KE

PT/M

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Audra Williams (2nd nomination)

B6186 road

Orgono

philia

Ultravio

let m

ap

Cash

ville

Reco

rds

Fat wank

List of Catholic musicians

Three Ravens

Wor

k sp

ouse

Crocke

r High

lands

Elemen

tary S

choo

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Martin Keating

Luis

Barra

gan

(exe

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Star

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pris

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pro

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Fairmon

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demy (

2nd n

omina

tion)

Denial of lesbianism

List

of U

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anad

ian

box

offic

e bo

mbs

List

of f

ictio

nal t

elev

isio

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Every time you masturbate God kills a kitten

Loserz (second nomination)

The 9/11 REcom

mission R

eport

British-India Holocaust

Jeop

ardy

! in

popu

lar c

ultu

re (2

nd n

omin

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Shit funnel

Centr

alsko

lan, S

taffan

storp

Michael Q. Schmidt (3rd nomination)

Hayah

Cooper Manning

Easte

rn A

llianc

e

JSP (Internet Celebrity)

Philadelphia mayoral election, 2007

List of Italian divisions in WWII

Indian Institute of Management Calcutta

Assembly of American Unionists

Universal Day of th

e Jedi

Sam's Dance Troupe

Republican/Democrat In Name Only

Nocturne (movie)

Battle of Yonkers

Samantha brett

TV Single Dads (2nd nomination)

Paranoia MUD

Localdataplace

Gordon Lennon

Central Unified School District 2006-2007 school year calendar

Fazeri

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lkai R

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Yaho

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St M

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Cath

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City

of t

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Mecha as Practical War Machines

Commun

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nocid

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Hol

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teel

List

of f

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The betterthanbest entmap2

Joi Ito

Mic

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Adscam blog disclosure

Matthew (given name)

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Ehren Watada

Transformers 3

Joshua P. Warren

List of largest empires (3rd nomination)

Polar

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NewsMax Media

Turkification

Reu

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Res

taur

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Fordso

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Schoo

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Anthony Carboni

Roma p

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natio

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team

(2nd

nomina

tion)

Am

y Lo

ftus

The White Rose Society (website)

Blanket clemency

Korean Liberal Democrats

The Goldfish Network

Kathy Vavrick-O'Brien

Agnostic theism

John Bullen Middle School

Scott

ish in

venti

ons

List o

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KO

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Community Middle School

List of terms associated with the color...

Chuc

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Charleston County School of the Arts

Saabs in popular culture

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Edwin Curry

Lasst die Daten sprechen!Datenvisualisierung und Informations-Aesthetik

Moritz Stefaner

decoded conference // München, Oktober 2010

Moritz Stefaner

1998 – 2002

2002 – 2005

2005 – 2007

2007 – 2009

2004 –

Web designer

BSc Cognitive Science

MA Interface Design

Research assistant FH Potsdam

Freelance information visualizerworking for Skype, World Economic Forum, dpa, Yahoo…

DATA VISUALIZATION & INFORMATION AESTHETICSPAST, PRESENT, FUTURE

~ 6000BC: early maps

First quantitative abstractions

infografe.com.br

Milestones in the Historyof Data Visualization• Up to the 17th century: Maps and diagramsThe origins of visualization are in geometric diagrams, in tables of the positions of stars and maps. In the 16th century,with the maritime expansion of Europe, new techniques and instruments were developed and, with such, new andmore precise forms of visual presentation of knowledge. In 1436, Johannes Gutenberg invents the press

c.6200 b.C.The oldest map?Representation of a city inBabylon found in the region ofKirkuk, Iraque. The original wascarved in stone.

c. 550 a.C.The first world map?There are no copies, justdescriptions in the books ofHerodotus. Anaximander ofMiletus (c.610BC-546BC).

c. 950First known record of showingvariables graphically(position of the sun, moon andplanets during the year).

c. 1500The Italian Leonardo da Vinci(1452-1519) records hisknowledge in detailedillustrations to portray anatomy,machines and movements

c. 1350The French Bishop Nicole Oresme(1323-1382) proposes the use of bargraphics to demonstrate a variablethat depends on another value.

