Storytelling and Visualization: A Survey

Chao Tong

, Richard Roberts

, Robert S. Laramee

, Kodzo Wegba

, Aidong Lu

, Yun Wang

Huamin Qu

, Qiong Luo

and Xiaojuan Ma

Visual and Interactive Computing Group, Swansea University, U.K.

Department of Computer Science, University of North Carolina at Charlotte, U.S.A.

Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong

Keywords:

Storytelling, Narrative, Information Visualization, Scientiﬁc Visualization.

Abstract:

Throughout history, storytelling has been an effective way of conveying information and knowledge. In the

ﬁeld of visualization, storytelling is rapidly gaining momentum and evolving cutting-edge techniques that

enhance understanding. Many communities have commented on the importance of storytelling in data visuali-

zation. Storytellers tend to be integrating complex visualizations into their narratives in growing numbers. In

this paper, we present a survey of storytelling literature in visualization and present an overview of the com-

mon and important elements in storytelling visualization. We also describe the challenges in this ﬁeld as well

as a novel classiﬁcation of the literature on storytelling in visualization. Our classiﬁcation scheme highlights

the open and unsolved problems in this ﬁeld as well as the more mature storytelling sub-ﬁelds. The beneﬁts

offer a concise overview and a starting point into this rapidly evolving research trend and provide a deeper

understanding of this topic.

1 MOTIVATION

“We believe in the power of science, exploration,

and storytelling to change the world” - Susan Gold-

berg, Editor in Chief of National Geographic Maga-

zine, from “The Risks of Storytelling”, October 2015

(Goldberg, 2015).

“In a world increasingly saturated with data and

information, visualizations are a potent way to break

through the clutter, tell your story, and persuade

people to action” (Singer, 2014). -Adam Singer,

Clickz.com, “Data Visualization: Your Secret Wea-

pon in Storytelling and Persuasion”, October 2014.

Throughout history, storytelling has been an ef-

fective way of conveying information and knowledge

(Lidal et al., 2013). In the ﬁeld of visualization, story-

telling is rapidly developing techniques that enhance

understanding. Many communities have commented

on the importance of storytelling in data visualization

(Segel and Heer, 2010). Storytellers tend to be inte-

grating complex visualizations into their narratives in

growing numbers.

As contributions, we present a survey reviewing

storytelling papers in visualization and present an

overview of the common and important elements in

storytelling visualization. We also describe the chal-

lenges in this ﬁeld and present a novel classiﬁca-

tion of the literature on storytelling in visualization.

Our classiﬁcation highlights both mature and unsol-

ved problems in this area. The beneﬁt is a concise

overview and valuable starting point into this rapidly

growing and evolving research trend. Readers will

also gain a deeper understanding of this rapidly evol-

ving research direction.

Deﬁnition and Storytelling Elements. A story can

be deﬁned as “a narration of the events in the life of

a person or the existence of a thing, or such events as

a subject for narration” (Reference, a) or “a series of

events that are or might be narrated” (Dictionary, a).

Storytelling is a popular concept that is used in many

ﬁelds, such as media (Segel and Heer, 2010), educa-

tion (Zipes, 2013) and entertainment (Schell, 2008).

Storytelling is a technique used to present dynamic re-

lationships between story nodes through interaction.

According to Zipes (Zipes, 2013), storytelling can

involve animation and self-discovery, incorporating

models, ethical principles, canons of literature, and

social standards. In education, a storyteller can im-

prove and strengthen the literacy of students. Also,

the storyteller can engage audiences so they feel a de-

sire to read, write, act, and draw. Audience members

can learn to express themselves critically and ima-

ginatively with techniques they may learn from the

storyteller or teacher.

212

Tong, C., Roberts, R., Laramee, R., Wegba, K., Lu, A., Wang, Y., Qu, H., Luo, Q. and Ma, X.

Storytelling and Visualization: A Survey.

DOI: 10.5220/0006601102120224

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages

212-224

ISBN: 978-989-758-289-9

Classiﬁcation of Literature and Challenges in

Storytelling and Visualization. Although storytel-

ling has been developing in other ﬁelds for years,

storytelling is a relatively new subject in visualiza-

tion. As such, it faces many challenges. In this survey

we have extracted the fundamental characteristics of

storytelling both as an entity and as a creative pro-

cess. Our literature classiﬁcation is based on the lo-

gical notions of who are the main subjects involved

in storytelling for visualization (authoring tools and

audience), how are stories told (narratives and tran-

sitions), why can we use storytelling for visualization

(memorability and interpretation). From these cha-

racteristics we have then developed the following di-

mensions which are common to storytelling in visua-

lization.

Authoring-Tools: Authorship addresses who cre-

ates the story and narrative. Authorship commonly

refers to the state or fact of being the writer of a

book, article, or document or the creator of a work

of art (Dictionary, b) and its source or origin (Refe-

rence, b). Central to this deﬁnition is the writer or

author. Rodgers(Rodgers, 2011) deﬁnes an author as

“an individual solely responsible for the creation of a

unique body of work.”

