This was originally written as part of the Master of Digital Design, Griffith University.
Abstract
Web 2.0 and new technologies have instigated a huge interest in information visualisation. While famous data visualisation exponents are internet celebrities, does the clever use of graphical imagery to convey vast and changing data enhance understanding of complicated concepts or is it merely another presentation tool to persuade viewers in more sophisticated, subjective ways?
The second and third parts of this FRED cover a broad outline of the ways in which data is imported and rendered in a html interface and finally include a brief, beginner investigation of the D3 code library.
FRED-ref
InfoViz, data visualisation, data-mapping, web architecture; Javascript library
Web keywords
Infographics, Data visualisation, infographic, information visualisation; data driven documents, D3.
Content
Purpose
This page is the first asect of a three-part FRED, which will encompass a short academic overview and description of data visualisation, including its current uses and the possible ways in which it may be used to complicate or conceal rather than enhance clear comprehension of data information.
The second and third components of this FRED cover an outline of web architecture and a practical assignment wherein a beginner JavaScript coder investigates D3 (data driven documents) and attempts simple attempts at using the libraries to create imple sdata visualisations.
Information visualisation, data visualisation and infographics
Information visualisation is a broad term representing any organized visual representation of large collections of information fields (Friendly & Denis, 2001, paragraph 1-10). Keim et al (2006) (quoted in Infovis-wiki.net, 2014) define information visualisation (InfoViz) as “the communication of abstract data through the use of interactive visual interfaces.” This supposedly draws on the idea that the viewer’s strong visual perceptions may be used to gain understanding of complex and abstract levels of data, and importantly, to gain insights (Brodbeck, Mazza, Lalanne, 2009, p. 34).
Data visualisation is essentially a narrower term referring to the transformation of knowledge, information and data into a visual representation in order to enhance human comprehension through visual capabilities (Card, 1998, p. 9; Friendly & Denis, 2001, paragraph 1-10), incorporating various methods for supporting the understanding and analyses of large, complex and abstract sets of data (Moere & Purchase, 2011, p. 1). Tools and artifacts of data visualisation are seen as a “pipeline” through which tracts of raw data, of varying levels of abstraction, is transformed into imagery systems which are interpreted by perception systems (Brodbeck Mazza, Lalanne, 2009, p. 30)
Infographics and data visualisation are convergent fields, with several differences. Visualisations usually also carry the assumption that the visual displayed has been created via algorithms which can be applied to many data sets, whereas an infographic is essentially a graphic artifact which has been crafted for a specific display (Kosara, 2010).
A key difference from the viewer’s pint of view is that whereas data visualisations generally incorporate an entire data set which would be unwieldy to display in static text format, with the impression that they are objective, Infographics are subjective, telling a specific story for an intended audience, (Arena-media.co.uk, 2014).
Web 2.0 and information visualisation
The introduction of Web 2.0 has seen an exponential increase in information available to everyday web users—harnessing the “collective intelligence” as well as giving users the ease of capacity to produce information to such an extent that the gap between the availability of information and ones capacity to process it is widening. Brodbeck, Mazza, Lalanne quote Steve Johnson in noting that the exponential growth of information in our age has led to an idea of incomprehensible tracts of data require interfaces to decipher, utilizing human’s tendency to detect patterns and shapes, etc. (2009, p. 27).
While information visualisation has influenced traditional and digital media, popular media has influenced aspects of information visualisation and related research (Moere & Purchase, 2001, p. 4). As Alan (2014, p. 187) notes, the world wide web is a culture dominated by an emphases on the visual: with photographs, video, static and interactive diagrams taking primacy in the viewing mind, with Google search trends confirming that the term “infographic” has reached peak search interest in 2013. It is evident that the social media and the web have motivated a “renaissance” of interest in graphic imagery, with a wide variety of visualisation tools available to map the growing quantity of data (Rosenburg, 2012, paragraph 1). Interest in data visualisation has corresponded with advancing technologies, linked as it is to the recording, processing and dissemination of data (Brodbeck, Mazza, Lalanne, 2009, p. 27). Nicholas Felton has achieved preeminent designer status with the broadcast of his Feltron Annual Report, which uses data mapping to tracking digital traces of personal activities into infographics (Pousman, Stasko, 2007, p4). The relationship of social media to infographics reached a nadir when Felton was hired by Facebook to design the timeline component (Rosenburg, 2012, paragraph 9).
