Data visualization as creative art practice

Abstract

This article begins by tracing the evolution of data visualization from the fields of aesthetics to areas of creative practice, arguing that the emergence of big data presents creative potential for digital artists. Whereas conventional information visualization emphasizes the effective understanding of data, aesthetics considers the possibility that visualization can enhance the experience of data and support the acquisition of knowledge. In expressing an artistic intent or form, data-based creative practices synthesize and build upon techniques in communications and aesthetics. The article provides a review of recent digital art projects involving big data and suggests further directions for creative practice in an era of data proliferation.

Keywords

Aesthetics big data creative practice data art data visualization

1. Introduction

Since its beginnings in the field of computer science, data visualization has been associated with empirical research incorporating two- and three-dimensional techniques to facilitate valuable insights into data patterns. While serving technical or specialist purposes, visualization techniques also focus on the effective communication of the meaning of data to generalist audiences. In recent decades, data visualization has been expanded through applications in many different fields, including scientific areas, as well as aesthetics and digital art. In the case of aesthetics and digital art, the focus is not entirely on data but rather on the creative potential of data itself and novel techniques of visualization. More specifically, aesthetics has become a vital dimension of data visualization. In digital art practices, data visualization has been used as a creative approach, especially since the late 1990s (Corby, 2008). Research has explored the benefits of data visualization in contemporary visual arts practice and the production of creative artifacts. Such data-based digital art emphasizes multi-dimensional engagement with actual data rather than the visual appearances of data visualization only or the genesis of data-driven metaphors (Viegas and Wattenberg, 2007). Also termed ‘data visualisation art’ or ‘data art’, this form of digital art usually uses computer technology to create artistic works. Computer software is employed to convert numeric data into visual or audio forms. During this process, data existing on a hard drive or the internet (or, indeed, generated by the artist) is reinterpreted according to the artist’s creative purposes (Wands, 2006).

This article provides an overview of data visualization as works of data art. It reviews some key examples of data art practice within a discussion of aesthetic approaches to the visual representation of data. The steady proliferation of data of all kinds (big data) presents multiple opportunities for the development of new digital art practices. Recently, big data has emerged as an important topic in many fields. Different from conventional data, big data refers to very large or complex data sets that comprise internet, network, mobile applications, social network and other data sources (Manyika et al., 2011). Art practices using big data will become more common as data increasingly proliferates and continues to influences people’s lives in contemporary societies. This article suggests that digital practices involving big data offer valuable directions for future research and artistic production.

2. Aesthetic Approach To Data Visualization

An aesthetic approach to data visualization contains different values from other approaches, such as usability or functionality, especially in the domains of science or engineering. In data visualization, an aesthetic approach refers to an investigation of the judgement that examines the value of the visualization work according to the sensation it produces (Sack, 2011). The judgement about the value of visualization in scientific or engineering discipline depends on utilization, such as the speed, accuracy, or efficiency of the task (Card et al., 1999). For example, judgements about medical data visualization will depend on whether data visualization helps doctors make a diagnosis more quickly and accurately. Similarly, scientists often use mathematical models to visualize the physical world for identifying problems or issues. The process of discovery requires detailed observation, logical formalism and repeatability, with objectivity and reproducibility as the result. The aesthetics associated with, or resulting from, mathematical models are normally disregarded (Fracchia, 1995). However, Fracchia suggests that the aesthetic consideration of the visual representation of scientific problems before the development of mathematical models could contribute to the analysis and interpretation of massive scientific datasets.

Previous research into aesthetic approaches to data visualization mainly focuses on questions such as what constitutes good data visualization and whether data visualization needs to be beautiful (Herdeg, 1983; Tufte, 1997). The questions explored in previous research are primarily focused on 2D graphics or diagrams. Tufte (1997) argues for the importance of aesthetics in information graphical design and examines the combination of words, numbers and pictures for enhancing the visual quality of design. He compares ‘friendly’ and ‘unfriendly’ data graphics so as to guide the design of such data visualization (Tufte, 2001[1983]). A ‘friendly’ data graphic helps readers to understand the data while an ‘unfriendly’ data graphic will not enable readers to efficiently grasp the data.

