Discussion of
Readings
stat/engl 332
X by Y
Moritz Stefaner
What you wrote
Ars Electronica
over 37,000
contributions over 30
years
Data behind Illustration
Contributor
Country
Year
Category
Award
XXX
Austria
2001
Internet
Golden Nica
YYY
UK
2004
Music
nothing
...
...
Process
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Figure 13-3. A plot of submissions with missing country information, split up by year and
category
Figure 13-3. A plot of submissions with missing country information, split up by year and
category
Figure 13-4. Submissions by company or institution, colored by categories
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Figure 13-5. World map with submissions per country, split up by category
I also explored some of the data in Microsoft Excel, as it seemed superior at producing stacked charts we could use for investigating trends over the years or comparing
attribute distributions in subsets of the data. For instance, Figure 13-6 shows the rela-
Figure 13-6. Prizes won by different countries
These considerations led to the development of more fragile charts, with the interpolation areas toned down to support the notion of them being only connectors
between more “solid” yearly events.
Figure 13-7. A first attempt at displaying categories by country
Exploring stacked area charts for categories over the years revealed some additional issues to tackle from a conceptual point of view. The category structure of
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Figure 13-8. Categories over the years
I also became more interested in the evocative, implicit communication aspects of the
visualization as I explored the existing charts. I felt uncomfortable with their charac-
The packing algorithm
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Figure 13-10. Submissions as dots, packed like sunflower seeds
Despite its visual complexity, the underlying procedure for creating these types of
modifications to the points themselves, I decided to skip all positions in the sequence
that would occlude the number, if it were overlaid on the circle (see Figure 13-11).
That dot would simply go in the next available precalculated position, so the total
number of dots would remain the same but the circle size would increase marginally.
Obviously, this principle only works for circles with enough dots to create the number; accordingly, the number is only displayed for circles containing a minimum of 100
items.
The final design
Figure 13-11. Numbers created by skipping points in the placement sequence
Why the “Color
Chapter” was not a
reading
Color: The Cinderella
Data Visualizati
Michael D
Avoiding catastrophe becomes the first principle in
bringing color to information: Above all, do no harm.
—Edward Tufte, Envisioning Information (Graphics Press)
IS ONE OF THE MOST ABUSED AND NEGLECTED tools in data visualiz
e it when we make poor color choices, and we neglect it when we rely o
tware defaults. Yet despite its historically poor treatment at the hands of
nd end users alike, if used wisely, color is unrivaled as a visualization too
5
y position
0
–5
–10
–15
–5
0
5
x position
10
Figure 4-1. Location of pitches indicated in an x/y plane
But what if we’d like to know more? For instance, what kinds of pitches (curveballs,
fastballs) landed where? Or what was their speed? Visualizations occupy two dimensions, but the world they describe is rarely so confined.
Quick critique …
The defining challenge of data visualization is projecting high-dimensional data onto
a low-dimensional canvas. As a rule, one should never do the reverse (visualize more
changeup
fastball
sinker
slider
5
y position
0
–5
–10
–15
–5
0
5
x position
10
Figure 4-2. Location and pitch type indicated by plotting symbols
I consider this visualization an abject failure. There are two reasons why graphs like
this one make our heads hurt: because distinguishing glyphs demands extra attention
(versus what academics call “preattentively processed” cues like color), and because
w
–5
changeup
0
5
10
–5
fastball
sinker
0
5
10
slider
y position
5
0
–5
–10
–15
–5
0
5
10
–5
x position
0
5
10
Figure 4-3. Location and pitch type indicated by facets
Multiplying plots in space works especially well on printed media, which can display
more than 10 times as many dots per square inch as a screen. Additional plots can b
arranged in both columns and rows, with the result being a matrix of scatterplots (in
R, see the splom function).
In Figure 4-4, I’ve used color as a means of encoding a fourth dimension of our pitching data: the speed of pitches thrown. The palette I’ve chosen is a divergent palette
that moves along one dimension (think of it as the “redness-blueness” dimension) in
the Lab color space,* while maintaining a constant level of luminosity.
–5
changeup
0
5
10
–5
fastball
sinker
0
5
10
slider
y position
5
0
–5
–10
–15
–5
0
5
10
–5
x position
71
78
speed (mph)
0
5
10
85
Figure 4-4. Location and pitch type, with pitch velocity indicated by a one-dimensional color
palette
to recover some of the data lost by increasing the sizes of our plotting symbols.
–5
changeup
0
5
10
–5
fastball
sinker
0
5
10
slider
y position
5
0
–5
–10
–15
0
5
10
–5
x position
0
5
10
pitch density
–5
71
78
speed (mph)
85
Figure 4-5. Location and pitch type, with pitch velocity and local density indicated by a twodimensional color palette (see inset for details)
Animation for Visualization:
Opportunities and Drawbacks
Danyel Fisher
Why animations work at all:
Common Fate
• large numbers of objects are grouped together, if
they are traveling in the same direction and at the
same speed
• individual objects that take their own trajectories
will be seen as isolated, and will visually stand out
• limitations: difficulty tracking more than four or five objects
independently: the eye gives up, tracking only a few
objects and labeling other movement as noise
(Cavanagh and Alvarez 2005).
What does Fisher mean
by ...
• change the visualization stably
• staging
?
This transition does not work so well:
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Figure 19-7. Less successful bar chart to pie chart animation: long bars become long, fat
wedges on the pie; short bars become short, skinny wedges; then all wedges grow to full length
346
BEAUTIFUL VISUALIZATION
This transitions is much better:
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Figure 19-8. Better bar chart to pie chart animation: the lengths of the bars are maintained as
they are brought into the ring; the ring then fills to become a pie
CHAPTER 19: ANIMATION FOR VISUALIZATION: OPPORTUNITIES AND DRAWBACKS
347
Good Practice for
Animations
• Do one thing at a time
• Preserve valid mappings
• Maintain the invariant
Taxonomy of Animations
• Change view (pan, zoom)
• Change surface (axis transformation, polar to
euclidean)
• Filter data (remove points)
• Reorder data
• Change representation (bar to pie, colour of nodes)
• Change the data (move through time step, modify
data, change values)