Space/OrderQuanzhen Geng

(Master of Software Systems Program)

January 27, 2003

CS-533C Reading Presentation

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 ofSpatial Encodings

(1) Visualizing large information space (Large Maps, Tables, Documents etc.) through a relatively small window screen.

Lack of screen space

(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?

How to display 1,000,000 rows of table on screen?

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

DistortionsProblems: – Large Computer-Based Information Systems – Small Window as Single Access-Point – Difficult to Interpret Single Information

Items when Viewing it Outside of its ContextDefinition: 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

www.ifs.tuwien.ac.at/~silvia/wien/vu-infovis/PDF-Files/InfoVis-6.pdf

Fold

Project

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 Perspective Wall

www.sims.berkeley.edu/courses/is247/s02/lectures/ZoomingFocusContextDistortion.ppt

2D view 3D view

Perspective Wall:• 3D view• Center panel to view detail• Perspective panels to view context

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 WallExample 1 – project schedule

Map work charts onto diagram. x-axis is time, y-axis is project. (Mackinlay, Robertson, Card ’91)

Perspective WallExample 2 – file navigation

Typical example use is file navigation–Shown by date, type–However few files can be displayed at once

Perspective WallExample 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 Perspective Wall

Document Lens

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 Fisheye-view

http://davis.wpi.edu/~matt/courses/distortion/#fisheye

Fisheye View Application 1 –Map of Washington D.C.

web.mit.edu/16.399/www/course_notes/context_and_detail1.pdf

Fisheye ViewApplication 2 –viewing network nodes

Fisheye View Application 3 – fisheye menu

www.comp.leeds.ac.uk/kwb/VIS/v02_16.ppt

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

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

DOI

Adjust

DOI

Slide

DOI

Zoom, adjust and slide provides interactive focus manipulation

Table Lens

Parallel CoordinatesIssues:

• 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 -GoalsWe 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 data7 variables13 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 ClutterTaken from: “Hierarchical Parallel Coordinates”

Ying-Huey Fua, Elke A. Rundensteiner, Matthew O. Ward

16,384 records in 5 dimensions causes over-plotting.

Improvement: SummarizationTaken 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: BrushingTaken 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