User-engagement: Engagement is about the au-

dience and also concerns why we use storytelling.

How can we ensure that the message comes across

to the audience? Can we measure engagement?

Narratives: Narrative concerns how an author

tells a story. Narrative structures include events and

visualization of characters. Narrative visuals contain

the transition between events. This entails, “Using

a tool to visually analyze data and to generate visu-

alizations via vector graphics or images for presen-

tation,” and then deciding ”how to thread the repre-

sentations into a compelling yet understandable se-

quence.”(Hullman et al., 2013b)

Transitions: Transitions are about how authors

may tell the story. Transitions seamlessly blend

events within a story and are key to its ﬂow. Success-

ful transitions vary actions as little as possible to

strengthen overall coherence. Transitions in visuali-

zation can be either dynamic or static.

Memorability: Memorability addresses why aut-

hors present data in the form of a story. Memorability

is an important goal of storytelling. A good visualiza-

tion technique draws the viewer’s attention and incre-

ase a story’s memorability (Bateman et al., 2010).

Interpretation: Data interpretation refers to the

process of critiquing and determining the signiﬁcance

of important data and information, such as survey re-

sults, experimental ﬁndings, observations or narrative

reports.

When examined in the context of storytelling in

visualization each dimension raises interesting ques-

tions: Are current storytelling platforms taking into

account the role of the author and supporting the aut-

horship process? What forms of narrative structures

and visuals best apply to storytelling in visualization?

Are static transitions or dynamic transitions more ef-

fective for storytelling in visualization? Can visuali-

zation increase the memorability of data information

or knowledge? Does storytelling and visualization aid

with data interpretation? What is the most effective

way to engage an audience? Data preparation and

enhancement is another challenge for which there is

currently no literature. Thus we include it as a future

research direction but not in our classiﬁcation.

Starting from the logical notions of who, how,

why, and these open questions we have chosen these

dimensions to form the basis of our literature classiﬁ-

cation on storytelling in visualization. See Table 1. It

is important to note that some papers address multiple

topics in Table 1 and in our classiﬁcation. We placed

papers by what we determined to be the main focus of

the paper. This is very useful for obtaining an over-

view. However some papers address more than one

theme, e.g. authoring tools and narratives.

Classiﬁcation of Literature: The Second Dimen-

sion. In addition, the literature is also classiﬁed by

the ordering or sequence of events, which refers to the

traversal the path viewer takes through the visualiza-

tion. This dimension is adapted from Segal and Heer

(Segel and Heer, 2010). It forms our second categori-

zation for Table 1. The classiﬁcation includes:

Linear: A story sequence path in linear order is

prescribed by the author.

User-directed Path: The user selects a path

among multiple alternatives or creates their own path.

Parallel: several paths can be traversed or visua-

lized at the same time.

Random Access or Other: There is no prescri-

bed path. There is currently no literature prescribing

random order. Therefore we replace this with a co-

lumn called “overview”.

Literature Search Methodology. We search both

the IEEE and ACM Digital libraries for the

terms “storytelling”, “narrative visualization”, “me-

morability”, “transitions in visualization”, “user-

engagement”, and various combinations of these

phrases. We focus primarily on the IEEE TVCG pa-

pers. We check the references of each paper and

looked for related literature on storytelling. We also

search the visualization publication data collection

Storytelling and Visualization: A Survey

213

Table 1: Our classiﬁcation of the storytelling literature. The y-axis categories fall into who-authoring-tools and user-

engagement, how-narrative and transitions, why-memorability and interpretation. See section 1 for a complete description.

Linear User-directed/Interactive Parallel Overview

Who

Authoring-Tools

Gershon et al. 2001(Gershon and Page, 2001)

Lu and Shen, 2008(Lu and Shen, 2008)

Cruz et al. 2011 (Cruz and Machado, 2011)

Wohlfart, 2006 (Wohlfahrt, 2006)

Wohlfart et al. 2007(Wohlfahrt and Hauser, 2007)

Lidal et al. 2012 (Lidal et al., 2012)

Lee et al. 2013(Lee et al., 2013)

Lidal et al. 2013(Lidal et al., 2013)

Lundblad et al. 2013(Lundblad and Jern, 2013)

Fulda et al. 2016(Fulda et al., 2016)

Amini et al. 2017 (Amini et al., 2017)

Eccles et al. 2007(Eccles et al., 2008)

Kuhn et al. 2012(Kuhn and Stocker, 2012)

User

Engagement

Figueiras, 2014 (Figueiras, 2014a)

Boy et al 2016 (Boy et al., 2015)

Borkin et al,2016 (Borkin et al., 2016)

Mahyar et al.,2015 (Mahyar et al., 2015)

How

Narrative

Hullman et al. 2013 (Hullman et al., 2013a)

Hullman et al. 2013 (Hullman et al., 2013b)

Gao et al. 2014 (Gao et al., 2014)

Amini et al. 2015 (Amini et al., 2015)

Bach et al. 2016 (Bach et al., 2016)

Viegas et al. 2004(Vi

egas et al., 2004)

