The Aesthetics of Diagrams

Michael Burch

VISUS, University of Stuttgart, Stuttgart, Germany

Keywords:

Diagrams, Information Visualization, Aesthetics.

Abstract:

Diagrammatic representations are omnipresent and are used in various application domains. One of their

major goal, in particular for information visualization, is to make data visual in a way that a spectator can

easily understand the graphical encoding to ﬁnally derive insights from the data. As we see, there are various

different ways to visually depict data by using visual features in various combinations. In this paper we come

up with some thoughts about existing diagram styles, for which we ﬁrst discuss the beneﬁts and drawbacks

of each of them focusing on aesthetics based on readability. Additionally, we describe some initial results on

the aesthetics of diagrams which we recorded in a web-based experiment. In this, we asked participants to

vote for one of two given diagrams of a given repertoire of 70 of them covering all examined aspects which

focuses more on aesthetics in the sense of beauty, not readability. The major result of this experiment unhides

a trend towards colored, 3D, and radial diagrams which stands somewhat in contrast to readability user studies

in information visualization oftentimes tending towards 2D and Cartesian diagrams for data exploration.

1 INTRODUCTION

Nowadays, there are various diagram types occurring

in different styles combining several visual features

into a single representation. The major goal of those

diagrams is to graphically depict data or scenarios in

some intuitive way. In particular, for information vi-

sualization, those should represent abstract data in a

meaningful, intuitive, readable, and understandable

way as described by Bertin (2010) and Tufte (1983,

1990). Moreover, the visualization should be as com-

pact and space-ﬁlling as possible serving as a good

overview of as much data as possible. When talking

about these aspects, we refer to diagram readability

aesthetics. Aesthetics in the sense of beauty often-

times plays a second role.

But on the other side, the second kind of diagram

aesthetics also plays an important role since ’good

looking’ visualizations much more attract users’ at-

tention and consequently, those might be used from

a broader ﬁeld of data analysts and in particular,

by laymen which oftentimes only know diagrams

from newspapers and magazines. These assumptions

make diagram aesthetics an important topic worth dis-

cussing and studying.

In this paper we discuss the beneﬁts and draw-

backs of diagrams by looking at them from different

perspectives. As a ﬁrst attempt we come up with a

very ﬂat categorization in which we subdivide a dia-

gram type depending on one visual feature it is based

on. These features are in particular, if they are old

ones or developed recently, if color is used or not, if

they represent data in 2D or 3D, if they use animation

or depict the data statically, if they are mapped to a ra-

dial (circular) or to a Cartesian coordinate system, if

they are space-ﬁlling or not, or in which research ﬁeld

those are used more frequently, i.e. either in infor-

mation visualization or scientiﬁc visualization. Typi-

cally, many more categories can be found, but to this

end, we only explore this list to get some ideas for

future directions.

To get a hint about user preferences on diagram

aesthetics in the sense of beauty we conducted an un-

controlled web-based experiment in which we let peo-

ple vote for exactly one of two represented diagrams

chosen from a preselected repertoire of 70 of them.

In this preliminary study we found that people tend

more to colored, radial, and 3D diagrams which are

considered as being not that readable and explorable

as for example 2D diagrams mapped to a Cartesian

coordinate system making them more space-ﬁlling

and space-efﬁcient. Colored diagrams were consid-

ered more aesthetically appealing than non-colored

diagrams which is beneﬁcial because using colors is

important for both, readable as well as aesthetically

appealing diagrams.

It may be noted that this work is just a very ﬁrst

step towards understanding diagram aesthetics. There

262

Burch M..

The Aesthetics of Diagrams.

DOI: 10.5220/0005357502620267

In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pages 262-267

ISBN: 978-989-758-088-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

are too many parameters serving as independent vari-

ables in a user experiment that much more empirical

research must follow to come closer to solutions of

this problem.

2 RELATED WORK

The international conferences on the theory and ap-

plication of diagrams, held every two years, built a

platform to discuss recent developments in diagram

research, of which the last twelve years are surveyed

by Purchase (2014). But also the traditional visual-

ization conferences and journals provide various ap-

proaches including diagrammatic aspects focusing on

producing effective visualization techniques.