Tufte’s concept of the role of aesthetic theory in the design of a visualization argues that the successful design of data visualization should be as follows: ‘graphical elegance is often found in simplicity of design and complexity of data’ (Tufte, 2001[1983]: 177). Tufte emphasizes the minimalist representation of data, in which visual elements that are unnecessary, useless and non-informative to information communication should be avoided (Tufte, 1997).

However, it needs to be noted that most of Tufte’s theories and practices, which focus on graphic visualization of statistics, have rarely been explored or engaged with using computer technology, including computer interactive techniques. In his books on information design, Tufte gives many examples of charts or diagrams that emphasize the objective characteristics of data. However, Tufte’s information design lacks a discussion of the application of computing technology to data visualization, including interactive techniques. In the digital age, most data visualization, particularly scientific and engineering-based forms, relies on computing technology more or less.

More recent research demonstrates that aesthetics has been gaining attention as a means to promote a positive effect on perception of data in order to enhance experience and amplify the ability to obtain knowledge (Cawthon and Moere, 2007). This has been recognized within the design discipline, where research demonstrates that visual attractiveness is a major factor in how users visualize data (Gaviria, 2008). If data visualization has a high aesthetic value, users may be encouraged to engage in finding more or richer meaning within images (Lang, 2008). Li et al. (2013) propose a theoretical framework that explores data, information and aesthetics along a communication continuum. They suggest that the value of aesthetics emphasizes integrating perception and sensation to the design of data visualizations, which fosters emotional engagement with data by participants or audiences. The aesthetic consideration for design of data visualization could enhance end users’ experience.

Applying aesthetics to visualization has the ability, therefore, to invoke a sensation on two levels. Firstly, it can promote the traditional visualization focus on accuracy, efficiency and effectiveness, which is valuable in scientific data visualization (Card et al., 1999). At the same time, it can be used to prompt a subjective experience in the form of an emotional response. Emotions, such as pleasure, joy or a sense of fun, which are considered to be the prime motivators for human behaviour, should have some bearing on the performance elements of a visualization, e.g. its usability, utility and functionality (Norman, 2004). Research by Tateosian et al. (2007) demonstrates that visual analysis could be more effective and more productive if the artistic side of visualization is enhanced. They argue that effective visualization engages users’ attention, whereby their gazes react and attend to a stimulus.

Usability has been demonstrated as having value for data visualization. However, aesthetics and emotion are also important. For example, Norman (2004: 17) suggests that emotions and aesthetics play an important role in the product design because ‘attractive things work better’, which departs from the traditional design principle of data visualization that claims functionality as the dominant element. Norman asserts that eliciting positive emotions can enhance communication elements for learning and creativity, as ‘positive emotions are critical to learning, curiosity, and creative thought’ and a happy emotion ‘broadens the thought processes and facilitates creative thinking’ (p. 19). The key design theory in his book focuses on the human attributes governed by brain mechanisms at three different levels: the visceral, behavioural and reflective.

The visceral level is the automatic response part of the brain. It requires design involving ‘immediate emotional impact … to feel good, look good’ (p. 69);

The behavioural level involves ‘function, understandability, usability, and physical feel’ (p. 70). Function is considered the most important component in the behavioural design.

The reflective level conveys a message and a culture and its usability together. The reflective level is often considered an overall impression of a product (p. 88).

The visceral level emphasizes the importance of physical features, such as visual appearance, sensation or sound. It plays a role in how people receive ‘emotional signals from the environment that get interpreted automatically at the visceral level’ (p. 65). The behavioural level primarily deals with usability, in which performance, rather than appearance and rational, is the main consideration. The reflective level is about the experience of using a product in terms of memory and reassessment. It emphasizes the importance of emotion at the reflective level, which distinguishes the reflective level from the other two levels.

