Analyzing Information Visualization Projects on the Topic of

Economic Inequality

New Media Project

Project Paper

Ana Crisostomo

Student n. 10397124

04/04/2013

1

1. The Nature and Role of Speculative Visualization

The current paper focuses on information visualization projects related to the topic of economic

inequality which can, in this particular case, be more specifically categorized under “speculative

visualization” as defined by the authors Tanyoung Kim and Carl DiSalvo in their 2010 article

“Speculative Visualization: A New Rhetoric for Communicating Public Concerns”. According to these

academics, speculative visualization “represents socially and politically meaningful data in aesthetic

ways to provoke viewer’s interpretation and further elicit discussions” (Kim, DiSalvo 1).

With an increasing number of governmental [1] and non-governmental organizations making local

and global data publicly available, information can, at least theoretically, be more readily available

nowadays than ever before. Just to present a brief overview, the World Bank Data website

(http://data.worldbank.org/) provides more than 2.000 indicators from its data sets focusing on a

diversity of topics ranging from agriculture and rural development to social development, from

environment to poverty. The UN (United Nations) Data website (http://data.un.org/) claims to have

60 million records from 34 databases referring to population, trade, labor, finance among other

areas. The online library from the CIA (Central Intelligence Agency) highlights the World Factbook

(https://www.cia.gov/library/publications/the-world-factbook/index.html) which provides basic,

current, and estimative intelligence for 267 world entities on history, people, government, economy,

geography, communications, transportation, military, and transnational issues.

Even though debates surrounding poverty and economic inequality have been largely explored by

the media previously, the data are still relevant and can be used to illustrate disparity worldwide or

within certain regions in novel manners and raise awareness to problematic cases since the most

affected populations do not have privileged access to information and are often powerless towards

their own situation as accurately described by Sharon Daniel in this quote from “The Database: An

Aesthetics of Dignity”:

Certainly, wherever the interpretation of available data is privileged over embodied

experience and the consumer is the only acknowledged citizen, the material

conditions of the technologically and economically disenfranchised may be ignored,

their social role devalued, and their rights systematically erased. Because political

and economic power are increasingly dependent upon access to and presence within

the global information culture, the voices of the “underserved” are becoming less

and less audible. (Daniel 165)

Economic inequality can be examined on a national or on an international level, and the comparison

[1] For a comprehensive list of data catalogues from governmental institutions worldwide, visit

http://datacatalogs.org/.

2

can be made between developed democratic countries on a particular moment in time [2] or

considering countries from the whole economic and political spectrum in a historical perspective [3]

depending on the specific goal of the analysis.

Several information visualization pieces have been produced on this topical domain and the

objective of this paper is not, by any means, to provide an extensive account of all the work created,

but to focus on selected visualizations from different sources to examine them, establish

comparisons and eventually encounter commonalities or predominant features in terms of graphical

or rhetorical strategies. If this type of visualization aims mostly at raising awareness and initiate or

maintain a debate (rather than finding solutions as argued by Kim and DiSalvo), how is this goal

achieved and how can its effectiveness be evaluated?

The following section of this paper will then be focused on the classification of 12 information

visualization projects (see the Appendix for full list) related to the theme of economic inequality and

poverty (whenever possible from an international perspective) and the subsequent section will be an

attempt to assess the quality and the efficiency of the same.

[2] As an example, see Richard Wilkinson’s 2011 TED talk on “How Economic Inequality Harms Societies”

<http://www.ted.com/talks/richard_wilkinson.html>.

[3] As an example, see Hans Rosling’s 2007 TED talk on “New Insights on Poverty”

<http://www.ted.com/talks/hans_rosling_reveals_new_insights_on_poverty.html>.

3

2. Classifying Information Visualization Projects on Economic Inequality

In order to analyze information visualization projects on the topic of economic inequality and

poverty, it might be relevant to classify them according to a particular taxonomy providing a more

in-depth interpretation of the strategies utilized in each case. Rather than being exhaustive, the

present selection focuses on the diversity of projects produced under the same theme and,

potentially, sharing a common goal.

The taxonomy and procedure applied in this study was originally presented by Edward Segel and

Jeffrey Heer in their 2010 paper “Narrative Visualization: Telling Stories with Data” which classifies

visualization according to 3 main categories: Genre, Visual Narrative and Narrative Structure,

including a total of 40 sub-categories.

