1009 Research Theoretical & Empirical Xingyu Lan is with Fudan University and
a member of the Research Group of Computational and AI Communication at
Institute for Global Communications and Integrated Media; Yanqiu Wu and Nan
Cao are with Intelligent Big Data Visualization Lab at Tongji University.
E-mail: {xingyulan96, wuyanqiu.idvx<EMAIL_ADDRESS>Nan Cao is the
corresponding author.

# Affective Visualization Design:
Leveraging the Emotional Impact of Data

Xingyu Lan  Yanqiu Wu  and Nan Cao

###### Abstract

In recent years, more and more researchers have reflected on the
undervaluation of emotion in data visualization and highlighted the importance
of considering human emotion in visualization design. Meanwhile, an increasing
number of studies have been conducted to explore emotion-related factors.
However, so far, this research area is still in its early stages and faces a
set of challenges, such as the unclear definition of key concepts, the
insufficient justification of why emotion is important in visualization
design, and the lack of characterization of the design space of affective
visualization design. To address these challenges, first, we conducted a
literature review and identified three research lines that examined both
emotion and data visualization. We clarified the differences between these
research lines and kept 109 papers that studied or discussed how data
visualization communicates and influences emotion. Then, we coded the 109
papers in terms of how they justified the legitimacy of considering emotion in
visualization design (why emotion is important) and identified five
argumentative perspectives. Based on these papers, we also identified 61
projects that practiced affective visualization design. We coded these design
projects in three dimensions, including design fields (where), design tasks
(what), and design methods (how), to explore the design space of affective
visualization design.

###### keywords:

Information Visualization, Affective Design, Visual Communication, User
Experience, Storytelling

Introduction

In the 1880s, philosopher Friedrich Nietzsche wrote down his eloquent
argument, "thoughts are the shadows of our feelings" [60]. Although scientists
are still investigating the role and mechanism of emotion, there is a general
consensus that emotion is an indispensable part of human intelligence and has
a crucial influence on perception, cognition, and behavior [52]. In the
visualization community, although traditional criteria of evaluating data
visualization design emphasize rational metrics such as accuracy and
efficiency, an increasing amount of work has recognized the importance of
emotion in visualization [24, 90, 45]. For example, empirically, emotion has
been observed as a commonly pursued factor in fields such as artistic
visualization [41, 86, 90] and data storytelling [44, 47, 79, 92, 78], capable
of influencing user engagement and the recall of data [38, 47]. Theoretically,
researchers have introduced concepts such as emotional design and affective
design from domains such as industrial design and human-computer interaction
(HCI) to help examine the relationship between data visualization and emotion
[44, 45, 10]. Meanwhile, many data visualization projects that intend to
leverage the benefits of emotion have been proposed. For instance, researchers
have explored affective data visualization as a promising approach to
communicating data to a wide audience [8, 65]. In the wild, design projects
such as the U.S. Gun Death [64] have sparked heated discussion on the role of
visualization in evoking emotion and doing good for society [13].

However, the development of this thread of research still faces three main
challenges. Firstly, there is a need for a clear definition and
characterization of the research scope. For example, the concept, affective
visualization, has been used inconsistently in different papers (e.g., [10,
96]), thus hindering effective communication between researchers. Secondly,
there is still controversy surrounding the legitimacy of considering emotion
in visualization design. For example, from the perspective of traditional
rationalism-driven design norms, emotion seems to be at odds with reasoning.
Therefore, a frequently raised doubt is whether affective design will hinder
the objectivity of data [51, 24]. However, to our best knowledge, no work has
been done to systematically review the relevant arguments that are now
scattered in various publications and legitimize why emotion is important for
visualization design. Thirdly, we still lack a clear picture of how to
operationalize affective design on visualization, such as where to apply such
design, what tasks the design can undertake, and how to perform the design.
Only by answering such questions can we sort out existing knowledge and
practices in this growing field and suggest concrete strategies and techniques
for designers.

To address the above challenges, first, we conducted a literature review and
identified three research threads that examined both emotion and data
visualization. We clarified the differences between these research threads and
set one specific thread, affective visualization design (data visualization
designed to communicate and influence emotion), as the focus of this work
(Section 2). Next, we coded 109 papers concerning affective visualization
design based on how they argued for the legitimacy of considering emotion in
visualization design (in response to the why challenge mentioned above) and
categorized the arguments into five perspectives, including the perspectives
of application, usefulness, rhetoric, sociology, and humanism (Section 3).
Based on the 109 papers, we also identified 61 affective visualization design
projects whose authors have explicitly stated their intent to communicate and
influence emotion. We coded these design projects in three dimensions (design
fields, design tasks, design methods) corresponding to the where, what, and
how challenges to explore the landscape of affective visualization design
(Section 4). Last, we discuss our limitations and the research opportunities
arising from this work (Section 5).

##  1 Background

In this section, we review background knowledge about affective design and the
debate about emotion in visualization design.

###  1.1 Affective Design

Affect, as defined in the APA Dictionary of Psychology [1], represents any
experience of feeling or emotion, and is a critical component of human
intelligence (for simplicity, we follow prior work [66] and use affect and
emotion in the following texts interchangeably). Over the years, researchers
and practitioners have explored the mechanism and benefits of emotion and
applied the knowledge to various designs. For example, in the 1980s and 1990s,
a significant amount of psychological research was done to investigate the
relationship between color and emotion [83]. Today, color, along with other
emotional elements such as shapes, fonts, and iconography, are widely used in
graphic design to create emotive posters or web pages [88]. In the field of
industrial design, researchers started exploring emotional design [61] in the
early 2000s, focusing on eliciting emotion by manipulating the appearances and
functions of industrial products such as cars and furniture. In the field of
HCI, designing for emotion gained popularity especially after the
conceptualization of affective computing [66]. Ideas such as affective design
and affective user interface were proposed and embraced by HCI researchers to
develop affective chatbots, computer games, and VR envionments [11, 67, 35].

