22 M7U: Subjectivity in Data Visualization

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22.1 Introduction

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, signifying a death from gun violence, and the age of the victim appears on the screen. 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…”, concluded with the life expectancy of that person. 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 the user has picked. I’m writing this just after watching the visualization for 2018: 11,356 people killed, totaling 472,332 stolen years.

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 this one from Christopher Ingraham at the Washington Post? 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.

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?” (Cairo, 2013). At root for Cairo was the question of how detached and “neutral” a visualization should be. He wondered whether a visualization should 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 (2016) 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 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 (1996) puts it, “Quantification is a technology of distance.” Distance, he further 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 (Gray, 2019). 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.

22.2 Persuasion and the God Trick

But subjective persuasion is everywhere, even in spreadsheets. In fact, feminist philosopher Donna Haraway (1988) would likely argue that it is especially present 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. 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. In most cases of seemingly “neutral” visualizations, this perspective is the one of the dominant, default group.

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 (2015). 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. 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 facts, 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 (2015) because of its emphatic rejection of emotion.

The logic that sets up this false 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 (see Shields, 2002). A central focus of feminist scholarship has been to challenge false binaries like this one between reason and emotion and to point out how they establish hierarchies as well. We’ll discuss hierarchies and binaries elsewhere, but the important thing to note for now is how false 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 another feminist principle of data science: embrace emotion and embodiment.

22.3 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. However, 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 (2018) 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 (2016), 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 Klein, one of original authors of the material in this reading, created a website that reanimates the historical charts of Elizabeth Palmer Peabody, the nineteenth-century editor and educator, who used visualization in her teaching (Klein et al., 2017).

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 faculty of observing in any given case the available means of persuasion” (Aristotle, 1954). But rhetoric isn’t only found in political speeches made by men dressed in tunics with wreaths on their heads. 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). As some of you will remember (or may be interested in), we even discuss the rhetoric of video games (see Bogost, 2008) in my LIS 618 class—if a video game can be rhetorical, then it shouldn’t surprise us that data visualization can as well.

The question of rhetoric matters because “a rhetorical dimension is present in every design,” (Hullman & Diakopoulos, 2011). 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 fact, those might even be the most perniciously persuasive visualizations of all!

22.4 Editorial Choices in Visualization

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 (2016) discusses a data story from the New York Times about the September 2012 jobs report. The New York Times created two graphics from the report: one framed from the perspective of Democrats (the party in power at the time) and one framed from the perspective of Republicans. It’s worth taking a pause here to check out the data story before moving on: Seeing the visualizations will be more helpful than simply reading the words that follow.

Either of these graphics, considered in isolation, appears to be neutral and factual. 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 “Democrat” 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.” In contrast, the “Republican” graphic highlights the fact 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. 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 (see Borkin et al., 2016).

So neither graphic is neutral, but both graphics are factual. As Jonathan Stray (2016) says, “The constraints of truth leave a very wide space for interpretation.” When visualizing data, the only certifiable fact 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).

22.5 Visualization as Ideological Work

Fields very close to visualization, like cartography, have long seen their work as ideological. But discussions of rhetoric, editorial choices, and power have been far 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 (Hullman & Diakopoulos, 2011). 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 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 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 (2016) 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. 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 faithfully 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 facts reign and everyone gets a gold medal for having an opinion. But there are other options.

22.6 Feminist Objectivity

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 (1988) 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. 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 (1995), 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. This has come to be known as standpoint theory. To supplement these ideas, Linda Alcoff (1988) 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. All of these ideas offer alternatives to the quest for a universal objectivity—which is, of course, an unattainable goal.

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 (2002) 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 men, with women viewed as varying from this “norm” only by their smaller size (see also Criado-Perez, 2019). 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. In an earlier reading, I described a research project I completed with a co-author examining 1,400 tweets associated with a Twitter hashtag claiming to be religious in nature but that our findings show to also be influenced by White nationalism. You may remember that I used this as an example of a research assumption that “different people might come to different conclusions.” Because I’m deeply familiar with the religion in question, and my co-author is not, we see the data differently. Because my co-author has been the target of misogynistic abuse and I have not, we understand certain tweets differently. And yet, even the combination of our two perspectives do not add up to perfect objectivity: For example, both of us are straight, so despite our criticism of homophobic abuse in these tweets, it’s unlikely that we caught everything that a queer researcher would have.

With all of this in mind, it was important in our published research to be clear who we were and what perspectives we brought to the table. 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, while still recognizing that other people with other perspectives may have taken a different approach. This is standard practice in some circles of qualitative researchers, but it would be helpful for data scientists to embrace this approach 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.

22.7 Data Visceralization

This embrace of multiple perspectives and positionalities helps to rebalance the hierarchy of reason over emotion in data visualization. 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. 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, famously 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 (see Keller & McClintock, 1984).

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 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 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 facts 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. 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 (2014).

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. 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 (2018), 253 million people globally live with some form of visual impairment, on the spectrum from limited vision to complete blindness. For reasons of accessibility, Aimi Hamraie (2018), 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 surfacing information in multiple sensory formats.”

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). 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.

Guerrilla Girls - V&A Museum, London by Eric Huybrechts is licensed CC BY-SA-ND 2.0

As you might guess, this history has mostly consisted of a parade of White male European “masters.” 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” (Guerilla Girls, 1995). The data seem pretty clear, though: 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.

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 face 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, 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 (see Kosara et al., 2009). 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 (1996): “Overview first, zoom and filter, then details-on-demand.” 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 D’Ignazio and artist Andi Sutton led walking tours of the future coastline of Boston based on sea level rise (D’Ignazio & Sutton, 2018). Interactive artist Mikhail Mansion (2011) made a leaning, bobbing chair that animatronically shifts based on real-time shifts in river currents. Nonprofit organizations in Tanzania staged a design competition for data-driven clothing that incorporated statistics about gender inequality and closed the project with a fashion runway show (Katuli, 2018). Artist Teri Rueb (2007) stages “sound encounters” between the geologic layers of a landscape and the human body that is affected by them. Simon Elvins (see Green, 2006) drew a giant paper map of pirate radio stations in London that you can actually listen to. A robot designed by Annina Rüst (2013) decorates real pies with pie charts about gender equality, and then visitors eat them.

22.8 Conclusion

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 preferences.

22.9 References

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