Hullman et al. 2011(Hullman and Diakopoulos, 2011)

Figueiras, 2014 (Figueiras, 2014a)

Figueiras, 2014 (Figueiras, 2014b)

Nguyen et al, 2014 (Nguyen et al., 2014)

Satyanarayan et al. 2014 (Satyanarayan and Heer, 2014)

Gratzl et al. 2016 (Gratzl et al., 2016)

Akashi et al. 2007(Akaishi et al., 2007)

Fisher et al. 2008(Fisher et al., 2008)

Hullman et al. 2011(Hullman and Diakopoulos, 2011)

Bryan et al. 2017(Bryan et al., 2017)

Segel and Heer, 2010(Segel and Heer, 2010)

Lee et al. 2015(Lee et al., 2015)

Static

Transitions

Ferreira et al. 2013(Ferreira et al., 2013)

Robertson, 2008(Robertson et al., 2008)

Chen et al. 2012(Chen et al., 2012)

Tanhashi et al. 2012(Tanahashi and Ma, 2012)

Liu et al. 2013(Liu et al., 2013)

Ferreira et al. 2013(Ferreira et al., 2013)

Animated

Transitions

Heer et al. 2007 (Heer and Robertson, 2007)

Liao et al. 2014 (Liao et al., 2014)

Bederson and Boltman, 1999(Bederson and Boltman, 1999)

Akiba et al. 2010(Akiba et al., 2010)

Why

Memorability

Bateman et al. 2010(Bateman et al., 2010)

Borkin et al, 2016(Borkin et al., 2016)

Saket et al. 2015 (Saket et al., 2015)

Interpretation Ma et al. 2012(Ma et al., 2012) Kosara and Mackinlay, 2013(Kosara and Mackinlay, 2013)

(Isenberg et al., 2015) for these major themes in vi-

sualization and storytelling. Google scholar is also

used as part of our search methodology.

In summary, our literature search includes:

1. IEEE EXPLORE Digital Library

2. ACM Digital Library

3. Visualization publication data collection (Isen-

berg et al., 2015)

4. the annual EuroVis conference

5. the Eurographics Digital Library

Several other papers were discovered by looking

at the related work section of the papers we found.

Survey Scope. The storytelling visualization pa-

pers summarized in this survey include the subjects

of scientiﬁc visualization, information visualization,

geo-spatial visualization, and visual analytics. In or-

der to manage the scope of this survey, storytelling

papers from other ﬁelds are not included, such as:

Virtual Reality and Augmented Reality: For ex-

ample, Santiago et al. (Santiago et al., 2014) present

“mogre-storytelling” as a solution to interactive story-

telling. This tool provides different functionalities for

creating and the customization of scenarios in 3D,

enables the addition of 3D models from the Internet,

and enables the creation of a virtual story using mul-

timedia and storytelling elements.

Education: For example, Cropper et al. (Crop-

per et al., 2015) address the extent of how scientiﬁc

storytelling beneﬁts our communication skills in the

sciences, and the connections they establish with the

information itself and others in their circle of inﬂu-

ence.

Gaming: Alavesa et al. (Alavesa and Zanni, 2013)

describe the development of a small scale pervasive

game which can take storytelling from camp-ﬁre sites

to modern urban environments.

Multi-media and Image Processing: For exam-

ple, Chu et al. describe a system to transform any

temporal image sequence to a comics-based storytel-

ling visualization (Chu et al., 2015). Correa and Ma

present a narrative system to generate dynamic narra-

tive from videos (Correa and Ma, 2010). Image pro-

cessing falls outside the scope of this survey. Video

processing also falls outside the scope of the survey

(Amini et al., 2015).

Language Processing: Theune et al. (Theune

et al., 2006) develop a story generation system. It can

create story plots automatically based on the actions

of intelligent agents living in a virtual story world.

The derived plots are converted to natural language,

and presented to the user by an embodied agent that

makes use of text-to-speech.

There are other ﬁelds that study storytelling as

well. In the next sections we describe the literature

on storytelling in visualization. Our classiﬁcation is

presented in Table 1.

2 AUTHORING-TOOLS FOR

STORYTELLING AND

VISUALIZATION

Authorship refers to writing or creating a book, arti-

cle, or document, or the creator of a work of art ac-

cording to The Oxford English dictionary(Dictionary,

b) , especially with reference to an author, creator

or producer (Reference, b). For our purposes, we

will adopt a deﬁnition of author described by Rod-

gers(Rodgers, 2011), “An author is best described as

an individual solely responsible for the creation of

a unique body of work.” Hullman (Hullman et al.,

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

214

2013b) et al. state, “Story creation involves sequential

processes of context deﬁnition, information selection,

modality selection, and choosing an order to effecti-

vely convey the intended narrative”.

All papers in this section focus on authoring-

tools for storytelling. Wohlfart and Michael (Wohl-

fahrt, 2006) create new volume visualization sto-

ries for medical applications. Gershon (Gershon and

Page, 2001) and Cruz (Cruz and Machado, 2011)

present general storytelling for information visualiza-

tion. Kuhn (Kuhn and Stocker, 2012), Lee (Lee et al.,

2013) and Plowman (Plowman et al., 1999) all deve-

lop unique creator tools for storytelling visualization.

