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ABSTRACT Snow, a physician, prove the source of the outbreak was a was outbreak the of source the provephysician,Snow, a helped which 1), (Fig. London in outbreakcholera 1854 the Snow’s of John include map visualizations early significant chartbarpurelywas for quantitative comparison Other [9]. His timeline. a on or (coordinates) space in data sitioning representingwithoutpo visually datafor system a veloped de first Playfaircentury.William 18th the since developed tation. of how this problem was overcome through visual represen as early [7], and as 570 tree diagrams [8], are early examples them [6]. Visual thinking tools, such as the abacus, invented compareto exercisingneeded thanmentalagility easier the mary function of visualization; plotting a few points is much databetween points [5]. image of the underlying data, its structure and relationships mental a form can viewer the that so seeable, information definitions: combines making it both because Vis,useful is form a mental image [4]. Information Visualization, or infoto also is visualize to visualization; of side one only is this visual processing systems of the human brain [3]. However, complex the of because powerful is visualization Data [2]. eye the to visible something make Tosimplyto is visualize Visualization has as been described an overloaded term [1]. of thedatadomaingaindeep,meaningfulinsightsintoinformation. in aclarifiedcontext,canhelpanaudiencewithlittleunderstanding Theliteratureshowsthatbeautifulpresentationsofdata, to artistic. methodsandpracticesofvisualizationfromtheanalytical different reviewsrelevantliteratureon Thisarticle insights intoinformation. users withdatainawaythatgivestheaudiencedeepandreflective assisted byvisualization.Visualizationhasthepotentialtoengage art Engaging ageneralaudiencewithscientificresearchcanbeeffectively totheArtistic From theAnalytical GENERAL NOTE ©2017 ISAST with thisissue. See
contexts, from analytical to the artistic. the Academic research has investigated visualization in different DESCRIB Tufte uses Minard’sTufte uses in map and bountiful detail, with readers unaware of the design [15]. diverse through data, rich and image the between relation intimate an prescribes He data. the of meaning the power self Tufte’sa of [14]. rateis visualization what atof philosophy should answer: how many, how often,where, how much and visualization a thatquestions of kind the describes mation, inforquantitative ofrepresentation the on books known disastrous campaign against Russia [13]. diagrams [12]; and Joseph Minard’s (1869) map of Napoleon’s Nightingale’sFlorence [10,11]; pumpcoxcomb water public File:Snow-cholera-map-1.jpg>) Public domain.(Imagesource:
JohnSnow’s mapofthe1854outbreakcholera. ING VISUA ENRO o.5,N.1 p 75,21 47 LEONARDO, Vol. 50, No.1,pp.47–52, 2017 LIZAT ION to to Evidence Beautiful establish fundamental principles of analytical design [16]. His princi algorithms to represent the data, to exploit the best of both ples guide the development of qualitative data visualizations, human and computer analysis [20,21]. outlining good practice in what to show the user, how to show It is necessary to establish a broader scope for infoVis than it and how to maintain integrity and credibility. Th is work for Tuft e’s quantitative charts, or the advanced data mining establishes that analytical presentations ultimately stand, or of visual analytics, to include qualitative data. Lev Mano fall, depending on the quality, relevance and integrity of their vich summarizes the evolution of visualization since Playfair: content [17]. Tuft e’s principles support the communication of infoVis uses a method of reduction of the dataobjects to quantitative information and can be applied broadly to many graphical primitives and leverages spatial variables to show contexts within quantitative data sets. variation and relation among key data points [22]. Users are Visual analytics aims to take advantage of the overwhelm able to recognize variation in spatial variables, such as scale, ing amount of data that is generated by business, govern signifi cantly better than nonspatial variables, such as color. ment and personal activities. Getting lost in this data—the Th us Manovich diff erentiates infoVis from other methods information overload problem [18]—wastes time and money for visual representation such as photography, video, MRI or for scientifi c and industrial applications. Visual analytics use radar: Th e goal of infoVis is to reveal the structure of data, so multidisciplinary collaboration and analytical reasoning to data with an existing structure is not suitable [23]. help users gain insight to support assessment, planning and To illustrate this point, we can examine a familiar piece, decisionmaking [19]. Using many of the same processes as such as the map of the London Underground by Harry Beck infoVis, visual analytics aims to fi nd the optimal automated (1933). It uses reduction, spatial variables and graphical en coding (Fig. 2). Beck reduced the information shown to the user by removing geographical information; the lines between stations were always straight and at either 90 or 45 degrees, as in Fig. 3, since a bend in the actual rail line is irrelevant in formation once you are already on the train. Th ese spatial cues were the primary source of information for the passenger, leaving second ary information (the diff erent train lines) to be coded using color, a visual variable. However, the antecedent geographical structure of a rail network means that the rail map is not infoVis; there is no new structure being revealed to the user. Manovich describes this activity as information design rather than information visualization [24]. Card also describes this difference [25], referring to Fig. 2. A map of the London Underground showing geographical relationships between the information design as the design of external stations; information that is not relevant once the passenger is on the network. Generated as part of the London Underground geographic maps project by software written by ed g2s and representations, where the existing, external, James D. Forrester utilizing GPS data (CC-BY-SA 3.0). (Image source:
Fig. 3. A map in the style of Harry Beck’s redesign. Created by Dominic Sayers (