Traditional data visualization can be considered a behavioural objective. The design of aesthetic data visualization should have visceral appeal. Reflective design helps to achieve a balance between the visceral attraction of people’s attention and behavioural usability, thus balancing aesthetics and functionality. Assessment of aesthetic effects underpins the important role of aesthetics in data visualization. Cawthon and Moere (2007) conducted an online survey to test the aesthetic effectiveness of data visualization. Two methods, individual ranking and group ranking, were used to assess respondents’ subjective responses to aesthetics in visual form, including TreeMap, IcicleTree, SpaceTree, Windows Explorer, BeamTrees, StarTree, Dendogram Tree, ReingoldTilfer Polar View, StepTree, Botanical Viewer and SunBurst (p. 10) (see Figure 1). For individual ranking, participants were required to rate their perception of the visual effect using a slider interface to assess the ugliness or beauty of the visualization. Group ranking was conducted to rate the same set of seven visualizations. The results show that SunBurst had the highest ranking in both individual and group rankings. SunBurst has a more harmonious visual form with the potential to raise delight and emotion. This assessment suggests that information designers should consider aesthetics as part of design conception and evaluation of techniques for data visualization.

Figure 1.

Eleven different tested data visualization techniques (Cawthon and Moere, 2007). Reproduced with permission.

The key issue identified in the research into data visualization and aesthetics is the focus on the utility and forms of the visualization. Friedman (2008) indicates that the successful design of data visualization often requires achieving a balance of aesthetics and utility. Depending on the context of the design, data visualization may emphasize functionality or usability, may prioritize aesthetic experience, or may totally ignore either usability or aesthetic issues. Lau and Moere (2007) propose a model of how aesthetics and functionality work together. The authors suggest a range of novel techniques that might be developed to support this model, such as museum technology designed for interactive installation. This interactive installation attempts to serve both as ‘art’ for the appreciation of collections from the museum and as a ‘tool’ to perform different tasks for information about the museum (Boehner et al., 2005). In addition, Kosara (2007) suggests one possible method for combining aesthetics and data visualization. He proposes the term ‘interaction in particular’ (p. 1), in which the visual representation has the quality of the sublime and, at the same time, the user can interact with the work for generating specific patterns or images. Therefore, users can choose which side of the visualization they would like to see: the artistic visualization (aesthetic effects) or the pragmatic visualization (empirical meaning).

3. Data-Based Creative Art Practice

Data-based digital art can be broadly divided into two categories: static data art and dynamic data art. While static data art often adopts digital technology as a tool to create images, dynamic data art focuses on using digital technology as a medium to create interactive artefacts. Dynamic data art represents the majority of data-based digital practice because it allows participants to ‘navigate visual and textual information and experience changes over time’ (Paul, 2003: 177).

3.1 Static data art

The following section will discuss a number of data-based art examples, including static data art, dynamic data art and a specific data-based art practice, which facilitates the experience of the power of digital technology.

Jason Salavon’s digital artwork Homes for Sale (1999–2002) presents a series of photographs representing every home for sale in the USA (Viegas and Wattenberg, 2007). The artwork was created by averaging the colour values of each image (Figure 2). The outcome presents a blurred image of a city’s pattern of weather and ghostly looking images of the houses. This work, based on numeric data, displays a sublime quality that has often been a criterion for the judgement of artworks. These static data visualizations involve the creation of digital artworks through digital compression technologies that enable complex data to be converted into compact forms for easy transfer or storage in digital media (Hope and Ryan, 2014). In visual art, compression technologies often use common file formats, such as JPEG, MP3 or MPEG.

Figure 2.

Salavon (1999–2002), Chicagoland (left) and LA/Orange County (right). Reproduced with permission.

