DHQ: Digital Humanities Quarterly
Volume 12 Number 4
2018 12.4  |  XMLPDFPrint

Critically engaging with data visualization through an information literacy framework

Steven Braun <s_dot_braun_at_northeastern_dot_edu>, Northeastern University Library, Digital Scholarship Group


The proliferation of tools that enable anyone to create visualizations of their data, even with limited experience or skills, has made data visualization more accessible than ever before. This is true in its use in both teaching and learning, as data visualization has increasingly taken on an important pedagogical role in the classroom and in scholarly research. However, with this proliferation of tools there has been a concomitant awareness that visualization needs to be employed through a critical lens that acknowledges its constructedness as explanatory medium and as a product of situated knowledges. Here, I describe one approach to teaching this notion of constructedness via a framework oriented around information literacy, which encourages critical engagement with data, the tools we use to interrogate them, and the visualizations we design to represent them. I describe this approach through a collection of “critical dichotomies” used to evaluate the authority and value of visualizations, which are mapped to a subset of the core information literacy competencies defined in the ACRL Framework for Information Literacy in Higher Education. To put these dichotomies into practice, I further describe an interactive activity called “Choose Your Own Adventure, with Data Visualization,” in which participants are given paper and markers to create booklets in the style of Choose Your Own Adventure books and asked to consider the relationship between active choices in the design process of a visualization and how a given visualization is interpreted. In the process, I explore how this framework can encourage us all, as critical practitioners of visualization, to think about the practical relationship between data visualization and information literacy more generally.

From situated data to constructed visualization

In “Humanities Approaches to Graphical Display” (2011), Johanna Drucker urged the adoption of new language to encourage scholars to think about data, their visual representation, and the nature of our interpretation of them in a discourse more inclusive of humanistic and qualitative modes of inquiry: capta. “Data are capta,” Drucker writes, continuing that data are “taken not given, constructed as an interpretation of the phenomenal world, not inherent in it.” Whereas capta are collected and parametrized in ways that depend directly upon the modes through which we observe them – an act in which we ourselves are implicated as collectors, reporters, and creators – data are often conceived as existing a priori, waiting around somewhere to be observed. Thus, whereas data presumes observer independence and absolutism, capta acknowledges the situatedness and observer codependence of the interpretive act. Reinterpreted through another frame, these sentiments are echoed by scholars such as Catherine D’Ignazio and Hill et al. who write on feminist perspectives on data, noting that feminist frameworks about the situatedness of knowledge can be helpful for thinking about how we engage with data and their representations as objective representations of reality [D’Ignazio 2015] [Hill et al. 2016]. Zooming out further, such interrogations of the social and technological pedigrees of objectivity and neutrality have been increasingly found in other disciplines as well, such as science and technology studies [Daston and Galison 2007], critical cartography [Crampton and Krygier 2006], and critical race theory [Gillborn et al. 2018], to name a few.
Examined through these lenses, it appears there is no shortage of humanistic inquiry into the privileged authority granted to objective expressions of the phenomenal world that are pervasive in quantitative research, reinvigorated by the data-dense environment of the current age. Moving from data to their visual representation, then, requires few leaps to see the consequences that a framework oriented around situated knowledge has on how we engage with the media we use to interpret them. To think in these terms is to conceive of data communicated in visually or graphically motivated form – a data visualization – as constructed space, one in which the value of a visualization is inflected by the cultural and political forces embedded within the design choices of its creator and the interpretive act of the user. While we may perceive visualizations as objective, complete, and authoritative [Kennedy et al. 2016], the reality is that they too are constructed, just as the capta that underlie them are themselves. And yet, the visualization still often stands for, in one-to-one identity, that which it actually represents, as a statement of fact. As Drucker more recently notes (2017), we continue to prioritize approaches to visualization that assume this one-to-one correspondence:

In a representational paradigm, the relation between data and display is uni-directional, the data precede the display, and the data are presumed to have some reliable representational relation to the phenomena from which they have been abstracted. The display functions as a surrogate for the data — which is itself a surrogate, adequate or inadequate, for some phenomena. Simply put, the display stands for the data, is a re-presentation of the data. But visualizations are generally taken to be a presentation, a statement (of fact, or argument, or process), rather than a representation (surrogate) produced by a complex process…Instead, we should consider that visualizations are usually representations (constructions) passing themselves off as presentations (statements of self-evident fact).  [Drucker 2017]

