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title: Artificial intelligence as enabler for automation of Data Visualization:
Potentials and obstacles - an empirical analysis
(original title: Künstliche Intelligenz als Wegbereiter für die Automatisierung von
Datenvisualisierungen: Potentiale und Hindernisse – eine empirische Analyse)
thesis: master thesis, Universität Salzburg
author: Matthias Tratz
year: 2021
page numbers and numbering of the illustrations deviate from original
this work was originally published in German
2.1.4. Visual representation of data
Data visualizations can help communicate content more easily. They can show patterns and
relationships, make data accessible, and serve as an exploration and analysis tool (Kennedy
und Allen 2017, S. 3; Telea 2018, 2). Because of this potential, one can see an increasing
use of data visualization in various fields, such as journalism, science, design, business and
public relations (Weber 2019, S. 336). Although visual signs are centuries-old human forms
of communication, data visualizations are so specific that the scientific examination of them
is comparatively new (Krämer 2016, S. 15; Frutiger und Heiderhoff 2004, S. 119–121). A
special feature is the multimodality of the visual, which can result from syntactical density as
well as from semantic ambiguity (Bucher 2019, S. 655–656).
A variety of disciplines are involved in the research of data visualizations: Communi-
cation and media studies, philosophy, perceptual psychology, cognitive science, educational
science, social sciences, linguistics, art and image science, political science, cartography,
statistics, computer science, and information design (Zacks und Franconeri 2020, S. 53–54;
Weber 2019, S. 345). In this respect, various conceptual approaches and definitions are in
use. In addition to data visualization, there are other terms such as information visualization,
infographics or information design, which are often used imprecisely. However, the terms can
be distinguished with regard to their field of application, their reception and the data material
used.
Friendly and Denis define data visualization as: „the science of visual representation
of ‚data‘, defined as information which has been abstracted in some schematic form, includ-
ing attributes or variables for the units of information“ (2001). The focus here is on „visualiza-
tions of quantitative (numerical) and categorical data: statistical graphs and thematic maps“
[transl. by author] (Weber 2019, S. 337). Kirk (2019) expands the definition to include the
type of preparation and a specific objective when he writes: „representation and presentation
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of data to facilitate understanding“ (S. 29). Data visualizations can thus be defined as appli-
cations for sense-making, in the context of data analysis, and for communication. They are
tools for gaining knowledge and for research to detect new questions (Grandjean 2015, S.
111; Rehbein 2017, S. 332; Few 2014). In doing so, they can create evidence as well as im-
prove the ability to remember and make decisions (Heer et al. 2010, S. 59–60; Weber 2019,
S. 341).
The distinction between data visualization and information visualization is fluid, as
both areas are related to each other (Unwin et al. 2008, S. 6). Information visualization can
rather be classified as a scientific research field with a proximity to computer science (Card
2008, S. 515). The connection of abstract data and their technical processing is in the fore-
ground. The pioneers Card, Mackinlay and Shneiderman define information visualization in
this way: „the use of computer supported, interactive visual representations of abstract data
to amplify cognition“ (Card et al. 1999, S. 7). Here, there is also a concrete objective, namely
the reinforcement of cognitive abilities. Visualizations are designed to help people perform
tasks more effectively and facilitate decision-making (Munzner 2015, S. 1). When comparing
the terms data visualization and information visualization, Kirk emphasizes the different per-
spectives. The former focuses on the input, i.e., visualization by means of data, while the lat-
ter is directed at the output, i.e., the information as the result of the visualization (Kirk 2019,
S. 27). Data visualization is considered the more modern and general term (Unwin et al.
2008, S. 6).
A key differentiator is the data basis. Information visualization refers to representa-
tions of abstract data that do not contain explicit spatial structural references (nonspatial
data), such as business and demographic data or network graphs (Keim et al. 2008, S. 160;
Rehbein 2017, S. 330). In contrast, scientific visualization uses data instances with 2- or 3-
dimensional references within the data, such as molecules, geometries, or medical data gen-
erated by computed tomography (CT) or magnetic resonance imaging (MRI) (Telea 2018, S.
3–4; Koponen und Hildén 2019, S. 236). These data can be described as: „typically 3D ge-
ometries or can be understood as scalar, vectorial, or tensorial fields with explicit references
to time and space“ (Keim et al. 2008, S. 160). Data visualizations also use abstract data.
Thus, representations that do not process abstract data can be excluded, such as:
Flowcharts, Unified Modeling Language, technical drawings, illustrations, scientific jargon,
conceptual diagrams, information design, infographics, or interface design (Ware 2013, S.