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Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/LEON_a_00959 by guest on 27 September 2021 uses abstract, nonphysical data to amplify cognition [26]. The aesthetic treatment—the graphical construction of the (Notably, Card also defines infoVis as interactive, because visualization—is insufficient for visualizations to be beau visualization is an activity that is supported by computer tiful but it is necessary [39]. A model of information aes graphics; data graphics are these noninteractive represen thetics developed by Andrea Lau and Andrew Vande Moere tations.) A familiar visualization that reveals an otherwise acknowledges that infoVis techniques are often combined invisible structure in data is Mendeleev’s periodic table of with creative design principles. This model places infoVis at elements. The structure of the table reflects the periodicity one end of a continuum, with a direct mapping and intrin of valence electrons of the elements. Beautiful visualizations sic focus on data. At the other end is visualization art, with are the result of this kind of elegant solution combined with interpretive mapping and an extrinsic focus on data [40]. good visual aesthetics. Artists Fernanda Viégas and Martin Wattenberg also de scribe visualization art: visualizations made with the intent of BEAUTY, AESTHETICS AND THE ARTISTIC creating art. This definition is useful because it avoids the dif Noah Iliinsky outlines what makes visualization beautiful ficult philosophical question “What is art?” and also avoids [27]. He prescribes four criteria for making beautiful info the issue of beauty. As aesthetically pleasing as a visualiza Vis: Visualization must be novel, informative, efficient and tion of, for example, deep space astrophysics, microscopy or aesthetic. However, these are not goals but intrinsic attributes any scientific experiment may be, it lacks artistic intent and of beautiful visualization. therefore is not art [41]. Novelty is important to engage a reader. Graphic designer and author Nigel Holmes says that information graphics MORE THAN COGNITION should appeal to the reader and have a true accounting of But when the field of infoVis is focused on its use as a cogni the story, whether statistical, geographic or diagrammatic tive tool, why should an artist set out to visualize data? The [28], which also applies to interactive infoVis. The novelty of beauty inherent in visualization is the idea behind the image; the presentation and the data can be considered. One study when well crafted, a visualization is visually beautiful and in published at IEEE VIS 2013 shows that novel or unexpected tellectually stimulating [42]. Viégas and Wattenberg suggest visualizations are more memorable to the user. Novel visual that the availability of data sources and ubiquity of computers construction helps users remember other visual features has enabled artists to visualize data in a new context [43]. The without extra mental encoding and may be a useful factor artistic context and the artist’s statement clarify the artwork, to exploit when communicating science to the general pub allowing the audience to access its message, which separates lic [29]. Novel data is also important. Visualization designer it from scientific visualization and visual analytics. Alberto Cairo suggests that the opposite of data novelty is Visualization art can be evocative, especially when emerg redundancy [30]. Original representations of novel data are ing from interdisciplinary collaboration. Collaboration may beautiful [31]. Remixing visualization methods out of context be key to creating meaningful, evocative experiences for a is a bankrupt activity; it is a sign of poor design. general audience, and artists can leverage this to commu Poor design gets in the way of the data; visualizations nicate their points of view [44,45]. There are many ways in need to be informative to be beautiful [32]. This is a fun which a nonexpert can represent knowledge; artists use tacit damental principle echoed by most authors but eloquently knowledge of their craft and visual and linguistic represen outlined by Cairo. He states that the relationship between art tation of data to communicate with the audience. Art then and visualization is analogous to the relationship between does not need to become science to be useful to scientific journalism and literature [33]. While a journalist borrows research and communication [46]. Art can make scientific tools and methods from literature, the story that results metaphors tangible, engage the emotions of the audience and should not become literature. Similarly, there is always a build awareness that can motivate action on issues such as burden of truth on visualization, even when it is an artistic climate change. However, this is an area that is still in need interpretation. of research [47]. Artistic visualization should still be efficient: allowing Experiencing information through feelings is essential to straightforward access to the message of the visualization personal and collective decisionmaking, which could be without sacrificing relevant complexity [34]. Effective visu used to encourage action on climate change [48]. One work alization research is typically focused on the domain expert with such an intention is When I Was a Buoyant (2012) by user [35]. With an understanding of the information domain, Josh Wodak [49] (Fig. 4). This photography exhibition acts complex analysis can be optimized with the help of visualiza as a portal to understanding of the “hockey stick” graph of tion, such as in the visual analytics or scientific visualization climate change (Fig. 5). The similarities between this graph approach. This approach provides little guidance to the user and the relationship between the human arm and head are and is designed to be neutral, although some research as explored using digital projection onto the body. serts that a truly neutral visualization of data is not possible One obvious danger is misinformation through poor de [36,37]. The efficient approach is not necessarily neutral. Re sign or misrepresentation; clarification is not the same as ducing irrelevant information will make a visualization more misrepresentation. Duxbury shows how mainstream media efficient, but as Nigel Holmes argues [38], the data should still sending mixed messages can be confusing to viewers and be reinforced with appropriate visual cues. can have a detrimental effect on climate change action [50].
