CS-533C Reading Presentation
Space/Order
Quanzhen Geng
(Master of Software Systems Program)
January 27, 2003
Space/Order Encodings
Definition:
Space/order encodings transform data in information
space into a spatial representation (size and order) in
display space that preserves informational
characteristics of the dataset and facilitates our visual
perception and understanding of the data.
Importance:
Finding a good spatial representation of the
information at hand is one of the most difficult and
also the most important tasks in information
visualization.
Two challenges of
Spatial Encodings
(1) Visualizing large information space
(Large Maps, Tables, Documents etc.)
through a relatively small window screen.
Lack of screen space
How to display 1,000,000
rows of table on screen?
(2) Visualizing multi-dimensional data (n>3) in 2D space
How to effectively present more than 3 dimensions of
information in a visual display with 2 (to 3) dimensions?
What does 10-D
space look like?
Solving the Problems in
Spatial Encodings
Two important spatial representation
techniques:
• Spatial distortions
solve the lack of screen space problem
• Parallel coordinates
Non-projective mapping between N-D and 2-D
Distortions
Problems:
– Large Computer-Based Information Systems
– Small Window as Single Access-Point
– Difficult to Interpret Single Information Items
when Viewing it Outside of its Context
Definition:
Distortion is a visual transformation that modifies a
Visual Structure to create focus+context views.
Want to achieve:
– Focus: to see detail of immediate interest
– Context: to see the overall picture
Want to solve:
The problem of displaying a large information space
through a relatively small window, i.e., lack of screen
space problem.
Principles of distortions
Transformation function
Magnification function
Distortions
• Methods of distortions (focus+context views):
--Bifocal Display
--Perspective wall
--Document lens
--Fisheye views
--Table lens
• Major differences of these methods:
--Transformation function
--Magnification function
Bifocal Display
• First suggested by Spence and Apperley
(1980?).
• Combination of a detailed view and
two distorted sideview.
• One-dimensional form.
Bifocal Display
Fold
Project
www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/PDF-Files/InfoVis-6.pdf
What is the Bifocal Display Doing?
• Transform the
information space to
the display space with
Visual transformation
functions
www.comp.leeds.ac.uk/kwb/VIS/v02_16.ppt
Early implementation of
Bifocal Display (1980)
www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/PDF-Files/InfoVis-6.pdf
Perspective Wall
• A technique for viewing and navigating large,
linearly-structured information (for instance,
chronological / alphabetical data), allowing the
viewer to focus on a particular area while still
maintaining some degree of location or context.
• Extension or descendant of Bifocal Display.
• 3D aspect decreases cognitive load.
Perspective Wall vs. Bifocal Display
Bifocal Display
2D view
Perspective Wall
3D view
Perspective Wall:
• 3D view
• Center panel to view detail
• Perspective panels to view context
www.sims.berkeley.edu/courses/is247/s02/lectures/ZoomingFocusContextDistortion.ppt
Perspective Wall
[Mackinlay et al.c 1991]
Perspective Wall
• In terms of transformation function, the
situation is closer to the bifocal display.
• Perspective gives smoother transition from
focus to context.
Perspective Wall
Example 1 – project schedule
Map work charts onto diagram. x-axis is time, yaxis is project. (Mackinlay, Robertson, Card ’91)
Perspective Wall
Example 2 – file navigation
Typical example use is file navigation
–Shown by date, type
–However few files can be displayed at once
Perspective Wall
Example 3 – file navigation
Features of Perspective Wall
• Folding is used to distort a 2-D layout into
a 3-D visualization,using hardware
support for 3-D interactive animation.
• Perspective panels are shaded to enhance
the effect of 3-D.
• Vertical dimension can be used to
visualize layering information.
Disadvantage:
• Wastes the corner areas of the screen.
Document Lens
Why: -Text too small to read but yet needed to
perceive patterns.
-Perspective wall wastes corner areas of screen
What: General visualization technique based on a
common strategy for understanding paper
documents when their structure is not
known.
How:
3D Visualization Tool For Large
Rectangular Presentations
Document Lens Features
• Lens – rectangular – interested in text
that is mostly rectangular
• Sides are elastic and pull the surrounding
parts towards the lens creating a pyramid
Document Lens
Document lens, 3-D effect, no waste of corner space
Comparison with other approaches
Bifocal Display
Document Lens
Perspective Wall
Fisheye View (Distortion)
• When people think about focus+context views, they
typically think of the Fisheye View (Distortion)
• First introduced by George Furnas in his 1981 report
• “Provide[s] detailed views (focus) and overviews
(context) without obscuring anything…The focus area
(or areas) is magnified to show detail, while preserving
the context, all in a single display.”