1375Catalan book gathers anastonishing visual,cosmography, perpetualcalendar and thematicrepresentations of theknown world. AbrahamCresques 91325-1387),Mallorca, Spain,

1569Invention of the cylindricalprojection to portray the globe.The Belgian Gerardus Mercator(1512-1594), also divided hisworld map in pages to facilitatethe use and printed the image ofthe god Atlas on the cover

1595Rumold Mercator (1545-1599)son of Gerardus continues hiswork and publishes this worldmap in 1595, one year after hisfather’s death

c. 150Spherical projection of the Earth using latitudes andlongitudes, by Ptolomy (c. 85-c. 165), Alexandria, Egypt.The above map is a reproduction of the world map ofPtolomy made by Johan Scotus in 1505.

M Denis and MJ Friendly : “Milestones in the History of Data Visualization”

14th century: Abraham Cresques

~1500: Leonardo da Vinci

Yay! The first pie chart! (Playfair 1801)

19th century: The golden age of infographics

GOLDEN AGE 519

FIG. 12. Spiral time-series diagram. “Five-year changes in the population by department from 1801 to 1881” (Mouvement quinquenialde la population par départment depuis 1801 jusqu’en 1881). The graphic design was intended to highlight the relative changes in eachdepartment over time. Source: Album de Statistique Graphique, 1884, Plate 25.

proved popular, and included pie maps, and diagramsor maps using stars, radial axes, polar time-series,and proportional circles as visual symbols for quanti-ties shown. Some special and novel graphic forms in-cluded maps with mosaic displays [multiply dividedsquares; see Friendly (2002a) for a history of thisgraphic form], anamorphic maps and “planetary” di-agrams. Basic chart forms were also used—line, barand time-series graphs, sometimes embedded in an-other graphic or map to show some aspect of the datanot represented in the principal display.

The selection of images from the Albums describedbelow is not representative; for that, see Palsky (1996),pages 141–161 and Wainer (2003) for two more ex-amples. Instead, I focus on the images I consider most

notable and imaginative; these are mostly taken fromthe occasional, special topics of the Albums.

Demographic data were not often presented in theAlbums, but Figure 12 from the 1881 album providesa particularly attractive graphic design. It uses polartime-series diagrams on a map of France to show thechanges in population by department at the 5-year in-tervals of the national census from 1801 to 1881. Foreach department, the central white circle shows thevalue in 1841, the middle of the series; the sectors areshaded red for years where the population was less thanthat value and are shaded blue–green for years wherethe population exceeded the 1841 value. The depart-ments ceded to Germany in 1871 are handled differ-ently and shaded in black. In addition to being visuallypleasing, the scaling relative to the central value for

19th century: The golden age of infographics

datavis.ca/milestones

Otto Neurath

John Tukey: Exploratory data analysis

John Tukey et al: PRIM-9 system (1972)

Open Street maps: One year of edits (2008) ITO WORLD

“INFOPORN” density design lab for wired.it

http://manyeyes.com

1HR IN FRONT OF THE TV http://www.flickr.com/photos/the_bumblebee/2229041742/sizes/o/

Microsoft: Sustainability (vision video, 2009) http://ohhello.tv/index.php/work/view/microsoft_sustainability/

PROJECTSwell–formed.eigenfactor

X by Y

revisit

map your moves

well-formed.eigenfactorMoritz Stefaner & Eigenfactor Team, 2009 | http://well-formed.eigenfactor.org

Demos

Demos

Demos

Demos

Hierarchical Edge Bundling

Danny Holten: Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data, IEEE Transactions on

Visualization and Computer Graphics, Vol. 12, No. 5, September/October 2006

Danny Holten and Jarke J. van Wijk: Force-Directed Edge Bundling for Graph Visualization, Eurographics/ IEEE-VGTC

Symposium on Visualization 2009, Volume 28 (2009), Number 3

Visual tweaks

X by Yhttp://moritz.stefaner.eu/projects/x-by-y/

First guesses of what might be interesting

Ein-reichung

X

Ein-reichung

X

X

X

X

X

Winner?

Winner?

Keywords

XKategorie

Jahr X

X XXLand

XAutor XX

KeywordsKategorieJahrLandAutor

What’s in the databases http://well-formed-data.net/archives/306/dbcounter-quick-visual-database-stats

Wattenberg. A Note on Space-Filling Visualizations and Space-Filling Curves .