In the visualization community, it is not until recent years that researchers
began to highlight the opportunities of incorporating affective design in data
visualization [10, 45, 44]. Following the call, this work conducts a
systematic review of existing research and practices on affective
visualization design and outline the latest advances in this emerging cross-
disciplinary field.

###  1.2 The Debate About Emotion in Visualization Design

Although affective design is a well-recognized design paradigm in many fields,
it remains underexplored in the visualization community [45]. As mathematical
and factual depictions of information, data does not seem directly related to
emotion. The mainstream visualization design norms highlight that a good
visualization should be accurate, concise, and clear [82]. Under such a
rationalism and minimalism design paradigm, the legitimacy of including
emotional elements in data visualization faces challenges. For example, in the
debate about chart junk, the use of emotion-laden embellishments was
criticized as redundant and distracting [82]. Another common concern is that
including emotion may reduce the objectivity of data. According to historian
Porter [69], the pursuit of objectivity is a core spirit of modern science,
and quantification is viewed as "a technology of distance", where people
should separate their subjective emotions from objective facts.

On the other hand, different opinions constantly exist. As early as 1995, a
panel discussion was held at the IEEE Visualization Conference to reflect on
"Is visualization struggling under the myth of objectivity?" [36]. In this
panel, researchers discussed the tension between viewing visualization as
objective science and the inherent subjectivity of visual design. One
researcher argued that "visualization is resistant to the systematic
evaluation and assessment procedures common to science; it still remains
difficult to ascertain if a particular instantiation has been ’successful’".
In recent years, more and more arguments, reflections, and critiques have been
proposed. For example, Dragga [25] responded to the chart junk debate by
arguing that although pictographs are statistically redundant, they are not
emotionally redundant. Kennedy et al. [39] thought that the binary view of
emotion and reason has viewed emotion as irrational and has made emotion
intentionally undervalued in data science for a long time. In the wild, many
practitioners have created emotion-laden visualizations and defended the
necessity of communicating emotion with data [47, 13]. However, so far, such
arguments and practices remain highly scattered and have not been
systematically reviewed by the visualization community. For example, no
systematic work has been done to clarify why it is important to consider
emotion in visualization design. Additionally, no summarization has been done
to clarify what industries or needs can be served by affective visualization
design and how it should be designed. To fill these gaps, this work aims to
synthesize relevant research work and design practices about affective
visualization design to clarify the values of emotion in visualization and
derive design implications.

##  2 Corpus Collection

This section reviews the literature on affective visualization design. Below
we first introduce how we collected and refined the corpus, and then provide
an overview of the corpus.

###  2.1 Methodology

To start with, we went through papers published in three well-recognized
leading venues of data visualization research (IEEE TVCG, ACM CHI, and
EuroVis) in the recent three years (2020-2022) to identify an initial set of
qualified papers. As prior literature has suggested that affective
visualization design is close to art practice [90, 41], we also went through
papers published in IEEE VIS Arts Programs in the recent three years.
Specifically, we considered a paper qualified if: (i) it is about data
visualization, and (ii) it explicitly mentions emotion or emotion-related
words (e.g., surprise, joy) in its keywords, title, abstract, or major
findings. As a result, we identified 24 qualified papers [45, 44, 47, 48, 51,
20, 65, 72, 7, 73, 71, 94, 9, 74, 26, 70, 62, 57, 53, 50, 27, 58, 5, 37].

For example, Samsel et al. [74] designed a color tool for creating affective
scientific visualization. Lan et al. [47] investigated how visualization
design facilitates the communication of negative emotions in serious data
stories. Based on the 24 papers, we used the backward snowballing method
(going through the reference lists of existing papers to identify new papers
to include) to identify more potentially qualified papers and used the
aforementioned two criteria to judge whether a paper is truly qualified. For
example, some papers about user experience were deemed as potentially relevant
during the snowball process. However, a further search in their texts showed
that emotion is not a focal point of their research. Therefore, these papers
were not included in our corpus. If two papers have highly overlapping content
(e.g., a short paper and a full paper written by the same authors and dealt
with the same research problem), we kept the paper whose research is more
comprehensive. We iterated the above process until no more qualified papers
were identified.

###  2.2 Concept Clarification and Corpus Refinement

When constructing the corpus, a challenge that emerged immediately was the
ambiguity of research boundaries and concept usage. For example, although some
papers have indeed studied both visualization and emotion and were tagged as
qualified in the first cycle of paper collection, their research goals are
different. For example, we noticed that affective visualization has been used
in different contexts to refer to different meanings. To address this
challenge and also clarify the research scope of this work, we categorized the
papers into three research threads to elucidate their differences and refine
our corpus.

Emotion as the object being visualized. This thread of research performs
visual analytics on the data that reflects emotion, such as heart rates,
facial expressions, and gestures. In other words, emotion is the object being
visualized and analyzed. For example, Zhang et al. [96] developed a visual
analytics system to help mine the emotion in music videos and called the
visualization in the system affective visualization. Recently, when studying
virtual reality, a group of researchers used affective visualization to
describe the visualization of people’s affective states in immersive
environments [67]. Other similar work includes visualizing the emotion in
presentations and TED talks [95, 89].

Emotion as a precondition of viewing visualization. This thread of research
views emotion as a precondition that exists before viewing visualization. For
example, Harrison et al. [31] studied the priming effect of emotion by first
using pictures to evoke positive or negative emotions from the participants,
then asking the participants to view charts, and examining whether the
emotions would influence the accuracy of graphical perception. Thoresen et al.
[80] studied whether people’s anxiety levels would influence their modes of
reading maps.

Emotion as a result of visualization design. This thread of research studies
emotion as a result of viewing data visualization and highlights the need of
considering viewers’ subjective experiences in visualization design. For
example, Bartram et al. [10] defined affective visualization as a type of
visualization that "uses visual features to evoke a mood, feeling or
impression" and proposed a set of color palettes that can effectively convey
emotion in chart design. Lan et al. [44] presented Kineticharts, an animation
design scheme for creating animated charts that express five emotions commonly
desired by data storytelling.