From this point of view, we can subdivide exist-

ing diagram types into several categories of which we

know that this is by far not a complete list. Based on

these categories, a discussion on beneﬁts and draw-

backs can be started to get more insights on which

ones are more suitable for readability tasks and which

of those look more aesthetically pleasing in the sense

of beauty. The best diagrams, at least in our opinion,

are those which fulﬁll both aesthetics criteria.

In general, the term ’aesthetics’ comes from the

ﬁeld of philosophy which contains aspects such as the

general nature of art, beauty, and taste. In a scien-

tiﬁc sense it can be more understood as the study of

sensory-emotional values by judging applying senti-

ment and taste as described in Zangwill (2008). The

deﬁnition of this kind of aesthetics is consequently

not driven by a speciﬁc user task as information visu-

alizers would understand it.

In contrast to that, diagram aesthetics in the sense

of readability and effectiveness is more based on de-

sign principles such as the reduction of chart junk de-

scribed in Bateman et al. (2010), a balanced lie factor

illustrated by Tufte (1983), and a reduction of visual

clutter as discussed in Rosenholtz et al. (2005). In

particular, in the ﬁeld of graph visualization, graph

drawing aesthetics are important to produce read-

able node-link diagrams which is researched by Harel

(1997) and Ware et al. (2002). For example, Bennett

et al. (2007) discuss the aesthetics of graph visual-

ization but they deﬁne aesthetics more in the sense

of readability which they evaluate in a comparative

user study. It is not clear if a viewer also ﬁnds the

presented graph beautiful, i.e. aesthetically appeal-

ing. There are some user studies which indicate that

participants might ﬁnd node-link diagrams with acute

crossing angles not that nice as node-link diagrams

with angles close to ninety degrees which was inves-

tigated in an eye tracking study by Huang (2007).

van Wijk (2005) discussed the value of visual-

ization. He measured the value of it by using effec-

tiveness and efﬁciency. Based on this he discusses

why visualizations are used in practice and why not.

Finally, he explores two views on diagrams by look-

ing at them as art or as scientiﬁc discipline. As a

good example for aesthetics in the sense of beauty

and in the sense of readability and explorability of

data, van Wijk discusses the work of Kleiberg et al.

(2001) on botanical trees. Those are inspired by the

metaphor of 3D trees in nature for visualizing hierar-

chical datasets.

In our work we try to subdivide diagram types into

several categories based on visual features. Those cat-

egories are then used to discuss beneﬁts and draw-

backs and ﬁnally, we ask a larger population how they

judge the aesthetics of them. This may help to gen-

eralize the beauty of diagrams and to compare them

with results of typical existing empirical user studies

on diagram readability aesthetics.

3 CATEGORIZATION OF

DIAGRAMS

Diagrams come in a variety of forms combining all

imaginable visual features to produce depictions of

data that beneﬁt from a high degree of readability.

Consequently, they allow to detect visual patterns

which can lead to derive insights when those can be

remapped to the original data.

In this section we propose a list of possible cat-

egories in which a diagram can fall in. These cate-

gories are used as basis to discuss beneﬁts and draw-

backs concerning insight detection as well as diagram

aesthetics in the sense of beauty.

• Old vs. New. This reﬂects if a diagram appears

before the invention of the computer (before 1941

as illustrated in Rojas (1998)) or after it. Con-

sequently, diagrams of the ﬁrst type are either

hand-drawn or produced mechanically, see Bertin

(2010). Diagrams of the second type are gener-

ated with computer aid, i.e. either by using a clas-

sical visualization tool which reads in datasets or

by manually composing the diagram of graphical

primitives.

• Colored vs. Non-colored. By these categories

we subdivide diagrams into those types that only

depict data as colored or non-colored images.

• 2D vs. 3D. Diagrams might also be categorized if

they make use of 3D, i.e. if they use a third di-

mension to project data on or not. This makes in

TheAestheticsofDiagrams

263

particular sense for data that already has an inher-

ent spatial dimension but it is more questionable

for data with an abstract 2D structure.