Authoring-tools for Linear Storytelling. Gershon

and Page state that storytelling enables visualization

to reveal information as effectively and intuitively as

if the viewer were watching a movie (Gershon and

Page, 2001).

Lu and Shen propose an approach to reduce the

number of time steps that users required in order to

visualize and understand the essential data features by

selecting representative datasets (Lu and Shen, 2008).

Lu and Shen (Lu and Shen, 2008) is based on the

previous work of time-vary visualization (Hansen and

Johnson, 2011) and design a general method for com-

paring data dissimilarities.

Cruz et al. (Cruz and Machado, 2011) present ge-

nerative storytelling as a conceptual framework for in-

formation storytelling.

Authoring-tools for User-directed and Interactive

Storytelling. A large body of research has been car-

ried out for authors wishing to create their own user-

oriented or interactive stories. This literature focu-

ses on interactive, user-driven authorship (as opposed

to automatic or semi-automatic authorship). Story-

telling is a relatively new form of interactive volume

visualization presentation (Wohlfahrt, 2006). Wohl-

fart presents a volumetric storytelling prototype ap-

plication, which is based on the RTVR (real time vo-

lume redering) Java library (Mroz and Hauser, 2001)

for interactive volume rendering. Each story action

group stores the scene changes relative to its prece-

ding action group (or story node) (Wohlfahrt, 2006;

Wohlfahrt and Hauser, 2007).

Wohlfart is based on previous work of volume vi-

sualization (Gooch et al., 1998; Haber and McNabb,

1990; Viola, 2005) and interactive visualization (Do-

nald, 1993) and combine these concepts together to

develop a storytelling model for volume visualization.

Lidal et al. (Lidal et al., 2012)(Lidal et al., 2013) pre-

sent a sketch-based interface for rapid modelling and

exploration of various geological scenarios.

Lidal et al. is based on a previous storytelling mo-

del (Wohlfahrt, 2006) for scientiﬁc visualization (Ma

et al., 2012) and develops a storytelling model for ge-

ological visualization.

Lee et al. present SketchStory, a data-enabled di-

gital white board to support real-time storytelling. It

enables the presenter to stay focused on a story and

interact with charts created during presentation(Lee

et al., 2013). Lee et al. is based on previous work

for storytelling of information visualization (Gershon

and Page, 2001; Segel and Heer, 2010) and sketch-

based interaction (Li et al., 2012), and develops the

SketchStory system to enhance storytelling in a pre-

sentation.

Lundblad and Jern (Lundblad and Jern, 2013)

present geovisual analytics software with integrated

storytelling. Lundblad and Jern is based on the pre-

vious work of the storytelling concept (Gershon and

Page, 2001) and work of web-based geovisual tools,

integrates storytelling with geovisual analytics soft-

ware.

Authoring-tools for Parallel Storytelling. In this

category of literature, authors create stories in paral-

lel. In other words there may be multiple authors wor-

king in parallel i.e. simultaneously for the ﬁnal out-

come. This is opposed to a single author as in the

previous subsection.

Eccles et al. (Eccles et al., 2008) presents the Ge-

oTime stories prototype that combines a geo-spatial

map with narrative events to produce a story frame-

work. This system uses a similar approach to Sense.us

(Heer et al., 2007a).

The CodeTimeline visualization by Kuhn and

Stocker (Kuhn and Stocker, 2012) enables developers

who are new to a team to understand the history of the

system they are working on. Prior to Kuhn and Stoker,

Ogawa (Ogawa and Ma, 2010; Ogawa and Ma, 2009)

presents “software evolution storylines” and “Code

Swarm”, which focus on the interactions between de-

velopers on projects but do not focus on telling a story

about the software history. Codebook, a concept pre-

sented by Begel et al(Begel et al., 2010), outlines a

social network that connects software engineers with

their shared code base.

3 USER ENGAGEMENT

The literature in this category addresses an impor-

tant but less developed research topic, namely user

engagement. In other words, who do we engage with

storytelling and how can we engage an audience?

Storytelling and Visualization: A Survey

215

Mahyar et al. (Mahyar et al., 2015) address how

prior research in different domains deﬁne and mea-

sure user engagement. Their work is based on pre-

vious work of Bloom’s taxonomy (Bloom, 1974) and

adapts it to information visualization.

User Engagement for User-directed Visualization.

The literature in this subsection focuses on inte-

ractive, user-driven visualization for user engage-

ment. Engagement speciﬁcally focuses on each user’s

investment in the exploration of a visualization (Boy

et al., 2015). Boy et al. use low-level user interaction

e.g. the number of interactions with a visualization

that impact the display to quantify user engagement.

Boy et al is based on previous work on narrative

visualization (Hullman and Diakopoulos, 2011) and

user-centred metrics (Gotz and Wen, 2009).