While most widely known visualisations, graphs, are 2-dimensional which requires reworking to accommodate more than two variable relationships (Friendly & Kwan, 2003, p. 18), ongoing advances in technology allow exploration of issues such as multidimensional data may be viewed as 2 dimensional screen artifacts and countless pockets of data can be viewed in one screen view. (Brodbeck, Mazza, Lalanne, 2009, p. 39.) Though visual considerations are vital for effective data visualisations, interactivity including seamless, responsive, rapidly updated and responsive user experience is vital for optimum user experience (Elmqvist, Moere, Jetter, Reiterer & Jankun-Kelly, 2011, p. 328). Consequently, interactive visual depictions are a focus in information visualisation, which may assist in lowering barriers to understanding of information (Afzal, Maciejewski, Jang, Elmqvist, & Ebert, 2012, p. 3). Where technology has also heralded new tools for visualisation of personally relevant information (Sorapure, 2009, p. 59), Silver noted that the digital generation see media as resources which they create as much as consume, where the emphases is on sharing and creating content additionally to conversing about it (2008, paragraph 11).
With the availability of new technology, the idea that visualized data is strictly the domain of specialists such as statisticians etc. is outmoded and data visualisations are common for everyday users (Sorapure, 2006, p61). While previously much research into visualisation was driven by scientific fields, in order to create simulations of quantities of data collected, the past ten years have seen the web drive a new interest (Card & Gershon, 1998, p. 2). As the consumer group of information visualisations has broadened from experts to lay masses, (Van Moere, Purchase, 2011, p.1), much of the work surrounding information visualisation makes assumptions that the viewing population are inherently visually literate and with proficiency in analyses (Pousman, Stasko, 2007, p. 1).
Communicating through visualisation
The notion of the diagrammatic use of imagery and words to enhance information comprehension is not new, having been in usage since ancient times (Tufte, 2006, pp. 80-97). In modern times, visual design has been viewed the intermediate step between raw information and comprehension (Richard Grefe quoted in Katz, 2012, p. 17). Though effective visual communication is an essential part of everyday life, as well as education in the 21st century (Rosenquist, 2012, p. 2), information visuals are not simply communicators of information, but instruments of power, which may have positive and negative consequences (Brasseur, 2003, p2).
Peter Hall notes that information visualisation has become “an unlikely form of mass entertainment”(SVA MA Design Research, 2014). While Hans Rosling’s Gapminder has enthralled TED conference audiences and turned it’s creator into a multimedia celebrity (Rosling, 2014; YouTube, 2014), environmental groups question whether the statistician’s use of dynamic data increases the depth of understanding of global population impacts or disguises it (Lack, 2013).
The prevalent thinking assumes that data visualisation is a truthful and transparent medium; in which data are solid facts are guilelessly given expression (SVA MA Design Research, 2014)Data visualisation pioneers espouse the idea that effective graphics exemplify deep and truthful design analyses (Tufte, 2006, p. 124). While Rosling emphasizes data visualisation as a tool to convey raw data (McCandless, 2014; Rosling, 2014),it must be acknowledged that information visualisation—and the selection, omission and inclusion of raw data itself—is done with a viewpoint and viewer in mind (Hall, 2012, p. 171).
As Lamb, Polman, Newman and Smith state, infographics and data visualisations represent multiple layers of information with varying depths and interrelations between variable. Tufte and Guterman assert that the intellectual quality of enhancement visuals for business is often dubious, with confusion between analyses and persuasion (2009, p. 37). Can information visualisations be trusted to accentuate comprehension of meaning or does the visual shorthand involved make them more usable for persuasion and deception? (Brasseur, 2003, p. 1)?
Truth and interpretation in data visualisation
The nature of integrating words in diagrams requires interpretation by the designer, as Tufte states: making a visual presentation is a “moral act, as well as an intellectual activity” (2006, p. 141). Drucker refers to visualisation tools as akin to “Trojan horse” vehicles by which viewers suspend critical judgment and absorb the persuasive intent and assumptions within the presentations of data (2011, p. 1).
