This document discusses the importance of visualizing data through information visualization. It begins with an introduction of the author, Ana Figueiras, and her work researching information visualization. It then discusses key concepts in visualization, including its definition as using interactive visual representations to amplify cognition. The document also covers why visualization is important, highlighting its ability to integrate humans in the data exploration process by applying perceptual abilities to large datasets. It discusses challenges in visualization including working with gradients or qualitative data. Finally, the power of narrative visualization is discussed, with strategies like using time, interactivity, empathy, and gamification.

1. Painting a clearer
picture: the importance
of visualizing data
Ana Figueiras
iNOVA Media Lab

2. iNOVA Media Lab
ABOUT ME
Ana Figueiras is a research scientist at iNova Media Lab, where she
coordinates the research line in Information Visualization and Data
Analysis (ViDA). Before joining iNOVA, she received her Ph.D. in
Digital Media from Universidade Nova de Lisboa (UT Austin Program |
Portugal). Ana also holds a degree in Communication Sciences and a
Master's degree in New Media. Her main research area is information
visualization with a focus on visual forms of storytelling. She is also
interested in new methodologies for performance and experience
evaluation in visualization. Ana also integrates a project for the
creation of interactive educational resources for primary school
children, supported by the Operational Human Capital Program.
2
Researcher
ANA FIGUEIRAS

3. VISUALIZATION | What is it?
”
“The use of computer-supported, interactive, visual
representations of abstract data to amplify cognition.”
[Card, Mackinlay, and Shneiderman, 1999]
3

4. DATA
”
“Data mining and machine learning researchers tend to believe in the power of their
statistical methods to identify interesting patterns without human intervention.
Information visualization researchers tend to believe in the importance of user
control by domain experts to produce useful visual presentations that provide
unanticipated insights.”
[Card, Mackinlay, and Shneiderman, 1999]
4

5. DATA
”
“For data mining to be effective, it is important to include the human in the data exploration
process and combine the flexibility, creativity, and general knowledge of the human with the
enormous storage capacity and the computational power of today’s computers. Visual data
exploration aims at integrating the human in the data exploration process, applying its
perceptual abilities to the large data sets available in today’s computer systems.”
[Keim, et al. 2003]
5

6. 6
The power players
ICIJ
ClintonCircle
Kevin Hu, et al.
OPEN DATA, WIKILEAKS, etc.
Regional Well-being
OECD

7. VISUALIZATION
”
“Visualizations aren’t truly visual unless they are seen. Getting your work out there for others
to see is critical, and publishing on the Web is the quickest way to reach a global audience.
Working with web-standard technologies means that your work can be seen and experienced
by anyone using a recent web browser, regardless of the operating system (Windows, Mac,
Linux) and device type (laptop, desktop, smartphone, tablet).”
[Murray, 2012]
7

8. WHY TO VISUALIZE?
”
“To affect thro’ the Eyes what we fail to convey to the public through their
word-proof ears.”
[Nightingale, 1858]
8

9. Is It Better to Rent or Buy?
Aid decision-making
9
How Many Households are Like
Yours?
Answer questions
Film Dialogue
WHY TO VISUALIZE?
See data in context

10. Japanese multiplication
technique
10
Fatal Force
Find patterns
England Riots
Tell a story
WHY TO VISUALIZE?
Expand memory

11. 11
Gauging Your Distraction
Raise awareness to issues
Watch how the measles outbreak spreads
Raise awareness to issues
WHY TO VISUALIZE?

12. 12
HOW CAN WE DO IT?

13. 13
LEARNABILITY vs FLEXIBILITY
Hard and slow Simple and fast
Abstract and
flexible
Blackbox and
limited
Canvas API
A means to draw using
JavaScript the element HTML
<canvas>
SVG and HTML DOMs
Multiplatform conventions to
programatically manipulate objects
(e.g., D3)
Grammars
Declarative languages to create,
save, and share visualization
projects (eg. Vega)
Turn key solutions
Solutions that don’t
require any programming
knowledge

14. 14
Carto Flourish Tableau Google Charts Plotly
Vega Bokeh D3 Matplotlib R charts
TOOLS

