1.
INTRODUCTION TO
DATA VISUALIZATION
February 3, 2015Hunter Whitney
1
DRAFT

2.
INTRODUCTION
HUNTER WHITNEY
2
! UX Design and Data Visualization Consultant
! Author and Contributing Editor
! @hunterwhitney"

3.
INTRODUCTION
HELLO!
‣ Who are you?
‣ What do you do?
‣ What’s your learning goal for today?
‣ Is there a topic you’d like to
visualize in the exercise today?
3

4.
Sections:
1) What is Data Visualization?
2) Data Visualization Purposes
3) Data and Design
4) People and Process
5) Examples to Discuss
6) Class Exercise
7) Resources and Conclusions
4

5.
CLASS EXERCISE PRELIMINARIES
DISCUSSION
Toward the end of class, we’re going to split up into groups and create data visualization concept
designs. As we go through each section, think about applying the ideas we cover to a project you might
choose.
Topic suggestion for the final exercise - create a visualization that shows how a series of events
unfolds over time. Be creative. It doesn’t have to be just a timeline on an x-axis.
This can be applied to many areas including - business (e.g., patterns of timing from VC funding to
IPO), sports (e.g., changes ball possession during a game), medicine (e.g., the spread of an epidemic)
START THINKING…
5

6.
KEY QUESTIONS TO ADDRESS IN YOUR PROJECTS
‣ What is the purpose/value of the visualization?
‣ Who are the intended users?
‣ How was the data selected and acquired?
‣ What design elements were used and why?
CLASS EXERCISE PRELIMINARIES 6

7.
! We’re only scratching the
surface of every topic
presented here
! The main goal is for you to
look at data visualization
with a holistic perspective
! Whatever your levels of
skill and experience are,
you have something to
offer
KEEP IN MIND… 7

8.
INTRODUCTION TO DATA VISUALIZATION
SECTION 1: WHAT IS
DATA VISUALIZATION?
8

9.
9
VISUALIZATIONS MAKE IT EASIER TO SEE
PATTERNS IN DATA
SECTION 1: WHAT IS DATA VISUALIZATION?
http://data.oecd.org/healthcare/child-vaccination-rates.htm

10.
The key to effectively exposing
meaningful patterns in data comes
down to thoughtful visual encoding.
http://www.gapminder.org/
SECTION 1: WHAT IS DATA VISUALIZATION? 10

11.
720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
How does encoding work?
Guess how many ‘7’s there
are in this set-
SECTION 1: WHAT IS DATA VISUALIZATION? 11

12.
720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
They’re the same set of
numbers, but now the
7’s pop out at us.
Now, try guessing again-
SECTION 1: WHAT IS DATA VISUALIZATION? 12

13.
720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
Effective visualizations
require thoughtful
encoding.
SECTION 1: WHAT IS DATA VISUALIZATION? 13

14.
Design decisions have a
big impact on what
people will see in the
data.
SECTION 1: WHAT IS DATA VISUALIZATION? 14
720349656089226535931140790070
720349656089226535931140790070

15.
A substantial portion of the human brain is devoted to visual processing
Source:
http://www.flickr.com/photos/orangeacid/234358923/
Creative Commons Attribution License
Source:
http://en.wikipedia.org/wiki/File:Brodmann_areas_17_18_19.png
GNU Free Documentation License
WE ARE WIRED FOR VISUALIZATION
10 Million Bits
Per Second
Source:
Current Biology (July 2006) by Judith McLean
and Michael A. Freed
SECTION 1: WHAT IS DATA VISUALIZATION? HUMAN BRAIN 15

16.
TAPPING IN TO OUR PERCEPTUAL POWERS
The pop-out effects are due to your brain’s pre-attentive processing
SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING 16
COLOR HUE ORIENTATION TEXTURE POSITION & ALIGNMENT
COLOR BRIGHTNESS COLOR SATURATION SIZE SHAPE

17.
What is easier to
distinguish here - color
or shape differences?
Some attributes pop out more
than others.
17SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING

18.
http://www.slideshare.net/slideshow/view?login=johnwhalen&title=cognitive-science-of-design-in-10-minutes-or-less
SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING
SHAPE
18

