iliinsky visualization for UX

Effective Visualization Design
for UX Practitioners
Noah Iliinsky
ComplexDiagrams.com
@noahi
About the Speaker
Noah Iliinsky
!  Noah Iliinsky is the author of
Designing Data Visualizations and the
technical editor of, and a contributor to,
Beautiful Visualization, both published by
O’Reilly Media.
!  He has spent the last several years
researching and teaching effective
approaches to creating diagrams and data
visualization.
!  He has a master’s in Technical
Communication from the University of
Washington, and a bachelor’s in Physics
from Reed College
Why Visualization?
Why Visualization?
http://en.wikipedia.org/wiki/Anscombe%27s_quartet
Visualization makes data accessible.
http://en.wikipedia.org/wiki/Anscombe%27s_quartet
Your brain is a pattern-detecting machine.
We’re extremely good at detecting
patterns and pattern violations:
• trends
• gaps
• outliers
The Four Pillars
A Successful Visualization
1. Has clear purpose
2. Includes (only) the relevant
content
3. Uses appropriate structure
4. Has useful formatting
A Successful Visualization
1.  purpose – why this visualization
2.  content – what to visualize
3.  structure – how to visualize it
4.  formatting – everything else
Today’s focus: structure and formatting.
Purpose
• Why am I creating this
visualization?
• Who is it for?
• What do they need to
understand?
• What actions do you need
to enable?
• How will it be consumed?
http://www.apple.com
Content
• What data matters?
• What relationships matter?
• Informed by purpose!
• What’s excluded is as important as
what’s included.
Content
http://www.businessinsider.com/iphone-bigger-than-microsoft-2012-2
Q: Why is structure so important?
Q: Why is structure so important?
A: Science!
Foundational papers:
Cleveland & McGill, 1984, 1985
Graphical Perception: Theory Experimentation, and
Application to the Development of Graphical Methods
Determined relative accuracy of perception &
interpretation of various visual encodings.
1.  Aligned, nonaligned Position
2.  (nonaligned) Length
3.  Angle / Slope
4.  Area
5.  Volume, Saturation
6.  Hue
http://www.jstor.org/stable/2288400
Encodings have properties, and represent data types
http://ComplexDiagrams.com/properties
Different data types require different encodings.
http://ComplexDiagrams.com/properties
Ordered-or-not is based in inherent interpretation.
http://ComplexDiagrams.com/properties
Number of values is based on differentiability.
http://ComplexDiagrams.com/properties
The two most important considerations in
visualization
1.  Position is everything.
2.  Color is difficult.
- @moritz_stefaner
Different data types require different encodings.
http://ComplexDiagrams.com/properties
First: Place.
Because position is the most accurate &
easiest* to perceive, we must use it for our
most important data.
Then we can add other data dimensions using:
•  size
•  shape
•  color
•  connection
•  etc.
* usually
Ok, but how do we use that to
select a structure?
Structure serves your purpose
http://manyeyes.com/manyeyes/page/Visualization_Options.html
The short answer
You might just want a bar graph.
In praise of bar graphs
Bars are fantastic for comparison of all kinds of things.
A lot of common data has the form:
• Value vs. category (sales, product)
• Value vs. multiple categories (sales, product, time)
Bars: grouping (position) has meaning
Sales per product, grouped by
week.
Good to emphasize & compare
per-week performance of each
product.
Sales per week, grouped by
product.
Good to emphasize & compare
performance of each product
over several weeks.
Bars: many ways to encode meaning
The ordering of the bars
can have meaning.
Bar graphs can be
annotated with points or
lines to show targets or
thresholds.
Bullet graphs are bars
with shaded regions to
represent qualitative
ranges.
https://en.wikipedia.org/wiki/Bullet_graph
Lines indicate continuity
• Line graphs are the standard for change over time.
• Too many lines look like spaghetti.
Dot plots are like bar graphs (without the bars)
• Comparing by position is extremely easy.
• Less cluttered than bars would be.