Understood in this way, objectivity in visual representation is thus inherently compromised by the intervention of human hands; when the visualization itself stands in as surrogate for that which is beneath it, what results is the illusion that mediated knowledge is actually absolute if interrogated and framed in the rightly-phrased way.
These perspectives are not particularly new, but what is momentous is their interrogation in those domains that are grappling with the role of the computational and quantitative in the scholarly process – including digital humanities. Across the humanities disciplines that have integrated computational modes of analysis, data visualization (or information visualization) is an increasingly dominant force, and as such it carries with it the requisite challenges that accompany any new medium or discourse of analysis employed in humanistic scholarship and teaching. Given visual representation conceived as constructed space, how do we validate the authority and utility of visualization as both process and artifact of research, especially with the proliferation of tools that make creating data visualizations easy with minimal user intervention? From a critical theory perspective, this is an important question. To explore this question is to formulate a more holistic understanding of visualization and its consequences in both theory and practice, one that accounts for not only visualization in the methodological domain but also the social, ethical, political, and epistemological ones [Kennedy et al. 2016]. In short, this means literacies in many forms that extend beyond the written word – numerical, graphical/visual, information – that enable the individual to critically engage with data, the tools that organize and engage with them, and the visual representations we craft of them to facilitate interpretation.
Becoming critical practitioners of data and visualization in this way can happen via many modalities and in many spaces. Perhaps surprisingly, I argue that libraries are among those spaces that are naturally positioned to encourage this kind of engagement, given their interest in information literacy, and libraries can offer useful entry points for considering how such literacies around data and visualization might be integrated into the curriculum. A clear example of this is offered by the ACRL Framework for Information Literacy in Higher Education, which outlines six core frames or competencies in information literacy around which libraries are encouraged to provide support [ACRL Board 2016]. These frames emphasize the theoretical underpinnings necessary for handling information with a critical eye, defining information literacy “as the set of integrated abilities encompassing the reflective discovery of information, the understanding of how information is produced and valued, and the use of information in creating new knowledge and participating ethically in communities of learning.” Data, as one particular expression of information, falls within the purview of information literacy [Koltay 2017] – and by extension, I argue, as do those representations based on data with which we commonly engage, including visualization.
In the Northeastern University Library, the competencies described in the ACRL Framework have become an integral component in how concepts and principles in information design are taught in workshops, lectures, and consultations. Given the university’s strategic orientation towards data- and computation-motivated modes of scholarly inquiry, exploring what it means from the perspective of librarianship to critically engage with data visualization in increasingly interdisciplinary spaces has been a useful exercise for collaborations with digital humanities researchers on campus. In this paper, I describe how these elements of the Framework manifest in my approach to teaching information literacy through data visualization. I then describe one interactive activity I have employed to teach these concepts that comes in the form of a Choose Your Own Adventure book, using data visualization instead of prose as the medium of narrative. In the process, I discuss the role that data visualization can more generally play in teaching core competencies in information literacy.

