How might ritual be necessary for maintaining a dominant perspective and how might it help to change people’s minds?

Data Feminism

On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints Catherine D'Ignazio and Lauren Klein

D'Ignazio, Catherine and Lauren Klein. "On Rational, Scientific, Objective Viewpoints from

Mythical, Imaginary, Impossible Standpoints." Data Feminism. MIT Press, 26 July 2020.

Retrieved from https://data-feminism.mitpress.mit.edu/pub/5evfe9yd

License: Creative Commons Attribution 4.0 International License (CC-BY 4.0)

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Principle: Elevate Emotion and Embodiment

Data feminism teaches us to value multiple forms of knowledge, including the knowledge that

comes from people as living, feeling bodies in the world.

In 2012, twenty kindergarten children and six adults were shot and killed at an elementary school in

Sandy Hook, Connecticut. In the wake of this unconscionable tragedy, and of the additional acts of gun

violence that followed, the design firm Periscopic began a new project: to visualize all the gun deaths

that took place in the United States over the course of a calendar year. Although there is no shortage of

prior work on the subject in the form of bar charts or line graphs, Periscopic, a company with the

tagline “Do good with data,” took a different approach.

When you load the project’s webpage, you first see a single orange line that arcs up from the x-axis on

the left-hand side of the screen. Then, the color abruptly changes to white. A small dot drops down,

and you see the phrase, “Alexander Lipkins, killed at 29” (figure 3.1a). The line continues to arc up

across the screen and then down, coming back to rest on the x-axis, where a second phrase appears:

“Could have lived to be 93.” Then, a second line appears—the arc of another life. The animation speeds

up and the arcs multiply. A counter at the top right displays how many years of life have been “stolen”

from these victims of gun violence. After several excruciating minutes, the visualization completes its

count for the year: 11,419 people killed, totaling 502,025 stolen years (figure 3.1b).

The visualization uses demographic data and rigorous statistical methods to arrive at these numbers,

as is explained in the methods section on the site. But what makes Periscopic’s visualization so very

different from a more conventional bar chart of similar information, such as “The Era of ‘Active

Shooters’” from the Washington Post (figure 3.2)? The projects share the proposition that gun deaths

present a serious threat. But unlike the Washington Post bar chart, Periscopic’s work is framed around

an emotion: loss. People are dying; their remaining time on earth has been stolen from them. These

people have names and ages. They have parents and partners and children who suffer from that loss as

well.

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Figure 3.1: An animated visualization of the “stolen years” of people killed by guns in the United States in 2013. The first image (a)

shows the beginning state of the animation and the second image (b) shows the end state. Images by Periscopic.

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This message was clearly received, as was the project overall. It was featured in Wired magazine, and

even won an Information is Beautiful award. But it also caused some stewing on the part of the

visualization community. Alberto Cairo, the author of the visualization book The Truthful Art,

expressed his concerns about the use of emotion and persuasion in the project: “Is it clear to a general

audience that what they see is the work of professionals who actively shape data to support a cause,

and not the product of automated processes?”1 At root for Cairo was the question of how detached and

“neutral” a visualization should be. He wondered, Should a visualization be designed to evoke

emotion?

The received wisdom in technical communication circles is, emphatically, “No.” In the recent book A

Unified Theory of Information Design, authors Nicole Amare and Alan Manning 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.”2 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 work only to stir

up emotions and cloud the viewer’s rational mind.

They’re not the first ones to posit this belief. In the field of data communication, any kind of ornament

has long been viewed as suspect. Why? As historian of science Theodore Porter puts it, “Quantification

Figure 3.2: A bar chart of the number of “active shooter” incidents in the United States between 2000 and 2015. Images by

Christopher Ingraham for the Washington Post.

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is a technology of distance.”3 And distance, he explains, is closely related to objectivity because it puts

literal space between people and the knowledge they produce. This desire for separation is what

underlies the nineteenth-century statistician Karl Pearson’s exhortation, echoed in Cairo’s comments

about the Periscopic visualization, for people to set aside their “own feelings and emotions” when

performing statistical work.4 The more plain, the more neutral; the more neutral, the more objective;

and the more objective, the more true—or so this line of reasoning goes. At a data visualization master

class in 2013, workshop leaders from the Guardian newspaper held up spreadsheet data—spreadsheet

data!—as an ideal for the communication of quantitative data, calling it: “Clarity without persuasion.”5

But persuasion is everywhere, even in spreadsheets, and—as feminist philosopher Donna Haraway

would likely argue—especially in spreadsheets. In the 1980s, Haraway was among the first to connect

the seeming neutrality and objectivity of data and their visual display to the ideas about distance that

we’ve just discussed. She described data visualization, in particular, as “the god trick of seeing

everything from nowhere.” The view from nowhere—from a distance, from up above, like a god—may

be data visualization’s most signature feature. It’s also the most ethically complicated to navigate for

the ways in which it masks the people, the methods, the questions, and the messiness that lies behind

clean lines and geometric shapes. Haraway calls it a trick because it makes the viewer believe that they

can see everything, all at once, from an imaginary and impossible standpoint. But it’s also a trick

because what appears to be everything, and what appears to be neutral, is always what she terms a

partial perspective. And in most cases of seemingly “neutral” visualizations, this perspective is the one

of the dominant, def ault group. Think back to the presumption of whiteness as def ault that we

discussed in the introduction, or—for an example of an actual visualization—to the redlining map

discussed in chapter 2. This is a good example of the god trick at work.6

The god trick and its underlying assumptions about neutrality and truth are baked into today’s best

practices for data visualization. This is largely due to the influence of one man: the renowned

statistical graphics expert Edward Tufte. Back in the 1980s, Tufte invented a metric for measuring the

amount of superfluous information included in a chart. He called it the data-ink ratio.7 In his view, a

visualization designer should strive to use ink to display data alone. Any ink devoted to something

other than the data themselves—such as background color, iconography, or embellishment—is a

suspect and intruder to the graphic. Visual minimalism, according to this logic, appeals to reason first.

As police officer Joe Friday says to every woman character on the American TV series Dragnet, “Just the

f acts, ma’am.” Decorative elements, on the other hand, are associated with messy feelings—or, worse,

represent stealthy (and, according to Tufte, unscientific) attempts at emotional persuasion. Data

visualization has even been named as “the unempathetic art” by designer Mushon Zer-Aviv because of

its emphatic rejection of emotion.8

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The logic that sets up this f alse binary between emotion and reason is gendered, of course, because 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 across many Western cultures. Indeed, psychologists

have called it a master stereotype and puzzled over how it endures even when certain emotions—even

extreme ones, like anger and pride—are simultaneously associated with men.9 A central focus of

feminist scholarship has been to challenge f alse binaries like this one between reason and emotion and

to point out how they establish hierarchies as well. (We discuss this more in chapter 4.) For now, the

important thing to note is how f alse binaries work to benefit a single one of Haraway’s partial

perspectives: that of the group already at the top—elite white men.