19.
http://www.slideshare.net/slideshow/view?login=johnwhalen&title=cognitive-science-of-design-in-10-minutes-or-less
SECTION 1: WHAT IS DATA VISUALIZATION? BRAIN SYSTEMS 19

20.
SECTION 1: DATA VISUALIZATION PROCESS AND PRACTICES
Adapted from Stephen Few.
20

21.
PUTTING THE PIECES TOGETHER
The components of visualizations fit into a larger context of goals, users,
and the media in which they are presented.
SECTION 1: WHAT IS DATA VISUALIZATION? BUILDING OUT 21

22.
SECTION 2: DATA
VISUALIZATION
PURPOSES
INTRODUCTION TO DATA VISUALIZATION 22

23.
Overview first, zoom and filter, then details-on-demand.
‣ Time Series and Event Sequences
‣ Part-to-Whole
‣ Geospatial
‣ Ranking
‣ Distribution
‣ Correlation
‣ Deviation
‣ Nominal Comparison
There can be overlaps in what can be shown and related
in one visualization
I CAN RELATE!
SECTION 2: DATA VISUALIZATION PURPOSES 23

24.
24
TIME-SERIES GRAPH
SECTION 2: DATA VISUALIZATION PURPOSES
http://www.businessinsider.com/india-and-america-come-meet-mum-2015-1

25.
25
STREAMGRAPH
SECTION 2: DATA VISUALIZATION PURPOSES

26.
26
TEMPORAL HEATMAP
SECTION 2: DATA VISUALIZATION PURPOSES

27.
SECTION 2: DATA VISUALIZATION USES 27
EARLY EXAMPLES

28.
28
NEAR REAL-TIME DATA
SECTION 2: DATA VISUALIZATION PURPOSES

29.
29
MORE TIME EXAMPLES
SECTION 2: DATA VISUALIZATION PURPOSES

30.
30
FOR A DEEPER DIVE INTO
TEMPORAL DATA VIS..
http://www.oreilly.com/pub/e/3139
http://uxmag.com/articles/its-about-time
SECTION 2: DATA VISUALIZATION PURPOSES

31.
Overview first, zoom and filter, then details-on-demand.
PART-TO-WHOLE: A TREEMAP OF TITANIC PROPORTIONS
SECTION 2: DATA VISUALIZATION PURPOSES 31
Overview first, zoom and filter, then details-on-demand.
Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/

32.
PART-TO-WHOLE: OTHER EXAMPLES
SECTION 2: DATA VISUALIZATION PURPOSES 32
* Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/
**
Pie Stacked Area
Parallel Sets Sankey Diagram

33.
FRUIT TREEMAPS: HIERARCHY AND PROPORTIONS
SECTION 2: DATA VISUALIZATION PURPOSES 33
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."

34.
34SECTION 2: DATA VISUALIZATION PURPOSES
GEOSPATIAL: THE POLITICAL LANDSCAPE

35.
GEOSPATIAL: EARLY EXAMPLE
Source:"
http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak"
SECTION 2: DATA VISUALIZATION PURPOSES 35

36.
http://uxmag.com/articles/leveraging-the-kano-model-for-optimal-results
RANKING
36SECTION 2: DATA VISUALIZATION PURPOSES

37.
37
http://datavizblog.com/category/distribution/
SECTION 2: DATA VISUALIZATION PURPOSES
DISTRIBUTION

38.
38
http://www.statsblogs.com/2014/08/20/creating-heat-maps-in-sasiml/
CORRELATION
SECTION 2: DATA VISUALIZATION PURPOSES

39.
39SECTION 2: DATA VISUALIZATION PURPOSES
DEVIATION

40.
SECTION 2: DATA VISUALIZATION PURPOSES 40
NOMINAL COMPARISON: BAR CHART
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."

