1d & 2d spaces for representing data mao lin huang

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1D & 2D Spaces for Representing Data Mao Lin Huang

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Page 1: 1D & 2D Spaces for Representing Data Mao Lin Huang

1D & 2D Spaces for Representing Data

Mao Lin Huang

Page 2: 1D & 2D Spaces for Representing Data Mao Lin Huang

1-D Representation of Data

Page 3: 1D & 2D Spaces for Representing Data Mao Lin Huang

1-D Textual Data

Page 4: 1D & 2D Spaces for Representing Data Mao Lin Huang

Keyhole Problem

• No context

• Lost, disoriented

• Where am I?

• Where can I go?

• Where do I want to go?

• How do I get there?

Page 5: 1D & 2D Spaces for Representing Data Mao Lin Huang

Visual Overview

• Map, organization (spatial layout of concepts)

• What information is (not) available?

• Adds context info, relationships

• Enables direct access

• Encourages exploration

• HCI metrics: • Improves user performance, learning time, error rates,

retention, satisfaction

Page 6: 1D & 2D Spaces for Representing Data Mao Lin Huang

Navigation Approaches

• Detail Only

• Zooming

• Overview+Detail

• Focus+Context (Distortion, fisheye)

Page 7: 1D & 2D Spaces for Representing Data Mao Lin Huang

1D Visual Representation

• Plaisant, “Lifelines”, pp 285See personal history

• Mackinlay, “Perspective Wall”, web

Page 8: 1D & 2D Spaces for Representing Data Mao Lin Huang

1D Visual Representation

• Eick, “SeeSoft”, p 419» Analyze 50,000 lines of code simultaneously by mapping each

line of code into a thin row.

• Eick, “Data Visualization Sliders”, p 251 (2 pages)

Page 9: 1D & 2D Spaces for Representing Data Mao Lin Huang

Navigation Strategies

• Detail Only

• Zooming

• Overview+Detail

• Focus+Context (Distortion, fisheye)

Page 10: 1D & 2D Spaces for Representing Data Mao Lin Huang

Fisheye Menus• http://www.cs.umd.edu/hcil/fisheyemenu/

• Very Fast• due to mouse mechanics, no clicking, mostly vertical sliding

• Alphabet overview helpful• Fisheye context not useful in this case?

• Might be more useful in SeeSoft where miniature representation gives important information

• Limits # of readable items to ~10• Wasted space at top- and bottom- right• Distortion problematic?• Alphabet overview distorted at A and Z• Scale limited?

• Possible improvement:• Same alphabet overview (without end distortion)• Remove fisheye, maximize readable items like scrolling

version• Same fast mouse mechanics, scroll fast on left, no scroll on

right

Page 11: 1D & 2D Spaces for Representing Data Mao Lin Huang

Music Animation Machine

• Good for visualizing music during serial playback, relate audio to visual structure

• Visualizing entire compositions: increase information density

• See patterns of phrases, instruments, etc.

Page 12: 1D & 2D Spaces for Representing Data Mao Lin Huang

2-D Representation of Data

Page 13: 1D & 2D Spaces for Representing Data Mao Lin Huang

2-D

• Image browsing

• Maps

Page 14: 1D & 2D Spaces for Representing Data Mao Lin Huang

Today• Bederson, “Pad++”, p 530

» a zooming graphic interface to replace icon-based window interface

• Furnas, “Space-Scale Diagrams”, web

Page 15: 1D & 2D Spaces for Representing Data Mao Lin Huang

Space-Scale

Page 16: 1D & 2D Spaces for Representing Data Mao Lin Huang

Pad++ on edge

• Like ray-tracing Info surface

window

zoom

Page 17: 1D & 2D Spaces for Representing Data Mao Lin Huang

Semantic Zooming

• Zooming in, red object turns to blue

Page 18: 1D & 2D Spaces for Representing Data Mao Lin Huang

Multiple Views

• Zoom factor ~ 20

Page 19: 1D & 2D Spaces for Representing Data Mao Lin Huang

Multiple levels = large scale

• Zoom factor = 20 * 20 * 20 = 8000

Page 20: 1D & 2D Spaces for Representing Data Mao Lin Huang

Multiple Foci

Page 21: 1D & 2D Spaces for Representing Data Mao Lin Huang

Multiple Overviews

• Can have different information types at each level

Page 22: 1D & 2D Spaces for Representing Data Mao Lin Huang

2-D + Attributes

• Dynamaps: dynamic queries on maps

Page 23: 1D & 2D Spaces for Representing Data Mao Lin Huang

2-D: Focus+Context Representation of Data

Page 24: 1D & 2D Spaces for Representing Data Mao Lin Huang

2-D• Robertson, “Document Lens”, p 562

»

• Spence, “Bifocal Lens”, p 331,333

Page 25: 1D & 2D Spaces for Representing Data Mao Lin Huang

Focus+Context

• Details within overview

• “Distortion-oriented display”

• “Fisheye”

• Leung, Apperley, “Taxonomy of distortion-oriented presentations”, book pg 350

Page 26: 1D & 2D Spaces for Representing Data Mao Lin Huang

Visual Transfer Functions

Information surface

Display surface

Identity function = normal flat overview

Bifocal

Page 27: 1D & 2D Spaces for Representing Data Mao Lin Huang

Magnification Functions

1st Derivative

Page 28: 1D & 2D Spaces for Representing Data Mao Lin Huang

Bifocal Display

• Spence, Apperley

Page 29: 1D & 2D Spaces for Representing Data Mao Lin Huang

Bifocal DisplayDisadvantage: 1 dimensional stretching on the 4 sides

Page 30: 1D & 2D Spaces for Representing Data Mao Lin Huang

Perspective Wall / Document Lens

Page 31: 1D & 2D Spaces for Representing Data Mao Lin Huang

NonLinear Magnification• http://www.cs.indiana.edu/hyplan/tkeahey/research/nlm/nlm.html

• http://www.cs.indiana.edu/hyplan/tkeahey/research/papers/infovis.98.html

Page 32: 1D & 2D Spaces for Representing Data Mao Lin Huang

“Bubble”Disadvantage: local context highly de-magnified

Page 33: 1D & 2D Spaces for Representing Data Mao Lin Huang

“Fisheye”, “wide-angle lens”Disadvantage: no flat area

Page 34: 1D & 2D Spaces for Representing Data Mao Lin Huang

Quiz: TableLens

• Bifocal!

Page 35: 1D & 2D Spaces for Representing Data Mao Lin Huang

Fisheye Menus

• Non-linear:

combination of Bubble + fisheye

Page 36: 1D & 2D Spaces for Representing Data Mao Lin Huang

Why not magnifying glass?

• Hides local context

Page 37: 1D & 2D Spaces for Representing Data Mao Lin Huang

F+C vs. O+D

• + Space efficient

• + Detail connected to context

• Smooth transition

• + matches human vision/processing?

• - Distortion

• - Longer learning time

• - no flat overview - Need a way to turn off focus

• - Content moves differently than mouse

• - hard to tell zoom factor

• + Scales up to larger data (zoom factor and chaining)

• + Multi foci easier

• + multiple overviews possible

• + Easy to implement, Less math!

•Fast system performance

• - >=2 places to look (cross-eyed!)

•Tracking field-of-view box hard

•Hand-eye coordination problem

• - detail and overview disconnected

• - Windows/space management

• - replicates detail data in overview