are fields of possibility. We must actively strive to question what
(and who) is at the center of the discourse of our field, what (and who)
is at the periphery. And then we must work to center the things and
people that have been cast off. The first step in this re-centering
process is to legitimize affect and embodiment in data visualization.
This means to moving emotion to the center of visualization design, and
to start to imagine data experiences for whole human bodies in all of
their glorious, situated, uncontainable excesses.
[^f1]: Resisting binary thinking is a multipurpose tool in the feminist
toolbox. We discussed the false gender binary in What Gets Counted
Counts. Feminist thinkers have demonstrated how other binaries also need
a complete re-thinking -- like reason/emotion, nature/culture,
subject/object, body/world, speaker/receiver, among others.
[^f2]: There is now almost thirty years of research on haptic forms of data
presentation. A great starting place for exploring this work is the 2010
paper by Sabrina Panëels and Jonathan C. Roberts called "Review of
Designs for Haptic Data Visualization."
[^f3]: The standard meteorological interpretation is that the statement is
actually evaluating tomorrow's weather based on today's conditions
along with historical data. A "30% chance of rain" tomorrow means that
based on how conditions are today, in 3 out of 10 cases historically
there would be rain the following day. Interestingly, a study by Gerd
Gigerenzer and his colleagues across five major cities found that
Europeans consistently preferred the "incorrect" interpretations
whereas a majority of New Yorkers stated the correct interpretation.
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