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Looking at Data Through the Eyes of Experts

/Theory/In/Practice

Beautiful Visualization

Edited by Julie Steele

& Noah Iliinsky

Revisithttp://moritz.stefaner.eu/projects/revisit-twitter-visualization/

SOCIALCOLLIDER.NET Sascha Pohflepp & Toxi | http://socialcollider.net/

Map your moves

Hermann Zschiegner | http://www.two-n.com

http://www.a-stranger.com/

http://www.scottleta.com/

EXPLORATION >< EXPLANATION

“The pure and simple truth is rarely pure and never simple.”

Oscar Wilde

New York Times graphics http://www.nytimes.com/interactive/2010/02/02/us/politics/

Segel and Heer. Narrative Visualization: Telling Stories with Data.

Table 1. Properties of Author-Driven and Reader-Driven Stories. Mostvisualizations lie along a spectrum between these two extremes.

Author-Driven Reader-DrivenLinear ordering of scenes No prescribed orderingHeavy messaging No messagingNo interactivity Free interactivity

complexity of the data, the complexity of the story, the intended au-dience, and the intended medium. There are clear cases in which agenre is more appropriate for a particular purpose. Business presenta-tions typically use Slide Shows instead of Comic Strips, and televisioncommercials use Videos instead of Flow Charts. These are commonand intuitive cases, but not all instances are so clear cut. For example,it is not obvious whether students might learn best from a Slide Showor a Video or even a Comic Strip. The right choice also depends onthe content being presented and the students’ background. In general,there will be no “right answer” a priori, but several possible candi-dates, each with advantages and disadvantages.

Both messaging and interactivity can be layered on top of these gen-res. Messaging is the use of text to provide observations and explana-tions about the images. Typically this text takes familiar forms such asheadlines, captions, labels, and annotations. For some visualizations,messaging can also include audio. Note that messaging is optional forany of the genres above, and can vary widely between instances of thesame genre. Interactivity allows the visualization to be manipulatedby the viewer. There are many possible types and degrees of interac-tivity, though common forms in narrative visualization include navi-gation buttons, hover highlighting, hover details-on-demand, filtering,searching, drill-down, zooming, and time sliders. Importantly, the ap-propriate use of messaging and interactivity will depend on a varietyof factors. Messaging might clarify visual elements but produce clut-ter. Interactivity might engage the user but detract from the author’sintended message. Again, these tradeoffs require context-specific con-sideration and judgment.

4.4 Balancing Author-Driven and Reader-Driven Stories

The visual narrative genres, together with interaction and messaging,must balance a narrative intended by the author with story discoveryon the part of the reader. We thus place narrative visualizations along aspectrum of author-driven and reader-driven approaches (Table 1).

A purely author-driven approach has a strict linear path through thevisualization, relies heavily on messaging, and includes no interactiv-ity. Examples include film and non-interactive slideshows. A stronglyauthor-driven approach works best when the goal is storytelling orefficient communication. We see this approach used in comics, art,cinema, commercials, business presentations, educational videos, andtraining materials.

A purely reader-driven approach has no prescribed ordering of im-ages, no messaging, and a high degree of interactivity. Examples in-clude visual analysis tools like Tableau or Spotfire. A reader-drivenapproach supports tasks such as data diagnostics, pattern discovery,and hypothesis formation.

Historically, many visualizations fall into the author-driven orreader-driven dichotomy. However, as we have seen throughout ourcase studies, most examples of narrative visualization fall somewherein-between, and an important attribute of narrative visualization is itsflexibility in balancing both elements. Visualizations are increasinglystriking a balance between the two approaches, providing room forlimited interactivity within the context of a more structured narrative.This is a relatively recent development, with most mainstream exam-ples coming from online journalism.

All the interactive examples in our dataset use a mix of the author-driven and reader-driven approaches. Despite the range of possiblecombinations, a few hybrid models have become most common. Be-low we discuss three common schemas. The first structure prioritizesthe author-driven approach, the second structure promotes a dialoguebetween the two approaches, while the third structure prioritizes thereader-driven approach.

