CSCI 552
Data Visualization
Shiaofen Fang
What Is Visualization?
 We observe and draw conclusions
– “A picture says more than a thousand
words/numbers”
– Seeing is believing, seeing is understanding
– Beware of ‘illusions’ (magicians)
 Visualization: transformation of
data/information into pictures
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Different Types of Data Visualization
 Scientific Visualization
– From science, engineering, medicine, etc.
– Is a method of computing: exploration,
simulation, discovery, insight.
– Data are usually homogeneous with predefined
spatial structures.
 Information Visualization
– Abstract Data: WWW documents, file
structures, relationships, financial data, etc.
– Usually heterogeneous without spatial
structures.
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Functions of Visualization
 A Representation of Information
 Aid for Understanding and Analysis
 Validation of Results
 A Tool for Communication
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Examples
Terrain geometry:
Terrain Texture:
(10,20,21), (12,13,14), (13,32,12),....,
(1,2,3), (2,4,5),(3,5,6),.....
(23,34,54), (23,34,23), (45,26,78),....
Time 0:
Volumetric cloud cover: 0, 0, 12, 14, 15, 15, 17, 12, 23, 45,.....
Wind vectors: (0.2, 0.3, 0.93,5), (0.4,0.5,0.76,12),...,
Time 1:
Volumetric cloud cover: 0, 0, 11, 12, 13, 16, 20, 12, 32, 45,.....
Wind vectors: (0.4,0.5,0.76,12),(0.5,0.5,0.7,6),...
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Info-graphics
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How Many “V”s
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Perpetual Ocean
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Visible
Human
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Graphical Design
 Can be more precise and revealing than
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numerical display
Can capture a large amount of information
in a very small space
Can extend to time-series display
Can be narrative
Can represent each data point by visual
information (graphic, icon, image, color,
pattern)
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Cholera map of central London,
1854, by Dr. John Snow
Train schedule Paris-Lyon, 1880s
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Napoleon’s Russia campaign, 1812, plots 6 variables
on a 2D graph
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Graphical Display (example)
fear-rage graph
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Graphical Integrity -What To Avoid In Visualization
The Lie Factor =
Size of effect shown in Graphic
Size of effect in Data
Example: fuel economy standards
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Graphical Integrity (2) ...
Visualizing data bearing
some dimension by
means of objects of
higher dimensions
Example:
the growing barrel
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Graphical Integrity (3) ...
Quoting data out of context and/or too sparse
Example: Connecticut traffic deaths
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Graphical Integrity (4) ...
 Is cosmetic decoration really needed to make data
more interesting
 Misleading graphical representation
Example: NCSA storm model
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Visual Perceptions
 Visual Memory is Limited
 We are sometimes not very sensitive to
small visual changes
 Visual perception can be influenced contrast
and surrounding environment
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How many black dots?
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Seeing parallel lines
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Seeing is not always believing
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Visualization Design Principles
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Show the data
Induce the viewer to think about the substance rather
than methodology, design, and the technology
Avoid distorting what the data have to say
Present large data sets coherently and concisely
Encourage comparison of different pieces of data
Reveal the data at several levels of detail
Serve a reasonably clear purpose: description, exploration,
tabulation, or decoration
Be closely integrated with the statistical and verbal
description of a data set
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Visualization Design
 Data Filtering
 Visual Mapping
 View Selection and Interactions
 Aesthetics in Visualization
 Metaphor in Visualization
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The Design Process
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Data Filtering
 Determining the optimal amount of
information a certain visualization process
can handle.
 Two approaches
1. Let the users choose the data scale to visualize
2. Multi-view or multi-display
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Visual Mapping
 From data elements to visual elements
 People’s prior knowledge can help
visual perception
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The Wind Map
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View Selection and Interaction
 Visual Interaction
– Zoom and Roll
– Controlling color
mapping.
– Controlling visual
mapping of data.
– Data zooming and filtering
 Level of Detail control
4D data visualization using scatter
plot and parallel coordinates
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Aesthetics in Visualization
 Focus
 Balance
 Simplicity
- Labels
- Networks
- Color
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Visual Metaphor
A visual metaphor maps the
characteristics of some well
understood source domain
to a more poorly understood
target domain (data) so as to
render aspects of the target
understandable in terms of
the source.
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Trees
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Rivers
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Ferris Wheel
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Sunflower
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Tools (InfoVis)
 Google
Refine
 Tableau
R
 Processing
 D3 (JS)
 ColorBrewer
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Tools (SciVis)
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