Once Segel and Heer’s taxonomy was put into practice for the purpose of this study, several

difficulties were found. One of the first complications regarded the Genre classification (see Table 1).

While in those authors’ empirical research the genres were mutually exclusive (even if they admitted

that theoretically this is not a requirement), in the current investigation it was found that this is not

necessarily the case for every situation. One project can be simultaneously classified under two or

more typologies: a visualization project can include an annotated graph and an animation or it can

present a partitioned poster and a video. Additionally, it was found that the categories considered

did not cater for all the cases where the visualization is actually initiated (and, occasionally, also

maintained) via the user’s direct input by means of a survey, a quiz or even a game in applications

which do not follow the “Slide Show” logic (see “How Rich Are You?”). For this reason, these options

were added to the matrix as an additional category.

Table 1 – Classification of Visualization Projects on Economic Inequality according to Genre

Visualization Projects

Genre

Mag

azin

e

Styl

e

An

no

tate

d

Gra

ph

/

Map

Par

titi

on

ed

Po

ste

r

Flo

w C

har

t

Co

mic

Str

ip

Slid

e S

ho

w

Film

/

Vid

eo

/

An

imat

ion

Gam

e /

Q

uiz

/

Surv

ey

A History of Poverty - + - - - - + - The Miniature Earth - - + - - - + - OECD Better Life Index - + - - - - - - GapMinder - + - - - + - - Yourtopia - + - - - - + + Worldshapin - + - - - - - - How Rich Are You? - - + - - - - + Visualizing the Voices of Vulnerable Populations in Times of Global Crisis

- + - - - + - -

Our Future Selves - + - - - + + + How Many Slaves Work for You? - - - - - - + + Measure of America - + - - - - - - Wealth Inequality in America - + - - - - + -

4

Moving forward to the classification according to the Visual Narrative features (see Table 2), some

of the 16 sub-categories established were not entirely self-explanatory (for instance, what does

“Feature Distinction” exactly stand for? What is the notion behind “Familiar Objects”?), while others

were not fully distinguishable (Can a “Close-Up” be considered a type of “Zooming” in certain cases

and vice-versa? And what is the precise distinction between those two features and “Motion”?).

Additionally, when a visualization project contains several different genres, it is not entirely clear

how each set of features should be examined – holistically in the context of the project or just in

relation to that specific genre? There is a reasonable amount of subjectivity when applying such

taxonomy to a diversity of projects, as acknowledged by the authors themselves, which occasionally

problematizes the replication of their analytical matrix to other cases.

Table 2 – Classification of Visualization Projects on Economic Inequality according to Visual Narrative

Features

Visualization Projects

Visual Narrative

Visual Structuring

Highlighting Transition Guidance

Esta

blis

hin

g Sh

ot

/ Sp

lash

Scr

ee

n

Co

nsi

ste

nt

Vis

ua

l Pla

tfo

rm

Pro

gre

ss B

ar /

Tim

eb

ar

“Ch

eck

list”

Pro

gre

ss T

rack

er

Clo

se-U

ps

Feat

ure

Dis

tin

ctio

n

Ch

arac

ter

Dir

ect

ion

Mo

tio

n

Au

dio

Zoo

min

g

Fam

iliar

Ob

ject

s

Vie

win

g A

ngl

e

Vie

we

r (C

ame

ra)

Mo

tio

n

Co

nti

nu

ity

Edit

ing

Ob

ject

Co

nti

nu

ity

An

imat

ed

Tra

nsi

tio

ns

A History of Poverty + + + + - + - + - + ? + - - + + The Miniature Earth + + - - - + - + + - ? - + + + + OECD Better Life Index - + + + + + - + - - ? - - - + + GapMinder + - + + + + + - + ? - + - + + Yourtopia + + - - - + - - - - ? - - - - - Worldshapin - + + + - + - - - - ? - - - + - How Rich Are You? + + - - - + - - - - ? - - - + - Visualizing the Voices of Vulnerable Populations in Times of Global Crisis