In summary, we identified multiple threads of research that stand at the
intersection of visualization and emotion. In pursuit of a better
conceptualization of these research threads and facilitate clearer dialogues
between researchers, we propose: (i) As the concept, affective visualization,
has been used in a broad sense and has already involved diverse work about
emotion, its definition can be more inclusive and encompassing. By referring
to the definition of affective computing [66], we define affective
visualization as a data visualization that relates to, arises from, or
influences emotion. (ii) What this work intends to focus on is the third
research thread (emotion as a result of visualization design). As the core
issue that distinguishes this research thread from the other two is its
emphasis on the role of design, we use the term affective visualization design
in the following texts to refer to a class of data visualizations designed to
communicate and influence emotion.

Next, we refined our corpus and only kept papers about affective visualization
design. Note that we also found four papers that addressed hybrid research
problems. For example, Qin et al. [70] proposed a bio-feedback system called
HeartBees to visualize the emotion of the crowd. Meanwhile, the authors also
intentionally used metaphorical shapes and animation to create an affective
visual representation. As they also contributed knowledge about affective
visualization design, we included the four papers in our corpus. The final
corpus contains 109 papers in total (see https://affectivevis.github.io/).

###  2.3 Corpus Overview

In general, the number of papers concerning affective visualization design has
grown remarkably over the years. Before 2006, at most one paper was published
every year, but in 2022 alone, 14 papers were published. We then analyzed
which research areas the papers belong to. We searched the publication venues
of these papers on the Web of Science (WoS) and identified the research areas
tagged to these venues. For example, the research area of IEEE TVCG and the
proceedings of ACM CHI was tagged as Computer Science on WoS. For 17 papers
that were not searchable on WoS, we searched the home pages of their venues
manually and coded their research areas based on how these venues introduced
and defined themselves using WoS’s taxonomy [3]. Two authors first coded
independently and then compared their codes (Cohen’s Kappa = 0.82) and
discussed until achieving 100% agreement. For venues that have multiple area
tags, we used their primary areas to perform the following statistics.

Figure 1: The distribution of the 109 papers in our corpus over time in terms
of their (a) research areas and (b) paper types.

Firstly, we counted the number of papers published in each research area and
identified five areas that have more than one paper concerning affective
visualization design, including Computer Science (75), Geography (18),
Communication (7), Art (3), and Sociology (2). Four papers were published in
other areas, such as Chemistry (1) and Thermodynamics (1). In other words, we
found that affective visualization design has attracted the interest of
researchers from multiple communities. In terms of temporal trends, as shown
in Fig. 1 (a), the community of Geography has shown earlier interest in this
research topic. For example, in 1991, Gilmartin and Lloyd [29] conducted a
user study to examine how different map projections influence people’s
emotional involvement with places. In the early 2010s, a group of geographers
proposed ideas such as affective cartography [33] and affective
geovisualization [4], which pushed this line of research to its peak. It is
also in the 2010s that the papers published in the area of Computer Science
began to mushroom. To date, papers belonging to this area have become the main
part of affective visualization design research.

Next, we coded the research types of these papers following the six categories
(Theoretical & Empirical, Applications, Systems & Rendering, Representations &
Interaction, Data Transformations, Analytics & Decisions) defined by the IEEE
VIS conference [2]. As a result, as shown in Fig. 1 (b), most papers belong to
the Theoretical & Empirical category (76). These papers either proposed
theoretical frameworks, critiques, and observations or examined the
relationship between visualization design and emotion by conducting user
studies. The second most identified paper type is Representations &
Interaction (28), which explored or proposed new visualization approaches to
influencing emotion. A small number of papers belong to the Applications
category (5), which built visualization systems or tools to help generate or
create affective visualization design. Chronologically, Theoretical &
Empirical has also been the dominant research type. However, in recent years,
we see more novel methods (Representations & Interaction) and design tools
(Applications) being proposed to enrich the toolkit of affective visualization
design and lower its authoring barrier.

##  3 Why Affective Visualization Design

As mentioned in Section 1.2, as a design paradigm that embraces subjective
experiences, affective visualization design still faces controversy. To
address the controversy, we coded the 109 papers in our corpus following the
methodology of thematic analysis [14] with the goal of distilling existing
arguments in support of affective visualization design. Two authors first read
the papers one by one and marked any sentences that explain the authors’
justifications for studying affective visualization design. Then, we went
through these sentences and generated codes independently to summarize the
rationales behind the arguments. To further identify different perspectives of
argumentation, we also grouped similar codes into high-level themes. For
example, Kosara [41] argued that emotion is important for the domain of data
art, because artists usually value the sublime quality of design more than the
pragmatic quality. Therefore, this argument was coded as "emotion is
prioritized in some domains" and was assigned to a theme called "from the
perspective of application". Then, we met to compare our codes, unify the
wording of similar codes, and resolve disagreements through iterative
discussion. Last, we tagged the papers again using the final coding scheme. In
total, we identified seven different arguments categorized into five
perspectives.

Figure 2: Examples of the five argumentative perspectives: (P1) affective data
stories published by news media during the COVID-19 pandemic [47], (P2)
emotional color palettes can facilitate the cognitive interpretation of maps
[7], (P3) two objective-looking but rhetorical charts [24], (P4) a vintage
pictograph that practiced Victorian values [42], (P5) a "cruel pie" about
human fatalities [25] and the call for data humanism [54].

###  3.1 From the Perspective of Application

As data visualization is serving a wide range of domains and users, the design
requirements of data visualization are becoming increasingly diverse. In our
corpus, 76 papers argued the legitimacy of affective visualization design from
the perspective of realistic needs.

Emotion is valued by certain domains. Different domains have different needs,
values, and priorities. For example, for data artists, emotion is a vital
channel of self-expression and can spark imagination and creativity [19, 90].
For the media industry, communicating emotion through visualization design is
also common, and multiple studies have observed the integration of affective
visualization design in various visual stories published by news agencies,
governments, or non-profits [45, 46]. For example, during the COVID-19
pandemic, many data stories were created to evoke shock, fear, or grief for
deaths [47] (Fig. 2 P1). In addition, emotion is also valued by domains such
as education and entertainment [30, 16]. For example, when investigating the
popularity of Wordle (a web-based tool for visualizing texts), Viégas et al.
[87] concluded that "emotional resonance seems to be a key reason for
preference of wordles over tag clouds" because it enables the public to play
with data and customize their own visualizations.