4.4.1 Martini Glass StructureThe Martini Glass visualization structure be-gins with an author-driven approach, initiallyusing questions, observations, or written articles to introduce the vi-sualization. Occasionally no text is used at all, as the visualizationinstead relies on an interesting default view or annotations. Once theauthor’s intended narrative is complete, the visualization opens up toa reader-driven stage where the user is free to interactively explore thedata. The structure resembles a martini glass, with the stem represent-ing the single-path author-driven narrative and the widening mouthof the glass representing the available paths made possible throughreader-driven interactivity. Using this image, we can think of varyingdegrees of authoring (question, observation, article) as correspondingto different stem types (short, long), and varying degrees of reader-ship (highlighting, filtering, path-choosing) corresponding to differentmouth shapes. With all these permutations, the general structure re-mains intact, with the author-driven narrative functioning first, thenfollowed separately by the reader-driven interactions. The authoringsegment may function as a jumping off point for the reader’s interac-tion, with questions, observations, and themes suggesting the types ofissues a reader might explore on his own. This structure is the mostcommon across the interactive visualizations we examined.

4.4.2 Interactive SlideshowThe Interactive Slideshow structure follows atypical slideshow format, but incorporates in-teraction mid-narrative within the confines of each slide. This structureallows the user to further explore particular points of the presentationbefore moving ahead to the next stage of the story. We saw this struc-ture in both the Budget Forecasts (§3.2) and Gapminder (§3.4) casestudies. Contrary to the martini glass, an interactive slideshow allowsfor interaction mid-narrative, a more balanced mix of author-drivenand reader-driven approaches. However, individual slides often func-tion in the martini glass style, again communicating author-intendedmessages prior to prompting the user to interact with the display.

Interactive slideshows work well with both complex datasets andnarratives. For complex datasets, this structure allows the author towalk the user through data-dimensions and manipulations step-by-step. This ensures that the user only moves forward in the presentationwhen he is ready to do so, and allows the user to repeat steps if desired.For complex narratives, this format allows the author to draw discreteboundaries between different story segments, similar to a cut in film.

4.4.3 Drill-Down StoryThe Drill-Down Story visualization structurepresents a general theme and then allows theuser to choose among particular instances of that theme to reveal addi-tional details and backstories. For instance, the theme might be “His-torical Bear Markets” [A31] and the visualization will allow the userto drill-down to a particular bear market to learn more about its his-tory. Or a map showing “North Korean Prison Camps” [A38] mayallow the user to learn more about individual camps by clicking on aspecific location on the map. This structure puts more emphasis on thereader-driven approach, letting the user dictate what stories are toldand when. Nevertheless, it still requires significant amounts of author-ing to determine the possible types of user interaction, what candidatestories to include, and the details included for each story.

5 DISCUSSION

In this paper, we conducted a design space analysis of narrativevisualization—visualizations intended to convey stories—based on acorpus of 58 collected examples from online journalism, graphic de-sign, comics, business, art, and visualization research. Our analy-sis highlights visual and interactive devices that support storytellingwith data, and we identify distinct genres of visualization using nar-rative structures such as the martini glass, interactive slideshow, anddrill-down story. In particular, we note a central concern in the de-sign of narrative visualizations: the balance between author-driven

Table 1. Properties of Author-Driven and Reader-Driven Stories. Mostvisualizations lie along a spectrum between these two extremes.

Author-Driven Reader-DrivenLinear ordering of scenes No prescribed orderingHeavy messaging No messagingNo interactivity Free interactivity

complexity of the data, the complexity of the story, the intended au-dience, and the intended medium. There are clear cases in which agenre is more appropriate for a particular purpose. Business presenta-tions typically use Slide Shows instead of Comic Strips, and televisioncommercials use Videos instead of Flow Charts. These are commonand intuitive cases, but not all instances are so clear cut. For example,it is not obvious whether students might learn best from a Slide Showor a Video or even a Comic Strip. The right choice also depends onthe content being presented and the students’ background. In general,there will be no “right answer” a priori, but several possible candi-dates, each with advantages and disadvantages.