4 NARRATIVE VISUALIZATION

AND STORYTELLING

Narrative structures include events and visualization

of characters. An example narrative can be a simple

interface that presents trends in keywords over time

(Fisher et al., 2008). Narrative visuals contain the

transition between events. It involves “using a tool

to visually analyze data and to generate visualizati-

ons via vector graphics or images for presentation” to

decide “how to thread the representations into a com-

pelling yet understandable sequence”(Hullman et al.,

2013b). Plowman et al (Plowman et al., 1999; Eccles

et al., 2008) report that a narrative speciﬁcally refers

to the macro-structure of a document in contrast to

the term story which refers to both structure and con-

tent. This structuring of evidence, combined with the

choice of appropriate rhetorical strategies, is referred

to as “the art of storytelling” among literary scholars

(Plowman et al., 1999). Research in narrative visu-

alization points to visualization features that afford

storytelling including guided emphasis and structu-

res for reader-driven storytelling. It also includes the

principles that govern effective structuring of transi-

tions between consecutive visualizations in narrative

presentations, and how different tactics for sequen-

cing visualizations are combined into global strate-

gies in formats like slideshow presentations. We se-

parate transitions into their own section, section 4 and

section 5, because of their importance.

All papers in this section develop methods or

structure on how to improve narrative storytelling vi-

sualization. Viegas et al. (Vi

egas et al., 2004) present

methods for improving data memorability. Fisher et

al. (Fisher et al., 2008) present ways for tracking nar-

rative events over time. Segal and Heer (Segel and

Heer, 2010) investigate the design of narrative visu-

alizations and identify techniques for telling stories

with data. Hullman et al. (Hullman and Diakopou-

los, 2011; Hullman et al., 2013a; Hullman et al.,

2013b) design the structure of a visualization to pre-

sent storytelling. Figueiras (Figueiras, 2014b; Figuei-

ras, 2014a) studies how to incorporate narrative ele-

ments as storytelling elements. Again, these papers

may cover more than one topic in Table 1. The bor-

ders between categories are not 100% black & white.

We place papers in the category reﬂecting their main

focus.

Narrative Visualization for Linear Storytelling.

The literature in this sub-section focuses on nar-

rative visualization using linear automatic or semi-

automatic approaches (as opposed to interactive ap-

proaches). The research here involves tools and

techniques with an emphasis on how stories are crea-

ted.

Hullman et al. describe a system called contex-

tiﬁer, which automatically produces custom, annota-

ted visualizations from a given article (Hullman et al.,

2013a). Hullman et al. is based on previous work in

storytelling in visualization (Segel and Heer, 2010)

and Kandogan’s automatic annotation analytics (Kan-

dogan, 2012).

Hullman et al. (Hullman et al., 2013b) outline how

automatic sequencing (the order in which to present

visualizations) can be approached in designing sys-

tems to help non-designers navigate structuring de-

cisions in creating narrative visualizations. This pa-

per is based on previous work of narrative sequen-

cing(Black and Bower, 1979) and narrative visuali-

zation(Hullman and Diakopoulos, 2011; Segel and

Heer, 2010), and demonstrates that narrative sequen-

cing can be systematically approached in visualiza-

tion systems.

Amini et al (Amini et al., 2015) identify the high-

level narrative structures found in professionally cre-

ated data videos and identify their key components.

Amini et al is based on previous work on storytelling

(Gershon and Page, 2001) and storytelling in infor-

mation visualization (Hullman et al., 2013a).

Bach et al. (Bach et al., 2016) develop graph co-

mics for data-driven storytelling to present and ex-

plain temporal changes in networks to an audience.

Bach et al. is based on previous work on network ex-

ploration (Herman et al., 2000) and data-driven story-

telling (Gershon and Page, 2001).

Narrative Visualization for User-Directed and In-

teractive Storytelling. The literature in this sub-

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

216

section focuses on interactive, user-driven narra-

tive visualization (as opposed to automatic or semi-

automatic). In other words, the papers focus on

techniques that enable users to create narratives in-

teractively. Viegas et al. (Vi

egas et al., 2004) summa-

rize two methods of visualizing email archives with

the aim of improving memorability of the data.

Previous visualizations of online social interaction

data have been focused on unravelling the data from

the researchers’ perspective, whereas these visualiza-

tions are for the beneﬁt of the user (Boyd et al., 2002;

Donath, 1995).

Hullman and Diakopoulos state that narrative in-

formation visualizations are a style of visualization

that often explores the interplay between aspects

of both exploratory and communicative visualization

(Hullman and Diakopoulos, 2011). Hullman and Di-

akopoulos is based on the previous work of Segel and

Heer (Heer et al., 2007b).

A narrative-based visualization attempts to create

a natural ﬂow whereby the data has an obvious pro-

gression and therefore permits easier understanding

and memorability (Figueiras, 2014b). Figueiras is ba-

sed on previous work of storytelling (Hullman and

Diakopoulos, 2011)(Ma et al., 2012)(Segel and Heer,

2010) and narrative visualization (Fisher et al., 2008),

and develops a model to add storytelling in narrative

visualization (Figueiras, 2014b).