Space and quantity used in data mapping and visualisation are mathematical concepts, which Brassard suggests are perceived by viewers to be free of varied perceptual and emotional interpretations and therefore objective (Brasseur, 2003, pp. 4-5). Whereas in scientific research and presentation, evidence is subjected to review and scrutiny and transparency of database findings, the shift towards visualisations in the humanities and business have not necessarily carried over this sense of intellectual guardedness (Tufte, Guterman, 2009, p. 37). Indeed, Katz outlines the ways in which design may knowingly include uninformation, misinformation and disinformation in order to knowingly mislead the viewer, within in well packaged visual (2012, pp. 13-17)
Mathematical mappings can give credence to viewpoints suggested—for instance, a cursory search of book and web resources, such as Phi and the golden ratio in art (Goldennumber.net, 2014), will show diagrammatic “evidence” of the use of the golden section in historical artworks. As Tufte postulates (BE2006, p. 31), these diagrams less represent that recording of scientific research than plausible yet extremely general interpretations of selective observations, as opposed to specific and scientifically testable diagrammatic mappings. Drucker notes that many data visualisations represent data of a more humanistic nature, which cannot be anchored by concrete matricular structure (2011. P. 23).
Measuring the success, accuracy or value of many forms of information visualisation is inherently highly subjective (Moere & Purchase, 2011, p. 6). Tufte identifies that displays of data inevitably involve selections of facts from larger pools, defining the scope of what is relevant and in turn, revealing which aspects they choose to (1997, p. 43). Statisticians concede that data in itself does not exist without a context of the designer or viewer (Drucker, 2011, p. 3) Similarly, how information is ordered affects how information in conceived in a visual display (Friendly & Kwan, 2003, p. 2).
Much of the discourse surrounding the communication of data visualisations involves comprehension and usability of the visual mapping (Hall, 2011, p. 171) and whether effective graphical techniques can speak effectively to the mythical viewer (Brasseur, 2003, pp. 3-4). Brasseur (2003, p. 3) recommends that texts regarding technical visuals and instructives regarding how visuals should be designed and read need to go beyond thoughts regarding how readers/viewers should be educated in visual literacy in order to universally comprehend technical visuals.
As Hall notes, a sophisticated sense of visual literacy is required to navigate the huge volume of data and now information visualisations available today and visual critical analyses must be awakened in order to question and comprehend the truthfulness and transparency of infoViz (2001, p. 184). Sorapure argues that critical examinations of information visualisation software can be utilized to enhance students’ general digital and critical competencies, incorporating reflection and analyses, as the novel nature of much data visualisation software and the inherent nature of them to force a user to visualize results in multiple ways underlines the notion that software and interpretations of use influences critical thinking as well as design (2009, p. 60).
Sound data visualisation
Visualisations are unavoidably dependent on the context of viewer and designer and as such, visualisations should be recognized as subjective and interpreted (Drucker, 2011. P. 8).
Data visualisations and diagrammatic mappings require serious and stringent standards in order to be relevant, rather than interpretations (Tufte, 2006, p. 31). Drucker postulates that data mining tools have captured the communal imagination to the extent that ambiguities of scale, representation and context are tolerated within this domain, which would not be tolerated within factual data represented in text (2011, p. 2). Yet critical scrutiny must be adopted when the rhetorical power of graphical artifacts is so strong (Drucker, 2011, p. 3).
Recognising that visualisation have inherent credence often beyond textual representation, the Neiman Watchdog called for information graphics to be governed by stringent ethical standards adhered to by journalism professionals, with incorporation of credited sources covering all inclusions (Niemanwatchdog.org, 2014). Friendly and Kwan recommend (2003, p. 5) recognising an acute understanding of the viewer and the communication goal and structuring content accordingly when creating visuals.