Both messaging and interactivity can be layered on top of these gen-res. Messaging is the use of text to provide observations and explana-tions about the images. Typically this text takes familiar forms such asheadlines, captions, labels, and annotations. For some visualizations,messaging can also include audio. Note that messaging is optional forany of the genres above, and can vary widely between instances of thesame genre. Interactivity allows the visualization to be manipulatedby the viewer. There are many possible types and degrees of interac-tivity, though common forms in narrative visualization include navi-gation buttons, hover highlighting, hover details-on-demand, filtering,searching, drill-down, zooming, and time sliders. Importantly, the ap-propriate use of messaging and interactivity will depend on a varietyof factors. Messaging might clarify visual elements but produce clut-ter. Interactivity might engage the user but detract from the author’sintended message. Again, these tradeoffs require context-specific con-sideration and judgment.

4.4 Balancing Author-Driven and Reader-Driven Stories

The visual narrative genres, together with interaction and messaging,must balance a narrative intended by the author with story discoveryon the part of the reader. We thus place narrative visualizations along aspectrum of author-driven and reader-driven approaches (Table 1).

A purely author-driven approach has a strict linear path through thevisualization, relies heavily on messaging, and includes no interactiv-ity. Examples include film and non-interactive slideshows. A stronglyauthor-driven approach works best when the goal is storytelling orefficient communication. We see this approach used in comics, art,cinema, commercials, business presentations, educational videos, andtraining materials.

A purely reader-driven approach has no prescribed ordering of im-ages, no messaging, and a high degree of interactivity. Examples in-clude visual analysis tools like Tableau or Spotfire. A reader-drivenapproach supports tasks such as data diagnostics, pattern discovery,and hypothesis formation.

Historically, many visualizations fall into the author-driven orreader-driven dichotomy. However, as we have seen throughout ourcase studies, most examples of narrative visualization fall somewherein-between, and an important attribute of narrative visualization is itsflexibility in balancing both elements. Visualizations are increasinglystriking a balance between the two approaches, providing room forlimited interactivity within the context of a more structured narrative.This is a relatively recent development, with most mainstream exam-ples coming from online journalism.

All the interactive examples in our dataset use a mix of the author-driven and reader-driven approaches. Despite the range of possiblecombinations, a few hybrid models have become most common. Be-low we discuss three common schemas. The first structure prioritizesthe author-driven approach, the second structure promotes a dialoguebetween the two approaches, while the third structure prioritizes thereader-driven approach.

4.4.1 Martini Glass StructureThe Martini Glass visualization structure be-gins with an author-driven approach, initiallyusing questions, observations, or written articles to introduce the vi-sualization. Occasionally no text is used at all, as the visualizationinstead relies on an interesting default view or annotations. Once theauthor’s intended narrative is complete, the visualization opens up toa reader-driven stage where the user is free to interactively explore thedata. The structure resembles a martini glass, with the stem represent-ing the single-path author-driven narrative and the widening mouthof the glass representing the available paths made possible throughreader-driven interactivity. Using this image, we can think of varyingdegrees of authoring (question, observation, article) as correspondingto different stem types (short, long), and varying degrees of reader-ship (highlighting, filtering, path-choosing) corresponding to differentmouth shapes. With all these permutations, the general structure re-mains intact, with the author-driven narrative functioning first, thenfollowed separately by the reader-driven interactions. The authoringsegment may function as a jumping off point for the reader’s interac-tion, with questions, observations, and themes suggesting the types ofissues a reader might explore on his own. This structure is the mostcommon across the interactive visualizations we examined.

4.4.2 Interactive SlideshowThe Interactive Slideshow structure follows atypical slideshow format, but incorporates in-teraction mid-narrative within the confines of each slide. This structureallows the user to further explore particular points of the presentationbefore moving ahead to the next stage of the story. We saw this struc-ture in both the Budget Forecasts (§3.2) and Gapminder (§3.4) casestudies. Contrary to the martini glass, an interactive slideshow allowsfor interaction mid-narrative, a more balanced mix of author-drivenand reader-driven approaches. However, individual slides often func-tion in the martini glass style, again communicating author-intendedmessages prior to prompting the user to interact with the display.

Interactive slideshows work well with both complex datasets andnarratives. For complex datasets, this structure allows the author towalk the user through data-dimensions and manipulations step-by-step. This ensures that the user only moves forward in the presentationwhen he is ready to do so, and allows the user to repeat steps if desired.For complex narratives, this format allows the author to draw discreteboundaries between different story segments, similar to a cut in film.