+ + - - - + - - - - ? - - - - -

Our Future Selves + - + + - + - + - - ? - - - - - How Many Slaves Work for You? + + - + - + + + - - ? - - + - + Measure of America + + - + - + - + - + ? - - - - + Wealth Inequality in America - + - - + - - - + - ? - + + + +

Finally, the analysis of the Narrative Structure (see Table 3) was based on more tangible features

which facilitated the projects’ examination according to the 17 pre-defined sub-categories. In the

original paper, the authors considered the three “Ordering” options to be mutually exclusive and,

once again, in the current study this was not necessarily the case as, for instance, “Random Access”

5

could be combined with “User Directed Path”. Other sub-categories such as “Stimulating Default

Views” or “Very Limited Interactivity” still contained a certain dose of subjectivity which was difficult

to account for – how should the level of appeal of a default view be determined? And should the

interactivity be measured in terms of number of options available for the user to select from (even if

they function mostly on a binary mode) or should the breadth and scope of the possible interaction

be taken into account? Since the classificatory features and the analytical procedures were not

clearly defined in the original paper, it is possible that other researchers may categorize the 12

projects below differently.

Table 3 – Classification of Visualization Projects on Economic Inequality according to Narrative

Structure Features

Visualization Projects

Narrative Structure Ordering Interactivity Messaging

Ran

do

m A

cce

ss

Use

r D

ire

cte

d P

ath

Lin

ear

Ho

ver

Hig

hlig

hti

ng/

De

tails

Filt

eri

ng/

Sele

ctio

n/S

ear

ch

Nav

igat

ion

Bu

tto

ns

Ve

ry L

imit

ed

Inte

ract

ivit

y

Exp

licit

Inst

ruct

ion

Taci

t Tu

tori

al

Stim

ula

tin

g D

efa

ult

Vie

ws

Cap

tio

ns/

He

adlin

es

An

no

tati

on

s

Acc

om

pan

yin

g A

rtic

le

Mu

lti-

Me

ssag

ing

Co

mm

en

t R

ep

eti

tio

n

Intr

od

uct

ory

Te

xt

Sum

mar

y/Sy

nth

esi

s

A History of Poverty - + - + + - - + - + + + + - - + -

The Miniature Earth - - + - - - + + - - + - + + + + +

OECD Better Life Index - + - + + + - - + + + - + - - + -

GapMinder - + - + + + - + + + + + - - - + -

Yourtopia - - + + + + - + - - + - + - - + -

Worldshapin - + - + + - + - + - + - + - - + -

How Rich Are You? - - + - - + + - - - + - + - + - +

Visualizing the Voices of Vulnerable Populations in Times of Global Crisis

+ + - + - + + - - - + - + - - - +

Our Future Selves + + - + + + - - + + + + + - - + -

How Many Slaves Work for You? - - + + - + - - + + + + + + + + +

Measure of America + + - + + + - + + - + + + - - - -

Wealth Inequality in America - - + - - - + - - + + + - + + + +

Despite the possible shortcomings and limitations of the taxonomy proposed by Segel and Heer, this

classification still holds the merit of systematically drawing one’s attention to a number of features

on a visual and narrative level enabling a more in-depth knowledge and the detection of particular

patterns.

On a top level analysis, there seems to be a correlation, for instance, between the number of

variables and the amount of user interaction allowed: the more data indicators the application

provides access to, the more likely it is that a higher level of user interaction is allowed. This

situation enables the user to select specific datasets and explore correlations which seem more

6

interesting for his particular case (see “Measure of America” and “Our Future Selves”) while

potentially diminishing complexity. Author driven and linear narratives with limited or no

interactivity usually explore a reduced number of indicators in one particular combination (see

“Wealth Inequality in America”).

A higher level of interaction was also usually connected to an open end narrative [4]. When a

considerable number of options can be simultaneously controlled by the user, it becomes

challenging to the author of the visualization to present a customized conclusion. The onus of the

story’s direction is, in those cases, transferred to the user. However, this is not a purely dual reality

and, as supported by Alberto Cairo, these projects exist in a continuum between presentation and

exploration and therefore always contain properties of both elements to a certain degree. It is within

this space that the three types of narratives defined by Segel and Heer – “Martini Glass Structure”,

“Interactive Slideshow” and “Drill-Down Story” - can be found.

From the collection of projects analyzed, there was no clearly defined style which was predominant.