Storytelling aims to simplify concepts, create

emotional connection, and provides capacity to help

retain information (Figueiras, 2014a). Figueiras pre-

sents the results of a focus group study on collecting

information on narrative elements. Figueiras is ba-

sed on previous work of narrative visualization (Segel

and Heer, 2010), and storytelling visualization (Ko-

sara and Mackinlay, 2013; Ma et al., 2012).

Nguyen et al. (Nguyen et al., 2014) develop a

new timeline visualization, SchemaLine, to gather, re-

present, and analyze information. Their work is ba-

sed on previous work of timeline visualization (Ma

et al., 2012; Tanahashi and Ma, 2012) and sensema-

king with timeline (Pirolli and Card, 2005).

Narrative Visualization for Storytelling in Parallel.

In this category of literature, the structure of events is

layed out in parallel. The research here focuses on

tools and techniques that create multiple narratives at

once, in other words simultaneously. These can be

useful for groups.

Information visualization systems enable users to

ﬁnd patterns, relationships, and structures in data

which may help users gain knowledge or conﬁrm

hypotheses (Akaishi et al., 2007).

Fisher et al. (Fisher et al., 2008) present narrative

as a way of presenting temporally dynamic data. In

this case, narratives help the user by tracking concepts

found in news stories that change over time. Fisher et

al. show how to piece together complex information

and examine multiple variables.

Fisher et al. is based on previous work in topic de-

tection and tracking(Dubinko et al., 2007) (Swan and

Jensen, 2000), and temporal visualization (Van Wijk

and Van Selow, 1999), and presents narrative as a new

technique in visualization (Fisher et al., 2008).

Narratives Visualization Overviews. Segel and

Heer state that storytelling is revealing stories with

data and using visualization to function in place of

written story (Segel and Heer, 2010). They also

discuss directions for future reader-centric research

(Heer et al., 2007b). Segel and Heer is based on pre-

vious work of narrative structure, visual narratives,

and storytelling with data visualization (Heer et al.,

2007b) and observes the storytelling potential of data

visualization and drawn parallels to more traditional

media.

5 STATIC TRANSITIONS IN

STORYTELLING FOR

VISUALIZATION

A transition refers to the process or a period of chan-

ging from one state or condition to another according

to the Oxford English Dictionary (Dictionary, c). In

the visualization literature, transitions may be the fo-

cus of visualization and include both dynamic and sta-

tic which are alternatives of presenting visualization.

Static visualizations are those that do not rely on ani-

mation. Transitions may be considered part of narra-

tive storytelling. However, we designate the literature

here in its own category to reﬂect the importance of

transitions and to keep related literature on this topic

together. Several research papers focus on the transi-

tions in storytelling. This is why they are separated

into a special group.

In this section, the visual designs of transitions

is generally static. The authors focus on presen-

ting the trend of data along timelines. Robertson et

al(Robertson et al., 2008) evaluate three approaches

of using bubble charts and attempts to discover which

one works best for presentation and analysis. Tana-

hashi and Ma (Tanahashi and Ma, 2012) presents a

storyline visualization which consists of a series of

lines, from left to right along the time-axis. Liu et

al. (Liu et al., 2013) design a storyline visualization

system, StoryFlow, to generate an aesthetically ple-

Storytelling and Visualization: A Survey

217

asing and legible storyline visualization. Ferreira et

al(Ferreira et al., 2013) propose a method of visua-

lizing a large amount of taxi data consisting of both

spatial and temporal dimensions.

Static Transitions for User-directed and Inte-

ractive Storytelling. The literature in this sub-

section focuses on interactive user-driven transitions.

The user creates static transitions interactively, i.e.

using a process they have some control over (as op-

posed to automatically).

TaxiVis proposes a method of visualizing a large

amount of taxi data consisting of both spatial and tem-

poral dimensions (Ferreira et al., 2013). Taxi beha-

viour is a popular focus of research. Among others,

Veloso et al. explored patterns and trends in taxi ride

data looking at the relationship between pick up and

drop off points (Veloso et al., 2011a; Veloso et al.,

2011b). Liao et al. developed a visual analytics sy-

stem to error check GPS data streamed from taxis

(Liao et al., 2010).

Static Transitions for Parallel Storytelling. In this

category of literature, the static transitions are shown

in parallel. In other words, many transitions can occur

simultaneously. Robertson et al. (Robertson et al.,

2008) deﬁne a trend in data as an observed general

tendency.

The gapminder trendalyzer uses a bubble chart to

show four dimensions of data, life expectancy is map-

ped to the x axis, infant mortality is mapped to the y

axis, population is mapped to bubble size and conti-

nent is mapped to color (Rosling, 2006).

An alternative multi-dimensional trend visualiza-

tion provides the user with the ability to select parti-

cular bubbles such that the animation shows a trace

line for the selected bubble as it progresses (Rosling,

2007). They are further grouped by continent and or-

dered alphabetically within each group (Tufte, 1990).

Robertson et al. is based on earlier work by Tversky

et al. (Tversky et al., 2002) and Baudisch et al. (Bau-

disch et al., 2006).