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Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/LEON_a_00959 by guest on 27 September 2021 Fig. 4. A selected image from When I Was a Buoyant by Josh Wodak. Photo-portraits of climate change data physically projected onto participants’ bodies to illustrate sea level rise measured against the human body. (© Josh Wodak)
Misrepresentation and guilt do not always promote behavioral change. Instead, appro priate visualization is effective, as shown by Rodgers and Bartram [51]. They confirmed the viability of ambient, artistic visualizations of power usage in a home context, providing nonexpert users with feedback to encourage positive behavioral change. Tufte states that among the most powerful data exploration methods are appropriate, beautiful reexpres sions and transformations of information [52].
UNDERSTANDING AN AUDIENCE
Clarifying data requires an understanding of Fig. 5. The Hockey Stick graph is commonly used to show global warming trends. Created by the intended audience; visual analytics for a Hanno (cc-by-sa-3.0). (Image source:
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Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/LEON_a_00959 by guest on 27 September 2021 course there is a continuum of insights that can be gained, as CONCLUSION insight can take on many forms: analytical, awareness, social, A description of visualization has emerged from the selected reflective and more. literature. It is an intentional act by an artist, using data rather One study by Vande Moere explored the kinds of insights than just factual metaphor, to communicate within an artistic that users get from casual visualization that complements context. Although it does not need to be visually beautiful, analytical insight. The comparison of different styles of pre its beauty rises out of novelty, efficiency, aesthetics and in sentation showed that users were able to gain deep insights formative qualities. A single, coherent message is reinforced from casual visualization, but the type of insight varied with by the artwork’s aesthetics and clarified through the artists’ the style of visualization, whether analytics, “magazine” or statement. The result of artistic clarification and reinterpre artistic [65]. This investigation found that visual embellish tation of information is not misinformation but a power ments tended to hide visual patterns, which prompted us ful way of providing the audience the opportunity for deep, ers to explore the content of the visualization. The differing meaningful or personal insights into the information being nature of insight shows that there can be different objectives presented. and motives for visualization art.