Perhaps one of the most interesting aspects was the hybrid character [5] of application styles where,

for example, video was added to complement graphs (see “The Miniature Earth”), author driven

narratives were intertwined with user driven elements (see “OECD Better Life Index”) or game

components became an intrinsic part of the visualization (see “How Many Slaves Work for You?”).

Other aspect worth mentioning refers to the user participation: nearly half of the projects examined

requested the user to provide his direct input at different levels. In some cases, the visualization

would only materialize itself via the user’s input (see “How Rich Are You?”, “How Many slaves Work

for you?” and “Yourtopia”) and in other cases the user’s participation served as a complement to the

existing data visualization (see “OECD Better Life Index” and ”Our Future Selves”). This strategy

allows the user to be integrated as an individual in the visual and rhetorical narrative and, therefore,

to become a direct participant in the story being told. Once the user is able, for instance, to compare

his personal economic well-being with the one of another (abstract) user of the same gender and

age group but from another country, the individual becomes directly involved and this connecting

moment can be explored in several manners in the path to raising awareness to problematic issues [6].

[4] Some authors even argue that data cannot conclude stories as “data are part of a process without an arc

that requires a dramatic ending. Instead, they proceed by insinuation, by involution – toward a beginning,

toward an aporia (the road without a name)” (Klein 91).

[5] On this matter, Manovich refers the hybrid nature of current new media where it is noticeable a “mix

between older cultural conventions for data representation, access and manipulation and newer conventions

of data representation, access and manipulation” (Manovich, “New Media from Borges to HTML” 10).

[6] As stated by Martin Wattenberg, “a moment of insight, in which people see facts and patterns for

themselves, can be rhetorically powerful” (Wattenberg, 2).

7

3. How to Assess the Quality and the Effectiveness of Information Visualization Projects?

As stated previously, a vast amount of official data is indeed publicly available, as well as an

increasing number of free online applications and user-friendly software [7] which enable a series of

operations from data processing to data visualization, but these facts do not imply that clear and

meaningful information is equally and easily attainable [8]. A specific set of multi-disciplinary skills is

required to produce an information visualization piece which is clear, informative and impactful. But,

even in this domain, there is not one unique set of criteria to assess the quality and/or the success of

an information visualization project.

The previous section examined a set of visualization projects according to the presence, absence or

degree of certain features, but how does this examination translate into a measure of quality? And is

it possible to state that one particular visualization piece produced more effective results than

another one on a similar topic?

If one would focus on the narrative element, then Stephen Few proposes the following 11

characteristics for an effective “statistical narrative” (the label he provides to a story based upon

quantitative information) – this should be: simple, seamless, informative, true, contextual, familiar,

concrete, personal, emotional, actionable and sequential (Few 2). Since some of these aspects are

rather subjective, they should be taken mainly as guiding principles and customized according to

other elements such as the medium and the audience [9]. As Nahum Gershon and Ward Page state in

the article “What Storytelling can do for Information Visualization”, “the choice of genre, as well as

the presentation medium, affects content, as well as what the audience gets from the process”

(Gershon, Page 36). The storytelling scenario also plays an important role on the presentation

structure and style as defined by Robert Kosara and Jock Mackinlay who distinguish between a self-

running presentation to a large audience, a live presentation by a speaker in front of an audience

and an individual (or small-group) presentation of results, each one of them with a distinct set or

requirements (Kosara, Mackinlay 4). An information visualization application embedded in a

presentation done in a particular scenario for a specific audience might produce different results

when the occasion and/or the audience change. There is a certain consensus on storytelling best

[7] As software examples, see IBM’s Many Eyes <http://www-958.ibm.com/software/analytics/manyeyes/> and

Tableau Public <http://www.tableausoftware.com/public/>.

[8] On this matter, Manovich refers “the large gap between what can be done with the right software tools,

right data, and no knowledge of computer science and advanced statistics - and what can only be done if you

do have this knowledge” (“Trending: The Promises and the Challenges of Big Social Data” 12) to highlight the

need for training and previous experience when dealing with a considerable amount of data to achieve

meaningful results. Beyond a matter of skills and experience, there is also a representation issue to be taken

into account. As Danah Boyd and Kate Crawford state, “working with big data is still subjective, and what it

quantifies does not necessarily have a closer claim on objective truth” (Boyd, Crawford 4).