15. “Resemblance, order and proportional are the three
signfields in graphics.”
[Bertin, 1983]
15
PERCEPTION OF COLOR

16. Which one is the brightest?
16
DETECT BRIGHTNESS

17. Which one is the brightest?
17
DETECT BRIGHTNESS
Hexadecimal: 7A7A7A
RGB Decimal:122, 122, 122
HSL: 0, 0, 48
HSV: 0, 0, 48
CMYK :0, 0, 0, 52
Hexadecimal: 909090
RGB Decimal:144, 144, 144
HSL: 0, 0, 56
HSV: 0, 0, 56
CMYK :0, 0, 0, 43

18. 18
Color intensity and brightness/darkness
SATURATION AND VALUE

19. 100% saturation 50% saturation
100% value 50% value
20% saturation
20% value
19
SATURATION AND VALUE | EXAMPLES

20. 20
1. Sequential schemes are suited to ordered data that progress from
low to high. Light colors for low data values to dark colors for high
data values.
2. Diverging schemes put equal emphasis on mid-range critical
values and extremes at both ends of the data range. The critical
class or break in the middle of the legend is emphasized with light
colors and low and high extremes are emphasized with dark colors
with contrasting hues.
3. Qualitative schemes do not imply magnitude differences between
classes, and hues are used to create the primary visual differences
between classes. Qualitative schemes are best suited to
representing nominal or categorical data.

21. • Colors that are too saturated look false;
• When using very saturated colors everywhere we are not
giving rest to the user's gaze;
• Very saturated colors draw a lot the attention of the user.
21
CONSIDERATIONS

22. • Don’t exagerate;
• Use saturation and value to guide and draw attention;
• Pure, bright or very strong colors can have extraordinary effects when
used sparingly or between more erased or less vibrant background tones.
• They can also use saturation and value to tell the story and to change the
mood of the visualization.
22
CONSIDERATIONS

23. 1. Sequential or divergent data that progress from the low
to high and therefore require gradients.
2. Qualitative data that require colors that clearly show
that the categories are not related to each other.
23
CHALLENGES

24. When working with gradients the
main concern is that the
differences between the steps are
sufficiently high, because it is
necessary for the user to clearly
differentiate between a light pink
and a lighter rose.
24
CONSIDERATIONS

25. When working with qualitative
data, the main concern is to find
colors that go well together and
attract the user's gaze. Find
good color compositions.
25
CONSIDERATIONS

26. “When considering these more functional applications, one
of the most reliable and versatile colour options is grey
(regardless of semantics about whether it is a colour or a
'colour without colour’).”
[Kirk, 2015]
26
USING GRAY

27. 27
Color brewer I want hue
TOOLS

28. nar.ra.ti.ve ● /nɐʀɐˈtiv/
a way of presenting or understanding a situation or
series of events, presented in order (cronological or
not), establishing relationships between the events.
NARRATIVE
28

29.  Para incutir valores
 Para educar
 Para entreter
 Para divulgar notícias
 Para registar eventos importantes
 Como catarse (que liberta as pessoas de seus medos e emoções
indesejadas)
29
WHY?

30. NARRATIVE
”
“Story is an interactive form of communication, where
information is brought into a context that people can
understand, remember, discuss and tell others about.”
[EJC, 2010]
30

31. When interacting with an object, the brain constructs a
simple wordless narrative composed of all the elements
of a narrative: characters (the organism and the object)
and a sequence of events that unfold in time (a beginning,
a medium and an end ).
31
WHY IT WORKS?

32. Until recently, visualizations were frequently used to support
traditional forms of storytelling, such as extra information or
supporting evidence. However, there has been a great effort
lately to turn visualizations into an independent form of
storytelling that can exist on its own without traditional
supports.
32
NARRATIVE VISUALIZATION

33. Gershon and Page (2001) were
the first to identify the valuable
contribution that narrative could
give to information
visualization.
Gershon and Page
NARRATIVE IN VISUALIZATION
33
2001

34. ”
“Our framework suggests design strategies for narrative
visualization, including promising under-explored approaches to
journalistic storytelling and educational media.”
[Segel and Heer, 2010]
34
NARRATIVE VISUALIZATION