The ACRL Framework in information visualization

Although the ACRL Framework focuses on competencies related to critical engagement with information sources more generally, it also provides a useful starting point for discussions on what it means to employ such competencies specifically in the context of data visualization. The Framework as a whole consists of six different frames, each of which focuses on a different facet of the information creation and consumption process:
  1. Authority is constructed and contextual
  2. Information creation as a process
  3. Information has value
  4. Research as inquiry
  5. Scholarship as conversation
  6. Searching as strategic exploration
When examined from the capta and visualization as constructed space perspectives described above, two of these prescribed frames emerge as being particularly significant. In the first of these, Authority is constructed and contextual, the Framework notes that the authority of information is a product of many intersecting influences and may be fluid depending upon the context in which it is created and used. Accordingly, mastery of this frame indicates an understanding of “the need to determine the validity of the information created by different authorities and to acknowledge biases that privilege some sources of authority over others, especially in terms of others’ worldviews, gender, sexual orientation, and cultural orientations.” Meanwhile, the second frame, Information creation as a process, emphasizes the idea that the quality, meaning, and value of information is a product of the processes of scholarship in which it is created. “Recognizing the nature of information creation,” this frame asserts, “experts look to the underlying processes of creation as well as the final product to critically evaluate the usefulness of the information.” In this way, the Framework offers a useful backdrop against which critical thinking about information visualization can be taught.
In the Northeastern University Library, where support services are available to researchers in the campus community around information visualization and design, these basic concepts are taught and discussed through “critical dichotomies” that can help guide our thinking about specific design choices we make in the creative process of data visualization (Figure 1). These dichotomies are conceptualized as modes of wayfinding for evaluating the design choices we make in the process of creating a visualization, emphasizing the idea that design is an embodied process, not merely an endpoint. By examining these dichotomies, I argue, we can become better-equipped to critically dissect the meaning, value, and authority of a particular visualization, following the guidelines of the frames described above, given the contexts in which it is created and interpreted. These dichotomies provide a theoretical foundation upon which practical design considerations may be evaluated and through which a data as capta perspective may be discussed.
Figure 1. 
Critical dichotomies in information visualization
The first of these dichotomies is proxy and artifact, which encourages design choices that principally reflect real observed changes in data as opposed to those that suggest the appearance of patterns that are actually artifacts of human perception. Research in cognitive science and psychology has shown that human visual perception is susceptible to biases that can distort what we see. Optical illusions, for example, achieve this effect of distortion by taking advantage of the limitations and inaccuracies of human vision to create mismatches between what the brain believes it is seeing and what it is actually seeing. As a result, human eyes can be tricked into seeing shapes and colors that are not physically present by merely combining elements of geometry, color, and space to artificially alter the perspective that the brain constructs in the process of perception [Meirelles 2013]. In the process of designing a visualization, these effects can arise as well in ways we may not notice. A good example is the use of perceptually non-uniform color palettes in visualizations, especially with respect to the widely-used rainbow color scheme, which can produce impressions of artificial boundaries between hues and consequently artificial boundaries in data [Borland 2007]. In the language of capta, the proxy/artifact dichotomy emphasizes the constructedness of interpreted meaning in a visualization, especially when that meaning is facilitated by design choices that prioritize approaches to visualization that presume a visualization stands in as declarative presentation of the data that precede it. In this sense, this dichotomy also maps to the frame Authority is constructed and contextual, emphasizing the role that design can specifically play in crafting the relative perceived authority of any given visualization.
The second critical dichotomy is parsimony and diminishing returns. Modeled after Edward Tufte’s data-ink ratio concept, which argues that the amount of ink in a visualization should change proportionately with changes in the data that such ink represents [Tufte 2001], this dichotomy aims to remind us that not all design choices are made equal in their contribution to a visualization. While the addition of data, ink, and complexity to a visualization may be accompanied by a proportional increase in meaning, value, and utility up to a certain point, there are additions to a design of the type that yield no appreciable return in the efficacy of what it is trying to communicate. Thus, parsimony and diminishing returns encourages an economical understanding of data visualization, arguing that good design is a careful balance between choices that maximize the message being communicated and the aesthetics of the medium through which that communication occurs. Such a framing encourages a tighter hermeneutical association between data and their visualization by minimizing the space for purely positivist and representational channels for crafting meaning in data. This dichotomy also points attention to the notion of information creation as process, whereby the meaning and value of a visualization is tightly coupled to the procedure by which it is created.
This dichotomy is followed by reductionism and holism, which describes the wide range of scales and resolutions across which visualizations operate in practical usage. In the language of Franco Moretti, this is also understood as the relationship between distant and close reading of a text; through visualization, we may switch between a close reading of a set of data that emphasizes a high degree of detail in the data or a more distant reading that emphasizes the bigger picture of patterns in the data [Moretti 2013]. When we design a visualization, we must maintain awareness of these different levels of understanding as the resolution at which we communicate our message informs the ways in which that message may be interpreted – put another way, the ways in which our data become crafted, composed, and graphically motivated.
Collectively, these first three dichotomies map to the Framework frame of Information creation as a process, highlighting the reality that the design of a visualization requires an ongoing reassessment of objectives and assumptions in the choices we make. The final critical dichotomy, which is authority and bias, maps primarily to the frame of Authority is constructed and contextual. In this dichotomy, we are reminded that there is no such thing as the singular visualization, the best possible visual representation of a set of data. Instead, any single visualization grabs only a differential slice of a larger narrative, and it is incumbent upon us as practitioners of information design to remember that for any one visual representation, there are many other possibilities that have not been expressed. In this way, signatures of authority and bias operate in tension with one another because a visualization is at once a statement of information authority and the product of a design process that is intrinsically biased by the motivations of the designer and interpreter.
The concepts behind these dichotomies are taught in the Northeastern University Library within a conceptual framework that discusses ways of building and shifting visual narrative, as shown in Figure 2. This framework is built upon a set of design elements (i.e., preattentive graphical elements and visual encodings used in visualization design, like color, symbol, size, and length [Ware 2008]) from which transformations, or manipulations of design elements, may be used to engender narrative shifts, or ways the meaning or interpretation of a visualization can be shifted. These transformations and shifts include typical manipulations commonly seen in visualizations, such as skewing or truncating axes to exaggerate or minimize trends, as well as less noticeable manipulations, like the use of perceptually non-uniform color palettes to exaggerate effects in data based on proportional design area (such as when coloring geographic or cadastral maps). By examining how these manipulations occur organically in examples of real visualizations seen in the academic literature and popular media, students become better-equipped to recognize manipulations in the visualizations they produce and encounter in their daily lives.
Figure 2. 
Building and shifting visual narrative
The use of real life examples of visualizations is a good starting point to help students understand these concepts, but I have employed other more creative strategies for enabling students to grapple with and apply them in direct, practical, and engaging ways. What does it mean to practically apply these concepts in the process of visualizing data, and how do the impressions they leave behind in the design process inform interpretation of the final visualization created? Put another way, what does it mean to create a visualization that is self-aware, one in which the situatedness of its data (or capta) is faithfully communicated? In one such strategy, I devised an activity titled “Choose Your Own Adventure, with Data Visualization.” In this activity, participants are asked to design their own flipbook in the style of Choose Your Own Adventure books[1], which are short novels in which the reader is invited to follow multiple different possible storylines based on prompts that follow branching page sequences. Using this format, it becomes possible to engage participants in discussion about how small changes in design can result in significant changes in the meaning communicated by a visualization, especially when framed around the critical dichotomies described above and their correlating interpretive shifts.

