Visual Overview Strategies

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Visual Overview Strategies

• cs5984 Information Visualization
• Chris North

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Where are we?

• Multi-D
• 1D
• 2D
• 3D
• Hierarchies/Trees
• Networks/Graphs
• Document collections

• Design Principles
• Empirical Evaluation
• Java Development
• Visual Overviews
• Multiple Views

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Quiz

• 4 focuscontext strategies
• bifocal
• Perspective
• Wide-angle lens
• bubble

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Why Overviews?
Data
Screen
a
a
data
data
a
data
data
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Advantages of Overviews

• Helps solve the Keyhole Problem
• 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
• Studies, e.g. BeardWalker, Leung, Plaisant,
  Chimera, North, etc.

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Visual Overview Design Goals

• Visual take advantage of human visual
  processing
• Information Rich show as much as you can!
  (while maintaining a clean design)
• Interaction Affordances enable quick access to
  details
• E.g. Zooming, OverviewDetail, FocusContext

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Data Scale

• Small scale data easy
• Just show everything
• But, theres always more data
• How much can you show?

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Cartography
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Overview Strategies for Large Scale
Data
Screen

• Screen Reduce visual representation size
• Pack more on the screen
• Data Reduce data scale
• Use less data to fit screen

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1. Reduce Visual Representation

• Hammer

Data
Screen
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Reduce Visual Representation

• Stasko, Information Mural
• Ben, Ahmed

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2. Reduce Data Scale

• Chainsaw

Data
Screen
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Data Scale

• Reduce data scale to fit screen
• Reduce attributes
• Reduce items
• Reduce value size
• 2 Approaches
• Eliminate
• Aggregate

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Reduce Attributes

• Eliminate attributes
• Scatterplot selects 2 attributes, ignores rest
• Aggregate attributes
• Column math grade (hw1 hw2) / 2
• Star Coordinates vector summaps n attributes
  to 2 (x,y)
• Multi-dimensional scalingstatistical technique
  to map n-D to 1,2,3-D usingdistance between
  points

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Reduce Items

• Eliminate items
• VIDA (Visual Info Density Adjuster) show high
  priority items (video)
• Human-Eye View focused info density
• Aggregate items
• Group many items into one
• SQL group by
• Snap-Together Visualization drill down (1M)
• Aggregate Towers
• Semantic zooming, Abstraction
• Pad, Jazz

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Aggregation with Zooming

• Rayson, Aggregate Towers
• Anil, Supriya

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Summary

• Reduce visual representation (Hammer)
• Reduce data scale (Chainsaw)
• Eliminate
• Aggregate

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DataWear

• Umer Farooq
• IEEE InfoVis 2001

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Assignment

• Thurs Multiple View Strategies
• Chi, Visualization Spreadsheet
• mudita, abhi
• North, Snap-Together Visualization
• varun, kumar

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Next Week

• Tues Trees
• Rao, Hyperbolic Trees
• david, harsha
• Robertson, Cone Trees
• anuj, atul
• Thurs Trees
• Johnson, Treemaps
• vishal, jeevak
• Beaudoin, Cheops
• jon, mudita

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Homework 3

• See website for important details
• Due Tues Oct 23
• Zoomable visualization design
• Use Jazz HiNote to create an information space
• Topic ideas hobby, life story, event, academic
  field
• Goal help someone learn about topic
• 1 page report analysis of zooming concept, your
  design
• Be creative, have fun!
• http//vtopus.cs.vt.edu/north/infoviz/hinoteapple
  t.html