Jussi Ängeslevä and Ross Cooper’s digital artwork Last Clock (2005) investigates the visual representation of time and space (Viegas and Wattenberg, 2007). The clock, similar to an analogue clock with a second hand, a minute hand and an hour hand, was created with video footage from surveillance cameras around cities (Figure 3). It consists of three rings, in which the outer ring is assigned to the second hand, the middle ring is assigned to the minute hand and the inner ring is assigned to the hour hand. Each hand represents a slice of live video. When the hands spin around the clock, ‘they leave a trace of what has been happening in front of the camera’ (p. 8). This digital artwork ‘displays a space’s history and rhythm. Trails behind its hands paint the clock face with a video feed, creating a mandala of archived time’ (Ängeslevä and Cooper, 2005: 20). The key to this work is its abstraction. Its ‘aesthetically pleasing quality lends itself better to the background awareness of the remote place than a literal, direct video feed would’.

Figure 3.

Last Clock (Ängeslevä and Cooper, 2005). Reproduced with permission.

John Simon’s data visualization Every Icon (1997) is considered a classic example for experiencing the magnitude of technology, which goes beyond human perception through the experience of the sublime. This work is presented as a grid of 32 x 32 squares that was designed online by systematically filling in each square every second in either white or black. Because there are 256 squares (32 x 32 = 256), there are 2²⁵⁶ possible icons, which will take billions of years to fill up. The result from this data visualization can cause difficulty in understanding its huge size and temporal framework.

3.2 Dynamic data art

The following section will discuss a number of dynamic data artworks, which often involves real time interactivity.

George Legrady’s data artwork Pockets Full of Memories (2001) is interactive, involving participant contributions (Wands, 2006: 175). This work was designed using an installation with a kiosk for data collection, a list of questions for audience members and a projection screen to display the outcome visually. It invited participants to contribute images of their personal possessions using digital scanning and to describe these objects with tags or keywords by answering the questions. A computer program was written to categorize these images of objects as a 2D map displayed on the screen, based on the description of these objects. Initially, the database of the work contained no data, but grew through participants’ input throughout the exhibition.

There is another type of data visualization art that focuses on visual representation as a specific kind of data or dataset, such as stock market or weather forecast data. John Klima’s data work Ecosystm (2000) is considered data art, based on a real-time visualization of the data of global currencies in 3D animation with a real-time weather report from John F Kennedy (JFK) Airport (Klima, 2000). Ecosystm is a simulation in which the artist converted the financial market data to an ecosystem, central to which are birds (Paul, 2003). The population and the behaviours of these birds were defined by the currency and its volatility. The birds’ territory was based on daily volatility that controlled the size of the territory in relation to the stability of the data. The weather of Ecosystm was decided by the weather report from JFK Airport, where clouds affected the weather data.

Garvin Baily and Tom Corby’s information visualization work Cyclone.soc (2006) is a noteworthy example of exploring scientific data through aesthetic approaches (Fujisawa et al., 2008). This immersive installation makes use of data from the USA satellite forecasting. The artists visualized this data as vector animations with information concerning depth and dimension, and then integrated textual data from internet newsgroups and the isobars of satellite data to produce ‘metaphoric waves of conversion’ (p. 393). The audience is able to interact with the work by ‘reading and responding to … postings’. This interactive work represents newsgroup messages as weather patterns, exploring ‘a suggestive link between these extreme belief systems and their potential wider ecological impacts on the material world’. Ecosystm and Cyclone.soc adopted stock market data for social and environmental commentary. However, other projects make use of scientific and health related data, such as sleep EEG data for creative practice.

Recently, sleep and EEG technology have been used by artists to explore the internal states of the human body in the context of digital technology (Wilson, 2002). These bodily experiments do not necessarily attempt to seek an improvement of existing knowledge, but to lead to novel directions in knowledge and understanding of issues concerning ‘the nature of the body in relation to social, political, scientific, ethical, economic, and culture shifts in the world as we know it’ (p. 178). A number of artworks specifically use EEG data as a creative medium. These artistic works include using brain waves to create an interactive work and the visual representation of EEG data as coloured abstracted images. For instance, Paras Kaul’s work Mind Garden (1997), a 3D game, explores a virtual garden, using EEG signals involving digital brainwave analysis and the internet (Kaul, 1997). In this work, participants were required to focus their attention to determine different forms and sounds; their brainwaves were recorded through EEG facilities. To explore the garden world, the brain theta and delta brainwave activity determined the course of the journey. The work aims to create a maximum level of experience based on users’ brain frequencies (Wilson, 2002).