Choose Your Own Adventure, with Data Visualization

In this activity, each participant is invited to create their own Choose Your Own Adventure book using paper and markers. The page templates, described in Table 1, are printed and cut out, and each participant is asked to take the required number of pages to create their book. Markers are handed out that participants may use for this process.
Type Quantity
Cover page 1
Introduction: “Lo, brave traveler!” 1
Data declaration page 1
Data selection page 1
Stop page: “How will you continue?” As many as needed
Outcome page As many as needed
Table 1. 
Page components required for the “Choose Your Own Adventure, with Data Visualization” activity
To begin, each participant is asked to select 2 different sets of data from a bank of sample data provided. These data sets, examples of which are shown in Table 2, are taken from Tyler Vigen’s website Spurious Correlations[2], a collection of visualizations that demonstrate humorous examples of variables that appear to be correlated statistically but have no real-life causal connection between them. After this, participants are asked to choose six to eight outcomes from the narrative shift options provided, as well as two to three different kinds of visualizations (for example, bar charts and line charts) to use in constructing those narrative shifts. For each narrative shift outcome, the participant draws out the different transformation step sequences required to reach it, using the design transformation techniques presented. Finally, once the sequential visualizations for each narrative shift outcome are all created, the participants number the pages, fill in any page references within the book structure, and assemble the book’s pages with a stapler.
Year 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Per capita consumption of whole milk in gallons
-- 7.7 7.4 7.3 7.2 7 6.6 6.5 6.1 5.9 5.7
Divorce rate in Washington per 1000 people
-- 4.6 4.5 4.6 4.4 4.3 4.3 4.1 4 3.9 3.9
Per capita consumption of cheese (pounds)
-- 29.8 30.1 30.5 30.6 31.3 31.7 32.6 33.1 32.7 32.8
Number of people who drown by falling into a pool
109 102 102 98 85 95 96 98 123 94 102
Number of films Nicolas Cage appeared in
2 2 2 3 1 1 2 3 4 1 4
Number of people killed by venomous spiders
6 5 5 10 8 14 10 4 8 5 6
Table 2. 
Example data sets, taking from Tyler Vigen’s Spurious Correlations
In the process of creating these books, participants are asked to think about the design choices they commonly make when creating visualizations of their own as well as the design elements they encounter in visualizations on a daily basis. To guide this thinking, the following questions are asked as participants work with their data:

  • What patterns appear to be intrinsic to the sets of data being used, and how do we validate the truthfulness of those patterns?
  • What conclusions emerge organically from those patterns, and what conclusions may be crafted?
  • How do we confirm or dispute the validity of those conclusions?
  • What knowledge do those conclusions impart, and how is that knowledge motivated by the way the data are visualized?
  • How can particular design choices be used to translate those patterns into knowledge in a way that is faithful to the data themselves?
In this way, participants are encouraged to think of visualizations as forms of dialogue rather than statements of fact – a nod to Drucker’s characterization of the declarative nature of representational approaches to visualization [Drucker 2017]. When elements of design are understood as the language and grammar used in expressing a narrative in graphical form, it becomes easier to acknowledge the subjective nature of visualization and representation.
Figure 3. 
Schematic diagram of pathways created in each book
Figure 4. 
Example pages from a completed book
Figure 3 provides the general process for creating a book, and Figure 4 provides an example of a small section from a completed book. In this example, two data sets are selected: one about per capita consumption of whole milk in gallons from the years 2000–2009, and another about the number of people killed by venomous spiders over the same range of years. The book begins with an appeal to its author:

Lo, brave traveler! The cosmic guardians of data visualization have entrusted you with the task of sharing their stories far and wide, spreading hope in the righteous use of information design. To assist you on your journey along paths of perilous design choices and misleading conclusions, the guardians have provided two sets of data to be your lantern. Write these data on the following page.

With this provocation, the participant is subsequently asked to create the visualization paths for the book. In this example, the data about venomous spider deaths is used to create a line chart. This line chart is then manipulated by skewing the range of the y-axis, which leads to an outcome page. Here, the outcome that results is a flattening of the variability in the data by reducing the range of the axis, making it appear as though deaths due to venomous spiders are low and invariable.
In this way, participants are invited to engage with the two frames from the ACRL Framework stated above. By physically going through the process of manipulating simple visualizations and creating their own books, participants are invited to reflect on the idea that information creation is indeed a process, one that is mired in a complex intersection of influences and biases that informs how a visualization may be interpreted. Likewise, participants also see that authority is indeed contextual, the value and meaning of a visualization being directly dependent upon those influences and biases. By engaging with the medium of data visualization through the “Choose Your Own Adventure” activity, participants are encouraged to consider what it means to be more critical practitioners of data visualization in these frames in their own scholarship and daily lives. And in that process, thinking about data as capta becomes an embodied act, particularly as participants explore what it means to parse out the design process of a visualization in a way that makes the visualization more self-aware.
This activity has been attempted twice, and both times it was received well overall by participants, who found it to be an exercise that was both useful and fun for thinking about the critical choices they make when designing a visualization. It also benefitted from the added bonus of providing participants with something tangible to take away from the workshop, to which they could refer in the future when designing their own visualizations. The only major challenge to the activity is the often limited availability of time – at least one hour is needed to provide plenty of space for creative expression and critical assessment.
The materials for these pages are available in the Northeastern University Library Digital Repository Service[3]. In the future, it may be possible to create an online interactive version of this exercise that automates the creation of pages while enabling users to create and manipulate their visualizations within a graphical user interface.