While Tufte recognizes that causal ambiguities and uncertainties should be clearly transparent (Tufte, 2006, p. 70) Moere & Purchase (2011, pp. 13-15) suggest that visualisation creation aspects should not be is not treated as purely an art or design issues, but rather as that designers have scientific responsibility to be transparent and articulate about their design aims and choices. As Tufte and Guterman state
Effective visualisation of data is harmonious with the viewer’s tasks requirement and data structure, while echoing the need for scrutiny and empirical evaluation of data visualisation in terms of truth and quality (Friendly and Kwan, 2003, p. 29).
Conclusion
While visualisations have become ubiquitous in representing and interpreting information in the web 2.0 age, it must be remembered that all data visualisations are directed interpretations of quantities of data. Until a stringent codes of ethics and transparencies is applied to development and viewing, it must be acknowledged that no matter how compelling or entertaining, visualisations are not entirely transparent and all are subjected to the intentions of the developers.
References
Alan, S. (2014). Emerging trends in data visualisation: implications for producers of official statistics. In: 59th ISI World Statistics Congress. Titchfield: Office of National Statistics.
Arena-media.co.uk, (2014). What’s the difference between an Infographic and Data Visualisation? | Blog - Arena UK. [online] Available at: http://www.arena-media.co.uk/blog/2012/09/whats-the-difference-between-an-infographic-and-data-visualisation/ [Accessed 14 May. 2014].
Robertson, G., Robertson, G., Fernandez, R., Fernandez, R., Fisher, D., Fisher, D., . . . Stasko, J. (2008). Effectiveness of animation in trend visualisation. IEEE Transactions on Visualisation and Computer Graphics, 14; 12(6), 1325-1332. doi:10.1109/TVCG.2008.125
Salvo, M. (2012). Visual rhetoric and big data: Design of future communication. Communication Design Quarterly Review, 1(1), 37-40. doi:10.1145/2448917.2448925
Brasseur, L. E., Sides, C. H., & ebrary, I. (2003). Visualizing technical information: A cultural critique. United States: Baywood Publishing Company, Incorporated.
Drucker, J. (2011). Humanities approaches to graphical display. Digital Humanities Quarterly, 5(1).
Elmqvist, N., Moere, A. V., Jetter, H., Cernea, D., Reiterer, H., Jankun-Kelly, T., Linnéuniversitetet. (2011). Fluid interaction for information visualisation. Information Visualisation, 10(4), 327. doi:10.1177/1473871611413180
Few, S. (2006). Tableau v2.0 puts on a graceful display of visual analysis -- in a world of "dancing bear" software, this upgrade delivers new filters and animations without adding awkward steps to an elegant data visualisation tool. San Mateo: United Business Media LLC.
Friendly, M. & Denis, D. J. (2001). Milestones in the history of thematic cartography, statistical graphics, and data visualisation. Web document, http://www.datavis.ca/milestones/. Accessed: May 15, 2014
Friendly, M., & Denis, D. (2005). The early origins and development of the scatterplot. Journal of the History of the Behavioral Sciences, XLI(2), 103-130.
Friendly, M., & Kwan, E. (2003). Effect ordering for data displays. Computational Statistics and Data Analysis, 43(4), 509-539. doi:10.1016/S0167-9473(02)00290-6
Gershon, N., Eick, S., & Card, S. (1998). Information visualisation ACM. doi:10.1145/274430.274432
Goldennumber.net, (2014). Art and applications of Phi, the golden ratio. [online] Available at: http://www.goldennumber.net/art-composition-design/ [Accessed 15 May. 2014].
Grimbeek, P., Bartlett, B., & Loke, K. (2004). Using Leximancer to identify themes and patterns in the talk of three high-distinction students. Educating: Weaving Research into Practice. Retrieved from http://hdl.handle.net/10072/2000
Guterman, J., & Tufte, E. (2009). How facts change everything (if you let them). MIT Sloan Management Review, 50(4), 35-38. Retrieved from http://search.proquest.com.libraryproxy.griffith.edu.au/docview/224966077?accountid=14543
Hall, P. A. (2012). Afterword: On mapping and maps. Design Philosophy Papers.