[9] On this account, the classic Rose Diagrams from Florence Nightingale are said to have been born out of the

necessity of making data more tangible to a rather diverse audience who would probably be unable, or

unwilling, to read statistical information (Brasseur 166).

8

practices, but there is absolutely no formula for guaranteed success.

If one would target the data and the graphics, then the most purist postulates from Edward Tufte

could be applied. In the 1982 classic Visual Display of Quantitative Information, the author proposes

five principles for graphical excellence, six principles for data integrity and five principles of data

graphics theory. According to his perspective, graphical excellence is defined by a visualization which

provides the viewer the greatest number of clear and precise ideas, based on truthful and

multivariate data, in the shortest time with the least ink in the smallest space (Tufte, Visual Display

of Quantitative Information 51). Those initial principles were mostly applied to visualizations in print

format and did not account for any user interaction so they can be considered incomplete when

analyzing interactive online applications. In his subsequent publication from 1990, Envisioning

Information, the author already moves beyond print in his attempt to escape the “flatlands” of the

two-dimensional paper and computer screen. However, the current platforms and interfaces require

much more specific interaction requirements. A rather complete taxonomy of “interactive

dynamics” within information visualization is proposed by Jeffrey Heer and Ben Shneiderman in the

2012 article “Interactive Dynamics for Visual Analysis”. These authors defend that meaningful visual

analysis is not derived from a one-time examination of a static image but that this process “consists

of repeated explorations as users develop insights about significant relationships, domain-specific

contextual influences, and causal patterns” [10] (Heer, Shneiderman 1) and propose 12 tasks grouped

into three high level categories [11].

There seems to be no single set of criteria to assess the quality of information visualization. If one

would agree that information visualization should be mainly perceived as technology – an extension

of ourselves and a means to reach goals (Cairo 29) - then this technology should be evaluated

according to its function and objective. In the case of (speculative visualization) projects under the

topic of economic inequality, one could state that the main goals are to raise awareness (see

“Visualizing the Voices of Vulnerable Populations in Times of Global Crisis”), challenge current

perceptions (see “Wealth Inequality in America”) and, eventually, lead to concrete actions (see “How

Rich Are You”). But how is it possible to measure awareness and compare the effectiveness of one

particular visualization project against another when no particular action is proposed to the user?

A basic measure could be based on social media sharing parameters such as how many times an

application was shared on specific platforms. Despite the viral potential [12] of some projects, these

[10] This dynamic and reiterative process is also reinforced by Johanna Drucker when mentioning that

“graphesis is premised on the idea that an image, like a text, is an aesthetic provocation, a field of

potentialities, in which a viewer intervenes. Knowledge is not transferred, revealed, or perceived, but is

created through a dynamic process” (Drucker 29).

[11] These categories and tasks are as follows: 1) Data & View Specification (visualize, filter, sort and derive); 2)

View Manipulation (select, navigate, coordinate and organize); 3) Process & Provenance (record, annotate,

share and guide) (Heer, Shneiderman 2).

[12] On this domain, Microsoft has unveiled earlier this year the ViralSearch: an application which allows the

identification and visualization of viral content making the viral concept more tangible and quantifiable – see

<http://research.microsoft.com/apps/video/default.aspx?id=185452>.

9

quantitative measures fall short on several fronts and can be deemed as superficial.

Would it be possible to perceive other phenomena originating from such visualizations? Kim and

DiSalvo highlight the importance of engaging the public in the several stages of these projects from

data capture to design (Kim, DiSalvo 8). Several data art projects [13] and events [14] have been based

on this participatory premise which can be a fundamental element in the new politics of mapping as

stated by Manovich:

Who has the power to decide what kind of mapping to use, what dimensions are

selected; what kind of interface is provided for the user – these new questions about

data mapping are now as important as more traditional questions about the politics of

media representation by now well-rehearsed in cultural criticism (who is represented

and how, who is omitted). (Manovich, “Data Visualization as New Abstraction and

Anti-Sublime” 3)

Allowing the users to directly collaborate in these projects might be one effective manner to foster

genuine public awareness. This participation does not necessarily need to be integrated in the

conceptualization, research and implementation phases: several of the applications examined in the

previous section requested the user’s personal input as the driver of a pre-defined narrative.