4.4.3 Drill-Down StoryThe Drill-Down Story visualization structurepresents a general theme and then allows theuser to choose among particular instances of that theme to reveal addi-tional details and backstories. For instance, the theme might be “His-torical Bear Markets” [A31] and the visualization will allow the userto drill-down to a particular bear market to learn more about its his-tory. Or a map showing “North Korean Prison Camps” [A38] mayallow the user to learn more about individual camps by clicking on aspecific location on the map. This structure puts more emphasis on thereader-driven approach, letting the user dictate what stories are toldand when. Nevertheless, it still requires significant amounts of author-ing to determine the possible types of user interaction, what candidatestories to include, and the details included for each story.

5 DISCUSSION

In this paper, we conducted a design space analysis of narrativevisualization—visualizations intended to convey stories—based on acorpus of 58 collected examples from online journalism, graphic de-sign, comics, business, art, and visualization research. Our analy-sis highlights visual and interactive devices that support storytellingwith data, and we identify distinct genres of visualization using nar-rative structures such as the martini glass, interactive slideshow, anddrill-down story. In particular, we note a central concern in the de-sign of narrative visualizations: the balance between author-driven

Table 1. Properties of Author-Driven and Reader-Driven Stories. Mostvisualizations lie along a spectrum between these two extremes.

Author-Driven Reader-DrivenLinear ordering of scenes No prescribed orderingHeavy messaging No messagingNo interactivity Free interactivity

complexity of the data, the complexity of the story, the intended au-dience, and the intended medium. There are clear cases in which agenre is more appropriate for a particular purpose. Business presenta-tions typically use Slide Shows instead of Comic Strips, and televisioncommercials use Videos instead of Flow Charts. These are commonand intuitive cases, but not all instances are so clear cut. For example,it is not obvious whether students might learn best from a Slide Showor a Video or even a Comic Strip. The right choice also depends onthe content being presented and the students’ background. In general,there will be no “right answer” a priori, but several possible candi-dates, each with advantages and disadvantages.

Both messaging and interactivity can be layered on top of these gen-res. Messaging is the use of text to provide observations and explana-tions about the images. Typically this text takes familiar forms such asheadlines, captions, labels, and annotations. For some visualizations,messaging can also include audio. Note that messaging is optional forany of the genres above, and can vary widely between instances of thesame genre. Interactivity allows the visualization to be manipulatedby the viewer. There are many possible types and degrees of interac-tivity, though common forms in narrative visualization include navi-gation buttons, hover highlighting, hover details-on-demand, filtering,searching, drill-down, zooming, and time sliders. Importantly, the ap-propriate use of messaging and interactivity will depend on a varietyof factors. Messaging might clarify visual elements but produce clut-ter. Interactivity might engage the user but detract from the author’sintended message. Again, these tradeoffs require context-specific con-sideration and judgment.

4.4 Balancing Author-Driven and Reader-Driven Stories

The visual narrative genres, together with interaction and messaging,must balance a narrative intended by the author with story discoveryon the part of the reader. We thus place narrative visualizations along aspectrum of author-driven and reader-driven approaches (Table 1).

A purely author-driven approach has a strict linear path through thevisualization, relies heavily on messaging, and includes no interactiv-ity. Examples include film and non-interactive slideshows. A stronglyauthor-driven approach works best when the goal is storytelling orefficient communication. We see this approach used in comics, art,cinema, commercials, business presentations, educational videos, andtraining materials.

A purely reader-driven approach has no prescribed ordering of im-ages, no messaging, and a high degree of interactivity. Examples in-clude visual analysis tools like Tableau or Spotfire. A reader-drivenapproach supports tasks such as data diagnostics, pattern discovery,and hypothesis formation.

Historically, many visualizations fall into the author-driven orreader-driven dichotomy. However, as we have seen throughout ourcase studies, most examples of narrative visualization fall somewherein-between, and an important attribute of narrative visualization is itsflexibility in balancing both elements. Visualizations are increasinglystriking a balance between the two approaches, providing room forlimited interactivity within the context of a more structured narrative.This is a relatively recent development, with most mainstream exam-ples coming from online journalism.