Hall, P. A. (2011). Bubbles, Lines, and String: How Information Visualisation Shapes Society. In A. Blauvelt & E. Lupton (Eds), Graphic Design: Now in Production (pp. 170-185) Minneapolis: Walker Art Center
Infovis-wiki.net, (2014). Information Visualisation - InfoVis:Wiki. [online] Available at: http://www.infovis-wiki.net/index.php?title=Information_Visualisation [Accessed 15 May. 2014].
Katz, J., 1943, & ebrary, I. (2012). Designing information: Human factors and common sense in information design. Hoboken, New Jersey: Wiley-Blackwell. [online]
[Keim et al., 2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualisation (IV 2006), IEEE, p. 9-16, 2006.
Kosara, R. (2010). The Difference Between Infographics and Visualisation. [online] eagereyes. Available at: http://eagereyes.org/blog/2010/the-difference-between-infographics-and-visualisation [Accessed 14 May. 2014].
Lack, M. (2014). Hans Rosling is ecologically illiterate « Population Matters. [online] Populationmatters.org. Available at: http://www.populationmatters.org/2013/population-matters-news/hans-rosling-ecologically-illiterate/ [Accessed 13 May. 2014].
Lamb, G., Polman, J. L., Newman, A., & Smith, C. G. (2014). Science news infographics. The Science Teacher, 81(3), 25.
Moere, A. V., & Purchase, H. (2011). On the role of design in information visualisation. Information Visualisation, 10(4), 356. doi:10.1177/1473871611415996
Niemanwatchdog.org, (2014). Editors, artists chafe at the errors and hype in bin Laden death story graphics. [online] Available at: http://www.niemanwatchdog.org/index.cfm?fuseaction=showcase.view&showcaseid=152 [Accessed 13 May. 2014].
Pousman, Z., Pousman, Z., Stasko, J. T., Stasko, J. T., & Mateas, M. (2007). Casual information visualisation: Depictions of data in everyday life. IEEE Transactions on Visualisation and Computer Graphics, 13(6), 1145-1152. doi:10.1109/TVCG.2007.70541
Rosenberg, D., & Grafton, A. (2012). A graphic renaissance. The Hedgehog Review, 14(3), 58+. Retrieved from http://go.galegroup.com/ps/i.do?id=GALE%7CA308600799&v=2.1&u=griffith&it=r&p=EAIM&sw=w&asid=720f276a6ff52b9e7b7d6a2aacc1f289
Rosling, H. (2014). Hans Rosling | Speaker | TED.com. [online] Ted.com. Available at: http://www.ted.com/speakers/hans_rosling [Accessed 16 May. 2014].
Rosling, H. (2014). Videos. [online] Gapminder.org. Available at: http://www.gapminder.org/videos/#.U3lNxBZF8t0 [Accessed 27 March. 2014].
Silver, D. (2008). History, Hype, and Hope: An Afterward. First Monday, [online] 13(3). Available at: http://firstmonday.org/article/view/2143/1950 [Accessed 12 May. 2014].
Sorapure, M. (2010). Information visualisation, web 2.0, and the teaching of writing. Computers and Composition, 27(1), 59-70. doi:10.1016/j.compcom.2009.12.003
SVA MA Design Research, (2014). Petere Hall: Infoviz: Where's the critique. [video] Available at: http://vimeo.com/35725312 [Accessed 13 May. 2014].
TEDGlobal (Producer). (2010, July). David McCandless:The beauty of data visualisation [Video file]. Retrieved from http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualisation#t-324962.
Tufte, E. R., 1942. (2006). Beautiful evidence. Cheshire, Conn: Graphics Press.
Troutner, J. (2010). Infographics defined. Teacher Librarian, 38(2), 44-47. Retrieved from http://search.proquest.com.libraryproxy.griffith.edu.au/docview/846786612?accountid=14543
YouTube, (2014). Hans Rosling's Demographic Party Trick #1, with Bill Gates. [online] Available at: https://www.youtube.com/watch?v=2nDh8MQuS-Y [Accessed 16 May. 2014].
Truth in data visualisation: Bibliography
Baer, K., & Vacarra, J. (2008). Information design workbook: Graphic approaches, solutions, and inspiration + 30 case studies. Beverly, Mass: Rockport.
Tufte, E. R., 1942. (1995). Envisioning information. Cheshire, Conn: Graphics Press.