New application scenarios are emerging. Several researchers justified the
necessity of considering emotion in data visualization design based on new
technology trends and market needs. For example, in 2004, given the trend that
"the contents of digital archives that permeate our daily lives become more
emotionally charged", a set of systems that visualize personal data were
developed [86]. Around 2010, Cartwright et al. [17] and van Lammeren et al.
[85] proposed that the maturity of Web 2.0 technology and 3D modeling
technology can expand the functions of maps. For example, people can now use
maps to fulfill more affective intents, such as annotating places with their
cherished stories or sharing the stories with beloved ones.

###  3.2 From the Perspective of Usefulness

Secondly, 67 papers justified the role of emotion in visualization design by
providing evidence from empirical studies or neuroscience to demonstrate
emotion is useful and can bring benefits.

Emotion is not the enemy of rationality. In response to the challenge that
emotion may hinder rational thinking about data, Kennedy and Hill [38] cited
various arguments from sociologists and philosophers to criticize the
stereotype of the "reason/emotion binary". Aitken and Craine [21, 4] argued
for the legitimacy of conveying emotion in cartography by citing work from
neuroscience, which has proven that emotion is not separate from, but is a
huge part of rationality. When studying the affective design of infographics,
Lan et al. [45] referred to the dual-process theory in psychology to
demonstrate that emotion is heavily involved in thought processes and can co-
exist with rationality. Similarly, Kostelnick [42] and D’ignazio et al. [24]
agreed that emotion can complement rather than contradict the logical analysis
of data. In several user studies [47, 44], researchers indeed observed that
affective visualization design was not at odds with data comprehension.
Anderson and Robinson [7] found that maps whose color palettes convey
congruent affective messages with content were felt more appropriate and less
confusing than those that use incongruent colors, showing that emotion can
work with or even amplify rationality (Fig. 2 P2).

Emotion brings additional benefits. First, emotion has been found useful for
enhancing user engagement with data. For example, Lan et al. [44] found that
in data storytelling, emotion showed a significantly positive correlation with
engagement-related metrics such as focused attention, enjoyment, interest, and
likability. Qualitatively, Kennedy and Hill [38] observed that people were
more likely to spend time with a data visualization when being emotionally
aroused by its design or content. In addition, emotion has also been observed
to enhance people’s perceived connection with data [29, 44] and influence how
they memorize data [47]. Emotion also influences people on a deeper level,
such as changing attitudes, values, or behaviors. For example, Boy et al. [13]
and Morais et al. [58] cited prior work from psychology and sociology to
illustrate that emotions such as empathy can help people perceive others’
misery and promote prosocial behavior. Lan et al. [47] used a set of serious
data stories that convey negative messages about COVID-19 as stimuli and found
that appropriate design methods helped evoke deep emotions and strengthened
contemplative thoughts and self-reflection.

###  3.3 From the Perspective of Rhetoric

Seventeen papers argue that data visualization design is a rhetorical device
that always integrates subjective factors.

Objective data visualization does not exist. In response to the challenge that
adding emotion to visualization may make data less objective, many researchers
argued that no data visualization is truly objective, because data
visualization design, as all design forms, is rhetorical [15, 59, 25, 51, 24,
39]. For example, D’ignazio and Klein [24] took two charts posted on New York
Times as examples (Fig. 2 P3); the two charts both look concise, "objective",
and are high in the data-ink ratio, but they deliver different messages (the
blue chart shows the unemployment rate in the US decreased during the
Democrats’ administration while the red chart shows the rate is constantly
high from a Republican perspective). D’ignazio and Klein discussed the
rhetorical intentions behind these designs and argued that charts that create
the "illusion of objectivity" can be even more deceptive than affective
charts. Similarly, Kennedy et al. [39] thought the use of clean layout and
geometric shapes are common conventions that create "the aura of objectivity"
in data visualization. Recently, Lee-Robins and Adar [51] also argued that
data is not neutral.

###  3.4 From the Perspective of Sociology

The arguments of nine papers emphasize that the current tendency to avoid
emotion in visualization design is not a golden rule, but is constructed by
the historical context, culture, and social values.

The emphasis or suppression of emotion is a social phenomenon. Emotion was
once highlighted in history. For example, Kostelnick [42] found that emotion
played an important role in chart design in the 1800s when Romantic values
(which emphasized individualism, emotion, and imagination) dominated all forms
of design. At that age, visualization designers were also bold to use lavish
color, pictorial elements, or epic narratives to enhance emotional appeal
(e.g., Fig. 2 P4). However, after the Enlightenment, rationalism has become a
dominating value, breading the tendency to undervalue emotion [42, 38]. In a
study, van Koningsbruggen and Hornecker [84] found that participants described
feeling not being allowed to emotionally connect to visualizations and emotion
was intentionally got rid of, and they argued that "a form of post-hoc
rationalisation takes place, which obscures people’s initial connections and
affective responses to visualisations".

###  3.5 From the Perspective of Humanism

Five papers argue from the angle of humanism, which claims that data
visualization should be humane, ethical, and do good to society.

Data should not be cold and cruel. For example, Dragga [25] pointed out that
statistical charts designed in a minimalistic style can be emotionless and
ignore the real people behind the data. He took several charts about
fatalities and disasters as examples, criticizing them as "cruel bars" and
"cruel pies" (Fig. 2 P5, left). Likewise, Mccleary [56] thought that a
conventional map design would appear indifferent if it visualizes data about
genocide. In recent years, some researchers [6, 15, 72] have recognized or
applied the idea of data humanism proposed by visualization designer Giorgia
Lupi [54] (Fig. 2 P5, right). In Lupi’s original essay, she called, "to
envision ways to use data to feel more empathic, to connect with ourselves and
others at a deeper level…we have to bring data to life - human life."