How can we let go of this binary logic? Two additional questions help challenge this reductive way of

thinking and the oppressive hierarchies that it supports. First, is visual minimalism really more

neutral? And second, how might activating emotion—leveraging, rather than resisting, emotion in data

visualization—help us learn, remember, and communicate with data? Exploring these questions helps

get us closer to the third principle of data feminism: embrace emotion and embodiment.

Visualization as Rhetoric

Information visualization has diverse origins. Its history is often traced from the explosion of

European men mapping their colonial conquests in the late fifteenth and early sixteenth centuries,

through the development of new visual typologies like the timeline and the bar chart in the

seventeenth and eighteenth centuries, to the adoption of those forms by powerful nations as they

amassed increasing amounts of data on the populations they sought to control. But feminist scholars

are increasingly challenging this simple narrative of progress, as well as its cast of characters, which is

predominantly white and male. Whitney Battle-Baptiste and Britt Rusert recently published a new

edition of the visualization work of W. E. B. Du Bois, the renowned Black sociologist and civil rights

activist, who created his “data portraits” of African American life for the 1900 Paris Exposition. Laura

Bliss, in a blog post that went viral, called attention to the “narrative maps” of Shanawdithit, a

member of the Beothuk (Newfoundland) tribe, which she created around 1829 at the urging of a

visiting anthropologist. And Lauren, one of the authors of this book, created a website that reanimates

the historical charts of Elizabeth Palmer Peabody, the nineteenth-century editor and educator, who

used visualization in her teaching (figure 3.3).10

Each of these early visualization designers understood how their images could function rhetorically.

But in more recent history, many of data visualization’s theorists and practitioners have come from

technical disciplines aligned with engineering and computer science and have not been trained in that

most fundamental of Western communication theories. In his ancient Greek treatise, Aristotle defines

rhetoric as “the f aculty of observing in any given case the available means of persuasion.”11 But

rhetoric isn’t only found in political speeches made by men dressed in tunics with wreaths on their

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heads.12 Any communicating object that reflects choices about the selection and representation of

reality is a rhetorical object. Whether or not it is rhetorical (it always is) has nothing to do with

whether or not it is true (it may or may not be).

The question of rhetoric matters because “a rhetorical dimension is present in every design,” says

visualization researcher Jessica Hullman.13 This includes visualizations that do not deliberately intend

to persuade people of a certain message. It especially and definitively includes those so-called neutral

visualizations that do not appear to have an editorial hand. In f act, those might even be the most

perniciously persuasive visualizations of all!

Editorial choices become most apparent when compared with alternative choices. For example, in his

book The Curious Journalist’s Guide to Data, journalist Jonathan Stray discusses a data story from the

New York Times about the September 2012 jobs report.14 The New York Times created two graphics from

the report: one framed from the perspective of Democrats (the party in power at the time; figure 3.4a)

and one framed from the perspective of Republicans (figure 3.4b).

Either of these graphics, considered in isolation, appears to be neutral and f actual. The data are

presented with standard methods (line chart and area chart respectively) and conventional

positionings (time on the x-axis, rates expressed as percentages on the y-axis, title placed above the

graphic). There is a high data-ink ratio in both cases and very little in the way of ornamentation. But

the graphics have significant editorial differences. The Democrats’ graphic emphasizes that

unemployment is decreasing—in its title, the addition of the thick blue arrow pointing downward, and

the annotation “Friday’s drop was larger than expected.” Whereas the Republicans’ graphic highlights

the f act that unemployment has been steadily high for the past three years—through the use of the “8

percent unemployment” reference line, the choice to use an area chart instead of a line, and, of course,

the title of the graphic.15 So neither graphic is neutral, but both graphics are f actual. As Jonathan Stray

says, “The constraints of truth leave a very wide space for interpretation.”16 When visualizing data,

the only certifiable f act is that it’s impossible to avoid interpretation (unless you simply republish the

September jobs report as your visualization, but then it wouldn’t be a visualization).

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Fields very close to visualization, like cartography, have long seen their work as ideological. But

discussions of rhetoric, editorial choices, and power have been f ar less frequent in the field of data

visualization. In 2011, Hullman and coauthor Nicholas Diakopoulos wrote an influential paper

reasserting the importance of rhetoric for the data visualization community.17 Their main argument

was 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

Figure 3.3: (a) Elizabeth Palmer Peabody’s chart of “Significant Events of the 17th Century United States” (1865). (b) Peabody’s

chart recreated in digital form by Lauren’s Digital Humanities Lab (2017). (c) A rendering of Peabody’s chart reimagined as an

interactive quilt by Lauren’s Digital Humanities Lab (2019). Images by (a) Elizabeth Palmer Peabody, A Chronological History of the

United States (1856), (b) the Georgia Tech Digital Humanities Lab, and (c) Courtney Allen for the Georgia Tech Digital Humanities

Lab.

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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 readers may go out and download the data themselves. But this choice also functions as what

Hullman and Diakopoulos call provenance rhetoric designed to signal the transparency and

Figure 3.4: A data visualization of the September 2012 jobs report from the perspective of

Democrats (a) and Republicans (b). The New York Times data team shows how simple

editorial changes lead to large differences in framing and interpretation. As data journalist

Jonathan Stray remarks on these graphics, “The constraints of truth leave a very wide

space for interpretation.” Images by Mike Bostock, Shan Carter, Amanda Cox, and Kevin

Quealy, for the New York Times, as cited in The Curious Journalist’s Guide to Data by

Jonathan Stray.

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trustworthiness of the presentation source to end users. Establishing trust between the designers and

their audience in turn increases the likelihood that viewers will believe what they see.

Other aspects of data visualization also work to displace viewers’ attention from editorial choices to

reinforce a graphic’s perceived neutrality and “truthiness.” After doing a sociological analysis, Helen

Kennedy and coauthors determined that four conventions of data visualization reinforce people’s

perceptions of its f actual basis: (1) two-dimensional viewpoints, (2) clean layouts, (3) geometric shapes

and lines, and (4) the inclusion of data sources at the bottom.18 These conventions contribute to the

perception of data visualization as objective, scientific, and neutral. Both unemployment graphics

from the New York Times employ these conventions: the image space is two-dimensional and abstract;

the layout is “clean,” meaning minimal and lacking embellishment beyond what is necessary to

communicate the data; the lines representing employment rates vary smoothly and f aithfully against a

geometrically gridded background; and the source of the data is noted at the bottom. Either the

Democrat or the Republican graphic would have been entirely plausible as a New York Times

visualization, and very few of us would have thought to question the graphic’s framing of the data.

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 who 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 alternative f acts reign and everyone gets a gold

medal for having an opinion. But there are other options.

Rather than valorizing the neutrality ideal and trying to expunge all human traces from a data product

because of their bias, feminist philosophers have proposed a goal of more complete knowledge. Donna

Haraway’s idea of the god trick comes from a larger argument about the importance of developing

feminist objectivity. It’s not just data visualization but all forms of knowledge that are situated, she

explains, meaning that they are produced by specific people in specific circumstances—cultural,

historical, and geographic.19 Feminist objectivity is a tool that can account for the situated nature of

knowledge and can bring together multiple—what she terms partial—perspectives. Sandra Harding,

who developed her ideas alongside Haraway, proposes a concept of strong objectivity. This form of

objectivity works toward more inclusive knowledge production by centering the perspectives—or

standpoints—of groups that are otherwise excluded from knowledge-making processes.20 This has

come to be known as standpoint theory. To supplement these ideas, Linda Alcoff has introduced the

idea of positionality, a concept that emphasizes how individuals come to knowledge-making processes

from multiple positions, each determined by culture and context.21 All of these ideas offer alternatives

to the quest for a universal objectivity—which is, of course, an unattainable goal.