Visual Storylines, by Chen et al. is designed to

summarize video storylines in an image composition

while preserving the style of the original videos (Chen

et al., 2012). Chen et al. is based on the work of video

summarization (Yahiaoui et al., 2001) and ﬁrst clus-

ters video shots according to both visual and audio

data to form semantic video segments.

Storyline visualization is a technique that portrays

the temporal dynamics of social interactions by pro-

jecting the timeline of the interaction onto an axis

(Tanahashi and Ma, 2012). Tanahashi and Ma (Tana-

hashi and Ma, 2012) is based on the idea of XKCD’s

hand-drawn illusion ”Movie Narrative Charts” (Ogie-

vetsky., 2009) and develops an algorithm for general

storyline visualization.

Liu et al. (Liu et al., 2013) design a storyline visu-

alization system, StoryFlow, to generate an aestheti-

cally pleasing and legible storyline visualization. Liu

et al. is based on previous work of Tanahashi et al.

(Tanahashi and Ma, 2012).

6 ANIMATED TRANSITIONS IN

STORYTELLING FOR

VISUALIZATION

Gonzalez and Cleotilde deﬁne animation as a series

of varying images presented dynamically according

to user actions, in ways that help the user to perceive

a continuous change over time and develop a more ap-

propriate mental model of the task (Gonzalez, 1995).

Animated Transitions for Linear Storytelling.

The literature in this sub-section focuses on animated

transitions using automatic, or semi-automatic appro-

aches(as opposed to interactive techniques to anima-

ted transitions).

Heer and Robertson investigate the effectiveness

of animated transitions in traditional statistical data

graphs, such as bar charts, pie charts, and scatter plots

(Heer and Robertson, 2007). Heer and Robertson is

based on the previous work of Bederson and Boltman

(Bederson and Boltman, 1999) but builds upon it by

testing different transitional events.

Animated Transitions for User-directed and In-

teractive Storytelling. The literature in this sub-

section focuses on interactive, user-driven transitions.

The user or users create animated transitions interacti-

vely (as opposed to automatically as in the previous

section). Bederson and Boltman examine how ani-

mating a viewpoint change in a spatial information

system affects a user’s ability to build a mental map

of the information in the space (Bederson and Bolt-

man, 1999). Bederson and Boltman is based on Gon-

zalez (Gonzalez, 1996) and Donskoy and Kaptelinin

(Donskoy and Kaptelinin, 1997) which address the

relationship between animation and users’ understan-

ding.

Akiba et al. introduce an animation tool named:

AniVis for scientiﬁc visualization exploration and

communication(Akiba et al., 2010). Akiba et al. is

based on previous animation support (Childs et al.,

2005) and an animation enhanced system (Correa and

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

218

Silver, 2005) and develops template-based visualiza-

tion tools for animation.

7 MEMORABILITY FOR

STORYTELLING AND

VISUALIZATION

Memory refers to the faculty by which things are re-

membered; the capacity for retaining, perpetuating,

or reviving the thought of things past according to the

Oxford English Dictionary (Dictionary, d). Memora-

bility is an important goal of storytelling. A good vi-

sualization technique engages the viewer’s attention

and increases a story’s memorability (Bateman et al.,

2010).

All papers in this section evaluate the effects of

visualization on memorability. Bateman et al. (Bate-

man et al., 2010) explore the effects of embellishment

on comprehension and memorability. Saket et al. (Sa-

ket et al., 2015) illustrate that map-based visualization

can improve accuracy of recalled data comparing with

node-link visualization.

Borkin et al. (Borkin et al., 2013) develop an on-

line memorability study using over 2000 static visu-

alizations that cover a large variety of visualizations

and determine which visualization types and attribu-

tes are more memorable.

Memorability for Linear Visualization. The lite-

rature here shows and tests visual designs in linear

order. Users are asked to compare the visual de-

signs (e.g. standard bar charts) verses embellished bar

charts. In other words, users are tested on their ability

to recall one visual design at a time in linear fashion.

Bateman et al. examine whether embellishment is

useful for comprehension and memorability of charts

(Bateman et al., 2010). Fourteen embellished charts

are selected from Nigel Holmes’ book Designer’s

Guide to Creating Charts and Diagrams (Holmes,

1984), and converted to plain charts. Previous stu-

dies have suggested that minor decoration in charts

may not hamper interpretation (Blasio and Bisantz,

2002), and work in psychology has shown that the use

of imagery can affect memorability (Gambrell and Ja-

witz, 1993), but there is very little work that looks at

how chart imagery can affect the way people view in-

formation charts.

Borkin et al. (Borkin et al., 2016) present the ﬁrst

study incorporating eye-tracking as well as cogni-

tive experimental techniques to investigate which ele-

ments of visualizations facilitate subsequent recogni-

tion and recall. Borkin et al. is based on previous

work on perception and memorability of visualization

(Bateman et al., 2010) and eye-tracking evaluation vi-

sualization (Blascheck et al., 2014).

Memorability for Parallel Visualization. In this

subsection, users are presented with a large number of

relation data in parallel (as opposes to one at a time).