##  4 Exploring the Landscape of Affective Visualization Design: Where, What,
and How

In this section, we analyze design projects that have practiced affective
visualization design and explore what the design space is.

###  4.1 Methodology

Based on the 109 papers we collected earlier, first, we went through all
papers and kept papers that are affective visualization design projects
themselves (design studies). As a result, 36 out of 109 papers belong to this
situation. For example, Concannon et al. [20] designed a data film to enhance
the emotional closeness between people and data. Lan et al. [44] proposed a
design scheme called Kineticharts for creating animated charts that express
emotions. Next, we also searched the reference lists of the 109 papers to
identify more in-the-wild design projects that are potentially qualified. For
example, U.S. Gun Death [64] is a design project cited by multiple papers [47,
51] as a representative example of affective visualization design. However,
although such design projects were considered affective by researchers, we
lacked first-hand justifications and explanations from their authors, thus
preventing us from understanding their true design intents or tasks.
Therefore, for each of the design projects, we searched its publication page,
the author’s personal website, or any interviews or talks that introduced the
project to double-check the design intents. For example, we found that the
designers of U.S. Gun Death have indeed recognized their affective intents by
saying that "it’s a massive collection of human emotion hidden within rows and
rows of numbers" [63] and "we need people to react, we need people to sort of
get riled up about things, get excited about things, and want to make a change
in the world" [68]. Therefore, we viewed it as a qualified project of
affective visualization design. After the double-checking process, we
identified 25 in-the-wild design projects whose designers have explicitly
stated their intent to communicate and influence emotion using data
visualization. Combining with the 36 qualified projects identified earlier, we
in total collected 61 projects as the exemplars of affective visualization
design.

Then, based on the collected papers or materials where the designers explain
their design intents, considerations, and procedures, we coded the 61 design
projects in three dimensions: (i) design fields, (ii) design tasks, and (iii)
design methods. Specifically, (i) concerns where a design problem is formed
and originated, (ii) is about what goals a design intends to achieve, and
(iii) concerns how to perform a design. For (iii), we followed the design
analysis of narrative visualization [77] and coded both genres and techniques.
Specifically, genre refers to a high-level category of design characterized by
a particular style or form, while technique refers to low-level methods used
to create that design. Similar to the coding methodology in Section 3, two
authors first independently went through the design projects and generated
codes and themes regarding the three dimensions. To ensure appropriate wording
of the techniques, we familiarized ourselves with conventional concepts in
design literature. For example, anthropomorphism has been used by
visualization researchers [13, 53, 34] to describe the technique of showing
concrete human behind data. After this round of coding, we achieved consensus
on 11 (61%) techniques, such as color, typeface, and anthropomorphism. For the
remaining techniques, we discussed and refined their wording until achieving
100% consensus. For example, one coder initially used the conventional
concept, animation, to describe the technique that utilizes motion to convey
emotion. However, given that not all the design projects in our corpus are
displayed on the screen, animation is not suitable to describe the kinetic
effects designed for genres such as installations. To enhance the
generalizability of this design technique, we finally refined its wording as
kinetic movement.

###  4.2 Design Space

After the coding process, we derived the design space in Table 1. The table
contains 61 rows, each representing one affective visualization design
project. The columns represent the three dimensions mentioned above and are
divided into several sub-columns. The table uses categorical hues to represent
different genres. For other cells, the table uses a check mark colored in grey
to indicate the presence of a particular factor or class. Examples of these
design projects can be seen in Fig. 3.

####  4.2.1 Where: Design Fields

Figure 3: Examples of affective visualization design projects.

Design fields concern where a design need originated, thereby indicating the
problem that the design seeks to address and suggesting the scenarios for its
application. Among the 61 design projects, 16 (26%) projects deal with issues
about environmental sciences & ecology. For example, Fig. 3 A, M, N were
motivated by global warming. Fig. 3 O and Q address the issue of pollution and
Fig. 3 E presents the facts of water waste. 15 (25%) projects address social
issues, such as public security, welfare, injustice, or inequality. For
example, Fig. 3 C and L visualize the gun problem in the US. Fig. 3 B and I
visualize the deaths in wars. Fig. 3 H reveals the problem of sexual
harassment, and Fig. 3 K focuses on the caring of disadvantaged groups. 11
(18%) projects are about health & well-being, such as illness and mental
stress. As an example, Fig. 3 P visualizes suicide data to showcase the
problem of depression. Other fields identified include news & media (7%),
daily life (5%), business (3%), urban studies (3%), and history (3%). Four
projects (7%) address mixed topics and two projects (3%) have not specified
which fields their design problems originated from.

####  4.2.2 What: Design Tasks

We identified 10 different tasks of affective visualization design.

Inform (N = 35). This task concerns communicating certain information to the
audience successfully (e.g., making people aware of or understand a fact) with
the help of evoking emotion. For example, Grewingk Glacier (Fig. 3 A) is a
data-driven installation that visualizes the shrinking speed of the glacier in
Alaska. As time passes, the architecture of this installation collapses more
quickly, mimicking the fast recession of the glacier while creating a strong
emotional impact. Through the design, the designer aims to "bridge the gap
between reason and emotion" and "synthesize information into knowledge through
an intently subjective human experience" [76].

Engage (N = 27). This task is about motivating people to consume and spend
time with visualization (e.g., attracting attention, sparking interest,
enticing likes or sharing) through eliciting emotion. For example, Iraq‘s
bloody toll (Fig. 3 B) is an infographic that visualizes the number of
soldiers who died in the Iraq war. It turns a red bar chart upside down as if
blood is dripping down. As explained by the designer, the design choice was
made deliberately to create a striking visual and draw readers into the
graphic [75].

Experiment (N = 14). This task focuses on realizing the needs of designers
themselves to test new design approaches, exercise novel ideas, or stretch the
boundaries of affective design. For example, given the new possibilities posed
by VR technology, Ivanov et al. [34] created a data story about gun shootings
(Fig. 3 C) to explore the elicitation of emotional responses through an
immersive environment.