Furthermore, Bruce Gilchrist and Johnny Bradley’s work Divided by Resistance (1996) emphasizes interactive communication between participants and a sleeping performer experiencing REM (Rapid Eye Movement) sleep (Gilchrist and Bradley, 1996). Participants were invited to sit on a large vibrating chair named ‘the seat of consciousness’ (p. 2). Its vibrating pads allowed participants to interactively communicate with sleepers’ brain states through electrodes that were connected to the sleepers. This work explored in a digital practice context ‘how signals from the external environment are symbolically incorporated and represented by the dreaming mind/brain’ (p. 4).

Daria Migotina, Carlos Isidoro and Agostinho Rosa’s visual representation of sleep EEG data from the normal sleeping brain, created in 2011, is an example of the visual representation of sleep EEG data as static images. It explores how sleep EEG data can be represented with aesthetic qualities, rather than as waveforms for communication only. In this work, the EEG data is rendered into abstract images (Migotina et al., 2011: 148). The visual representation of sleep EEG data occurred through two different modes. A single image combines five different images of five sleep stages. The objects are located in a spiral pattern. From the centre, the objects at first start to spiral outwards in a clockwise direction and in a chronological order. Through this method, the background colour has been generated by the information in each sleep stage. The result of the second method shows a similar aesthetic quality as the one generated by the first method. These works present a series of colour abstract images that have different visual effects based on waveforms. Presumably, human subjects cannot understand these abstract images. Migotina et al. claim that this abstract visual representation of sleep EEG data does not constitute an artwork but is only experimentation insofar as the traditional data visualization (involving the communication of information) is challenged.

Nina Sobell’s Interactive Brainwave Drawing Game (1974‒) explores how people influence each others’ brain signals. The installation consists of a monitor with two people sitting side by side in front of it, with EEG equipment. Their brainwaves are captured and displayed overhead with their real-time image on the monitor. These data-based artistic works explore data in the cultural realm that emphasizes aesthetic experience, rather than pure communication in the traditional sense of visualization with a scientific purpose of identifying patterns or relationships (Fujisawa et al., 2008). These works challenge traditional techniques in the fields of data visualization and enable people to engage more with the images ‘on emotional and conceptual levels by employing the visualization process to produce expressive works of visual art’ (p. 393).

Another example adopts traditional Chinese Taoist principles to create a new interactive data art practice (Figure 4). Richard Li (2015) created an interactive data visualization work Taiji through the Kinect platform. The work explored traditional Chinese yijing aesthetics and Taoist body philosophy in the digital environment (Li, 2015). The creative work adopted sleep EEG data to explore the potential application of the concept of Taoist data visualization in the medical field. The work presented possibilities for interactivity between users, data and the technological system. The work emphasized harmonious, immersive and imaginative experience through gesture-based technology.

Figure 4.

Richard Li, Taiji (2015). Reproduced with permission.

The last example is related to big data visualization and used data from different cities around the world. Lanke Frank Tarimo Fu, Danielle Griego, Nikola Marinčić and Jorge Orozco created a data visualization work, It feels like (2016), built on a website. The project compares the weather between two cities among 27 cities. It allows users to explore information about current weather conditions in the database to compare weather conditions of other cities (Kaufman, 2016). It provides users with the visual information about the city’s seasons that they can experience virtually. The work emphasizes a sense of exploring the world through viewing the weather of the city the user is currently living in. It also promotes feelings about other cities around the world, using local weather information as an indicator (Tarimo et al., 2016).

These data-based creative works indicate that data visualization is not only for communication and comprehension or identifying insights within visual patterns of data but also for enjoyment, user engagement or the production of sublime qualities in a digital artwork.