References and Notes 21 S.K. Card, J.D. Mackinlay and B. Schneiderman, Readings in Infor- mation Visualization: Using Vision to Think (San Francisco: Morgan 1 M. Chen, L. Floridi and R. Borgo, “What Is Visualization Really Kaufmann, 1999). For?” in The Philosophy of Information Quality (Springer Interna tional Publishing, 2013) pp. 75–93. 22 See Thomas and Cook [19]. 2 visualise, in The Oxford English Dictionary (Oxford, U.K.: Oxford 23 See Manovich [5]. Univ. Press: 2013). 24 See Manovich [5]. 3 C. Ware, Information Visualization: Perception for Design (San Diego: 25 See Card et al. [21]. Morgan Kaufmann, 2012). 26 See Card et al. [21]. 4 See [2]. 27 N. Iliinsky, “On Beauty,” in J. Steele and N. Iliinsky, eds., Beautiful 5 L. Manovich, “What Is Visualisation,” Visual Studies 26 (2011) Visualisation (Sebastopol, CA: O’Reilly, 2010) pp. 1–14. pp. 36–49. 28 S. Heller, Nigel Holmes: On Information Design (Jorge Pinto Books 6 A. Cairo, The Functional Art—Design and Infographics | Journalism Inc., 2006). Interactive Conference 2013. Conference Video. 29 M.A. Borkin et al., “What Makes a Visualization Memorable?” IEEE 7 N. Holmes, Designer’s Guide to Creating Charts & Diagrams (New Transactions on Visualization and Computer Graphics 19 (2013) York: WatsonGuptill Publications, 1984). pp. 2306–2315. 8 M. Lima, Visual Complexity (Princeton Architectural Press, 2011). 30 A. Cairo, The Functional Art: An Introduction to Information Graph- ics and Visualization (New Riders, 2012). 9 J.R. Beniger and D.L. Robyn, “Quantitative Graphics in Statistics: A Brief History,” The American Statistician 32 (1978) pp. 1–11. 31 See Iliinsky [27]. 10 E.R. Tufte,The Visual Display of Quantitative Information (Cheshire, 32 See Iliinsky [27]. CT: Graphics Press, 1983) p. 2. 33 See Cairo [30]. 11 J. Snow, On the Mode of Communication of Cholera (London: John Churchill, 1855). 34 See Iliinsky [27]. 12 F.B. Viégas and M. Wattenberg, “Artistic Data Visualisation: Beyond 35 A. Vande Moere and H. Purchase, “On the Role of Design in Visual Analytics,” Second International Conference, OCSC (Beijing: Information Visualization,” Information Visualization 10 (2011) Springer Berlin Heidelberg, 2007) pp. 182–191. pp. 356–371. 13 E. Tufte, Beautiful Evidence (Cheshire, CT: Graphics Press, 2006) 36 See Vande Moere and Purchase [35]. p. 213. 37 M. Hohl, “From Abstract to Actual: Art and DesignerLike Enquiries 14 E. Tufte, Visual Explanations (Cheshire, CT: Graphics Press, 1997). into Data Visualisation,” Kybernetes 40 (2011) pp. 1038–144. 15 E. Tufte, Envisioning Information (Cheshire, CT: Graphics Press, 38 See Holmes [7]. 1990). 39 See Iliinsky [27]. 16 See Tufte [13]. 40 A. Lau and A. Vande Moere, “Towards a Model of Information Aes thetics in Information Visualization,” 11th International Conference 17 See Tufte [13]. Information Visualization (IV ’07) (IEEE, 2007) pp. 87–92. 18 D. Keim et al., “Visual Analytics: Definition, Process, and Chal 41 See Viégas and Wattenberg [12]. lenges,” in A. Kerren et al., eds., Information Visualization (Springer Berlin Heidelberg, 2008) pp. 154–175. 42 J. Hagy, “Visualization: Indexed,” in J. Steele and N. Iliinsky, eds., Beautiful Visualisation (Sebastopol, CA: O’Reilly, 2010) pp. 353–367. 19 J.J. Thomas and K.A. Cook, “A Visual Analytics Agenda,”IEEE Com- puter Graphics and Applications 26 (2006) pp. 10–13. 43 See Viégas and Wattenberg [12]. 20 See Thomas and Cook [19]. 44 See Viégas and Wattenberg [12].
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Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/LEON_a_00959 by guest on 27 September 2021 45 See Hohl [37]. 57 See Cairo [30]. 46 L. Duxbury, “BreathTaking: Creating Artistic Visualisations of 58 Z. Pousman, J. Stasko and M. Mateas, “Casual Information Visual Atmospheric Conditions to Evoke Responses to Climate Change,” ization: Depictions of Data in Everyday Life,” IEEE Transactions on Local-Global: Identity, Security, Community 10 (2012) pp. 34–45. Visualization and Computer Graphics 13 (2007) pp. 1145–1152. 47 S.R.J. Sheppard, “Landscape Visualisation and Climate Change: The 59 See Vande Moere and Purchase [35]. Potential for Influencing Perceptions and Behaviour,”Environmental Science & Policy 8 (2005) pp. 637–654. 60 See Purchase et al. [53]. 48 See Duxbury [46]. 61 C. Ziemkiewicz et al., “Understanding Visualization by Understand ing Individual Users,” IEEE Computer Graphics and Applications 32 49 J. Wodak, archAngle,
54 See Vande Moere and Purchase [35]. Manuscript received 2 February 2014. 55 P. Gough, C.d.B. Wall and T. Bednarz, “Affective and Effective Vi sualisation: Communicating Science to Nonexpert Users,” in 2014 IEEE Pacific Visualization Symposium (Yokohama: IEEE, 2014) PHILLIP GOUGH is a PhD candidate from The University pp. 335–339. of Sydney. He is studying visualization for nonexpert users 56 See Cairo [30]. (NEUVis) at the university’s Design Lab and at CSIRO.
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