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The belief that universal objectivity should be our goal is harmful because it’s always only partially put

into practice. This flawed belief 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.22 The vast majority of scientific studies—not just of heart

disease, but of most medical conditions—are conducted on men, with women viewed as varying from

this “norm” only by their smaller size.23 The key to fixing this problem is to acknowledge that all

science, and indeed all work in the world, is undertaken by individuals. Each person occupies a

particular perspective, as Haraway might say; a particular standpoint, as Harding might say; or a

particular set of positionalities, as Alcott might say. And all would agree that research by only men and

about only men cannot be universalized to make knowledge claims about all other people in the world.

Disclosing your subject position(s) is an important feminist strategy for being transparent about the

limits of your—or anyone’s—knowledge claims. Thus, for example, we (the authors) included

statements about our own positionalities in the introduction in order to disclose the gender,

race/ethnicity, class, ability, education, and other subject positions that informed the writing of this

book. Rather than viewing these positionalities as threats or as influences that might have biased our

work, we embraced them as offering a set of valuable perspectives that could frame our work. This is

an approach that we would like to see others embrace as well. Each person’s intersecting subject

positions are unique, and when applied to data science, they can generate creative and wholly new

research questions.

Data Visceralization

This embrace of multiple perspectives and positionalities helps to rebalance the hierarchy of reason

over emotion in data visualization.24 How? 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. (We discuss affect further in

chapter 7.) This work challenges the thinking that casts emotion out as irrational and illegitimate, even

as it undeniably influences the social, political, and scientific processes of the world. Evelyn Fox Keller,

a physicist turned philosopher, f amously employed the Nobel Prize–winning research of geneticist

Barbara McClintock to show how even the most profound of scientific discoveries are generated from a

combination of experiment and insight, reason and emotion.25

Once we embrace the idea of leveraging emotion in data visualization, we can truly appreciate what

sets Periscopic’s “US Gun Deaths” graphic apart from the Washington Post graphic or from any number

of other gun death charts that have appeared in newspapers and policy documents. The Washington

Post graphic, for example, 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 United States or

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haystacks in Kansas or exports from Malaysia or any other statistic. In contrast, the Periscopic

visualization leads with loss, grief, and mourning—primarily through its rhetorical emphasis on

counting “stolen years.” This draws the attention of viewers to “what could have been.” The counting is

reinforced by the visual language for representing the “stolen years” as grey lines, appropriate for

numbers that are rigorously determined but not technically f acts because they come from a statistical

model. The visualization also uses animation and pacing to help us first appreciate the scale of one life,

and then compound that scale 11,419-fold. The magnitude of the loss, especially when viewed in

aggregate and over time, makes a statement of profound truth revealed to us through our own

emotions. It is important to note that emotion and visual minimalism are not incompatible here; the

Periscopic visualization shows us how emotion can be leveraged alongside visual minimalism for

maximal effect.

Skilled data artists and designers know these things already, or at least intuit them. Like the

Periscopic team, others are pushing the boundaries of 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

representations of data that the whole body can experience, emotionally as well as physically—data

that “we see, hear, feel, breathe and even ingest,” writes media theorist Luke Stark.26

The reasons for visceralizing data have to do with more than simply creative experimentation. First,

humans are not two eyeballs attached by stalks to a brain computer. We are embodied, multisensory

beings with cultures and memories and appetites.27 Second, people with visual disabilities need a way

to access the data encoded in charts and dashboards as well. According to the World Health

Organization, 253 million people globally live with some form of visual impairment, on the spectrum

from limited vision to complete blindness.28 For reasons of accessibility, Aimi Hamraie, the director of

the Mapping Access project at Vanderbilt University, advocates for a form of data visceralization,

although not in those exact terms: “Rather than relying entirely on visual representations of data,”

they explain, “digital-accessibility apps could expand access by incorporating ‘deep mapping,’ or

collecting and surf acing information in multiple sensory formats.”29

At the moment, however, examples of objects and events that make use of multiple sensory formats

are more likely to be found in the context of research labs and galleries and museums. For example, in

A Sort of Joy (Thousands of Exhausted Things), the theater troupe Elevator Repair Service joined forces

with the data visualization firm the Office of Creative Research to script a live performance based on

metadata about the artworks held by New York’s Museum of Modern Art (MoMA).30 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

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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.31 As you might guess, this history has mostly consisted of a parade

of white male European “masters,” as the Guerrilla Girls project pictured in figure 3.5 reminds us.

In 1989, the Guerrilla Girls, an anonymous collective of women artists, published an infographic: Do

Women Have to Be Naked to Get into the Met. Museum? The graphic makes a data-driven argument by

comparing the gender statistics of artists collected by another New York museum, the Metropolitan

Museum of Art (the Met) to the gender statistics of the subjects and models in the artworks. It was

designed to be displayed on a billboard, but it was rejected by the sign company because it “wasn’t

clear enough.”32 If you ask us, it’s pretty clear: the Met readily collects paintings in which women are

the (naked) subjects but it collects very few artworks created by women artists themselves.

After being thwarted by the sign company, the Guerrilla Girls then paid for the infographic to be

printed on posters displayed throughout the New York City bus system, until the Metropolitan

Transportation Authority (MTA) cancelled the contract, stating that the figure seemed to have more

than a f an in her hand. It is definitely more than a f an, but this deliberate understatement reveals the

MTA’s discomfort with this provocative, activist image.33

A Sort of Joy deploys wholly different tactics to similar ends. The performance starts with a group of

white men standing in a circle in the center of the room. They f ace out toward the audience, which

surrounds them. The men are dressed like stereotypical museum visitors: collared shirts, slacks, and

so on. 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 the names of the 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,

Figure 3.5: Do Women Have to Be Naked to Get into the Met. Museum? An infographic (of a sort) created by the Guerrilla Girls in

1989, intended to be displayed on a bus billboard. Courtesy of the Guerrilla Girls.

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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 Marías are barely

in the register. Several minutes later, after the men say “Michael,” “James,” “George,” “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, spoken by the female performers, the first sound they’ve

made.

For audience members, the experience is slightly confusing at first. Why are the men in a circle? Why

do they randomly speak someone’s name? And why are those women walking around so intently? But

“Mary” becomes a kind of aha moment, highlighting the highly gendered nature of the collection—

exactly the same kind of experience of insight that data visualization is so good at producing,

according to researcher Martin Wattenberg.34 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: counting and grouping. A

bar chart or a tree map of first names could easily have represented the same results. But presenting

the dataset as a time-based experience makes the audience wait and listen and experience. It also

runs counter to the mantra in information visualization expressed by researcher Ben Shneiderman in

the mid-1990s: “Overview first, zoom and filter, then details-on-demand.”35 In this data performance,

we do not see the overview first. We hear and see and experience each datapoint one at a time and only

slowly construct a sense of the whole. 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 woman’s name will arise. We feel the gender differential, rather

than see it.

