Visual Overview Strategies

cs5984: Information Visualization

Chris North

Where are we?

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

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

Quiz

• 4 focus+context strategies:• bifocal

• Perspective

• Wide-angle lens

• bubble

Why Overviews?

Data Screen

dataa dataadata dataa

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. Beard&Walker, Leung, Plaisant, Chimera, North, etc.

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, Overview+Detail, Focus+Context

Data Scale

• Small scale data = easy• Just show everything

• But, there’s always more data…

• How much can you show?

(3,2)

(5,7)

(9,9)

Attribute 1A

ttribute 2

Cartography

Overview Strategies for Large Scale

1. Screen: Reduce visual representation size• Pack more on the screen

2. Data: Reduce data scale• Use less data to fit screen

Data Screen

1. Reduce Visual Representation

“Hammer”

DataScreen

Reduce Visual Representation

• Stasko, “Information Mural”• Ben, Ahmed

2. Reduce Data Scale

“Chainsaw”

Data Screen

Data Scale

• Reduce data scale to fit screen

• Reduce # attributes

• Reduce # items

• Reduce value “size”

• 2 Approaches:• Eliminate

• Aggregate

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 scaling:statistical technique to map n-D to 1,2,3-D usingdistance between points

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 (1:M)

– Aggregate Towers

• Semantic zooming, Abstraction– Pad++, Jazz

Aggregation with Zooming

• Rayson, “Aggregate Towers”• Anil, Supriya

Summary

1. Reduce visual representation (Hammer)

2. Reduce data scale (Chainsaw)• Eliminate

• Aggregate

DataWear

• Umer Farooq

• IEEE InfoVis 2001

Assignment

• Thurs: Multiple View Strategies• Chi, “Visualization Spreadsheet”

» mudita, abhi

• North, “Snap-Together Visualization”» varun, kumar

Next Week

• Tues: Trees• Rao, “Hyperbolic Trees”

» david, harsha

• Robertson, “Cone Trees”» anuj, atul

• Thurs: Trees• Johnson, “Treemaps”

» vishal, jeevak

• Beaudoin, “Cheops”» jon, mudita

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/hinoteapplet.html