Provoke (N = 12). This task guides people to think, reflect, contemplate, and
inspire new thoughts through emotion. For example, EdiPulse [27] (Fig. 3 D) is
an edible visualization designed to "provoke critical discourse" about
physical activities. The designers translated users’ heart rate data into
3D-printed slogans, emojis, and flower-like graphs using chocolate to create a
playful and fun experience.

Advocate (N = 11). This task recommends ideas, values, or proposals to viewers
and regards eliciting emotion as a means to persuade participation and action.
For example, The Water We Eat (Fig. 3 E) is an interactive website that
advocates action in saving water. This web page visualizes the huge amount of
water consumption every day and urges viewers strongly to change their living
habits [32].

Socialize (N = 11). This task views emotion as an essential factor for social
activities such as sharing, gift-giving, and opinion exchange. For example, in
the Dear Data project [55], two designers drew visualizations on postcards
based on their personal data collected every week and sent the postcards to
each other (e.g., Fig. 3 F are postcards drawn to show "A week of envy"). The
designers used these visualizations to collaborate and make friends [23].

Heal (N = 5). This task intends to increase the well-being of people through
the elicitation of positive emotions and the reduction of stress or pain. For
example, StressTree (Fig. 3 G) maps people’s stress levels to the colors of a
tree metaphorically, thus guiding them to regulate their breathing to make the
tree look more healthy. This visualization was designed to help people relax
and reduce negative thoughts [93].

Empower (N = 4). This task focuses on making people (especially vulnerable
populations) stronger and more confident in expressing their feelings,
controlling their life, and claiming their rights. For instance, The Academia
is Tied in Knots (Fig. 3 H) surveyed women who have experienced sexual
harassment and transformed the data into an interactive interface (the "knots"
can be unfolded to show their testimonies) to help bring the sensitive and
uneasy stories to the foreground [26].

Commemorate (N = 3). This task makes people honor and remember someone or
something. For example, Blood Swept Lands and Seas of Red [22] (Fig. 3 I)
installed 888,246 ceramic poppies (each representing a dead soldier) engulfing
the Tower of London during the 100th anniversary of Britain’s participation in
the World War I. Its designer said, "we couldn’t think of a more appropriate
and fitting tribute to the remembrance of those that fell during the World
Wars…the Tower of London plays the perfect backdrop to this humbling tribute."
[81].

Archive (N = 2). This task is about the documentation of personally meaningful
experiences and feelings. For example, to archive her walking experience in a
park, Gardener [28] captured data (e.g., colors, physical objects such as
leaves and petals) that triggered her sensory experiences at different
locations along the walking route, and then visualized the data as space-time
cubes (Fig. 3 J) to facilitate subjective interpretation and recollection.

####  4.2.3 How: Design Methods

This section reports the methods of affective visualization design, including
high-level genres and low-level techniques.

Genres. As illustrated in Fig. 4, we identified a total of six genres of
affective visualization design. The first one is the interactive interface,
where users can interact with (e.g., click, scroll, rotate) data visualization
on web pages or visual systems. For example, Fig. 3 C, E, H, L all belong to
this genre. The second one is the video, which presents data visualization
dynamically as a series of continuous frames. For example, Fig. 3 K is a data
film produced to elicit empathy for young adults who leave the care system in
England. The third genre is the static image/painting, such as infographics
drawn in computer software and graphs drawn by hands (e.g., Fig. 3 B, F). The
fourth genre, installation, refers to the data-driven display installed at
places such as museums, streets, and galleries. For example, Fig. 3 A, I, Q
were all installed outdoors and can be encountered by any passers-by. The
fifth genre is called artifact, which denotes the physical object made by
hands or devices (e.g., a 3D printer). Compared to installations, artifacts
are often small objects that are casually used or placed, such as the data-
driven scarf in Fig. 3 N and the ice cubes in Fig. 3 J. The last genre is
called event, namely an activity intentionally designed for people to
participate in and experience data. For example, Fig. 3 M is a “walking data
visualization” which asked the participants to walk along the future coastline
of a city. Many participants experienced a sense of urgency and concern
because the streets they walked on will be flooded by sea water in the future.
Overall, compared to the design genres identified in the previous decade [77],
the genres of affective visualization design have significantly expanded and
diversified.

Figure 4: Six genres of affective visualization design. Table 1: Analysis of
61 affective visualization design projects. The splits in the "design genre"
column indicate that the corresponding design projects are a combination of
two or more different genres. More detailed information can be browsed at
https://affectivevis.github.io/.

Techniques. We identified a total of 18 low-level design techniques used by
the 61 affective visualization design projects to influence emotion. We
categorized these techniques into four strategies (S1-S4).

S1: Sensation. Appealing to various senses is ranked as the most applied
strategy for affective visualization design. In terms of specific design
techniques, the use of realistic/metaphorical imagery such as photographs,
figurative data glyphs, and visual metaphors was mentioned extensively by
designers. For instance, the installation in Fig. 3 A uses the imagery of ice
to evoke viewers’ intuitive perception of glaciers. Fig. 3 B presents a bar
chart metaphorically to create an imagery of blood. Fig. 3 P uses figurative
symbols to represent different types of suicide and deliberately composes them
into a peaceful landscape. Fig. 3 I utilizes the symbolic meaning of poppies
to mourn for deaths. Apart from this technique, the use of color, kinetic
movement, sound, shape, and typeface has also been mentioned by designers as
approaches to influencing emotion. A few projects have also leveraged the
emotional quality of smell and taste.

S2: Narrative. The second most applied strategy for affective visualization
design is narrative, namely using storytelling methods to communicate data
affectively. The most mentioned technique in this category is personalized
setting. Given that people generally show stronger emotion to things that
relate to themselves, designers have explored various means of improving the
sense of proximity. For example, Fig. 3 K lets viewers select their own
location and then shows them a customized data film based on the chosen
location. The second most identified technique in this category is
anthropomorphism, namely showing the real people behind data [57]. For
example, Fig. 3 H uses one glyph to show one testimony from a victim of sexual
harassment, and Fig. 3 C allows viewers to "meet" the casualties of gun
shootings and read their identities, thus transforming abstract numbers into
people with flesh and blood. In addition, designers have adopted techniques
such as narrative structure and wording/phrasing to manipulate the flow of
data narrative and augment emotion through textual expressions.

