INFORMATION
VISUALIZATION
CMPT 481
How to represent data?
How effective is a visual attribute in conveying information?


1
2
3
4
E.g. How might number be best conveyed?
Information Visualization
What is visualization?
Information visualization basics
Visual variables
“The power of the unaided mind is highly overrated.
Without external aids, memory, thought and
reasoning are all constrained. But human intelligence
is highly flexible and adaptive, superb at inventing
procedures and objects that overcome its own limits.
The real power comes from devising external aids
that enhance cognitive abilities.”

Donald Norman
What is visualization?


Understanding and seeing
“To visualize”
 Previously:
“to construct a visual image in the mind”
 Now: “a graphical representation of data or concepts”

Definition of visualization:
 The
use of computer-supported visual representations
of data to amplify cognition
 “Using vision to think”

The purpose of visualization is insight, not pictures
Anscombe’s quartet
Anscombe’s quartet
Anscombe’s quartet
What InfoViz is not

Not scientific visualization
 where
data is not abstract
History of Visualization


William Playfair (1780’s)
First systematic charting of abstract data
line graph
 bar chart
 pie chart
 circle graph

Napoleon's march on Moscow
Example
http://www.smartmoney.com/map-of-the-market/
Example

http://www.babynamewizard.com/voyager
Important: Perceptual Foundations

We can take advantage of
pre-attentive processing:

But should avoid illusions:
 Distortions
 Ambiguities
 Paradoxes
 Hallucinations
Mapping Data to Visual Form
Types of attribute data

Nominal data



Ordinal data



Ordering and ranking based on < and >
e.g. restaurant ratings
Interval data



Category data that can only be compared for equality
e.g. apple, orange
Ordering and arithmetic possible but no natural zero
e.g. dates, temperature
Ratio data


Ordered, natural zero
e.g. height, weight, age, length
Selecting the Mappings

Most variables are mapped on to only 6 high-level
visual encodings
 Spatial
(most important)
 Marks
 Connection
 Enclosure
 Retinal
Properties
 Temporal Properties
Adding interactivity


View transformations to increase information content
Location probes
 use
location to reveal additional info
 e.g. tool tips, detail screen

Viewpoint controls
 zoom
and pan
Mapping data to graphics

Data table
 determine
what is to be visualized
 filter data appropriately

Determine visual structure
 choose
mappings for attributes
important  horizontal and vertical
 don’t overmap visual variables
 most
 determine

appropriate scales for axes
Determine interactivity
 probes
and viewpoint controls
Where to live in the U.S.
City
Climate
Housing
HlthCare
Crime
Transp
Educ
Abilene
521
6200
237
923
4031
2757
Akron
575
8138
1656
886
4883
Albany
468
7339
618
970
Albany
476
7908
1431
Albuquerque
659
8393
Alexandria
520
Allentown
Arts
Recreat
Econ
Long
Lat
Pop
996
1405
7633
-99.689
32.559
110932
2438
5564
2632
4350
-81.518
41.085
660328
2531
2560
237
859
5250
-84.158
31.575
112402
610
6883
3399
4655
1617
5864
-73.7983
42.7327
835880
1853
1483
6558
3026
4496
2612
5727
-106.65
35.083
419700
5819
640
727
2444
2972
334
1018
5254
-92.453
31.302
135282
559
8288
621
514
2881
3144
2333
1117
5097
-75.4405
40.6155
635481
Alton
537
6487
965
706
4975
2945
1487
1280
5795
-90.1615
38.794
268229
Altoona
561
6191
432
399
4246
2778
256
1210
4230
-78.395
40.515
136621
Amarillo
609
6546
669
1073
4902
2852
1235
1109
6241
-101.849
35.383
173699
Ab
ile
ne
Ak
ro
n
Al
ba
ny
Al
ba
Al
ny
bu
qu
er
qu
Al
e
ex
an
dr
ia
Al
le
nt
ow
n
Al
to
n
Al
to
on
a
Am
ar
illo
Climate
Climate
700
600
500
400
300
200
100
0
Climate
Crime vs. climate
700
600
Climate
500
400
300
200
City
100
Linear (City)
0
0
200
400
600
800
Crim e
1000
1200
1400
1600
Multiple attributes – radar plot
HousingCost
8000
7000
Econ
Climate
6000
5000
4000
3000
2000
Recreat
HlthCare
1000
0
Arts
Crime
Amarillo
Albany
Educ
Transp
Geographical orientation
Spatial