35. Two approaches in visualization:
 as visualizações orientadas
pelos autores da visualização;
 e as visualizações orientadas
pelos leitores.
Three types of common structures in
narrative visualization:
 estruturas Martini Glas;
 estruturas Interactive
Slideshow;
 as estruturas Drill-Down
Story.
35
NARRATIVE VISUALIZATION

36. There is still no consensus on what encompasses a data story (or a
narrative visualization) and most research is more concerned with
how the different components of visualization can improve the
feeling of storytelling. It would be important to limit the scope of
what is called a data story, for example by distinguishing between a
data story and a traditional data visualization.
36
NARRATIVE IN VISUALIZATION

37.  a set of specific stories or facts;
 annotations or narration that help to highlight the data;
 a significative order or relationship between the
narrative elements.
37
FREQUENTLY HAVE:

38.  The relationship between time and narrative;
 The role of interactivity in providing context
(annotations);
 The role of empathy strategies;
 The use of gamification.
38
STRATEGIES IN VISUALIZATION

39. VISUAL METAPHORS
”
“One of the fundamental features of
stories is that they provide a temporal
structure, even if not necessarily linear.”
[Kosara and Mackinlay, 1990]
Gapminder
TIME AND NARRATIVE
39
Hans Rosling

40. Timelines | Histography Animation | American Workday
40
STRATEGIES

41. Steppers | A History of Sumo Scrollers | How the Recession Reshaped the Economy
41
STRATEGIES

42. ”
“The story creator then must decide how
to thread the representations into a
compelling yet understandable
sequence.”
[Hullman et al., 2013]
 Dialogo (Pergunta e resposta; Who, What, When, Where,
Why, How)
 Temporais (cronológica, invertida ou futura);
 Causais (sequências de causa explicita ou de realidades
alternativas);
 Granularidade (geral para específico e vice versa);
 Comparação (comparação de dimensões ou de
medidas);
 Espaciais (sequências de aproximação espacial).
42
TRANSITIONS
Transições mais utilizadas:

43. As anotações têm a capacidade de adicionar
contexto que, de outra forma, seria muito
difícil de dar, facilitando a interpretação do
utilizador, sugerindo conclusões e
orientando a interação do utilizador. Esta
informação de contexto é mais fácil de
assimilar do que um artigo denso e pode
servir como pequenos momentos de
storytelling.
ANNOTATIONS
Fatal Force
INTERATIVITY AND CONTEXT
43
The Washington Post

44. INDIVIDUALS INSTEAD OF NUMBERS
”
“People remember the gist,
message, and the feeling,
not the numbers.”
[Sam, 2016]Home and Away
EMPATHY
44
CNN

45. PLAYFULL DATA
”
“Gamification is an informal umbrella term
for the use of video game elements in non-
gaming systems to improve user experience
(UX) and user engagement”
[Deterding et al., 2011]SPENT
GAMIFICATION
45
McKinney

46. There is an intense discussion in research in visualization about
whether or not the introduction of narrative is beneficial. However,
most seem to agree that when done correctly, it can be a powerful
way to create a structured interpretation path. Good narrative
visualizations allow the user to engage with data, makes it gain more
insights and helps them cope with their short periods of attention
and lack of data literacy.
46
STORYTELLING: GOOD OR BAD?

47. Most seem to agree that when done correctly, it can be a
powerful way to create a structured interpretation path.
Good narrative visualizations allow the user to engage
with the data, gain insights, and help them cope with their
short attention spans and lack of data literacy.
47
STORYTELLING: GOOD OR BAD?

48. There has also been growing concern about how much narrative incorporation will
affect data exploration and whether or not it distracts the user from the data. The
investigation has revealed that having flexible narratives that indicate particular
milestones for the user to explore, but that also allow free exploitation of
intermediate reference points, is a good option. However, more research is still
needed on how narrative influences the interpretation process and how to effectively
create these narratives (what rhetorical techniques can be used and whether it is
possible to build a set of techniques that work different datasets).
48
STORYTELLING: GOOD OR BAD?

49. ANA FIGUEIRAS
anafigueiras@fcsh.unl.pt
vidainovamedialab@fcsh.unl.pt
http://anafigueiras.com
49
http://inovamedialab.org/
THANK YOU