S3: Behavior. Behavioral design is a design method that seeks to shape or
influence human behavior. During our analysis, we identified three main
techniques of behavior design. The first technique asks people to
manipulate/operate a data visualization. For example, the designers of Fig. 3
O prepared a set of river lanterns that encoded the data of river pollution in
a community and invited the residents to release the lanterns into the river,
thus engaging the residents emotionally with the data. The second technique
asks people to create/build a data visualization by themselves, such as the
knitting of data-driven scarves in Fig. 3 N. The third technique makes people
experience data during a walk/wander. For example, Fig. 3 M asked people to
walk along the route of the future coastline to subjectively experience
climate change, and Fig. 3 Q asked people to step into parks, streets, and
neighborhoods to monitor air quality in an embodied manner.

S4: Context. Context means the surrounding environment when people view or
interact with a data visualization. For example, a viewer may read a data
visualization from home or on the road, which can distance him/her from the
content being portrayed in the visualization. To cope with such disconnection,
several design projects put a visualization at a situated place (somewhere
related to the content being presented in a data visualization design). For
example, apart from creating visualizations about river pollution, Fig. 3 O
also brought local residents to the polluted river to heighten the emotional
impact. Fig. 3 I chose the Tower of London as the place to install the poppies
because it is a representation of national honor and is related to the theme
of the data visualization, namely sacrificing for the country. We also found
three projects that used VR technology or large screen projection technology
to create an immersive environment, such as Fig. 3 C.

####  4.2.4 Design Space Observations

By observing how the cells distribute in Table 1, we summarize implications
from three main aspects.

Task distribution. The most mentioned tasks of affective visualization design
are inform (mentioned by 57% of all projects) and engage (44%), followed by
experiment (23%), provoke (20%), advocate (18%), socialize (18%), heal (8%),
empower (7%), commemorate (5%), and archive (3%). Most projects claimed hybrid
tasks, and the average number of tasks undertaken per design project is 2.03
(SD = 1.06). Two projects claimed as many as five tasks. For example, Dear
Data [55] project (Table 1 #35) was initiated by the motivation to make
friends (socialize) and experiment how to visualize personal data through
hand-drawing postcards. During the drawing process, the two designers also had
more reflections on their own lives (provoke) and used the drawing as a
therapy to face themselves honestly (heal). Meanwhile, they also published the
postcards to engage more people with data visualization [23]. We also
calculated how these design tasks interact with different design fields. For
example, in the field of environmental sciences & ecology, 69% projects
reported the inform task, 50% reported the engage task, and 31% reported the
socialize task. In the field of social issues, 53 % projects reported the
inform task, 47% reported the engage task, and 40% reported the advocate task.
When designing for daily life, 67% projects intended to archive, provoke, and
socialize. In general, although inform and engage are the dominant tasks in
most cases, different fields still show varied priorities in design tasks.

Genre distribution. The most popular affective visualization design genre in
our corpus is the interactive interface (adopted by 44% of all projects),
followed by installation (25%), artifact (18%), event (13%), static
image/painting (11%), and video (10%). Although most design projects belong to
a single genre, we identified 12 projects that belong to hybrid genres. For
example, three projects (Table 1 #11-13) published two versions of their
design, including an interactive interface and a video. Three projects (Table
1 #44-46) created a data-driven installation while organizing an outdoor
event. Project #47 is the only design work that spans three genres. Its
designers first set a data-driven installation (a cluster of balloon sensors
that monitor air quality) at public sites, and then distributed the balloons
to volunteers and asked them to wander in the city and play with the balloons
(event). In addition, they also organized a workshop for people to create the
balloons by themselves (artifact). As for the interaction between genres and
design fields, interactive interface is the primary genre within most fields,
such as news & media (adopted by 75% projects within this field), social
issues (67%), and health & well-being (45%). However, for environmental
sciences & ecology, compared to interactive interface (19%), the dominant
genre is installation (63%). One reason may be that environmental issues are
closely related to the physical world, so many designers decided to invite
people to experience data in outdoor space rather than just showing data on
screens.

Technique distribution. Overall, sensation is the most applied strategy to
elicit emotion, and among all the techniques within this strategy, the most
adopted techniques are realistic/metaphorical imagery (adopted by 52%
projects), color (28%), kinetic movement (26%), and sound (21%). The primary
sense being designed is the visual sense, followed by the auditory sense. The
strategy of narrative has also been practiced by many projects, with
personalized setting (26%) and anthropomorphism (16%) being the most
frequently applied techniques. The two techniques both serve to enhance the
individuality of data, either by making it relevant to viewers themselves or
by showing other people behind data. It has also been observed that some
techniques are preferred by specific genres. For example, genres such as
interactive interface, video, and static image/painting often evoke emotion
through sensation and narrative. By contrast, genres such as event and
artifact tend to appeal more to sensation and behavior. This distinction may
be attributed to the nature of these genres, as the former three genres are
more digital, whereas the latter two genres are more physical. However, we did
not find an absolute boundary between these design genres in terms of using
design techniques. For example, digital genres can use VR technology to create
a sense of physical context (e.g., Table 1 #6) or employ interactive features
to manipulate user behavior online (e.g., Table 1 #16). Likewise, physical
artifacts can also integrate digital elements (e.g., sensors, 3D printing) to
dynamically generate personal narratives (e.g., Table 1 #56, 57). These design
practices suggest that the fusion of online and offline experiences and the
exploration of cross-media design are taking place in affective visualization
design.

##  5 Discussion and Future Work

Below we discuss our limitations and future research opportunities.