• Animated vs. Static. ’Animation is used for pre-

sentation, static displays for exploration’ is often-

times heard when dealing with this aspect of vi-

sual representation. We base one categorization

on the fact if data is shown in an animated fashion

or not.

• Radial vs. Cartesian. Some diagrams might be

mapped to polar coordinates making them to ra-

dial (or circular) representations whereas others

are mapped to a Cartesian coordinate system.

• Infographic vs. Traditional Diagram. Info-

graphics are typically used in newspapers and

magazines making a diagram more attractive to

the readership. Traditional diagrams are those

that are used in scientiﬁc research which typically

avoid visual ornaments.

• Space-ﬁlling vs. Non-space-Filling. The dis-

play space might be used efﬁciently by avoiding

empty spaces in which no data is encoded. In this

case one might speak of a space-ﬁlling diagram

whereas a non-space-ﬁlling diagram is the result

in the second case.

• Scientiﬁc vs. Information Visualization. Dia-

grams can also be categorized by the research ﬁeld

in which they are used most frequently. Scientiﬁc

visualization more frequently uses 3D visualiza-

tions since the analyzed data there has typically

an inherent spatial dimension and is of continu-

ous nature. In contrast, information visualization

typically deals with discrete (non-continuous) and

abstract data.

4 DIAGRAM READABILITY

AESTHETICS

Although there are many diagram types it is often

questionable which diagram is best suited for a given

scenario and task to be solved. In the sense of read-

ability aesthetics, comparative user studies can be

conducted focusing on ﬁnding a good candidate for a

certain purpose. The intent of this section is to brieﬂy

discuss diagram readability aesthetics and the beneﬁts

and drawbacks when diagrams of a certain category

described in Section 3 are used to visually support a

data analyst.

• Old diagrams typically use simple shapes and

simple geometrical primitives. In many cases, the

used color codings cannot compete with those of

today’s high-resolution displays. Consequently,

we expect that those old-fashioned diagrams are

not that useful for readability. Moreover, interac-

tion techniques as we understand them as com-

puter users are missing in such diagrams and they

do not scale to large datasets. Examples of such

diagrams are those designed by Bertin (2010).

• Colored diagrams have one great advantage over

non-colored diagrams. They have an additional

visual dimension on which a data attribute can be

visually encoded. But due to perceptual issues

we cannot use that many colors in a diagram as

illustrated in the book on perception for design

by Ware (2000), which can also be challenging

for people suffering from color deﬁciencies and

color blindness.

• Typical negative aspects such as occlusion effects

and visual clutter come into play when an ana-

lyst is dealing with 3D diagrams. Interaction tech-

niques such as rotation are a meaningful concept

to achieve more readable 3D diagrams. This is for

example also discussed in Heinrich et al. (2014)

who described the use of 1D, 2D, and 3D visual-

izations for molecular graphics. They came to the

conclusion that in this speciﬁc ﬁeld researchers

tend more to the 3D representations. Compared

to them, only a few number of 2D candidates ex-

ists. In the ﬁeld of software engineering, instead,

various 2D visualizations are used but 3D as in

software cities as described in Wettel and Lanza

(2007) is rarely applied.

• In particular, in ﬁelds which have to deal with

time-varying data, the question arises if it is dis-

played as a natural time-to-time mapping (anima-

tion) or as a time-to-space mapping (static). For

example, in dynamic graph visualization, there

exist these two concepts as surveyed in Beck et al.

(2014). Animated diagrams soon lead to prob-

lems to preserve the mental map, in particular

when subsequent diagrams have to be compared

to explore them for time-dependent visual pat-

terns. This is much easier in static displays for

dynamic data but there, visual scalability prob-

lems might occur. Moreover, the application of

interaction techniques is difﬁcult in animated dia-

grams. An interesting discussion on these aspects

is described in the work of Tversky et al. (2002).

