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Stronger Research Reporting Using Visuals
Applying Visual Design for Better Research – VCU Workshop
5th October, 2011
We live in a time of
unprecedented
Information
Overload
2
“ The highest-paid person in the
first half of the next century
will be the ‘storyteller.’ ”
Rolf Jensen, 1996
3
As Story-tellers, we learn..
To write for the reader, not for yourself
4
As Story-tellers, we learn..
To write for the reader, not for yourself
A story needs a logical flow
5
As Story-tellers, we learn..
To write for the reader, not for yourself
A story needs a logical flow
To have a point of view
6
As Story-tellers, we learn..
To write for the reader, not for yourself
A story needs a logical flow
To have a point of view
Only to report data that is vital to
telling the story
7
How can visuals help in storytelling?
Attention
The eyes are drawn like a magnet to images.
Comprehension
Less cognitive processing required, especially if
image is familiar.
Complexity
Best way to summarise / represent complexity.
Understanding
Can reveal patterns and relationships that would
otherwise be hard to interpret or spot
Retention
Presence of illustrations significantly improves
retention.
Aesthetics
What’s wrong with wanting it to look good?
Timing
Graphics reduce time required to explain.
Emotion
Pictures do a far better job of communicating
emotion, and emotion does a far better job of
inspiring action.
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Types of Visuals
Graphs
Illustrations
Data Viz
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“Best 100 non-fiction
books of the twentieth
century”
- amazon.com
Graphs
“When a graph is made, quantitative and categorical
information is encoded by a display method. Then the
information is visually decoded. This visual perception is
a vital link. No matter how clever the choice of the
information, and no matter how
technologically impressive the encoding,
a visualization fails if the decoding fails.”
(William S. Cleveland, The
Elements of Graphing Data, Hobart Press, 1994, p. 1)
To 3D or not to 3D?
5
4
3
2
1
0
Series 3
Series 1
Series 1
Series 2
Series 3
To 3D or not to 3D?
6
4
2
0
Series 1
Series 1
To 3D or not to 3D?
6
4
2
0
Series 1
Series 1
To 3D or not to 3D?
6
4
2
Series 1
0
Category Category Category
1
2
3
CategorySeries 1
4
To 3D or not to 3D?
6
4
2
0
Series 1
To 3D or not to 3D?
6
4
2
0
Series 1
To 3D or not to 3D?
6
4
2
0
Series 1
To 3D or not to 3D?
6
5
4
Series 1
3
Series 2
Series 3
2
1
0
Category 1 Category 2 Category 3 Category 4
Losing Perspective
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Losing Perspective
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Losing Perspective
Areas, Volumes and Magnitudes
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Category 1
Category 2
Category 3
Category 4
Areas, Volumes and Magnitudes
1
0.9
0.8
0.7
Ratio of size from Cat 1 to 2 is 1:2
BUT ratio or shape area is 1:4
0.6
0.5
0.4
0.3
0.2
0.1
0
Category 1
Category 2
Category 3
Category 4
Areas, Volumes and Magnitudes
Areas, Volumes and Magnitudes
1
0.5
0
Category
1 Category
2
Category
3
Category
4
Lie factor = size of effect shown in graphic / size of effect in data
Areas, Volumes and Magnitudes
Areas, Volumes and Magnitudes
14
12
10
Series 3
8
Series 2
6
Series 1
4
2
0
Category 1
Category 2
Category 3
Category 4
Areas, Volumes and Magnitudes
Who ate all the Pies?
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Who ate all the Pies?
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Who ate all the Pies?
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
Who ate all the Pies?
We make angle judgments when we read a pie chart,
but we don’t judge angles very well. These judgments
are biased; we underestimate acute angles (angles
less than 90°) and overestimate obtuse angles
(angles greater than 90°).
(Naomi Robbins, Creating More Effective Graphs, Wiley, 2005, p. 49)
Who ate all the Pies?
Who ate all the Pies?
8%
Sales
1st Qtr
17%
2nd Qtr
3rd Qtr
4th Qtr
22%
58%
Who ate all the Pies?
Q2
Q1
8%
8%
17%
13%
17%
22%
58%
62%
Q3
9%
Q4
10% 6%
8%
26%
23%
60%
58%
Who ate all the Pies?
70%
60%
50%
Apples
40%
Oranges
30%
Bananas
20%
Grapefruit
10%
0%
Q1
Q2
Q3
Q4
Hollands and Spence found that trends are best analyzed with line graphs
than with a series of pie charts. When estimating trends with line graphs,
people can use a slope estimation procedure; with pie charts, they must
perform multiple size discriminations between pie slices.
Hollands JG, Spence I. Judgments of change and proportion in graphical perception. Hum Factors 1992;34:313-34.
Chart Junk & Data Ink
Category 4
5
2.8
Category 3
4.5
3
1.8
3.5
2
Category 2
4.4
2.5
2
Category 1
$0.00
$1.00
$2.00
2.4
$3.00
4.3
$4.00
$5.00
$6.00
Chart Junk & Data Ink
Lipkus I M , Hollands J G J
Natl Cancer Inst Monogr
1999;1999:149-163, Oxford
University Press
Gillan and Richman found that participants had faster response times and were
more accurate when the data-ink ratio was high than when it was low. In
addition, integrated tasks (e.g., global comparisons or synthesis judgments)
appear to be more affected by the data-ink ratio than are focused tasks (e.g.,
selecting the value of a data point).
Gillan DJ, Richman EH. Minimalism and the syntax of graphs. Hum Factors 1994;36:619-44
Chart Junk & Data Ink
Recap…
Data Integrity – avoid:
1.
2.
3.
4.
3 dimensional treatments
Tricks of perspective
Lie-factors of area or volume
Too many pies
Data Clarity – avoid:
5. Unnecessary clutter
6. A low data-to-ink ratio
Tufte’s 5 principles of GOOD information design
1. Enforce visual comparisons between groups
2. Show or suggest causality
3. Show multivariate data (more than 2 dimensions)
4. Content driven—all about explaining the data
5. Completely integrate words, numbers and images
1. Enforce comparison
In other words, we must always ask the question, “compared to
what?”.
Fortunately, visual comparison is faster and easier than mathematical or
conceptual comparison:
“visualization made it possible to see the effects
of design changes on the pressure distribution
of an airplane wing, for example. The same thing
could be done with number crunching in theory,
but it was a lot more immediate and obvious where
things went wrong when the model was actually
shown as an image”
- Robert Kosara, http://stat-computing.org/newsletter/issues/scgn-22-1.pdf
1. Enforce comparison
London’s Daily Greenhouse Gas Contribution
139 thousand tonnes of carbon dioxide would fill a sphere 521 metres across.

