Introduction to Data Visualization Fundamentals

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

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

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

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

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

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

! 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

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

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

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

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-

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-

720349656089226535931140790070
322302076958689027429003358787
115045223998424533087922668417
382319480046553364246202505406
711172160430997890121737608183
566145635519888049583302306957
749597705315240714467203496560
892265359311407900703223020769
586890274290033587871150452239
984245330879226684173823194800
465533642462025054067111721604
309978901217376081835661456355
Effective visualizations
require thoughtful
encoding.

Design decisions have a
big impact on what
people will see in the
data.
720349656089226535931140790070
720349656089226535931140790070

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

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

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

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

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

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

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

SECTION 2: DATA
VISUALIZATION
PURPOSES
INTRODUCTION TO DATA VISUALIZATION 22

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

25
STREAMGRAPH

26
TEMPORAL HEATMAP

SECTION 2: DATA VISUALIZATION USES 27
EARLY EXAMPLES

28
NEAR REAL-TIME DATA

29
MORE TIME EXAMPLES

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

PART-TO-WHOLE: A TREEMAP OF TITANIC PROPORTIONS
Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/

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

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

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

GEOSPATIAL: EARLY EXAMPLE
Source:"
http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak"

http://uxmag.com/articles/leveraging-the-kano-model-for-optimal-results
RANKING

37
http://datavizblog.com/category/distribution/
DISTRIBUTION

38
http://www.statsblogs.com/2014/08/20/creating-heat-maps-in-sasiml/
CORRELATION

DEVIATION

NOMINAL COMPARISON: BAR CHART

41
DIFFERENT PERSPECTIVES: NOMINAL COMPARISON AND
PART-TO-WHOLE

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

SECTION 3: DATA AND
DESIGN

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

THE MARRIAGE OF DESIGN AND DATA
DATA CAN BE BROKEN INTO TWO MAJOR CLASSES: DISCRETE AND CONTINUOUS
45
SECTION 3: DATA AND DESIGN

THE MARRIAGE OF DESIGN AND DATA
46

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

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

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

CHART
EFFECTIVENESS
Source:
Enrico Bertini, Assistant Professor at NYU-Poly (@filwd)

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

Which one
is bigger?
A B
A
B
53
Think about good design practices: proximity

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

COLOR AND VALUE
http://blog.visual.ly/building-effective-color-scales/

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

57
CONSTANCY

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

What do you see here?
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/

‣ 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...

! Visualization Display Choices
http://scitechdaily.com/scientists-manage-flood-big-data-space/ http://www.steema.com/tags/mobile

A FEW DATA
VISUALIZATION
DEVELOPMENT
TOOLS:

SECTION 4: PEOPLE AND
PROCESS

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

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

VISUALIZATION IS
PART OF AN
ITERATIVE PROCESS
66
SECTION 4: PEOPLE AND PROCESS

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.”

ROLE
• RESEARCHER
• PUBLIC
PRIOR KNOWLEDGE
• NONE
• SUBJECT EXPERT
USE FREQUENCY
• ONCE A DECADE
• EVERY HOUR
USERS
USER QUESTION 1 - WHO VIEWS THE DATA?

PURPOSE
HYPOTHESIS?
• WHAT ARE WE 
TRYING TO LEARN OR
SHOW?
• HOW DO WE KNOW 
IF WE ACHIEVED IT?
GOAL?
• WHAT ARE THE 
BOUNDARIES?
PARAMETERS?

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

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

72
“Applied field ethnography”, data, and map visualizations

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

SECTION 5: EXAMPLES
TO DISCUSS

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
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

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

78SECTION 5: EXAMPLES TO DISCUSS

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

80
https://source.opennews.org/en-US/articles/nyts-512-paths-white-house/

SECTION 6: CLASS
EXERCISE

‣ 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

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

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

SECTION 7: RESOURCES
AND CONCLUSIONS

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

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.

‣ 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]

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

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

QUESTIONS?
CONTACT:
HUNTER WHITNEY
HUNTER@HUNTERWHITNEY.COM
@HUNTERWHITNEY

Introduction to Data Visualization Fundamentals

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