Ga + sxsw data visualization from a ux perspective class v.0.5

INTRODUCTION 2
HAPPY SUPER PI DAY! 3.14.15…
The number π is a mathematical constant, the ratio of a circle's circumference to its
diameter, commonly approximated as 3.1415..9
NOT PIE DAY…

DATA VISUALIZATION
FROM A UX PERSPECTIVE
March 14, 2015Hunter Whitney
1+ SXSW

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

Sections:
1) What is Data Visualization?
2) Data Visualization Purposes
3) Data and Design
4) UX Considerations for DV
3DATA VISUALIZATION FROM A UX PERSPECTIVE

INTRODUCTION TO DATA VISUALIZATION
SECTION 1: WHAT IS
DATA VISUALIZATION?
4

6
VISUALIZATIONS MAKE IT EASIER TO SEE
PATTERNS IN DATA
SECTION 1: WHAT IS DATA VISUALIZATION?
http://data.oecd.org/healthcare/child-vaccination-rates.htm

Selective encoding makes data easier
to grasp and explore.
http://www.gapminder.org/
SECTION 1: WHAT IS DATA VISUALIZATION? 6

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 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 can find
in the data.
720349656089226535931140790070
720349656089226535931140790070

12SECTION 1: WHAT IS DATA VISUALIZATION?
Scanning speed…
25 100500 75
Apple
Orange
Peach
Mango
Blueberry
Banana
Grapes
Chart Sub Title
Bar Graph
Orange
25 100500 75
Apple
Orange
Peach
Mango
Blueberry
Banana
Grapes
Chart Sub Title
Bar Graph
Orange

TAPPING IN TO OUR PERCEPTUAL POWERS
The “pop-out” effects are due to your brain’s pre-attentive processing.
COLOR HUE ORIENTATION TEXTURE POSITION & ALIGNMENT
COLOR BRIGHTNESS COLOR SATURATION SIZE SHAPE

What is easier to
distinguish here -
color or shape
differences?
Combinations can
tell a more layered
story.
Some attributes pop out more
than others.

Adapted from Stephen Few’s work.

Consider encoding strategies to direct attention toward
what’s critical.

A substantial portion of our brains 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 16

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
17

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 18

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 19

SECTION 2: DATA
VISUALIZATION
PURPOSES
INTRODUCTION TO DATA VISUALIZATION 20

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.
CAN YOU RELATE?
SECTION 2: DATA VISUALIZATION PURPOSES 21

23
TIME SERIES GRAPH
SECTION 2: DATA VISUALIZATION PURPOSES

23
STREAMGRAPH

24
TEMPORAL HEATMAP

SECTION 2: DATA VISUALIZATION USES 25
EARLY EXAMPLES OF TEMPORAL DATA VIS

26
NEAR REAL-TIME DATA

28
Class, Age, Sex, Survived, Quantity
1st,adult,male,yes,57
1st,adult,male,no,118
1st,adult,female,yes,140
1st,adult,female,no,4
1st,child,male,yes,5
1st,child,male,no,0
1st,child,female,yes,1
1st,child,female,no,0
2nd,adult,male,yes,14
2nd,adult,male,no,154
2nd,adult,female,yes,80
2nd,adult,female,no,13
2nd,child,male,yes,11
2nd,child,male,no,0
2nd,child,female,yes,13
2nd,child,female,no,0
3rd,adult,male,yes,75
3rd,adult,male,no,387
3rd,adult,female,yes,76
3rd,adult,female,no,89
3rd,child,male,yes,13
3rd,child,male,no,35
3rd,child,female,yes,14
3rd,child,female,no,17
crew,adult,male,yes,192
crew,adult,male,no,670
crew,adult,female,yes,20
crew,adult,female,no,3
crew,child,male,yes,0
crew,child,male,no,0
crew,child,female,yes,0
crew,child,female,no,0
PART-TO-WHOLE
http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/
Here is some data about
the makeup of the
passengers and crew of
the Titanic’s last
voyage.
How would you
visualize relationships
between these groups?

29SECTION 2: DATA VISUALIZATION PURPOSES
PART-TO-WHOLE

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

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

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

FLOW MAP OF WEBSITE VISITOR TRAFFIC

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

35
http://datavizblog.com/category/distribution/
DISTRIBUTION

36
http://www.statsblogs.com/2014/08/20/creating-heat-maps-in-sasiml/
CORRELATION

DEVIATION

SECTION 3: DATA AND
DESIGN

THE MARRIAGE OF DATA AND DESIGN
DATA CAN BE BROKEN INTO TWO MAJOR CLASSES: DISCRETE AND CONTINUOUS
38
SECTION 3: DATA AND DESIGN

39
THE MARRIAGE OF DATA AND DESIGN

NOMINAL COMPARISON: BAR CHART

42
DIFFERENT VIEWS OF THE DATA

SECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 42
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."

44SECTION 3: DATA AND DESIGN
CONTEXT AND PERCEPTION
http://blog.visual.ly/building-effective-color-scales/

45
http://swiked.tumblr.com/post/112073818575/guys-please-help-me-is-this-dress-white-and
THE COLOR COMBO THAT BROKE THE INTERNET

CONTEXT AND PERCEPTION
46
http://www.lottolab.org/articles/illusionsoflight.asp http://adaynotwasted.com/2010/02/light-and-color-illusionsgin-art/

47
COLOR CONSTANCY

SECTION 4: UX
CONSIDERATIONS FOR
DV

SECTION 4: UX CONSIDERATIONS FOR DV 48
http://cnr.ncsu.edu/geospatial/wp-content/uploads/sites/6/2014/02/earth_observation-574_crop1-1500x600.jpg

50
Where did this come from? What can I do with it?What is it showing me ?
Do I have other options for
visualizing these data?
SECTION 4: UX CONSIDERATIONS FOR DV

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
50SECTION 4: UX CONSIDERATIONS FOR DV

52
http://www.oum.ox.ac.uk/visiting/images/Wallace_papilio.jpg http://images.nationalgeographic.com/wpf/media-live/photos/000/000/cache/blue-morpho-butterfly_89_600x450.jpg
STATIC DYNAMIC

LET’S CONSIDER DYNAMIC HIGH USER CONTROL VISUALIZATIONS…
52
• Context and Transitions
• Workflow and Pivoting
• Strategically breaking out different views of data
• Divide-and-Conquer Versus All-in-One Visualizations

54SECTION 4: UX CONSIDERATIONS FOR DV (EXAMPLE 1)
Thank you Jeff Chang, MD.

Thank you Brad Friedman, Ph.D.
More info: http://bioconductor.org/packages/devel/bioc/html/
AnalysisPageServer.html

Thank you, Joe Preston, CXO
Momentum Design Lab
http://momentumdesignlab.com/

57
Courtesy of Joe Preston CXO Momentum Design Lab
SECTION 4: UX CONSIDERATIONS FOR DV (EXAMPLE 3)

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.

VISUALIZATION IS
PART OF AN
ITERATIVE PROCESS
63
SECTION 4: UX CONSIDERATIONS FOR DV (EXAMPLE 3)

RESOURCES

DATA VISUALIZATION RESOURCES
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:
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.
RESOURCES 88

A FEW DATA
VISUALIZATION
CREATION
TOOLS:
62SECTION 3: DATA AND DESIGN

QUESTIONS?
CONCLUSIONS - CLASS CLOSING / Q&A
CONTACT:
HUNTER WHITNEY
HUNTER@HUNTERWHITNEY.COM
@HUNTERWHITNEY
91

Ga + sxsw data visualization from a ux perspective class v.0.5

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