4. Conclusion

This article has presented key theories of aesthetic approaches to data visualization and data-based creative art practices. It emphasized the importance of aesthetics to data visualization, which promotes a positive effect on the perception of data. In perception terms, the aesthetic consideration of data visualization in a design field thus could enhance the experience and emotional influence from data visualization and amplify the ability to obtain knowledge.

Visual representation of data not only focuses on communication and understanding data but is also a creative practice. In an era of big data, how to understand large and complex data is a very important aspect to effective communications. And the perception of the power of big data can also enhance the aesthetic experience of our society and everyday lives.

This article has highlighted two main aspects. The first is the aesthetic experience of data as creative practice. It is clear that data art and using data as a creative medium do not necessarily attempt to enhance communication or comprehension, but to evoke emotional experience and to facilitate experiences of big data in our everyday life. The second is the use of technology, such as gaming platforms, to enhance interactivity in data visualization. It is particularly important to create interactive experience by adopting technology.

I suggest that future studies adopt 3D technology to generate immersive experience in big data environments. Instead of using gesture-based or gaming technology, further research could adopt other technologies to explore immersive interactive experience. For example, this could include using wearable technology, motion capture technology and iDome. Subsequent research could also focus on big data on social media and mobile devices. Using big data as a creative medium is important to experiencing the flow and impact of data in contemporary societies.

Footnotes

Funding

This project, Project Emotion Measurement and Intelligence Interactivity Design (DB17032), was supported by Shanghai IV Summit Discipline in Design. There is no conflict of interest.

Biographical Note

QI LI is a scholar, artist and designer who is currently lecturing in the School of Art and Design in the Shanghai University of Engineering Science after obtaining his PhD degree from Edith Cowan University in Perth, Australia. His research examines aesthetic approaches to data visualization through the combination of gaming technology and traditional Chinese philosophies. He is interested in how digital technologies enhance creativity and artistic practices. Li has exhibited his work internationally in England, Australia, New Zealand and China. In addition to his book, Interpretation of Contemporary Online Gaming (Beijing United Publishing Company, 2018), he has published papers on the application of traditional Chinese aesthetics to data visualization and environmental art in the journals Environmental Values and Humanities and Technology Review. Address: Qi Li, Shanghai University of Engineering Science, 333 Longteng Rd, Songjiang District, Shanghai, China. [email: qili@sues.edu.cn]

References

Ängeslevä

Cooper

(2005) Last clock. Computer Graphics and Applications, IEEE, 25(1): 20–23.

Boehner

et al . (2005) Opening the frame of the art museum: Technology between art and tool. Proceedings of the Digital Arts and Culture 2005 Conference, Copenhagen.

Card

Mackinlay

Schneiderman

(1999) Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann.

Cawthon

Moere

(2007) Qualities of perceived aesthetic in data visualization. Proceedings of CHI Conference on Human Factors in Computing Systems, San Jose, CA.

Corby

(2008) Landscapes of feeling, arenas of action: Information visualization as art practice. Leonardo 41(5): 460–467.

Fracchia

(1995) Is visualization struggling under the myth of objectivity? Proceedings of the 6th IEEE Visualization Conference (VISUALIZATION ‘95), Atlanta, GA, 412–415.

Friedman

(2008) Data visualization and infographics. Available at: http://www.smashingmagazine.com/2008/01/14/monday-inspiration-data-visualization-and-infographics/ (accessed 20 October 2015).

Fujisawa

et al . (2008) Visualization of scientific arts and some examples of applications. Journal of Visualization 11(4): 387–394.

Gaviria

(2008) When is information visualization art? Determining the critical criteria. Leonardo 41(5): 479–482.

10.

Gilchrist

Bradley

(1996) Divided by resistance. Available at: http://www.artemergent.org.uk/dbr/dbr.html (accessed 12 October 2015).

11.