41.
41
DIFFERENT PERSPECTIVES: NOMINAL COMPARISON AND
PART-TO-WHOLE
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 2: DATA VISUALIZATION PURPOSES

42.
CLASS EXERCISE (KEEP IN MIND)
DISCUSSION KEY QUESTIONS TO ADDRESS
‣ What are the main functions
(e.g., exploratory, tracking,
explanatory, etc.?)
‣ What kinds of design elements
might you want to use?
‣ What level of interactivity
might be good to include?
For whichever subject area you choose, think about the
basic design elements and functions that might work
best. These questions will come into sharper focus as
you learn more about the goals of the users.
CONSIDERATIONS FOR YOUR CLASS PROJECT
42

43.
SECTION 3: DATA AND
DESIGN
INTRODUCTION TO DATA VISUALIZATION 43

44.
http://phys.org/news/2013-10-visualization.html
THERE ARE ENDLESS FORMS OF VISUALIZATION
SECTION 3: DATA AND DESIGN 44

45.
THE MARRIAGE OF DESIGN AND DATA
DATA CAN BE BROKEN INTO TWO MAJOR CLASSES: DISCRETE AND CONTINUOUS
45
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN

46.
THE MARRIAGE OF DESIGN AND DATA
46
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN

47.
Nominal Scale: This is simply putting items
together without ordering or ranking them (e.g.,
an apple, an orange, and a tomato).
Ordinal Scale: Elements of the data describe
properties of objects or events that are ordered by
some characteristic.
THE MARRIAGE OF DESIGN AND MEASUREMENTS
47
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN

48.
Interval Scale: These are data that are
measured on some kind of scale, often
temporal (e.g., the days of the week, hours of
the day).
THE MARRIAGE OF DESIGN AND MEASUREMENTS
Ratio Scale: An ordered series of numbers
assigned to items (objects, events, etc.)
that allow for estimating and comparing
different measures in terms of multiples,
such as “half as many” or “four times as
heavy.”
48
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 2: DATA VISUALIZATION PURPOSES

49.
STATISTICAL SUMMARIZATION AND ANALYSIS
Visualizations can clarify or obscure the statistical summarization of
http://blog.visual.ly/using-visual-reasoning-to-understand-numbers/
49SECTION 3: DATA AND DESIGN

50.
50
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN

51.
CHART
EFFECTIVENESS
Source:
Enrico Bertini, Assistant Professor at NYU-Poly (@filwd)
51SECTION 3: DATA AND DESIGN

52.
Think about good design practices: selective labeling
52
Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209
SECTION 3: DATA AND DESIGN

53.
Which one
is bigger?
A B
A
B
53
Think about good design practices: proximity
SECTION 3: DATA AND DESIGN

54.
Think about good design practices: multiples
54
Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209
SECTION 3: DATA AND DESIGN

55.
55SECTION 3: DATA AND DESIGN
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
COLOR AND VALUE
http://blog.visual.ly/building-effective-color-scales/

56.
YOUR VISUAL SYSTEM
56
http://www.lottolab.org/articles/illusionsoflight.asp http://adaynotwasted.com/2010/02/light-and-color-illusionsgin-art/
SECTION 3: DATA AND DESIGN

57.
57
CONSTANCY
SECTION 3: DATA AND DESIGN

58.
Idea: Forms or patterns transcend the stimuli used to
create them.
Why do patterns emerge?
Under what circumstances?
Principles of Pattern Recognition:
“Gestalt” is German for “pattern” or “form,
configuration”.
GESTALT PRINCIPLES
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm
58SECTION 3: DATA AND DESIGN

59.
What do you see here?
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/
59SECTION 3: DATA AND DESIGN

60.
‣ How do you design the “perfect” visualization?
‣ There’s no perfect visualization: the design space is just too big!
‣ But it’s up to you to design the one that fits...
60SECTION 3: DATA AND DESIGN

61.
! Visualization Display Choices
http://scitechdaily.com/scientists-manage-flood-big-data-space/ http://www.steema.com/tags/mobile
61SECTION 3: DATA AND DESIGN

62.
A FEW DATA
VISUALIZATION
DEVELOPMENT
TOOLS:
62SECTION 3: DATA AND DESIGN

63.
SECTION 4: PEOPLE AND
PROCESS
INTRODUCTION TO DATA VISUALIZATION 63

64.
SECTION 4: PEOPLE AND PROCESS 64
http://cnr.ncsu.edu/geospatial/wp-content/uploads/sites/6/2014/02/earth_observation-574_crop1-1500x600.jpg

65.
VISUALIZATION IS ONLY THE TIP
OF THE ICEBERG
Data visualization is only a part of a
much larger process that includes
identifying the purpose of the
visualization, the kinds of people who
will use it, the types of data that can
be collected and analyzed, and good
design choices.
65SECTION 4: PEOPLE AND PROCESS

