Visualization Design Principles

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Visualization Design Principles

• cs5984 Information Visualization
• Chris North

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Quiz

• What is the purpose of visualization?

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Basic Visualization Model
Data
Visualization
Visual Mapping
Interaction
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Data Table
Attributes (aka dimensions, variables,
columns, )

• Data Values
• Data Types
• Quantitative
• Ordinal
• Categorical/Nominal

Items (aka cases, tuples, data points, rows, )
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Visual Mapping

• Map data items ? visual marks
• Visual marks
• Points
• Lines
• Areas
• Volumes

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Visual Mapping

• Map data items ? visual marks
• Map data item attributes ? visual mark
  attributes
• Visual mark attributes
• Position, x, y
• Size, length, area, volume
• Orientation, angle, slope
• Color, gray scale, texture
• Shape

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Example

• Hard drives for sale price (), capacity (MB),
  quality rating (1-5)

C
Color rating
P
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Example Spotfire

• Film database
• Year ? X
• Length ? Y
• Popularity ? size
• Subject ? color
• Award? ? shape

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Ranking Visual Attributes

• Position
• Length
• Angle, Slope
• Size
• Color

Increased accuracy for quantitative data -W.S.
Cleveland
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Basic Visualization Model

• So far simple static charts

Data
Visualization
Visual Mapping
Interaction

• Most of semester
• more complex mappings
• interaction strategies

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Visualization Design Process

• Primary Inputs
• Data
• User Task
• Compare, known item search, patterns, outliers,
• Scale
• items
• attributes
• User characteristics
• Standards/guidelines
• System resources

• Bag of tricks
• View types
• Interaction strategies

Design
Visualization
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Issue Scale

• of attributes (dimensionality)
• of items
• of possible values (e.g. bits/value)

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Design Principles

• 5 HCI Metrics
• User performance
• Time, success rate, recovery, clicks, actions
• Learning time
• Error rate
• Retention time
• User satisfaction

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Cost of Knowledge

• Frequently accessed info should be quick
• Infrequently accessed info can be slow

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Increase Data Density

• Calculate data/pixel

A pixel is a terrible thing to waste.
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Eliminate Chart Junk

• How much ink is used for non-data?
• Reclaim empty space ( screen empty)
• Attempt simplicity(e.g. am I using 3djust for
  coolness?)

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Interactivity

• Interaction to handle increased scale
• Direct Manipulation
• Visual representation
• Rapid, incremental, reversible actions
• Pointing instead of typing
• Immediate, continuous feedback
• Encourage exploration

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Information Visualization Mantra

• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• Overview first, zoom and filter, then details on
  demand
• E.g. Spotfire

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The Insight Factor

• Avoid the temptation to design a form-based
  search engine
• More tasks than just search
• How do I know what to search for?
• What if theres something better that I dont
  know to search for?
• Hides the data

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Open your mind

• Think bigger, broader
• Does the design help me explore, learn,
  understand?
• Reveals the data

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Class Motto

• Show me the data!

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Information Types

• Multi-dimensional databases,
• 1D timelines,
• 2D maps,
• 3D volumes,
• Hierarchies/Trees directories,
• Networks/Graphs web, communications,
• Document collections digital libraries,

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Assignment Presentations

• Book Ch. 1, 3, 4
• Read for Tues
• Inselberg Multidimensional detective (parallel
  coordinates)
• ganesh, christa
• Kandogan Star Coordinates
• rohit, david
• Read for Thurs
• Rao Table Lens
• harsha, vishal
• Keim VisDB
• ming, binli

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Homework 1

• Due Sept 6 (1 week)
• Data
• Eye-tracking data
• Web logs
• Multi-D engineering data
• Bio-informatics?
• User study data
• GTA Purvi
• Demos in 104c Friday 4pm
• Available in 104c F,M-W 4-7pm

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Projects

• Proposal due Sept 13

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Presentations

• Mapping
• Show pictures / demo / video
• Strengths, weaknesses
• Hci metrics, insight factor