This feeling is affect. It comprises the emotions that arise when experiencing the performance, as well

as the physiological reactions 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.

Data visceralizations that leverage affect aren’t limited to major art institutions. Catherine and artist

Andi Sutton led walking tours of the future coastline of Boston based on sea level rise.36 Interactive

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artist Mikhail Mansion made a leaning, bobbing chair that animatronically shifts based on real-time

shifts in river currents.37 Nonprofit organizations in Tanzania staged a design competition for data-

driven clothing that incorporated statistics about gender inequality and closed the project with a

f ashion runway show.38 Artist Teri Rueb stages “sound encounters” between the geologic layers of a

landscape and the human body that is affected by them.39 Simon Elvins drew a giant paper map of

pirate radio stations in London that you can actually listen to.40 A robot designed by Annina Rust

decorates real pies with pie charts about gender equality, and then visitors eat them.41

These projects may seem to be speaking to another part of brain (or belly) than your standard

histograms or network maps, but there is something to be learned from the opportunities opened up

by visceralizing data. Deliberately embracing emotions like wonder, confusion, humor, and solidarity

enables a valuable form of data maximalism, one that allows for multisensory entry points, greater

accessibility, and a range of learning types.

Visceralizing Uncertainty

Scientific researchers are now proving by experiment what designers and artists have known through

practice: activating emotion, leveraging embodiment, and creating novel presentation forms help

people grasp and learn more from data-driven arguments, as well as remember them more fully.42 As

it turns out, visceralizing data may help designers solve one particularly pernicious problem in the

visualization community: how to represent uncertainty in a medium that’s become rhetorically

synonymous with the truth. To this end, designers have created a huge array of charts and techniques

for quantifying and representing uncertainty. These include box plots, violin plots (figure 3.6), gradient

plots, and confidence intervals.43 Unfortunately, however, people are terrible at recognizing

uncertainty in data visualizations, even when they’re explicitly told that something is uncertain. This

remains true even for some researchers who use data themselves!44

For example, let’s consider the Total Electoral Votes graphic displayed as part of the New York Times

live online coverage of the 2016 presidential election (figure 3.7). The blue and red lines represent the

New York Times’s best guess at the outcomes over the course of election night and into the following

day. The gradient areas show the degree of uncertainty that surrounded those guesses, with the

darker inner area showing electoral vote outcomes that came up 25 percent to 75 percent of the time,

and the lighter outer areas showing outcomes that came up 75 percent to 95 percent and 5 percent to 25

percent of the time, respectively. If you look closely at the f ar left of the graphic, which represents

election night (everything prior to the 12:00 a.m. axis label), the outcome of Trump winning and

Clinton losing easily f alls within the 5 to 25 percent likelihood range.

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Although many election postmortems pronounced the 2016 election the Great Failure of Data and

Statistics, because most simulations and other statistical models suggested that Clinton would win,

most forecasts did include the possibility of a Trump victory. The underlying problem was not the

f ailure of data but the difficulties of depicting uncertainty in visual form. People are just not

sufficiently trained to recognize uncertainty in graphics such as this. Rather than interpreting the

gradient bands as probabilities (e.g., Trump had a 20 percent chance of winning at 6 p.m.), people may

interpret them as votes (e.g., Trump had 20 percent of the vote at 6 p.m.). Or they may ignore the

gradient bands altogether and look only at the lines. Or they see Clinton on top and assume she is

winning. This is called heuristics in psychology literature—using mental shortcuts to make judgments—

and it happens all the time when people are asked to assess probabilities.45 A large part of the

problem is that visualization conventions reinforce these misjudgments. The graphics look so certain,

even when they are trying their very hardest to visually illustrate uncertainty!

Figure 3.6: What is the best way to communicate uncertainty in a medium that looks so certain? Designers have created diverse

chart forms to try to solve this problem. Depicted here are five violin plots; each shows the distribution of data along with their

probability density (the purple part). You could also think of this form as a beautiful purple vagina, as the comic xkcd has observed;

see https://www.xkcd.com/1967/. Images from the Data Visualisation Catalogue.

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Jessica Hullman, whose work on rhetoric we’ve already mentioned, offers one solution to this problem.

Instead of creating fixed plots as in the New York Times example that represents uncertainty in

aggregate or static form, Hullman advocates for rendering experiences of uncertainty.46 In other

words, leverage emotion and affect so that people experience uncertainty perceptually. Or, to invoke a

common refrain from rhetorical training and design schools, “show, don’t tell.” Rather than telling

people that they are looking at uncertainty while employing a certain-looking graphic style—which

creates conditions ripe for those pesky heuristics to intervene—make them feel the uncertainty.

We can see a good example of showing uncertainty in action on the same New York Times live election

coverage webpage. At the top of the page was a gauge (figure 3.8) that showed the New York Times’s

real-time prediction of who was likely to win the race, with a gradient of categories that ranged from

medium blue (“Very Likely” that Clinton would win) to medium red (“Very Likely” that Trump would

win). But the needle did not stay in one place. It jittered between the twenty-fifth and seventy-fifth

percentiles, showing the range of outcomes that the New York Times was then predicting, based on

simulations using the most recent data. At the beginning of the day, the range of motion was f airly

wide but still only showed the needle on the Hillary Clinton side. As the night went on, its range

narrowed, and the center moved closer and closer to the red side of the gauge. By 9 p.m., the needle

jittered just a little, and on the Trump side only.

A number of New York Times readers were aggressive in their dislike of the jitter, calling it

“irresponsible” and “unethical” and “the most stressful thing I’ve ever looked at online and I’ve seen a

lot of stressful shit.”47 In response, Gregor Aisch, one of the designers of the gauge, defended it,

explaining that “we thought (and still think!) this movement actually helped demonstrate the

Figure 3.7: A 2016 chart from the New York Times that uses opacity—darker and lighter shades of blue and red—to indicate

uncertainty. Images by Gregor Aisch, Nate Cohn, Amanda Cox, Josh Katz, Adam Pearce, and Kevin Quealy for the New York Times.

Data Feminism 3. On Rational, Scienti�c, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

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uncertainty around our forecast, conveying the relative precision of our estimates.”48 So was this

“unethical” design, or the sophisticated communication of uncertainty?

Building off of Hullman’s work, we’d say that the answer is the latter. The jittering election gauge was

actually exhibiting current best practices for communicating uncertainty. It gave people the

perceptual, intuitive, visceral, and emotional experience of uncertainty to reinforce the quantitative

depiction of uncertainty. The f act that it unsettled so much of the New York Times readership probably

had less to do with the ethics of the visualization and more to do with the outcome of the election. So

score one for emotion in the task of representing uncertainty.

Don’t Never Do a God Trick

So does this mean that all election graphics should jitter? Or that data visceralizations are categorically

superior to visual graphics? Or that a data visualization framed around emotion is always the “better”

choice for the design task at hand? Or that designers should never use the god trick to present a view

from above?