15–16; Friendly und Denis 2001).
Visual Analytics (VA) can be seen as a functional extension of information visualiza-
tion (Nazemi 2016, S. 20). Its focus is on the generation of knowledge: „Detect the expected
and discover the unexpected“ (Keim et al. 2008, S. 157). Interactive visualizations, supported
by automated analysis techniques, are intended to facilitate understanding, assessment, and
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decision making on large and complex data sets. The cognitive process is integrated into the
interactive analysis (Rehbein 2017, 332; Keim et al. 2008, S. 157). The Figure 1 shows fur-
ther fields of application that can be distinguished from data visualizations with regard to their
data basis and their objective. Related areas such as information visualization or extensions
such as visual analytics can be seen.
Figure 1: Context of the terminology in the field of data visualization (own work)
Infographics (information graphics) are mostly used for explanation and are not necessarily
based on data. They often represent combinations of different elements such as charts, illus-
trations, photos, diagrams, pictograms and texts, which are characterized by a narrative
structure (Koponen und Hildén 2019, S. 26, 93; Weber 2019, S. 337; Kennedy und Allen
2017, S. 3). Areas of application are often journalism or marketing (Masud et al. 2010, S.
447). Based on the usage of graphical elements, there is an intersection between in-
fographics and pictorial statistics, which were popularized by Otto Neurath and Gerd Arntz.
However, pictorial statistics use quantifying pictograms to represent facts and can insofar be
understood as part of data visualizations (Hartmann und Bauer 2006, S. 58, 65; Weber 2019,
S. 350–351).
Information design can be defined as the process of selecting, organizing and pre-
senting information in order to make it more accessible. Both target groups and their needs
are taken into account, which is why application-oriented aspects such as usability and user
experience (UX) are also relevant. In contrast to this, graphic design or visual communication
(design field) deals with design with regard to advertising impact and reception (Stapelkamp
2013, S. 18, 20; Koponen und Hildén 2019, S. 23). Another complex area is knowledge
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visualization. Visual representations are examined for their ability to improve the transfer and
creation of knowledge between at least two people. Knowledge, whether implicit or explicit, is
the basis of transfer. This can include a wide range of variants: Experiences, statements,
facts, processes, reasons and expert knowledge. In addition, the visual design is extensive:
from sketches and diagrams, to 2D or 3D representations (Burkhard 2005, S. 242–244).
Outside of data visualization research departments, the interest in visual representa-
tions is growing (Weber 2019, S. 345). In the Anglo-American world, the research field of vis-
ual communication has existed since the 1970s. In contrast, there is no uniform approach in
Europe. The perspectives vary in the countries depending on the research tradition and the
range of disciplines involved (Müller 2007, S. 7–8). The complexity becomes apparent, for
example, in the different approaches to the visual and the concept of the image. On the one
hand, the visual is difficult to separate from the communication process, and on the other
hand, the conceptual definitions vary (Müller 2007, S. 13, 19). While the German language
speaks of "Bild", the English distinguishes between „image, picture, visuals“ (Müller 2007, S.
11), with several levels of meaning. This is also apparent in the scientific orientation and the
interdisciplinary references. In German there is an orientation towards the image sciences,
while in English the focus is more on visual studies (Lobinger 2012, S. 41; Müller 2007, S.
17).
The study of visual forms of representation received increased attention through the
so-called turn to the image (Wende zum Bild) in the 1990s. At roughly the same time, Gott-
fried Boehm (1994) proclaimed the „iconic turn“ and William J. T. Mitchell (1992) the „pictorial
turn“. This was accompanied by the demand for a "turning away from this logocentrism of
Western societies" [transl. by author] (Lobinger 2012, S. 29). The reference to the linguistic
turn should be a marker for the influence of the visual on everyday life as well as on cognition
(Lobinger 2012, S. 28–29). Visual communication has established itself as a sub-discipline in
communication science (Beck 2017, S. 178; Müller 2007, S. 18). It follows the tradition of the
empirical social sciences and investigates production, distribution and reception processes of
visual representations in a social, cultural and economic context, with the research goal of:
„understanding and explaining current visual phenomena and their implications for the imme-
diate future“ (Müller 2007, S. 24). Data visualization is investigated rather sporadically in vis-
ual communication, as shown in studies by Brinch (2020), Kennedy et al. (2020), Nærland
(2020), Weber (2019), Geise (2019) oder Metag (2019). The complexity of the research field
makes it difficult to deal with data visualizations. The network graph (see Figure 2) is in-
tended to illustrate the diversity of the various disciplines with their different perspectives.