-(Shneiderman, DTUI, 1998)
www.cc.gatech.edu/classes/AY2002/cs7450_spring/ Talks/10-focuscontext.ppt
Principles of Fisheye View
1D Fisheye
2D Fisheye
–Continuous Magnification Functions
–Can distort boundaries because applied radially rather than x y
http://davis.wpi.edu/~matt/courses/distortion/#fisheye
Fisheye-view vs. Bifocal display
Bifocal Display
http://davis.wpi.edu/~matt/courses/distortion/#fisheye
Fisheye-view
Fisheye View
Application 1 –Map of Washington D.C.
web.mit.edu/16.399/www/course_notes/context_and_detail1.pdf
Fisheye View
Application 2 –viewing network nodes
Fisheye View
Application 3 – fisheye menu
Dynamically change the size of a menu
item to provide a focus area around
the mouse pointer, while allowing all
menu items to remain on screen
• All elements are visible but items
near cursor are full-size, further
away are smaller
• “bubble” of readable items move
with cursor
www.comp.leeds.ac.uk/kwb/VIS/v02_16.ppt
Fisheye View
Application 4 – fisheye table
Table Lens
The Table Lens:
Merges Graphical and Symbolic Representations in an
Interactive Focus + Context Visualization for Tabular
Information.
(Ramana Rao and Stuart K. Card)
Table Lens Features
• Focus + context for large datasets while retaining
access to all data
• Works best for case / variable data & flexible,
suitable for many domains
• Cell contents coded by color (nominal) or bar
length (interval)
• Tools: zoom, adjust, slide
• Search / browse (spotlighting)
• Create groups by dragging columns
Table Lens
• Distortion in each dim. is
independent
• Multiple focal areas
• Degree of Interest (DOI)
• Interactive Focus
Manipulation
DOI (Degree of Interest)

Maps from an item to a value that indicates the
level of interest in the item.
Table Lens Focus Manipulation
Zoom, adjust and slide provides interactive
focus manipulation
DOI
DOI
DOI
Zoom
Adjust
Slide
Table Lens
Parallel Coordinates
Issues:
• How to effectively present more than 3
dimensions of information in a visual
display with 2 (to 3) dimensions?
• How to effectively visualize very large,
often complex data sets?
www.sims.berkeley.edu/courses/is247/s02/lectures/MultidimensionalDataAnalysis.ppt
Parallel Coordinates -Goals
We want to:
Visualize multi-dimensional data
• Without loss of information
• With:
– Minimal complexity
– Any number of dimensions
– Variables treated uniformly
– Objects remain recognizable across transformations
– Easy / intuitive conveyance of information
– Mathematically / algorithmically rigorous
(Adapted from Inselberg)
www.sims.berkeley.edu/courses/is247/s02/lectures/MultidimensionalDataAnalysis.ppt
Parallel Coordinates:
Visualizing N variables on one chart
• Create N equidistant vertical axes, each corresponding
to a variable
• Each axis scaled to [min, max] range of the variable
• Each observation corresponds to a line drawn through
point on each axis corresponding to value of the variable
www.comp.leeds.ac.uk/kwb/VIS/v02_14.ppt
Parallel Coordinates
-- Correlations may start to appear as the
observations are plotted on the chart
-- Here there appears to be negative correlation
between values of A and B for example
-- This has been used for applications with
thousands of data items
www.comp.leeds.ac.uk/kwb/VIS/v02_14.ppt
Cartesian vs. Parallel Coordinates
Dataset in a Cartesian coordinate
Same dataset in parallel coordinates
infovis.cs.vt.edu/cs5984/students/parcoord.ppt
Parallel Coordinates
Example 1: Correlations
Detroit homicide data
7 variables
13 observations
Parallel Coordinates Example 2: Air traffic control
Cartesian Coordinates
Parallel Coordinates
http://www.caip.rutgers.edu/~peskin/epriRpt/ParallelCoords.html
Parallel Coordinates:
Advantages
• Multi-dimensional data can be visualized in
two dimensions with low complexity.
• Each variable is treated uniformly.
• Relations within multi-dimensional data can
be discovered (“data mining”).
• Because of its visual cues, can serve as a
preprocessor to other methods.
Parallel Coordinates:
Disadvantages
• Close axes as dimensions increase.
• Clutter can reduce information perceived.
• Varying axes scale, although indicating
relationships, may cause confusion.
• Connecting the data points can be misleading.
Disadvantage: Level of Clutter
Taken from: “Hierarchical Parallel Coordinates”
Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward
16,384 records in 5 dimensions causes over-plotting.
Improvement: Summarization
Taken from: “Hierarchical Parallel Coordinates”
Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward
.
Improvement: Level-Of-Detail (LOD)
Taken from: “Hierarchical Parallel Coordinates”
Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward.
Improvement: Brushing
Taken from: “Hierarchical Parallel Coordinates”
Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward.
Summary
• Spatial encoding the most important encoding
• The good and bad of spatial distortion
• The advantages and disadvantages of parallel
coordinates