The answer to these questions may surprise you: definitively no! If there is any single rule in design,

it’s that context is queen. A design choice made in one context or for one audience does not translate to

other contexts or audiences. Simply stated: it is never a good idea to say “never” in design. We delve

deeper into the importance of context when working with data in chapter 6.

Let’s take the god trick as an example. Even though the god trick can do harm—for example, in the

form of those racist, objective-looking redlining maps from chapter 2—there are also good reasons to

use the god trick as a form of recuperation, contestation, or empowerment. As renowned data

Figure 3.8: The controversial “jittering” election gauge featured in the New York Times

coverage of the 2016 presidential election. Images by Gregor Aisch, Nate Cohn, Amanda

Cox, Josh Katz, Adam Pearce, and Kevin Quealy for the New York Times.

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visualization designer Fernanda Viégas says, “The kind of overview that data visualization provides is

one of the superpowers I treasure the most.”49

It is this “superpower”—the aerial view from no body—that we see put into practice in the map

Coming Home to Indigenous Place Names in Canada (figure 3.9a). Margaret Pearce, a cartographer and

member of the Citizen Potawatomi Nation, spent fifteen months collecting Indigenous place names

from First Nations, Métis, and Inuit peoples. The map depicts the land that is known in a

contemporary Anglo-Western context as Canada, but without any of the common colonial orientation

points, like the boundaries of the provinces or the locations of major cities like Ottawa, Montréal, and

Nova Scotia. For example, you can see in figure 3.9b that places like Kinoomaagewaabikaang

(“Teaching rocks”) and Odawa (“Traders”) and Kazabazua gajiibajiwan (“River runs under”) fill the

area usually described as Toronto. As the publisher’s description states, “The names are ancient and

recent, both in and outside of time, and they express and assert the Indigenous presence across the

Canadian landscape in Indigenous languages.”50

Coming Home to Indigenous Place Names in Canada leverages the authority of the god’s-eye view to

challenge the colonizer’s view, to advocate for a “reseeing” of the land under terms of engagement that

recognize Indigenous sovereignty and respect Indigenous homelands. The extent of geographic

territory included and the sheer number of names asserts the Indigenous presence as major, originary,

and ongoing. This is by design. Pearce intentionally created the map with the same paper size, fold,

scale, and projection as the map published by Natural Resources Canada, the country’s geographic

authority.51 By replicating these design features, Coming Home proposes an alternative yet equally

authoritative conception of national identity.52

There is actually another twist on top of its intentionally authoritative view: the map does not reveal

everything.53 As the cartographer, Pearce proceeded with the Indigenous methodologies of respect,

responsibility, and reciprocity.54 What this meant in practice was caring for each name as Indigenous

cultural property—securing the permissions for each name and respecting when communities did not

want to share English translations of the name. She is definitive about the f act that the names are not

data: “The place names aren’t datasets. The place names are cultural property being shared with the

map that come from people.” Protecting that cultural property also meant protecting the exact

geographic location of each site from being shared with outsiders. This is where the scale of the god

trick has protective effects: because it is generalized, at 1:5,000,000, it serves to communicate general

location without pinpointing exact location. In this case, the god trick communicates Indigenous

authority while preserving Indigenous autonomy.

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Data Feminism 3. On Rational, Scienti�c, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

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Although the map has been published and the project is ostensibly finished, Pearce’s commitment to

and care for the Indigenous names continues. Each time the map is reproduced, such as in the detail

image in figure 3.9b, Pearce writes to the communities to whom the names belong, explains the

proposed context of the names, and requests permission for the names to be reproduced in that

context. You can see these permissions as they were granted in the caption for the figure. Pearce

proceeds with sensitivity to her own positionalities, as well as the depth of meaning of each particular

place, its name, and the community. Place names are “relations,” she says. “And they’re not my

relations, it’s not my territory. ... They co-exist as relations that are incorporated into the community.”

Cartography, then, becomes not a straightforward representation of “what is” in some absolute sense.

Rather, Coming Home is a map of relations, conversations, and shared investments across difference in

the landscape.

Figure 3.9: Overview and detail view of Coming Home to Indigenous Place Names in Canada (2017). (a) The map depicts First

Nations, Métis, and Inuit place names collected from many tribes and nations across what is today more commonly called Canada.

(b) The detail view depicts Indigenous names from the area around Toronto. Map by Margaret W. Pearce; map design copyright

2017 Canadian-American Center, University of Maine.

Place names in this detail image shared by permission of the following:

Alan Corbiere

Hiio Delaronde and Jordan Engel, “Haudenosaunee Country in Mohawk,” The Decolonial Atlas,

decolonialatlas.wordpress.com/2015/02/04/haudenosaunee-country-in-mohawk-2/, by permission of the authors.

Charles Lippert and Jordan Engel, “The Great Lakes: An Ojibwe Perspective,” The Decolonial Atlas,

decolonialatlas.wordpress.com/2015/04/14/the-great-lakes-in-ojibwe-v2/, by permission of the authors.

Kitigan Zibi Anishinabeg.

Brian McInnes, Sounding Thunder: The Stories of Francis Pegahmagabow (East Lansing: Michigan State University Press, 2016), by

permission of Brian McInnes, with gratitude to James Dumont and Wasauksing First Nation.

Woodland Cultural Centre, place names from Frances Froman, Alfred Keye, Lottie Keye, and Carrie Dyck, English-Cayuga/Cayuga-

English Dictionary (Toronto, Ontario: University of Toronto Press); and Marianne Mithun and Reginald Henry, Wadewayęstanih. A

Cayuga Teaching Grammar (Brantford, Ontario: Woodland Publishing, The Woodland Cultural Centre, 1984), by permission of Amos

Key Jr. and Carrie Dyck.

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Elevate Emotion and Embodiment

The third principle of data feminism, and the theme of this chapter, is to elevate emotion and

embodiment. As we have shown, these are crucial if often undervalued tools in the data communication

toolbox. They help avoid inadvertently conveying the view from no body: the view from an imaginary

and impossible standpoint that does not and cannot exist.

How has the whole picture, the overview, or the god trick come to be seen as rational and objective at

all? How did the field of data visualization arrive at a set of conventions that prioritize rationality,

devalue emotion, and completely ignore the nonseeing organs in the human body? Who is excluded

when only vision is included?

Any knowledge community inevitably places certain things at the center and casts others out, in the

same way that male bodies are almost always taken as the norm in scientific studies while female

bodies are viewed as deviations, or that abled bodies are almost always taken as primary design cases

while disabled bodies require a design retrofit. Feminist human-computer interaction (HCI) scholar

Shaowen Bardzell asserts designers should look first to those at the margins: the people pushed to the

margins in any particular design context demonstrate who and what the system is trying to exclude.55

Subsequent work in HCI insists that designers then work to “demarginalize the ‘margins’ by

recognizing intersections that exist, and engaging solidarity to navigate towards equity and

inclusion.”56

In the case of data visualization, what is excluded is emotion and affect, embodiment and expression,

embellishment and decoration. These are the aspects of human experience associated with women,

and thus devalued by the logic of our master stereotype. But Periscopic’s gun violence visualization

shows how visual minimalism can coexist with emotion for maximum impact. Works like A Sort of Joy

demonstrate that data communication can be visceral—an experience for the whole body. And Coming

Home to Indigenous Place Names in Canada establishes that the god trick itself can be used to

simultaneously engender emotion and challenge injustice.