###  5.1 Limitations

In the corpus collection stage, we used the snowball search method to collect
as much qualified literature as possible. However, the corpus may not be
exhaustive as the snowball searching relies on the references cited in the
initial set of papers, potentially overlooking less-cited sources that are not
referenced in the initial set. Secondly, the 61 design projects used for
characterizing the design space of affective visualization design are either
research papers themselves or in-the-wild projects cited by research papers.
Therefore, although being typical examples of affective visualization design
in the eyes of researchers, they are by no means exhaustive nor representative
of the full landscape of affective visualization design practice. We believe
that more case studies and interviews with practitioners can be conducted to
help identify and analyze the affective visualization design practice in the
wild. Also, alternative analysis perspectives or taxonomies can be proposed to
further advance our understanding of such design practice.

###  5.2 Opportunities For Future Research

We identify nine specific research opportunities (marked as O1-O9) categorized
into three main avenues.

Scientific evaluation of affective design techniques. The design space in
Section 4 indicates a set of design techniques that designers thought can
influence emotion. However, as shown in prior work [47], sometimes there will
be a gap between designers’ expectations and people’s real experiences. Among
the identified techniques, although some have been validated by controlled
user studies as being effective in influencing emotion, such as color and
animation [10, 44, 46], a considerate number of techniques still lack
evaluation. Therefore, more future work needs to be done to (O1) assess the
relationship between visualization design techniques and emotion. Second,
controversy still exists in terms of the effectiveness of certain techniques.
For example, anthropomorphism was found insignificant in augmenting empathy by
Boy et al. [13], while it showed a small effect in another larger-scale study
[58]. In a study about map design, people got significantly more emotionally
involved with locations close to themselves [29], but in another study, such a
personalized narrative showed no significant effect [20]. To ensure the
reliability of research findings, future studies can (O2) replicate existing
results in diverse settings and cross-validate conflicting outcomes. Third, as
more and more state-of-the-art technology has been incorporated into affective
visualization design, the design space of affective visualization design will
go on to expand. It is important to study (O3) how novel techniques such as
AR/VR/MR, 3D printing, and multi-modal interaction (e.g., sound, smell)
influence emotion and how they interact with other design elements. Fourth,
more efforts can be put into enhancing the rigor of research methods.
Researchers can (O4) conduct studies with more diverse stimuli and larger
sample sizes, and try alternative measures (e.g., neuro-physiological measures
to understand emotion more in-depth.

Embracing more application scenarios and users. Through this work, we are
excited to see affective visualization design being applied to various domains
and being used as a novel approach to solving domain problems and serving
realistic needs. We see journalists, non-profits, and feminists use data
visualization to raise awareness of social injustice. We see environmentalists
and geographers use data visualization to call for green lifestyles. We see
artists create data visualization to touch the tender part of human hearts. We
see scientists transform complex, cold data into vivid, affective
representations. In the future, we believe that (O5) the idea of affective
visualization design can be introduced to a wider range of disciplines and
industries, leading to more interesting applications. Another important lesson
we have learned from this work is that, rather than seeing people as passive
information receivers, many design projects have treated people as active
agents or collaborators. Such a paradigm has been framed by some researchers
and designers as participatory action research, citizen science, or grassroots
movements [43, 8, 65]. Under this paradigm, designers in fact relinquish some
design rights to people and allow them to participate in or even dominate the
process of data collection and visualization. In the future, we may further
(O6) explore the approaches to stimulating people’s sense of agency in data
visualization design and bringing data democratization to a higher level.
Relating to this issue, (O7) more authoring tools can be developed to empower
ordinary people to create or customize affective visualization design.
Currently, this type of tool is surprisingly rare [91, 72, 18].

Evoking emotion appropriately and ethically. Although this work focuses on
presenting the arguments and design projects that support evoking emotion with
data visualization design, we do not claim that eliciting emotion is always
good or that objectivity and neutrality should be tossed away. Emotion is a
double-edged sword, and an important future research direction is to (O8)
examine the pitfalls or side effects of eliciting emotion with data
visualization. For example, emotion may make people impulsive and prejudiced.
Psychologist Bloom [12] argued for putting empathy aside in public decision-
making because "empathy is biased; we are more prone to feel empathy for
attractive people and for those who look like us or share our ethnic or
national background…and empathy is narrow; it connects us to particular
individuals, real or imagined, but is insensitive to numerical differences and
statistical data." Another viewpoint is embedding a pre-defined emotion in a
visualization may hinder viewers’ freedom to interpret the design emotionally
on their own terms. For example, cartographer Kent [40] thought that “while
the emotional association with a specific place would perhaps be affected by
the amount of detail apparent in its portrayal…the absence of detail inherent
to cartographic symbolization allows a free play of the imagination necessary
for the development of emotions associated with that sense of place”. In
addition, the power of emotion may be exploited by people with malevolent
intents. For example, social science has a long history of examining how
politicians and businessmen incite emotion to gain profit or deceive people
with low literacy, as well as how emotional contagion aids the spread of
violent or destructive behavior [49]. Therefore, it is crucial to (O9) study
the ethical issues surrounding affective visualization design and develop
guidelines that promote the responsible use of such design.

##  6 Conclusion

In this work, we have identified three distinct research threads that study
visualization and emotion and defined affective visualization design as data
visualizations designed to communicate and influence emotion. We have reviewed
109 papers concerning affective visualization design to characterize this
growing field and outline the necessity of considering emotion in
visualization design. We have also analyzed 61 affective visualization design
projects to explore the design space of such practices, including where to
apply the design, what tasks the design can undertake, and how to perform the
design.

Affective visualization design is an ongoing research field that is full of
creativity and innovation. It responds to various emerging needs in the real
world and also deepens our understanding of data visualization, such as what
constitutes a good data visualization, how a visualization should be
evaluated, and what visualization can do for individuals and society. We
believe that more investigation and discussion of affective visualization
design will help the visualization community embrace more ideas, values,
application scenarios, and wider users.

###### Acknowledgements.

This work was supported by NSFC 62072338 and NSF Shanghai 20ZR1461500. We
would like to thank all the reviewers for their valuable feedback.

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