As data visualization becomes an increasingly important and common tool in scholarship and teaching, there has been a concomitant increase in the recognized importance of being able to engage with visualization critically. Here, I described one approach to supporting this kind of critical engagement as expressed through competencies in information literacy, leveraging the ACRL Framework, a collection of critical dichotomies for evaluating visualizations, and an activity that seeks to teach these concepts in the format of a Choose Your Own Adventure book. Collectively, this approach emphasizes the active role the individual plays in designing and interpreting visualizations, encouraging students to interrogate what it means to conceptualize visualization as a creative, constructive medium where representation is discursive rather than absolutely defined.
Designing for data visualization in this way requires a holistic view of the complex relationships that exist between all entities involved, including data, representation (visualization), designer, and user. When considered as a complex system of interactions and conversations between these entities, the design of a visualization becomes an exercise in which we are actively engaged with the discursive dimensions of data and information. This discursive dimension is a reflection of Drucker’s definition of capta, which emphasizes the ways in which we are fundamentally implicated in the construction of data and their representation we may conventionally perceive as static, absolute, objective, and authoritative. An understanding of these relationships is essential for engaging holistically with visualization as a medium of knowledge, and as a result, it is important to acknowledge the influence that our conceptions of information, knowledge, and the relationship between them – as evidenced by the critical dichotomies described above – impart to the design process.
Through creative activities like the one described here, it becomes easier to effectively teach core competencies around critical thinking in information design, especially when framed around concepts in information literacy. As information design is itself a creative act, pedagogical approaches that are inherently creative and experientially oriented can play a significant role in generating tangible, hands-on understanding of highly abstract concepts. In the process, students can be encouraged to assess the ways in which they form relationships between information and their representation, at the same time tuning into the constructedness of data. By understanding visualization as a medium for exposing these relationships, students can become better equipped to be critical practitioners of all forms of visual representation, not only as designers but also as consumers.


[1]  The Choose Your Own Adventure series is based on a concept created by Edward Packard and was originally published by Bantam Books.
[3]  For access to these materials, see http://hdl.handle.net/2047/D20236096.

Works Cited

ACRL Board 2016 ACRL Board. “Framework for Information Literacy for Higher Education”, Association of College & Research Libraries, 2016. http://www.ala.org/acrl/standards/ilframework
Borland 2007 Borland, D., and Taylor, R. M. “Rainbow Color Map (Still) Considered Harmful”, IEEE Computer Graphics and Applications, 27.2 (2007): 14-17.
Crampton and Krygier 2006 Crampton, J. and Krygier, J. “An Introduction to Critical Cartography”, ACME, 4.1 (2006): 11-33.
Daston and Galison 2007 Daston, L. and Galison, P. Objectivity. MIT Press, Cambridge, MA (2007).
Drucker 2011 Drucker, J. “Humanities Approaches to Graphical Display”, “Digital Humanities Quarterly”, 5.1 (2011).
Drucker 2017 Drucker, J. “Non-representational approaches to modeling interpretation in a graphical environment”, Digital Scholarship in the Humanities, 33.2 (2017).
D’Ignazio 2015 D’Ignazio, C. “What would feminist data visualization look like?”, (2015). https://civic.mit.edu/feminist-data-visualization
Gillborn et al. 2018 Gillborn, D., Warmington, P., and Demack, S. “QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics”, Race Ethnicity and Education, 21.2 (2018): 158-179.
Hill et al. 2016 Hill, R., Kennedy, H., and Gerrard, Y. “Visualizing junk: big data visualizations and the need for feminist data studies”, Journal of Communication Inquiry, 40.4 (2016): 331-350.
Kennedy et al. 2016 Kennedy, H., Hill, R. L., Aiello, G., and Allen, W. “The work that visualization conventions do”, Information, Communication & Society, 19.6 (2016): 715-735.
Koltay 2017 Koltay, T. “Data literacy for researchers and data librarians”, Journal of Librarianship and Information Science, 49.1 (2017): 3-14.
Meirelles 2013 Meirelles, I. Design for Information. Rockport Publishers, Beverly, Massachusetts (2013).
Moretti 2013 Moretti, F. Distant Reading. Verso, London (2013).
Tufte 2001 Tufte, E. The Visual Display of Quantitative Information. Graphics Press, Cheshire, Connecticut (2001).
Ware 2008 Ware, C. Visual Thinking for Design. Morgan Kaufmann Publishers, Burlington, MA (2008).
2018 12.4  |  XMLPDFPrint