5
Figure 2: Research landscape and disciplines in the context of data visualization
(own work)
The presentation does not claim to be complete. The individual lines of connection are based
on literature research (see Appendix 1). Areas that conduct research on data visualization
with regard to forms of representation and application are highlighted in purple. Communica-
tion sciences are highlighted in orange. It becomes visible that disciplines within this re-
search space are widely dispersed, with emphases on statistics, computer science, percep-
tual psychology, as well as bibliometrics.
The size of the nodes results from the sum of the incoming and outgoing connecting
lines. In the present case, only the German and English language areas were used. Differen-
tiations were made between disciplines when the research traditions differ significantly, such
as between communication studies and communication studies.
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Figure 3: Research landscape of data visualization scaled by number of search results (own
work)
To represent the visibility of individual disciplines, the number of search results in Google
Scholar (see Appendix 2) was used as a proxy. They indicate the size of the nodes in the
second graph (see Figure 3). Especially mathematical-technical disciplines like statistics and
computer science have many references. The color grouping in Figure 3 was calculated us-
ing modularity clustering. Based on the density of connection structures, the algorithm calcu-
lates which areas are most likely to belong together and assigns them to a "community"
(Blondel et al. 2008, 2-3). Based on presuppositions, this results in clusters that are similar in
their theories or methods, such as art history and philosophy, or information science,
knowledge management and bibliometrics.
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List of appendices (according to excerpt)
Appendix 1: References to the network graph in the context of data visualization
source
target
reference
Viziometrics
Bibliometrics
Lee et al. 2018, S. 12
Library and information sci-
ence
Bibliometrics
Milojević und Leydesdorff 2020, S. 1
Art History
Bildwissenschaft
Lobinger 2012, S. 33
Philosophy
Bildwissenschaft
Lobinger 2012, S. 33
Semiotics
Bildwissenschaft
Prinz und Reckwitz 2012, S. 180
Psychology
Bildwissenschaft
Lobinger 2012, S. 33
Informatics
Bildwissenschaft
Schirra 2005, S. 269
Cognitive Science
Bildwissenschaft
Vogeley 2005, S. 97
Philosophy
Bildwissenschaft
Sachs-Hombach und Schürmann 2005, S.
109
Graphic Design
Communication Design
Schneider et al. 2009, S. 203
Information Design
Communication Design
Stapelkamp 2013, S. 20
Typography
Communication Design
Schneider et al. 2009, S. 203
Journalism
Communication Studies
Simonson 2015, S. 2
Scientific Visualization
Computer Graphics
Wright 2007, S. 2
Computer Graphics
Computer Science
Wright 2007, S. 1
Machine Learning
Computer Science
Duarte und Ståhl 2019, S. 27
Medienwissenschaft
Cultural Studies
Leschke 2014, S. 25
Infographic
Data Journalism
Weber 2019, S. 353
InfoViz / DataViz
Data Journalism
Weber 2019, S. 352
Computer Science
Data Science
Cutt 2015, S. 31
Statistics
Data Science
Cutt 2015, S. 31
Machine Learning
Data Science
Cutt 2015, S. 31
Mathematics
Data Science
Cutt 2015, S. 31
Data Mining
Data Science
Said und Torra 2019, S. 2
Machine Learning
Data Science
Said und Torra 2019, S. 2
Graph Theory
Discrete Mathematics
Pottmeyer 2019, S. 77
Typography
Graphic Design
Schneider et al. 2009, S. 157
InfoViz / DataViz
Human-Computer Interaction
Bae et al. 2019, S. 140
Informatics
Human-Computer Interaction
Carroll 2010, S. 4
Information Design
Human-Computer Interaction
Carroll 2010, S. 4
User Experience
Human-Computer Interaction
Carroll 2010, S. 4
Information Architecture