Rather than making universal rules and ratios (think: data-ink) that exclude some aspects of human

experience in f avor of others, our time is better spent working toward a more holistic and more

inclusive ideal. All design fields, including visualization and data communication, are fields of

possibility. Black feminist sociologist Patricia Hill Collins describes an ideal knowledge situation as one

in which “neither ethics nor emotions are subordinated to reason.”57 Rebalancing emotion and reason

opens up the data communication toolbox and allows us to focus on what truly matters in a design

process: honoring context, architecting attention, and taking action to defy stereotypes and reimagine

the world.58

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Footnotes

�. Alberto Cairo, “Emotional Data Visualization: Periscopic’s ‘U.S. Gun Deaths’ and the Challenge of

Uncertainty,” Peachpit, April 3, 2013, http://www.peachpit.com/articles/article.aspx?p=2036558. ↩

�. Nicole Amare and Alan Manning, A Unified Theory of Information Design: Visuals, Text and Ethics

(New York: Routledge, 2016). ↩

�. Theodore M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life

(Princeton, NJ: Princeton University Press, 1996). ↩

�. But is such distance possible? Pearson’s own work, like that of so many important figures in

statistical history, was influenced by his own deeply problematic belief s. For more on Pearson and

his support of the eugenics movement, in particular, see chapter 5. Pearson as quoted in Jonathan

Gray, The Data Epic: Visualisation Practices for Narrating Life and Death at a Distance, in Data

Visualization in Society, ed. H. Kennedy and M. Engebretsen (Amsterdam: Amsterdam University

Press, 2019). ↩

�. Adam Crymble, “The Two Data Visualization Skills Historians Lack,” Thoughts on Public & Digital

History (blog), March 13, 2013, http://adamcrymble.blogspot.com/2013/03/the-two-data-

visualization-skills.html. ↩

�. Haraway goes so f ar as to link the god trick of visualization with vision itself. She writes, “The

eyes have been used to signify a perverse capacity—honed to perfection in the history of science tied

to militarism, capitalism, colonialism, and male supremacy—to distance the knowing subject from

everybody and everything in the interests of unfettered power.” The redlining map in chapter 2 is a

perfect example of such unfettered power: the pretense of distance and omniscience in the service

of gender and race oppression. Donna Haraway, “Situated Knowledges: The Science Question in

Feminism and the Privilege of Partial Perspective,” Feminist Studies 14, no. 3 (1988): 575–599. ↩

�. Edward R. Tufte, The Visual Display of Quantitative Information, 2nd ed. (Cheshire, CT: Graphics

Press, 2015). For an example of how his ideas are used by contemporary practitioners, see

“Maximiizing the Data-Ink Ratio in Dashboards and Slide Decks,” plotly, December 11, 2017,

https://medium.com/@plotlygraphs/maximizing-the-data-ink-ratio-in-dashboards-and-slide-deck-

7887f7c1f ab. ↩

�. Mushon Zer-Aviv, “DataViz—The UnEmpathic Art,” October 19, 2015,

https://responsibledata.io/dataviz-the-unempathetic-art/. ↩

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�. Stephanie A. Shields, Speaking from the Heart: Gender and the Social Meaning of Emotion

(Cambridge: Cambridge University Press, 2002). ↩

��. Witney Battle-Baptiste and Britt Rusert, eds., WEB Du Bois’s Data Portraits: Visualizing Black

America (Hudson, NY: Chronicle Books, 2018); Laura Bliss, “The Hidden Histories of Maps Made by

Women: Early North America,” CityLab, March 21, 2016,

https://www.citylab.com/design/2016/03/women-in-cartography-early-north-america/471609/;

and Lauren Klein, Caroline Foster, Adam Hayward, Erica Pramer, and Shivani Negi, “The Shape of

History: Reimagining Elizabeth Palmer Peabody’s Feminist Visualization Work,” Feminist Media

Histories 3, no. 3 (Summer 2017): 149–153. More about all of these visualizations and the contexts in

which they were created can be found on Lauren’s interactive book in progress, Data by Design, at

http://dataxdesign.io/. ↩

��. Aristotle, The Rhetoric and the Poetics (New York: Random House, 1954). ↩

��. Thanks go to M. Richard Zinman for helping us f act-check whether the men wore robes or

tunics, as well as what their head garb consisted of. ↩

��. Jessica Hullman and Nicholas Diakopoulos, “Visualization Rhetoric: Framing Effects in Narrative

Visualization,” IEEE Transactions on Visualization and Computer Graphics 17, no. 12 (December 2011):

2231–2240. ↩

��. See Jonathan Stray, The Curious Journalist’s Guide to Data (New York: Columbia Journalism

School, 2016), https://legacy.gitbook.com/book/towcenter/curious-journalist-s-guide-to-

data/details; and Mike Bostock, Shan Carter, Amanda Cox, and Kevin Quealy, “One Report,

Diverging Perspectives,” New York Times, October 5, 2012,

https://archive.nytimes.com/www.nytimes.com/interactive/2012/10/05/business/economy/one-

report-diverging-perspectives.html. ↩

��. Since the 1950s, there has been a line of research focused on the important framing effects of

titles of news articles on interpretation. More recently, scholars are showing that titles of

visualizations are similarly important anchors for people to make sense of data graphics in popular

media. For example, see Michelle A. Borkin, Zoya Bylinskii, Nam Wook Kim, Constance May

Bainbridge, Chelsea S. Yeh, Daniel Borkin, Hanspeter Pfister, and Aude Oliva, “Beyond

Memorability: Visualization Recognition and Recall,” IEEE Transactions on Visualization and

Computer Graphics 22, no. 1 (2016): 519–528. ↩

��. See Stray, “The Curious Journalist’s Guide to Data.” ↩

��. See Hullman and Diakopoulos, “Visualization Rhetoric.” ↩

Data Feminism 3. On Rational, Scienti�c, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

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��. Helen Kennedy, Rosemary Lucy Hill, Giorgia Aiello, and William Allen, “The Work That

Visualisation Conventions Do,” Information, Communication & Society 19, no. 6 (March 16, 2016): 715–

735. ↩

��. Haraway, “Situated Knowledges.” ↩

��. Sandra Harding, “‘Strong Objectivity’: A Response to the New Objectivity Question,” Synthese

104, no. 3 (September 1995): 331–349. ↩

��. Linda Alcoff, “Cultural Feminism versus Post-Structuralism: The Identity Crisis in Feminist

Theory,” Signs: Journal of Women in Culture and Society 13, no. 3 (1988): 405–436. ↩

��. Nieca Goldberg, Women Are Not Small Men: Life-Saving Strategies for Preventing and Healing

Heart Disease in Women (New York: Ballantine Books, 2002). ↩

��. Most studies also continue to treat sex and gender as binary classifications, which they are not.