• Bar and Neta (2006) found out that humans rather

tend to prefer visual objects when they contain

curved shapes. This aspect speaks in favor of ra-

dial or circular diagrams as surveyed by Draper

et al. (2009). But Burch et al. (2011a, 2013) eval-

uated in an eye tracking experiment that radial

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264

Figure 1: Our voting framework for recording diagram preferences: A dynamic graph visualization with applied edge

splatting by Burch et al. (2011b) received 41.7 percent of the votes (58th place) (Left). A dynamic graph visualized as

TimeRadarTrees by Burch and Diehl (2008) received 58.9 percent of the votes (9th place) (Right).

tree layouts cause twice as long completion times

and more complicated visual task solution strate-

gies as when traditional Cartesian tree layouts are

used. Consequently, it seems as if radial diagrams

are aesthetically appealing but from a readability

and space-efﬁciency point of view they are rather

questionable.

• Infographics typically use visual ornaments to

make the data depiction more aesthetically ap-

pealing. In traditional diagrams those ornaments

are regarded as chart junk which are not needed to

make data explorable. In many cases the viewer

has to inspect more visual elements in Infograph-

ics until he can derive insights. Infographics typ-

ically display statistical data and cannot handle

large data sets as information visualizations can

do.

• If a diagram is space-ﬁlling it can display a larger

amount of data into a single view, i.e. an overview

of a large amount of that data is visible as a start-

ing point for further explorations. But also non-

space-ﬁlling diagrams have their beneﬁts. If we

think of hierarchy visualizations like treemaps or

node-link diagrams, the treemaps are more space-

ﬁlling but the hierarchical organization in node-

link diagrams becomes more apparent.

• Typical diagrams in SciVis have to deal with

continuous, three-dimensional, and time-varying

data. This makes the corresponding SciVis dia-

grams more complex to design and generate. Con-

sequently, more complex algorithms are used to

compute visualizations which can compete with

the vast amount of data. In contrast to that, in In-

foVis, diagrams are typically 2D representations

and visually encode discrete data by using simpler

algorithms.

5 AESTHETICS STUDY

The focus of this study is not on diagram readability

aesthetics but more on the fact if a diagram is aes-

thetically appealing in the sense of beauty. To ﬁnd

this out we asked people in a web-based experiment

to vote for one of two given diagrams. The goal of

this crowdsourcing experiment was to get an impres-

sion about how the mass of people decides when it

comes to aesthetics judgments for diagrammatic rep-

resentations.

5.1 Diagram Collection and Stimuli

We collected 70 diagrams by using the image search

functionality of Google. We strategically looked for

diagrams of all categories, i.e. we integrated diagrams

which are static as well as animated, are 2D as well

as 3D, are radial as well as Cartesian and so on. It

may be noted that a diagram can contain several of

those visual features, i.e. a 3D pie chart is three-

dimensional, radial, static, and is frequently used in

information visualization but less frequently in scien-

tiﬁc visualization.

All images were of high resolution and were

scaled to ﬁt to the horizontal and vertical display

space. The question ’Which of both diagrams do you

like more?’ is clearly visible above both diagrams all

the time, see Figure 1. We use animated gifs to vi-

sually depict animated diagrams in an endless replay

scenario. Each participant can vote for exactly one

of both diagrams by selecting either A or B and by

conﬁrming this decision by a mouse click. After that

a new voting scenario was created, i.e. two different

diagrams were randomly selected and displayed in a

similar way.

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265

5.2 Participants

Our study particpants were recruited by posting a link

to the experiment on an author’s web page. To take

part, a login and a password are required. We did not

record any additional information such as the partici-

pant’s professional level, age, or gender, because we

do not want to discourage people from taking part in

the experiment. After two weeks, 127 people have

taken part voluntarily in the experiment which voted

6,744 times in total. We also recorded the timestamp

of the voting and how long it took them from seeing

the diagrams and conﬁrming the vote.

5.3 Study Design and Procedure

We ran the voting experiment for exactly two weeks.

The web-based experiment could be let run in each

standard browser by just logging in and using the cor-

responding URL. The voting web page was imple-

mented in PHP. Technical problems during the exper-

iment could be reported but fortunately, nobody re-

ported one.

The single task for the participants was to vote

for exactly one of two displayed diagrams. For this,

they had as much time as they wanted. The data was

recorded and stored in a text ﬁle which was later used

for evaluation of the study results.