Human-Computer Interaction
Tullis und Albert 2013, S. 48
Cognitive Science
Human-Computer Interaction
Carroll 2010, S. 4
Communication Design
Infographic
Weber 2019, S. 350
Graphic Design
Infographic
Weber 2019, S. 350
Information Design
Infographic
Weber 2019, S. 350
Pictorial Statistics
Infographic
Peschke 2019, S. 112
Journalism
Infographic
Amit-Danhi und Shifman 2018, S. 3542
Discrete Mathematics
Informatics
Prabhu 2020, S. 2
Graph Theory
Informatics
Kasyanov 2001, S. 146
Information Science
Informatics
Prabhu 2020, S. 2
Computer Science
Informatics
Prabhu 2020, S. 2
Information Theory
Informatics
Prabhu 2020, S. 2
Cognitive Science
Informatics
Prabhu 2020, S. 2
Library science
Informatics
Prabhu 2020, S. 2
User Experience
Information Architecture
Rosenfeld et al. 2015, S. 177
Information Design
Information Architecture
Cardello 2014
Typography
Information Design
Gribbons 1993, S. 17
Bibliometrics
Information Science
Bawden und Robinson 2012, S. 5
13
Knowledge Management
Information Science
Bawden und Robinson 2012, S. 5
Computer Science
Information Science
Bawden und Robinson 2012, S. 6
Library and information sci-
ence
Information Science
Bawden und Robinson 2012, S. 6
Statistics
Information Theory
Cover und Thomas 2006, S. 28
Mathematics
Information Theory
Cover und Thomas 2006, S. 28
Computer Science
Information Theory
Cover und Thomas 2006, S. 28
Cognitive Science
Information Theory
Martignon 2015, S. 108
Visual Analytics
InfoViz / DataViz
Bae et al. 2019, S. 145
Psychology
InfoViz / DataViz
Bae et al. 2019, S. 140
Computer Science
InfoViz / DataViz
Zacks und Franconeri 2020, S. 54
Information Design
InfoViz / DataViz
Stapelkamp 2013, S. 21
Kommunikationswissenschaft
Journalism
Grimm und Delfmann 2017, S. 97
Data Journalism
Journalism
Lewis et al. 2020, S. 17
Graph Theory
Knowledge Management
Ríos-Zapata et al. 2017, S. 308
Cognitive Science
Knowledge Management
Dalkir 2017, S. 8
Library and information sci-
ence
Knowledge Management
Dalkir 2017, S. 8
Knowledge Management
Knowledge Visualization
Burkhard 2005, S. 243
Graphic Design
Knowledge Visualization
Burkhard 2005, S. 243
Psychology
Knowledge Visualization
Burkhard 2005, S. 243
Visuelle Kommunikation (KW)
Kommunikationswissenschaft
Müller 2007, S. 9
Politikwissenschaft
Kommunikationswissenschaft
Beck 2017, S. 171
Sociology
Kommunikationswissenschaft
Beck 2017, S. 171
Medienwissenschaft
Kommunikationswissenschaft
Beck 2017, S. 165
Zeitungswissenschaft
Kommunikationswissenschaft
Grimm und Delfmann 2017, S. 97
Library science
Library and information sci-
ence
Milojević und Leydesdorff 2020, S. 1
Medienwissenschaft
Literaturwissenschaft
Leschke 2014, S. 25
Data Mining
Machine Learning
Nguyen et al. 2019, S. 78
Statistics
Machine Learning
Nguyen et al. 2019, S. 81
Informatics
Mathematics
Prabhu 2020, S. 3
Discrete Mathematics
Mathematics
Pottmeyer 2019, IX
Communication Studies
Media Studies
Simonson 2015, S. 2
Sociology
Media Studies
Dierberg und Clark 2013, S. 1257
Literary Criticism
Media Studies
Dierberg und Clark 2013, S. 1257
Cultural Studies
Media Studies
Dierberg und Clark 2013, S. 1257
Filmwissenschaft
Medienwissenschaft
Leschke 2014, S. 22
Soziometrie
Network Science
Fuhse 2016, S. 26
Sociology
Network Science
Fuhse 2016, S. 26
Gestalt Psychology
Network Science
Fuhse 2016, S. 26
Graph Theory
Network Science
Dehmer et al. 2017, S. 576
Aesthetics
Philosophy
Prinz und Reckwitz 2012, S. 180
Graphic Design
Pictorial Statistics
Hartmann und Bauer 2006, S. 59
Psychology of Perception
Psychology
Ware 2013, S. 181
Gestalt Psychology
Psychology of Perception
Ware 2013, S. 181
Scientometrics
Science of Science
Fortunato et al. 2018, S. 1
Bibliometrics
Scientometrics
Schmid 2020, S. 1
Library and information sci-
ence
Scientometrics
Milojević und Leydesdorff 2020, S. 1
Viziometrics