(We address that in the next chapter.) And for more on what Carolina Criado-Perez calls the gender

data gap, check out her book Invisible Women: Data Bias in a World Designed for Men (New York:

Abrams, 2019). ↩

��. Resisting binary thinking is a multipurpose tool in the feminist toolbox. We discuss the f alse

gender binary in chapter 4. Feminist thinkers have demonstrated how other binaries also need a

complete rethinking—like reason/emotion, nature/culture, subject/object, body/world,

speaker/receiver, universal/particular, f acts/values, and traditional/modern, among others. In

short, beware binaries! They are probably hiding a hierarchy behind them. ↩

��. Evelyn Fox Keller and Barbara McClintock, A Feeling for the Organism: The Life and Work of

Barbara McClintock, 10th anniversary ed. (New York: Freeman, 1984). ↩

��. Luke Stark, “Come on Feel the Data (and Smell It),” Atlantic, May 19, 2014,

https://www.theatlantic.com/technology/archive/2014/05/data-visceralization/370899/. ↩

��. If you’re interested in appetites, Lauren’s historical research deals with the cultural significance

of appetite and eating in the early United States. See Lauren F. Klein, An Archive of Taste: Race and

Eating in the Early United States (Minneapolis: University of Minnesota Press, 2020). ↩

��. “Vision Impairment and Blindness,” World Health Organization, October 11, 2018,

http://www.who.int/news-room/f act-sheets/detail/blindness-and-visual-impairment. ↩

��. Aimi Hamraie, “A Smart City Is an Accessible City,” Atlantic, November 6, 2018,

https://www.theatlantic.com/technology/archive/2018/11/city-apps-help-and-hinder-

Data Feminism 3. On Rational, Scienti�c, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

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disability/574963/. ↩

��. You can see the full performance of A Sort of Joy (Thousands of Exhausted Things) at

https://vimeo.com/133815147. ↩

��. This critique is at least as old as Linda Nochlin’s canonical 1971 essay: “Why Have There Been No

Great Women Artists?,” in Woman in Sexist Society: Studies in Power and Powerlessness, ed. Vivian

Gornick and Barbara Moran (New York: Basic Books, 1971), 344–366. ↩

��. By Whitney Chadwick in Guerrilla Girls, Confessions of the Guerilla Girls (New York:

HarperCollins, 1995). ↩

��. Feminist ruckuses with the Metropolitan Transit Authority continue today. In 2015, the

menstruation start-up company THINX was also told their ads—featuring women with eggs and

grapefruit—were too suggestive and that the word period wouldn’t be allowed. After the public cried

foul (MTA trains were already cluttered with cleavage due to easily approved advertisements for

plastic surgery), the ads did run. In another subway-related incident, the woman-led sex toy

company Dame sued the MTA for censorship in 2019 when it refused to run Dame’s ads. See Rachel

Krantz, “THINX Underwear Ads on NYC Subway Are Up—but the Company Has Another Big

Announcement,” Bustle, November 9, 2015; and Leila Ettachfini, “MTA Quietly Bans Sex Toys from

Advertising on NYC Subway,” Vice, January 10, 2019. ↩

��. Wattenberg states: “A moment of insight, in which people see f acts and patterns for themselves,

can be rhetorically powerful” (2). See Robert Kosara, Sarah Cohen, Jérôme Cukier, and Martin

Wattenberg, “Panel: Changing the World with Visualization,” in IEEE Visualization Conference

Compendium (Piscataway, NJ: IEEE, 2009). ↩

��. In “The Eyes Have It,” Ben Shneiderman writes about the design of graphic user interf aces to

support data exploration for the purposes of analysis. There is clearly a different context and set of

goals than an artistic performance, yet folks in the information visualization community have also

showed how “overview first” doesn’t necessarily apply for all analytic tasks in user interf ace design

either. Still, it’s useful to think about when “the whole picture” doesn’t (and can’t) provide the whole

emotional picture and determine what strategies one might pursue to do so. See Shneiderman, “The

Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” The Craft of

Information Visualization, September 1996, 364–371; and Timothy Luciani, Andrew Burks, Cassiano

Sugiyama, Jonathan Komperda, and G. Elisabeta Marai, “Details-First, Show Context, Overview Last:

Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations,” IEEE Transactions

on Visualization and Computer Graphics 25, no. 1 (August 20, 2018): 1225–1235. ↩

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��. D’Ignazio and Sutton were struck by the similarity of Boston coastline maps from the past

(seventeenth and eighteenth centuries) with the future predictions based on climate change (the

year 2100 estimated with a seven-foot storm surge). The neighborhoods that Bostonians created in

the 1800s by trucking in gravel and dirt are the most vulnerable ones to rising sea levels in the

future. In Boston Coastline: Future Past, the artists led a walking tour of the past/future coastline that

was punctuated by microlectures from community members working on climate adaptation.

Participants wore messages as they walked and then stenciled them into a timeline on the Boston

Common to close the walk. Catherine D’Ignazio and Andi Sutton, “Boston Coastline: Future Past,”

kanarinka.com, 2018, accessed March 13, 2019, http://www.kanarinka.com/project/boston-

coastline-future-past/. ↩

��. Mikhail Mansion’s project is called “Two Rivers.” One chair sits on a platform in the Providence

River and logs data about the currents and shifts in the water. Visitors to a gallery are invited to sit

on a second chair and feel, in real-time, the motion of the Providence River at a distance. Mikhail

Mansion, “Two Rivers (2011),” Vimeo, October 1, 2011, https://vimeo.com/29885745. ↩

��. The Data Zetu project (“our data” in Swahili), in collaboration with a number of partners in

Tanzania, ran the Data Khanga Design Challenge in which participants designed khangas—f abrics

that traditionally carry social messages in East Africa. Designers worked to incorporate statistics

about gender equality and health into their khangas. Models wore the winning designs in a f ashion

show to which all participants were invited. Maana Katuli, “Young Artists Use Fashion and Data to

Promote Dialog on Sexual Health,” Medium, March 28, 2018, https://medium.com/data-zetu/young-

artists-use-f ashion-and-data-to-promote-dialog-on-sexual-health-517429662ec2. ↩

��. Core Sample is a GPS-based sound walk by Teri Rueb from 2007. Teri Reub, “Core Sample—

2007,” Teri Rueb (blog), 2007, accessed March 13, 2019, http://terirueb.net/core-sample-2007/. ↩

��. FM Radio Map from 2006 is a paper map that plots the location of commercial and pirate radio

stations in London. Viewers can use a modified radio to listen to each radio station by placing metal

contacts on the station locations. The back of the map uses graphite to conduct electricity from the

metal contacts to a small radio that tunes in to the station selected. Jo-Anne Green, “Simon Elvins’

Silent London,” Networked_Music_Review (blog), July 11, 2006,

http://archive.turbulence.org/networked_music_review/2006/07/11/simon-elvins-silent-london/. ↩

��. A Piece of the Pie by Annina Rüst leverages pie metaphors to make pie charts and literal, edible

pies. Her robot also tweets its data about gender representation in technical fields. Annina Rüst, “A

Piece of the Pie Chart”, 2013, accessed March 13, 2019, http://www.anninaruest.com/pie/. ↩

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��. A 2010 study by Scott Bateman and colleagues in computer science at the University of

Saskatchewan found that “embellished” charts—such as bar charts in the form of monsters—do not

hinder people’s ability to accurately read them—and in f act, they are actually easier to remember.