Where something occurs in space
Is perceptually dominant
Good for discriminating values and finding patterns
Mappings:
 Unstructured
(no axis, just something present or not)
 Nominal (a region is divided into subregions, and can
be present or not)
 Ordinal (the ordering of subregions is also meaningful)
 Quantitative (the regions has a metric)
Spatial Examples
Marks

Marks are visual things that occur in space
 Points
(0D)
 Lines (1D)
 Areas (2D)
 Volume2 (3D)
Connection and Enclosure

Allow relationships to be shown many other objects
Bubble Sets

http://www.youtube.com/watch?v=P6CgBmIiXaE
Retinal Properties

Encode other dimensions (attributes) of the data
Temporal

Maps some data to time
 E.g.
animation of the state of water as temperature
goes from +125 to -10 degrees Celsius
 Temperature is mapped to time
Often, time is mapped to time (compressed)
http://www.nytimes.com/interactive/2009/09/12/bu
siness/financial-markets-graphic.html

But do our mappings work?


visualizations have a specific purpose
Mappings must be expressive


Encodes only intended data relations and no other data relations
Certain visual encodings lend themselves to more uses
• E.g., size can be used for nominal, ordinal, or quantitative
• E.g., shape can only be used for nominal data
Caveats


Lie Factor
Chart Junk
Lie Factor

The representation of numbers, as physically
measured on the surface of the graphic itself, should
be directly proportional to the quantities represented.
[Tufte, 1991]
Calculating Lie Factor
Chart Junk

The interior decoration of graphics generates a lot of
ink which does not tell the viewer anything new. The
purpose of the decoration varies - to make the
graphic appear more scientific, to enliven the display,
to give the designer an opportunity to exercise artistic
skill. Regardless of the cause, it is all non-data-ink or
redundant data-ink, and it is often chartjunk."
 [Tufte, 1983]
Chart Junk
Data-Ink Ratio



‘Data ink’: the essential non-erasable ink used to
present the data
‘Non-data ink’: the redundant ink used to elaborate
or decorate the graph
The Data-Ink Ratio is defined as the percentage:
(100 x Data-ink) / (Total ink used on graphic)
Low data-ink ratio
High Data-Ink Ratio
So what is wrong with this?
Is this better?
Some research has shown


that both types of charts can be equally read
without errors and take about the same amount of
time to read
the junky charts are remembered significantly
better over a long period of time
 including

the existence of the chart and chart details
junky charts to provide a clearly biased message
If you are interested…
Bateman, S., Mandryk, R.L., Gutwin, C., Genest, A.M.,
McDine, D., Brooks, C. 2010. Useful Junk? The
Effects of Visual Embellishment on Comprehension
and Memorability of Charts. In ACM Conference on
Human Factors in Computing Systems (CHI 2010),
Best paper award.
http://hci.usask.ca/publications/view.php?id=173
Viz Techniques

Relationship Among Data Points
 ScatterPlot
 Network
Diagram
 Matrix Chart
Viz Techniques: Small multiples


Learn the representation once, use it many times
Invites comparisons
Viz Techniques

Compare a set of values
 Bar
Chart
 Block Histogram
 Bubble Chart
Viz Techniques

Track Rise and Falls Over Time
 Line
Graph
 Stack Graph
 w\
categories
Viz Techniques

Parts of a Whole
 Pie
Chart
 Tree Map
 For
comparison
Viz Techniques

Analyze Text
 Tag
Cloud
 Word Tree
 PhraseNet
Viz Techniques

Geographic Data
 Map
Overlays
Vis Techniques

Hierarchical Data
 Tree
Maps
 Radial Layouts
Vis Techniques

N-Dimensional
 Parallel
Coordinates
Other Interaction Techniques

Linking and Brushing
A new Take on Linking and Brushing

VisLinks: Linking across different visualizations
More Info and Links

Interesting visualizations
 http://www.informationisbeautiful.net/

General Viz Software and Toolkits
 Prefuse
(http://prefuse.org/)
 Ggobi (http://www.ggobi.org/)
 ManyEyes (http://manyeyes.alphaworks.ibm.com/)

InfoViz News and Resource:
 Information
aesthetics (http://infosthetics.com/)
 InfoVis Wiki (http://www.infovis-wiki.net/)