To most Londoners, '139 thousand tonnes of carbon dioxide' is not a very meaningful
quantity. Illustrating it in the context of London landmarks allows viewers to make it
meaningful for themselves.
simplified version
1. Enforce comparison
New York Weather for 1980

1980’s weather is compared against ‘normal’ weather averages allowing you to
immediately spot points of difference.
simplified version
2. Suggest Causality
Without an indication of cause, you can be left wondering what
the point is. i.e. if you show a trend, it begs the question, why is
this happening?
2. Suggest Causality
http://youtu.be/pLqjQ55tz-U
3. Show Multivariate Data
The world we seek to understand is multivariate.
The more variables, the more opportunities we have to see
relationships and patterns
simplified version
3. Show Multivariate Data
New York Weather for 1980
3 Dimensions:- Temperature
- Precipitation
- Humidity
simplified version

3. Show Multivariate Data
Increase in oil consumption
oil consumption (Y axis) by year
(X axis) and region (stacked area)
3. Show Multivariate Data
Increase in oil consumption
oil consumption (Y axis) by year
(X axis) and region (stacked area)

“Small Multiples”
Also called Trellis /
Lattice / Grid /
Panel Chart
3. Show Multivariate Data
How BI Customers Use their Platforms
Platforms, by type of usage, by volume
3. Show Multivariate Data
How BI Customers Use their Platforms
Platforms, by type of usage, by volume

3. Show Multivariate Data
Canadians think it time for a change of government, if they don’t see the
Government as being on the right track. And their vote intentions tend to
reflect that.
NF
Size of the circle is the amount of approval of
the premier/PM
Right Track
SK
Colour of Circle indicates vote difference
•
•
•
•
AB
Dark green = 15+ vote lead,
Light green is 5-14 lead,
White = +/- 5% lead/trail,
Red= 5-14 trail & dark red (no example here) is
trail by 15 or more
MN
BC
PQ
PEI
NB
ON
Feds
Time for Change
NS