When polled two to three weeks later, people were much more likely to recall the message of an

embellished chart over a minimalist chart that displayed the same data. People also thought the

“junk charts,” decorated with monsters, 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. Relating

the visual form to the topical content of a chart can really work. So, as data journalist Mona Chalabi

says, “If it’s about f arts, draw a butt for god’s sakes.” See Scott Bateman, Regan L. Mandryk, Carl

Gutwin, Aaron Genest, David McDine, and Christopher Brooks, “Useful Junk?: The Effects of Visual

Embellishment on Comprehension and Memorability of Charts,” in Proceedings of the SIGCHI

Conference on Human Factors in Computing Systems (New York: ACM, 2010), 2573–2582; Michelle A.

Borkin, Zoya Bylinskii, Nam Wook Kim, Constance May Bainbridge, Chelsea S. Yeh, Daniel Borkin,

Hanspeter Pfister, and Aude Oliva, “Beyond Memorability: Visualization Recognition and Recall,”

IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2015): 519–528; and Bryony Stone,

“‘If It’s about Farts, Draw a Butt for God’s Sakes’: Mona Chalabi Tells Us How to Illustrate Data,” It’s

Nice That (blog), March 8, 2018, https://www.itsnicethat.com/articles/mona-chalabi-illustration-

internationalwomensday-080318. ↩

��. You can read more about these chart forms and their purposes and functions at the Data

Visualisation Catalogue, https://datavizcatalogue.com/. ↩

��. According to work by Sarah Belia and colleagues, researchers themselves have a hard time

understanding confidence intervals. See Sarah Belia, Fiona Fidler, Jennifer Williams, and Geoff

Cumming, “Researchers Misunderstand Confidence Intervals and Standard Error Bars,”

Psychological Methods 10, no. 4 (2005): 389–396. ↩

��. Take, for example, the weather report. Forecasts such as “There’s a 30 percent chance of rain

tomorrow” are generally interpreted by the public to mean “It will rain 30 percent of the time” or “It

will rain in 30 percent of my area,” and not as a 30 percent probability of it raining. The standard

meteorological measure is probability of precipitation (PoP), which takes both time and geography

into account. PoP is calculated by multiplying a confidence measure (that rain will occur somewhere

in a geographic area in a given time period) by an area measure (the percentage of the geographic

area that will receive any rain in a given time period). In an installment of her series Just the Facts,

data journalist Mona Chalabi detailed how the weather industry has an acknowledged “wet bias,”

meaning forecasters consistently overpredict rain so as not to make people angry that they didn’t

bring umbrellas. Mona Chalabi, “Is the National Weather Service Lying to You?,” Guardian, March 17,

Data Feminism 3. On Rational, Scienti�c, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

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2017, https://www.theguardian.com/us-news/2017/mar/17/national-weather-service-forecasting-

temperatures-storms. ↩

��. Hullman and colleagues did a study on hypothetical outcome plots, which animate different

simulated outcomes for a single quantity. By viewers seeing where the outcomes tended to land

animated over time, they were able to infer more about the probability of variation than in standard

violin plots and error bars. Jessica Hullman, Paul Resnick, and Eytan Adar, “Hypothetical Outcome

Plots Outperform Error Bars and Violin Plots for Inferences about Reliability of Variable Ordering,”

PLOS ONE 10, no. 11 (2015): e0142444. ↩

��. Richard Porczak (@tsiro), “Straight up: the NYT needle jitter is irresponsible design at best and

unethical design at worst and you should stop looking at it,” Twitter, November 8, 2016, 9:58 p.m.,

https://twitter.com/tsiro/status/796185282718511104. J. K. Trotter, “The New York Times Live

Presidential Election Meter Is Fucking with Me,” Gizmodo, November 8, 2016,

https://gizmodo.com/the-new-york-times-live-presidential-meter-is-fucking-w-1788732314. ↩

��. Gregor Aisch, “Why We Used Jittery Gauges in Our Live Election Forecast,” Vis4.net, November

14, 2018, https://www.vis4.net/blog/2016/11/jittery-gauges-election-forecast/. ↩

��. Email to Catherine D’Ignazio and Lauren Klein, January 7, 2019. ↩

��. Margaret Wickens Pearce, “‘Coming Home’ Map,” Canadian-American Center, 2018, accessed

March 13, 2019, https://umaine.edu/canam/publications/coming-home-map/. ↩

��. Margaret Pearce, interview by Catherine D’Ignazio, March 15, 2019. ↩

��. As Pearce and Hornsby write, “To be ‘coming home’ is itself a kind of reconciliation, a moving

away from settler time and moving toward Indigenous time.” M. Pearce and S. Hornsby, “The

Making of Coming Home,” Canadian Geographer 64, no. 1 (2020). ↩

��. No god trick reveals everything because that would be impossible at any scale. The trick part is

that it gives the viewer the impression that everything is revealed. ↩

��. Pearce and Hornsby, “The Making of Coming Home.” ↩

��. Elizabeth Grosz, in “Architectures of Excess,” explains: “Communities, which make language,

culture, and thus architecture their modes of existence and expression, come into being not through

the recognition, generation, or establishment of universal, neutral laws and conventions that bind

and enforce them, but through the remainders they cast out, the figures they reject, the terms that

they consider unassimilable, that they attempt to sacrifice, revile and expel” (152). Grosz,

“Architectures of Excess,” in Architecture from the Outside: Essays on Virtual and Real Space

Data Feminism 3. On Rational, Scienti�c, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

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(Cambridge, MA: MIT Press, 2006), 151–166. Also see Shaowen Bardzell, “Feminist HCI: Taking Stock

and Outlining an Agenda for Design,” in Proceedings of the SIGCHI Conference on Human Factors in

Computing Systems (New York: ACM, 2010), 1301–1310. ↩

��. Michaelanne Dye, Neha Kumar, Ari Schlesinger, Marisol Wong-Villacres, Morgan G. Ames,

Rajesh Veeraraghavan, Jacki Oneill, Joyojeet Pal, and Mary L. Gray, “Solidarity across Borders:

Navigating Intersections towards Equity and Inclusion,” in Companion of the 2018 ACM Conference on

Computer Supported Cooperative Work and Social Computing—CSCW 18 (New York: ACM, 2018), 487–

494. ↩

��. Collins, Black Feminist Thought. ↩

��. As more designers and illustrators enter the field, a new generation of data visualizers is

challenging the antiemotion and antiembellishment dogma. These include Jessica Bellamy, Giorgia

Lupi, Stef anie Posavec, Federica Fragapane, and Kelli Anderson, among many others. On a practical

level, engineering productive collisions between data science people (sophisticated in analytic

methods and abstraction) and artists, designers, media folks, and humanists (sophisticated in

rhetoric, form, and embodiment) might be the surest way to overcome the f alse binary of reason

versus emotion. ↩