Involving the user’s data allows incorporating the same in the story being disclosed, building an

emotional bridge with what was, until then, an external and distant reality [15]. Such suggestion does

not imply that projects which do not cater for user interaction by default are inferior from a

qualitative perspective or are necessarily less efficient when it comes to raising awareness to certain

topics. As stated previously, the visualization should be customized taking into account elements

such as the medium, the audience and the specific objective. However, when the medium is the

internet, the audience is the general public and the topic is economic inequality, allowing direct

collaboration and interactivity might be a powerful strategy for an impactful communication.

Whatever the strategy selected is, knowing that “data can be an honest accounting of what we fail

to do, or how we hide” (Klein 89), then information visualization can be a way to acknowledge these

failures and initiate the work towards new solutions and, in the case of economic inequality,

ultimately contribute to a fairer and more balanced world. As succinctly put by Jerome Cukier: “the

most obvious way I see in which data visualization can change the world is through transmission of

knowledge from experts to the people” (Cukier 1).

[13] As a reference, see these two projects: YoHa’s Invisible Airs <http://yoha.co.uk/invisible> from 2011 and

MIT Senseable City Lab’s Trash Track <http://senseable.mit.edu/trashtrack/> from 2009.

[14] For more information on Open Data events, visit <http://opendataday.org/>.

[15] This does not exclude other strategies to create emotive data narratives which do not involve the public’s

direct participation. As an example, see Chris Jordan’s 2008 TED talk on “Turning Powerful Stats into Art”

<http://www.ted.com/playlists/56/making_sense_of_too_much_data.html>.

10

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Tufte, Edward. The Visual Display of Quantitative Information. Cheshire, Connecticut: Graphics Press

LLC, 2001.

Wattenberg, Martin. What Happens After “AHA!” - Changing the World with Visualization Panel.

IEEE Information Visualization Conference, October 2009, Atlantic City, New Jersey. 29

March 2013. <http://kosara.net/papers/2009/Kosara_InfoVisPanel_2009.pdf>.

Wilkinson, Richard. “How Economic Inequality Harms Societies.” TED. July 2011. 25 March 2013.

<http://www.ted.com/talks/richard_wilkinson.html>.

Yoha. Invisible Airs – Database, Expenditure & Power. 2011. 24 March 2013.

<http://yoha.co.uk/invisible>.

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Appendix

List of Information Visualization Projects Analyzed:

A History of Poverty. Christian Aid. 2011. <http://www.povertyover.org/index.php>.

GapMinder. Gapminder Foundation. 2005. <http://www.gapminder.org/>.

How Many Slaves Work for You? Slavery Footprint. 2011. <http://slaveryfootprint.org/>.

How Rich Are You? Poke. 2003. < http://www.globalrichlist.com/>.

Measure of America. Sarah Burd-Sharps, Kristen Lewis, Rosten Woo, Zachary Watson, Andrew

Thornton. Social Science Research Council. 2007. <http://www.measureofamerica.org/maps/>.

OECD Better Life Index. Moritz Stefaner, Frank Rausch, Jonas Leist, Marcus Paeschke and Timm

Kekeritz for Raureif design consultancy. OECD. 2012.

<http://www.oecdbetterlifeindex.org/#/45341221541>.

Our Future Selves. Jason Alcorn, Michael Keller and Emily Liedel. News21 – Columbia University

Graduate School of Journalism. 2011. <http://columbia.news21.com/our-future-selves/>.

The Miniature Earth. Luccaco. 2011. <http://www.miniature-earth.com/>.

Visualizing the Voices of Vulnerable Populations in Times of Global Crisis. Elena Paunova.

Visualizing.org and UN Global Pulse. 2011. < http://www.universeofatoms.com/un/index.html>.

Wealth Inequality in America. 2012. <http://youtu.be/QPKKQnijnsM>.

14

Worldshapin. Carlo Zapponi and Vasundhara Parakh. 2012. <http://worldshap.in/>.

Yourtopia. Created by Guo Xu, Dirk Heine, Rufus Pollock, Friedrich Lindenberg and Hannes

Bretschneide. Open Knowledge Foundation. 2010. <http://yourtopia.net/>.