66.
VISUALIZATION IS
PART OF AN
ITERATIVE PROCESS
66
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 4: PEOPLE AND PROCESS

67.
PERSPECTIVE: BIOTECHNOLOGY EXECUTIVE
67
‣ “We usually have an underlying narrative or hypothesis that is driving the
analysis, but even with that you have to be ready for a surprise. Be willing to
go where the data leads you, provided you have good data from multiple
sources.”
‣ “We try to have teams involved in the data collection and analysis process
‘from soup to nuts’. If people join only at the end of the process, you could be
setting yourself up for failure.”
‣ “If you rely on just one data set, you can be totally misled.”
SECTION 4: PEOPLE AND PROCESS

68.
ROLE
• RESEARCHER
• PUBLIC
PRIOR KNOWLEDGE
• NONE
• SUBJECT EXPERT
USE FREQUENCY
• ONCE A DECADE
• EVERY HOUR
USERS
USER QUESTION 1 - WHO VIEWS THE DATA?
68SECTION 4: PEOPLE AND PROCESS

69.
PURPOSE
HYPOTHESIS?
• WHAT ARE WE
TRYING TO LEARN OR
SHOW?
• HOW DO WE KNOW
IF WE ACHIEVED IT?
GOAL?
• WHAT ARE THE
BOUNDARIES?
PARAMETERS?
69SECTION 4: PEOPLE AND PROCESS

70.
DATA QUESTION 1 - WHO OWNS IT?
PRIMARY
• YOU COLLECT IT
• YOU OWN IT
• NOBODY ELSE HAS IT
• OTHERS COLLECT IT
• OTHERS OWN IT
• OTHERS HAVE IT
SECONDARY
DATA
70SECTION 4: PEOPLE AND PROCESS

71.
DATA QUESTION 2 - DOES IT CHANGE?
DYNAMIC
• CHANGES OFTEN
• COLLECTED OFTEN
• TIME WINDOW
MATTERS
• DOES NOT CHANGE
• COLLECT IT ONCE
• TIME WINDOW
MATTERS
STATIC
DATA
71SECTION 4: PEOPLE AND PROCESS

72.
72
“Applied field ethnography”, data, and map visualizations
SECTION 4: PEOPLE AND PROCESS

73.
USER CONTROL:
HIGH
STATIC
EXPLAINEXPLORE
(e.g., data-intensive research
applications)
(e.g., print infographic
advocacy )
(e.g., interactive infographic
journalism)
(e.g., data-rich visualizations with
limited interactivity)
DYNAMIC
USER CONTROL:
LOW
73SECTION 4: PEOPLE AND PROCESS

74.
SECTION 5: EXAMPLES
TO DISCUSS
INTRODUCTION TO DATA VISUALIZATION 74

75.
SECTION 5: EXAMPLES TO DISCUSS 75
After Nate Silver moved on to other things,
New York Times filled the gap with a data-
centric journalism section called “The
Upshot.”
Let’s discuss, deconstruct, and critique a few
examples from the site. These are screen
shots to you may not have full context, but
let’s see how these visualizations stand up.
You might want to visit the site and play with
it more on your own and practice evaluation
it based on what we’ve already discussed.
http://www.nytimes.com/upshot/

76.
76
http://www.nytimes.com/interactive/2014/07/08/upshot/how-the-year-you-were-born-influences-your-politics.html?abt=0002&abg=1
SECTION 5: EXAMPLES TO DISCUSS

77.
77SECTION 5: EXAMPLES TO DISCUSS
http://www.nytimes.com/newsgraphics/2014/senate-model/

78.
78SECTION 5: EXAMPLES TO DISCUSS

79.
79
http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html?abt=0002&abg=0
SECTION 5: EXAMPLES TO DISCUSS

80.
80
https://source.opennews.org/en-US/articles/nyts-512-paths-white-house/
SECTION 5: EXAMPLES TO DISCUSS

81.
SECTION 6: CLASS
EXERCISE
INTRODUCTION TO DATA VISUALIZATION 81

82.
‣ Get into groups 4 or more, and discuss the ideas and examples you
have in mind.
‣ Then...
• Select the purpose, audience, and data you want to use for a
visualization
• Design the visualization on the provided poster paper
• Be ready to share your results and describe your thought process
EXERCISE IDEA: THINK TIME
82SECTION 6: CLASS EXERCISE