Scientometrics
Lee et al. 2018, S. 12
Philosophy
Semiotics
Ware 2013, S. 6
InfoViz / DataViz
Statistics
Unwin et al. 2008, S. 4
Information Design
User Experience
Stapelkamp 2013, S. 20
InfoViz / DataViz
Visual Analytics
Keim et al. 2008, S. 158
Scientific Visualization
Visual Analytics
Keim et al. 2008, S. 158
Psychology
Visual Analytics
Keim et al. 2008, S. 158
Cognitive Science
Visual Analytics
Keim et al. 2008, S. 158
Psychology of Perception
Visual Analytics
Keim et al. 2008, S. 158
14
Data Mining
Visual Analytics
Keim et al. 2008, S. 158
Human-Computer Interaction
Visual Analytics
Wong und Thomas 2009, S. 306
Computer Graphics
Visual Analytics
Wong und Thomas 2009, S. 306
Visual Sociology
Visual Anthropology
Grady 1996, S. 11
Cultural Studies
Visual Culture
Pfurtscheller 2019, S. 37
Sociology
Visual Sociology
Müller 2007, S. 18
Cultural Studies
Visual Studies
Lobinger und Venema 2019, S. 2
Bildwissenschaft
Visual Studies
Müller 2007, S. 187
Aesthetics
Visual Studies
Prinz und Reckwitz 2012, S. 180
Art History
Visual Studies
Prinz und Reckwitz 2012, S. 180
Semiotics
Visual Studies
Prinz und Reckwitz 2012, S. 180
Visual Anthropology
Visual Studies
Prinz und Reckwitz 2012, S. 180
Communication Design
Visuelle Kommunikation (Dsg)
Schneider et al. 2009, S. 203
Art History
Visuelle Kommunikation (KW)
Müller 2007, S. 19
Philosophy
Visuelle Kommunikation (KW)
Müller 2007, S. 19
Sociology
Visuelle Kommunikation (KW)
Müller 2007, S. 14
InfoViz / DataViz
Visuelle Kommunikation (KW)
Müller 2007, S. 14
Psychology
Visuelle Kommunikation (KW)
Müller 2007, S. 14
Note: Visual communication (Dsg) refers to the design discipline, whereas visual communi-
cation (KW) refers to the field of research in communication science.
Appendix 2: Google Scholar search results for different disciplines
term
Google Scholar search results
Statistics
6 170 000
Mathematics
4 840 000
Sociology
4 840 000
Philosophy
4 670 000
Psychology
3 860 000
Computer Science
3 530 000
Machine Learning
3 260 000
Information Science
2 890 000
Communication Studies
2 640 000
Informatics
2 500 000
Data Mining
2 400 000
Aesthetics
2 260 000
Cognitive Science
2 110 000
Cultural Studies
1 980 000
Information Theory
1 790 000
Computer Graphics
1 670 000
Journalism
1 490 000
Knowledge Management
1 480 000
Graph Theory
1 030 000
Human-Computer Interaction
988 000
Literary Criticism
739 000
User Experience
676 000
Semiotics
652 000
Art History
593 000
InfoViz / DataViz 1
572 000
Data Science
525 000
15
Media Studies
419 000
Library and information science
337 000
Discrete Mathematics
336 000
Literaturwissenschaft
275 000
Visual Culture
256 000
Library science
240 000
Infographic
207 000
Typography
174 000
Graphic Design
155000
Politikwissenschaft
137 000
Bibliometrics
116 000
Network Science
110 000
Scientometrics
106 000
Science of Science
102 000
Medienwissenschaft
102 000
Information Design
79 600
Gestalt Psychology
61 700
Scientific Visualization
59 400
Information Architecture
54 200
Visual Analytics
52 100
Kommunikationswissenschaft
47 900
Communication Design
44 200
Visual Studies
42 300
Visual Anthropology
39 600
Psychology of Perception
12 600
Visual Sociology
9 170
Bildwissenschaft
8 280
Knowledge Visualization
7 870
Data Journalism
7 700
Visuelle Kommunikation (Dsg) 2
6 480
Visuelle Kommunikation (KW) 2
6 480
Filmwissenschaft
6 470
Sociometry
4 210
Zeitungswissenschaft
3 290
Pictorial Statistics
726
Viziometrics
63
Note: Data last checked on 05.03.2021, all search terms searched for exact match ("search
term"); Visual Communication (Dsg) refers to the design discipline, whereas Visual Commu-
nication (KW) refers to the field of research in communication science.
1 combined results for Data Visualization: 390 000, Information Visualization: 182 000
2 collectively surveyed as "Visual Communication“