• This example is a quantitative axis and category grouping
http://www.nytimes.com/interactive/2013/05/25/sunday-review/corporate-taxes.html
Two-axis dot plots are scatter plots
Width in mm
inches
More Svelte
26"
650b
700c
More Burly
Schwalbe Marathon Supreme
Schwalbe Marathon SmartGuard
Schwalbe Marathon XR
Schwalbe Marathon
Schwalbe Big Aple
Schwalbe 650b Fatty
Schwalbe 650b Middy
Fatty Rumpkin
Panaracer Pasela
Jack Brown Blue
Jack Brown Green
Nifty Swifty
Ruffy Tuffy
Panaracer Col de la Vie
Maxy Fasty
Roll-y Pol-y
28
32
1.25"
36
40
1.5"
44
48
1.75"
52
2"
56
• Shows relationship between two quantitative axes
• Trends become apparent
http://complexdiagrams.com/2009/03/tire-chart/
Scatter plots can easily encode 5-7 axes of data
• Horizontal
• Vertical
• Size
• Color
• Highlight
• Trace
• Shape
• Don’t get
carried away…
Free material from gapminder.org
• One (clever) way of dealing with three quantitative axes
http://www.nytimes.com/imagepages/2010/05/02/business/02metrics.html
What about composition?
Good pies are possible…
Pecan 8%
Blueberry 33%
Peach 17%
Pumpkin 17%
Cherry 25%
• Few relevant slices
• Not much precision required
• Slices ordered by size
Even mostly good pies are only OK.
• We’re bad at comparing angles
• Length is much more accurate
http://en.wikipedia.org/wiki/Pie_chart
Tree maps allow a lot of detail, at a price.
• Tree maps encode hierarchy with nesting
• Can encode other values with color
• We’re really bad at comparing areas
https://en.wikipedia.org/wiki/Tree_map
Please avoid these common
structural failures
These will distort your data; just say no. Use bars instead.
• Pies that don’t add up to 100%
• 3D anything
• Radar charts
• Circular bars or areas
http://en.wikipedia.org/wiki/File:Spider_Chart.jpg
http://www.presentation-process.com/doughnut-chart.html
http://www.socialmediaexaminer.com/
SocialMediaMarketingIndustryReport2013.pdf
Two major domains of formatting:
•  Encoding data
•  Revealing data
Formatting makes visualizations accessible and compelling.
Formatting
Structure
Content
Purpose
Q: What informs encoding choices?
Q: What informs encoding choices?
A: Science!
Color is difficult.
Wrong.
http://eusoils.jrc.ec.europa.eu/esdb_archive/serae/GRIMM/erosion/inra/europe/analysis/maps_and_listings/web_erosion/maps_and_listings/altitude_a3.gif
Color is difficult.
Color is not ordered.
Color is not ordered!
http://ComplexDiagrams.com/properties
Color is difficult.
Better.
http://mapsof.net/uploads/static-maps/topographic_(altitude)_map_tamil_nadu.png
Color is difficult.
Passable…
http://www.goldensoftware.com/gallery/gallery-11.shtml
Color is difficult.
Pretty good.
http://www.goldensoftware.com/gallery/gallery-11.shtml
Color is difficult.
Color has meaning!
Gender
Nationality
Politics
Religion
Morality
Nature
Encoding everything else.
Different properties for different data types.
http://ComplexDiagrams.com/properties
Scatter plots can easily encode 5-7
axes of data
• Horizontal
• Vertical
• Size
• Color
• Highlight
• Trace
• Shape
• Don’t get
carried away…
Free material from gapminder.org
Formatting to reveal data
Reveal the data
Ouch! Decorative 3D distorts and obfuscates data.
Reveal the data
Remove the noise.
Reveal the data
Much better!
•  less-saturated colors
•  removal of decoration
•  no tick marks
•  minimal grid
•  direct labeling
There are many valid options for
•  labeling
•  grid lines
•  legends
•  fonts & colors
•  etc.
Reveal the data
Get Cole Nussbaumer’s
Excel template!
(there’s a link from ComplexDiagrams.com)
Reveal the data
Pro tip:
A few (focused) graphs
with less data on each
are better than fewer graphs
with more (noisier) data.
Highlight what matters, remove the rest
• Geography is modified to
show logical meaning
• Colors encode party.
• Saturation encodes
turnout.
• Outlines group regions.
• All other details removed.
http://www.economist.com/blogs/graphicdetail/2013/04/mapping-britain
A Successful Visualization
1. Has clear purpose
2. Includes (only) the relevant
content
3. Uses appropriate structure
4. Has useful formatting
Conclusion:
Purpose informs choices,
and science guides them.
Thank you!
More resources at:
ComplexDiagrams.com
@noahi