83.
StreamgraphSpace Time CubeGantt Chart
83SECTION 6: CLASS EXERCISE
Food for thought..

84.
Food for thought..
84
http://www.gapminder.org
SECTION 6: CLASS EXERCISE

85.
SECTION 7: RESOURCES
AND CONCLUSIONS
INTRODUCTION TO DATA VISUALIZATION 85

86.
DATA VISUALIZATION RESOURCES
‣ Flowing Data (http://flowingdata.com/
‣ Fast Company Co.design (http://www.fastcodesign.com/)
‣ UX Magazine (http://uxmag.com/)
‣ The Human-Computer Interaction Lab (http://www.cs.umd.edu/hcil/)
‣ A Periodic Table of Visualization Methods (www.visual-literacy.org/
periodic_table/periodic_table.html)
Sites:
86SECTION 7: RESOURCES AND CONCLUSIONS

87.
DATA VISUALIZATION BOOKS:
‣ Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps. (Berg, W. J., Trans.) Redlands, CA: Esri
Press. (Original work published 1965)
‣ Card, S. K., Mackinlay, J. D., & Shneiderman, B. (Eds.). (1999). Readings in information visualization: Using
vision to think. San Francisco, CA: Morgan Kaufmann Publishers.
‣ Few, S. C. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA:
Analytics Press.
‣ Few, S. C. (2004). Show me the numbers: Designing tables and graphs to enlighten. Oakland, CA: Analytics
Press.
‣ Fry, B. (2008). Visualizing data. Sebastopol, CA: O’Reilly Media, Inc.
‣ Segaran, T., & Hammerbacher, J. (Eds.) (2009). Beautiful data: The stories behind elegant data solutions.
Sebastopol, CA: O’Reilly Media, Inc.
‣ Tufte, E.R. (1997). Visual explanations: Images and quantities, evidence and narrative. Cheshire, CT: Graphics
Press, LLC.
‣ Ware, C. (2008). Visual thinking for design. Burlington, MA: Morgan Kaufmann Publishers.
‣ Whitney, H. (2012) Data Insights New Ways to Visualize and Make Sense of Data Morgan Kaufmann/Elsevier
2012.
‣ Wilkinson, L. (2005). The grammar of graphics. Chicago, IL: Springer.
‣ Yau, N. (2011). Visualize this: The flowing data guide to design, visualization, and statistics. Indianapolis, IN:
Wiley Publishing, Inc.
87SECTION 7: RESOURCES AND CONCLUSIONS

88.
‣ Length Triesman & Gormican [1988]
‣ Width Julesz [1985]
‣ Size Triesman & Gelade [1980]
‣ Curvature Triesman & Gormican [1988]
‣ Number Julesz [1985]; Trick & Pylyshyn [1994]
‣ Terminators Julesz & Bergen [1983]
‣ Intersection Julesz & Bergen [1983]
‣ Closure Enns [1986]; Triesman & Souther [1985]
‣ Color (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991]Kawai et al.
‣ Intensity Beck et al. [1983]; Triesman & Gormican [1988]
‣ Flicker Julesz [1971]
‣ Direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992]
‣ Binocular luster Wolfe & Franzel [1988]
‣ Stereoscopic depth Nakayama & Silverman [1986]
‣ 3-D depth cues Enns [1990]
‣ Lighting direction Enns [1990]
88SECTION 7: RESOURCES AND CONCLUSIONS

89.
CONCLUDING THOUGHTS
•Data visualization involves learning about the rules and the process
•Start with the problem, not with the data or the visualization
•Think big: find the data you need
•Visualize your data in multiple ways
•Know your audience and their goals
89SECTION 7: RESOURCES AND CONCLUSIONS

90.
Keep in mind - the value of data depends on what you do with it
90
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012.
SECTION 7: RESOURCES AND CONCLUSIONS

91.
QUESTIONS?
CONTACT:
HUNTER WHITNEY
HUNTER@HUNTERWHITNEY.COM
@HUNTERWHITNEY
91SECTION 7: RESOURCES AND CONCLUSIONS