All the interactive examples in our dataset use a mix of the author-driven and reader-driven approaches. Despite the range of possiblecombinations, a few hybrid models have become most common. Be-low we discuss three common schemas. The first structure prioritizesthe author-driven approach, the second structure promotes a dialoguebetween the two approaches, while the third structure prioritizes thereader-driven approach.

4.4.1 Martini Glass StructureThe Martini Glass visualization structure be-gins with an author-driven approach, initiallyusing questions, observations, or written articles to introduce the vi-sualization. Occasionally no text is used at all, as the visualizationinstead relies on an interesting default view or annotations. Once theauthor’s intended narrative is complete, the visualization opens up toa reader-driven stage where the user is free to interactively explore thedata. The structure resembles a martini glass, with the stem represent-ing the single-path author-driven narrative and the widening mouthof the glass representing the available paths made possible throughreader-driven interactivity. Using this image, we can think of varyingdegrees of authoring (question, observation, article) as correspondingto different stem types (short, long), and varying degrees of reader-ship (highlighting, filtering, path-choosing) corresponding to differentmouth shapes. With all these permutations, the general structure re-mains intact, with the author-driven narrative functioning first, thenfollowed separately by the reader-driven interactions. The authoringsegment may function as a jumping off point for the reader’s interac-tion, with questions, observations, and themes suggesting the types ofissues a reader might explore on his own. This structure is the mostcommon across the interactive visualizations we examined.

4.4.2 Interactive SlideshowThe Interactive Slideshow structure follows atypical slideshow format, but incorporates in-teraction mid-narrative within the confines of each slide. This structureallows the user to further explore particular points of the presentationbefore moving ahead to the next stage of the story. We saw this struc-ture in both the Budget Forecasts (§3.2) and Gapminder (§3.4) casestudies. Contrary to the martini glass, an interactive slideshow allowsfor interaction mid-narrative, a more balanced mix of author-drivenand reader-driven approaches. However, individual slides often func-tion in the martini glass style, again communicating author-intendedmessages prior to prompting the user to interact with the display.

Interactive slideshows work well with both complex datasets andnarratives. For complex datasets, this structure allows the author towalk the user through data-dimensions and manipulations step-by-step. This ensures that the user only moves forward in the presentationwhen he is ready to do so, and allows the user to repeat steps if desired.For complex narratives, this format allows the author to draw discreteboundaries between different story segments, similar to a cut in film.

4.4.3 Drill-Down StoryThe Drill-Down Story visualization structurepresents a general theme and then allows theuser to choose among particular instances of that theme to reveal addi-tional details and backstories. For instance, the theme might be “His-torical Bear Markets” [A31] and the visualization will allow the userto drill-down to a particular bear market to learn more about its his-tory. Or a map showing “North Korean Prison Camps” [A38] mayallow the user to learn more about individual camps by clicking on aspecific location on the map. This structure puts more emphasis on thereader-driven approach, letting the user dictate what stories are toldand when. Nevertheless, it still requires significant amounts of author-ing to determine the possible types of user interaction, what candidatestories to include, and the details included for each story.

5 DISCUSSION

In this paper, we conducted a design space analysis of narrativevisualization—visualizations intended to convey stories—based on acorpus of 58 collected examples from online journalism, graphic de-sign, comics, business, art, and visualization research. Our analy-sis highlights visual and interactive devices that support storytellingwith data, and we identify distinct genres of visualization using nar-rative structures such as the martini glass, interactive slideshow, anddrill-down story. In particular, we note a central concern in the de-sign of narrative visualizations: the balance between author-driven

Martini glass structure

Drill–down story

Interactive slideshow

relevance

function

integrity

form

Information

Design

Based on a draft by David McCandless

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COMPUTER SCIENCE STATISTICS VISUAL DESIGN UI DESIGN

Based on Ben Fry’s PhD Thesis: Computational Information Design

acquire parse filter mine represent refine interact

COMPUTER SCIENCE STATISTICS VISUAL DESIGN UI DESIGN

Mind Sketch

create

read

Based on an illustration in Bill Buxton: Sketching User Experiences

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