Transcript Visualization Design cs5984: Information Visualization Chris North Spotfire Simple Visualization Model Data Visual Mapping View Port.

Visualization Design
cs5984: Information Visualization
Chris North
Spotfire
Simple Visualization Model
Data
Visual Mapping
View Port
Data Table
Attributes
Types:
•Quantitative
•Ordinal
•Nominal/Categorical
Items
(aka: cases,
tuples, data
points, …)
Visual Mapping
• Define a Space
• Map: data  marks
• Map: data attributes  graphical mark attributes
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Year  X
Length  Y
Popularity  size
Subject  color
Award?  shape
Ranking Visual Attributes
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Position
Length
Angle, Slope
Size
Color
Increased accuracy
for quantitative data
-Cleveland
Visualization Design Process
Primary Inputs:
• Data
• User Task
Bag of tricks:
•View types
•Interaction strategies
• Compare, known item
search, patterns, outliers,…
• Scale
• # items
• dimensionality
Design
• User characteristics
• Standards/guidelines
• System resources
Visualization
Design Principles
• 5 HCI Metrics:
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User performance ***
Error rate
User satisfaction
Learning time
retention
Interactivity
• Interaction to handle increased scale
• Direct Manipulation
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Visual representation
Rapid, incremental, reversible actions
Pointing instead of typing
Immediate, continuous feedback
• Encourage exploration
Increase Data Density
• Calculate data/pixel
“A pixel
is a
terrible
thing to
waste.”
Eliminate “Chart Junk”
• How much “ink” is used for non-data?
• Reclaim empty space
(% screen empty)
• Attempt simplicity
(e.g. am I using 3d
just for coolness?)
Information Visualization Mantra
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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
Query Previews
• http://www.cs.umd.edu/~egemen/demo.html
Data Space
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Multi-dimensional: databases,…
1D: timelines,…
2D: maps,…
3D: volumes,…
Hierarchies/Trees: directories,…
Networks/Graphs: web, communications,…
Document collections: digital libraries,…
Assignment
• Read for Tues:
– Inselberg “Multidimensional detective” (parallel
coordinates) book p107 (margaret, josh s)
– Kandogan “Star Coordinates” web (matt c, fanye)
• Read for Thurs:
– Rao “Table Lens” p343,597 (marty e, purvi)
– Feiner “Worlds within Worlds” p96 (scott m, ?)
• Find some interesting data
• Send me your picture