Users are tested on their ability to process relations-

hip data in parallel (all relationships simultaneously).

This is distinct from memorability for linear visuali-

zation where recall focuses on one visual design at a

time in linear order.

Saket et al. (Saket et al., 2015) illustrate that dif-

ferent visualization designs can effect the recall accu-

racy of data being visualized. Saket et al. is based

on previous work of visualization memorability (Ba-

teman et al., 2010) and a recalling experiment (Isola

et al., 2011).

8 INTERPRETATION FOR

STORYTELLING AND

VISUALIZATION

Interpretation refers to the action of explaining the

meaning of something, according to the Oxford Eng-

lish Dictionary (Dictionary, e). Martin(Martin, 1997)

refers to interpretation as a process to reach a deep

cognition level which contains the fundamental va-

lues of a story. To ﬁnd out the fundamental opposi-

tions and transformations underlying the story, inter-

pretation has to reduce all the oppositions found on

the ﬁgurative and narrative levels to one or two basic

umbrella oppositions.

Interpretation for User-directed Visualization.

The literature here focuses on interactive, user-driven

interpretation. In this class of literature, a user inter-

prets visual design interactively. In other words, the

observer chooses the order in which they view and

interpret visual designs. This is as opposed to a pres-

cribed or automated order of visual designs. Ma et al.

(Ma et al., 2012) state that a story that is well paced

exhibits deliberate control over the rate at which plot

points occur. Ma et al. is based on previous scientiﬁc

visualization work at NASA, based in the scientiﬁc

research center and scientiﬁc museum and describe

how visualization can be used to tell a good story, and

tell it well. This is a topic that the scientiﬁc visualiza-

tion research community paid little attention to at that

time.

Storytelling and Visualization: A Survey

219

Overviews of Interpretation for Visualization and

Storytelling. The literature in this subsection provi-

des overviews on the topic of interpretation for visua-

lization and storytelling.

Kosara and Mackinlay deﬁne a story as an ordered

sequence of steps with a clearly deﬁned path through

it (Kosara and Mackinlay, 2013). This paper presents

a selection of previous work on storytelling in visu-

alization and postulates storytelling as a fruitful area

of future research (Kosara and Mackinlay, 2013). Ko-

sara and Mackinlay is based on the previous history of

storytelling, deﬁnition and model of Segel and Heer

(Segel and Heer, 2010) and outlines a research pro-

gram to develop storytelling as a visualization task of

equal importance to exploration and analysis.

9 UNSOLVED PROBLEMS AND

CONCLUSION

This survey provides a novel up-to-date overview of

storytelling in visualization. The most important re-

cent literature is included and discussed. Since story-

telling in visualization is a recently new subject, we

expect an increase in research in the coming years.

Moreover we believe it will evolve into a popular to-

pic in the ﬁeld of visualization.

By reviewing Table 1, we can see storytelling vi-

sualization focuses on information visualization more

than scientiﬁc visualization, which conveys that more

challenges are left unsolved in this ﬁeld. However,

by reﬁning a storytelling model for scientiﬁc visuali-

zation (Wohlfahrt and Hauser, 2007), the implemen-

tation of storytelling in scientiﬁc visualization could

increase in the future. We can also see that story-

telling in visualization concentrates more on explo-

ration than on presentation. Like Kosara and Mackin-

lay (Kosara and Mackinlay, 2013) state: “visualiza-

tion techniques address the exploration and analysis

of data more than presenting data”.

In future work, there are many directions and un-

solved problems. Storytelling will gain importance

in data presentation and data exploration. Here is a

summary of some unsolved problems in storytelling

for visualization.

Measuring User Engagement:It is clear that ob-

jective measures of user-engagement is a relatively

unexplored area of research. Can we derive a mature

classiﬁcation of user engagement activities? Is user

engagement something we can clearly deﬁne?

Data Preparation and Enhancement: Virtually

no one has addressed the challenge of data prepara-

tion and enhancement for storytelling. Moreover, is

storytelling data best captured or derived from an ex-

isting data set or software system? Can a standard

data ﬁle format be developed?

Narrative Visualization for Scientiﬁc and Geo-

spatial Visualization: Why has there been such an

imbalance of research narrative visualization for in-

formation visualization but virtually none for scienti-

ﬁc and geo-spatial visualization?

Transitions for Scientiﬁc Visualization: The be-

neﬁts of static transition versus dynamic transitions in

visualization still remains relatively immature.

Memorability for Visualization: What are the

key elements for making a memorable visualization?

This is still an immature research direction.

Animated Transitions for Geo-spatial Visuali-

zation: Animated transitions for geo-spatial visuali-

zation remains an open research direction. This is sur-

prising given the popularity and importance of geo-

spatial visualization.

Interpretation for Scientiﬁc Information, and

Geo-spatial Visualization: Although we included

two papers that arguably touch upon the topic of ef-

fective interpretation of stories, this topic remains lar-

gely unexplored.

The classiﬁcation of literature, we present makes

it clear that many future research directions remain

open in storytelling and visualization.

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