5.4 Results

We counted how often a participant voted for a spe-

ciﬁc diagram and how often the diagram was involved

in a voting. This gives for each diagram a ratio of

positive votes and the number of total votes for that

diagram, giving a percentage value expressing the di-

agram aesthetics in the sense of beauty.

We base the ranking of all displayed diagrams in

our experiment on these percentages and computed

a decreasing order. Somewhat surprisingly, the 3D

pie charts (the only ones that we showed) were on

places one and two with 68.34 percent and 67.33 per-

cent, respectively. The diagram on the third place is

again a 3D diagram, but it is a very colorful three-

dimensional bar chart (Cartesian) representing quan-

tities in the plane (64.11 percent). This lets us assume

that colored and 3D diagrams tend to be more aesthet-

ically appealing to the viewer.

An also interesting phenomenon is the fact that

simple and well-known diagrams seem to be preferred

by the viewer, i.e. pie charts and bar charts as well as

weather maps with an animated hurricane on it (64.10

percent). A metro map (63.42 percent) also belongs

to the higher ranked diagrams.

Node-link tree diagrams, for example, of which

we showed eight, are only ranked lower than 53th

place and got an average percentage of only 38.59.

The lowest only got 32.95 percent. All the non-

colored diagrams are ranked very low which indicates

that colorful diagrams might be more attractive and

more beautiful.

What we also found interesting is the fact that

treemaps are ranked very low. A general treemap

only achieved 34.96 percent, which makes a 67th

place in the ranking. The cushion treemap is even

worse, getting only 29.37 percent at a 69th place. It

seems, as if there are two many rectangular shapes in

a space-ﬁlling representation, although the presented

treemaps are very colorful. Our assumptions might go

in the right direction if we inspect bubble treemaps,

being less space-efﬁcient, but using circular shapes.

Those bubble maps are ranked on a 6th place with a

percentage of 62.91.

Infographics are not considered that nice, all of

them are ranked in the second half in the ranking.

Maybe the color coding is responsible for that.

In summary, we can say that 3D, colored, and ra-

dial diagrams perform better in the study. Also ani-

mated diagrams are typically not ranked very low but

those are also not the winners in this experiment.

5.5 Threats to Validity

It may be noted that this is an uncontrolled experi-

ment, i.e. we know the login data from the participant

but we do not know who is actually taking part in the

study and we also do not know how much attention

and importance the participant is given to this study.

Consequently, we must be careful with the recorded

results and just present the results as a very prelim-

inary step towards the direction of investigating dia-

gram aesthetics. Many more studies must follow, also

in a controlled user study setting, maybe also by using

eye tracking techniques.

What is also problematic in this study is the miss-

ing comparability of the diagrams. This means, we

randomly show diagrams which depict different kinds

of data. A scenario showing different diagrams for

the same dataset would be beneﬁcial but then we can-

not compare, for example, diagrams for hierarchical

datasets with those for multivariate data. Moreover,

we only tested a limited number of diagrams. Since

there are various of them, it is nearly impossible to

test all of them. We tried to ﬁnd a ’good’ candidate

for many of them and to show them as representative

diagram for this class of diagrams in the study.

The evaluation of differences between user

groups, i.e. male vs. female, young vs. old, expe-

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266

rienced data analyst vs. layman etc. would also be

worth investigating. But in this study this is problem-

atic due to the uncontrolled web-based setting.

6 CONCLUSION AND FUTURE

WORK

In this paper we presented a discussion on the use

of diagrams in the ﬁeld of information visualization,

in particular we described beneﬁts and drawbacks by

categorizing them based on the visual features they

are based on. Apart from having a look at the useful-

ness for speciﬁc data analysis tasks we look more on

the aesthetics of the diagrams. To obtain better judg-

ments on such aesthetics and to strengthen or weaken

our subjective impressions of diagram types we con-

ducted a preliminary web-based user experiment. Par-

ticipants are confronted with two diagrams and have

to vote in favor of one diagram. The major result

of this uncontrolled comparative aesthetics study is

that our participants ﬁnd 3D, radial, and colored dia-

grams more aesthetically appealing than for example

2D, Cartesian, and non-colored representations.

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