3. Show Multivariate Data

4. Content-Driven
If there are elements that don’t serve the purpose of
explaining the data, they are probably chart junk.
4. Content-Driven
New York Weather for 1980
There is nothing on here that is irrelevant

4. Fully integrate words, numbers and images
Aim for the viewer to be able to take in the whole picture in one
glance, so avoid separate, complex legends which need to be
continually referenced to make sense of the data

4. Fully integrate words, numbers and images
New York Weather for 1980
Key annotations are present right within the chart
simplified version

4. Fully integrate words, numbers and images
Distinct Segments driven by exposure interactions and
psychographic engagement
Key annotations are present right within the chart
Napoleon’s March on Moscow illustrates the principles
Enforce visual
comparisons —
the width of the
tan and black
lines gives you an
immediate
comparison of the
size of
Napoleon’s army
at different times
during the march
The design
should be
content-driven —
Napoleon’s March
was designed as
an anti-war
poster…the
designer was
passionate about
the information
being presented.
The point of the
poster wasn’t the
design, it was the
information.
Completely
integrate
words,
numbers and
images—in this
map, number
sit comfortably
with words and
the only legend
is a scale to
give a sense of
distance
Show
multivariate
data —
Napoleon’s
March shows
six: army size,
location (in 2
dimensions),
direction, time,
and
temperature
Show causality — the map shows how the
temperature and river crossings defeated Napoleon.
simplified version
Quiz: Does this meet all of the criteria?
simplified version
Data Visualization
“Statistics journals rarely cover graphical methods… Outside of
statistics, though, infographics and data visualization are more
important. Graphics give a sense of the size of big numbers, dramatize
relations between variables, and convey the complexity of data and
functional relationships… sometimes to more efficiently portray masses
of information that their audiences want to see in detail (as with sports
scores, stock prices, and poll reports), sometimes to help tell a story (as
with annotated maps), and sometimes just for fun:.”
- Visualization, Graphics, and Statistics, Andrew Gelman and Antony Unwin, Statistical Computing &
Graphics, July, 2010
Data Visualization
Info-graphics
Dynamic Data
Visualization
Dashboards
Info-graphics
Summarize complex information using both decorative as
well as data-driven visual elements
Info-graphics
Info-graphics
Dynamic Data Vizualisation
Uses motion or other interactive elements to allow the user
themselves to explore a dataset for insight
Dynamic Data Vizualisation
- Some tools becoming available
Many Eyes, (www.many-eyes.com)
Dashboards
Summarize key statistics into one page or panel of charts
Dashboards
Dashboards
Using Excel ‘Slicers’ for a Dynamic Dash
MY AWESOME DASHBOARD
Gross Profit
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
Total Sales
Salesperson 5
Salesperson 4
Sum of Total GP
Sum of Total GP
Salesperson 3
Sum of Total sales
Sum of Total sales
Salesperson 2
Salesperson 1
0
Month
Product
Salesperson
Jan-09
Product A
Salesperson 1
Feb-09
Product B
Salesperson 2
Mar-09
Product C
Salesperson 3
Apr-09
Product D
Salesperson 4
May-09
Jun-09
Jul-09
Salesperson 5
50000
100000
150000
200000
Illustrations
“Ask yourself this: What information are you representing with the
written word on a slide that you could replace with a photograph (or
other appropriate image or graphic)?.. Images are powerful, efficient
and direct. Images can also be used very effectively as mnemonic
devices to make messages more memorable. If people cannot listen and
read at the same time, why do most PowerPoint slides contain far more
words that images? … It takes the realization that modern presentations
with slides and other multimedia have more in common with cinema
(Images and narration) …than they do with written documents.”
- Presentation Zen, Garr Reynolds, 2008
Illustrations
Use of decorative, non-data driven images to add meaning
to your reporting.
Source images from good
quality, legal sources
Think like a designer: Simple,
bold, colour-matched to your
palette, Rule of 3rds
But you don’t need to be
one: a tonne of image
manipulation tools right in
PowerPoint.
Don’t be afraid to try!
Use images along with
bold words to make
your headline points
For memorability or to
emphasise a point pick an
image that has an
emotional appeal cute,
comical, evocative
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That’s all folks! Questions?
Contact me:
Laura Davies, SVP
[email protected]
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