Herdeg

(1983) Graphis Diagrams: The Graphic Visualization of Abstract Data: Die Graphische Visualisierung Abstrakter Gegebenheiten. Zurich: Graphis Press.

12.

Hope

Ryan

(2014) Digital Arts: An Introduction to New Media. New York: Bloomsbury.

13.

Kaufman

(2016) Here’s what happens when you let artists play with big data. Available at: https://nextcity.org/daily/entry/big-data-art-projects-sense-your-city (accessed 25 September 2016).

14.

Kaul

(1997) Mind Garden. Los Angeles Convention Center.

15.

Klima

(2000) Ecosystm. Available at: http://www.cityarts.com/lmno/ecosystm.html (accessed 17 October 2015).

16.

Kosara

(2007) Visualization criticism: The missing link between information visualization and art. Proceedings of the Information Visualization, 2007, IV ’07, 11th International Conference, Zurich, IEEE, 631–636.

17.

Lang

(2008) Aesthetics in information visualization: Trends in information visualization. Hauptseminar Medieninformatik WS 2008/2009. Available at: http://141.84.8.93/pubdb/publications/pub/baur2010infovisHS/baur2010infovisHS.pdf#page=16 (accessed 7 September 2015).

18.

Lau

Moere

(2007) Towards a model of information aesthetics in information visualization. Proceedings of Information Visualization, 2007, IV’07, 11th International Conference, Zurich, IEEE, 87–92.

19.

(2015) Taoist data visualization: An embodied aesthetic approach to data visualization through gesture-based technology. Humanities and Technology Review 34: 1–33.

20.

McMahon

Haddad

(2013) An investigation into a new aesthetics of scientific data visualisation. In: The Australian Council of University Art & Design Schools: Locations of Practice, Sites for Creativity: From the Studio to the Cloud, Sydney. Available at: http://acuads.com.au/conference/2013-conference/article/an-investigation-into-a-new-aesthetics-of-scientific-data-visualization/ (accessed 23 August 2016).

21.

Manyika

et al . (2011) Big Data: The Next Frontier for Innovation, Competition, and Productivity. Philadelphia, PA: McKinsey Global Institute.

22.

Migotina

Isidoro

Rosa

(2011) Brain art: Abstract visualization of sleeping brain. Proceedings of GA 2011-XIV Generative Art Conference, Rome, 141–153.

23.

Norman

(2004) Emotional Design: Why We Love (Hate) Everyday Things. New York: Basic Books.

24.

Paul

(2003) Digital Art. London: Thames & Hudson.

25.

Sack

(2011) Aesthetics of information visualization. In: Lovejoy

et al . (eds) Context Providers: Conditions of Meaning in Media Arts. Bristol: Intellect Ltd.

26.

Salavon

(2007) Homes for sale. Available at: http://salavon.com/work/Homes/ (accessed 10 September 2016).

27.

Simon

(2007) Every icon. Available at: http://numeral.com/appletsoftware/eicon.html (accessed 30 August 2016).

28.

Tarimo

FLF

et al . (2016) It feels like, sense your city grand prize. Available at: http://datacanvas.org/project/it-feels-like/ (accessed 20 September 2016).

29.

Tateosian

Healey

Enns

(2007) Engaging viewers through nonphotorealistic visualizations. Proceedings of the 5th International Symposium on Non-Photorealistic Animation and Rendering. New York: Association for Computing Machinery, 93–102.

30.

Tufte

(1997) Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT: Graphics Press.

31.

Tufte

(2001[1983]) The Visual Display of Quantitative Information, 2nd edn. Cheshire, CT: Graphics Press.

32.

Viegas

Wattenberg

(2007) Artistic data visualization: Beyond visual analytics. In: Proceedings of the Second International Conference, OCSC 2007 Held as Part of HCI International 2007, Beijing. Berlin: Springer, 182–191.

33.

Wands

(2006) Art of the Digital Age. New York: Thames & Hudson.

34.

Wilson

(2002) Information Arts: Intersections of Art, Science, and Technology. Cambridge, MA: MIT Press.