Info Vis: Multi-Dimensional Data Chris North cs3724: HCI.
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Transcript of Info Vis: Multi-Dimensional Data Chris North cs3724: HCI.
Info Vis:Multi-Dimensional Data
Chris North
cs3724: HCI
Presentations
• jerome holman• john gibson
• Vote: UI Hall of Fame/Shame?
Quiz
• Why visualization?•
• Class motto:•
VisualizationDesign Principles
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 3djust for coolness?)
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
InfoVis Design Principles
• Increase data density
• Eliminate “chart junk”• Mantra: Overview first, zoom&filter, details on demand
• Insight factor• Does the design reveal the data?
• Does the design help me explore, learn, understand?
• Show me the data!
Visualizing Multi-dimensional data
Multi-dimensional Data TableAttributes (aka: dimensions, fields, variables, columns, …)
Items
(aka: data points, records,tuples, rows, …)
Data Values
Data Types:•Quantitative•Ordinal•Categorical/Nominal
Basic Visualization Model
Data VisualizationVisual Mapping
Interaction
Visual Mapping
1. Map: data items visual marks
• Visual marks:• Points
• Lines
• Areas
• Volumes
Visual Mapping
1. Map: data items visual marks
2. 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
Example
• Hard drives for sale: • price ($), capacity (MB), quality rating (1-5)
p
c
Example: Spotfire
• Film database
• Year X
• Length Y
• Popularity size
• Subject color
• Award? shape
Ranking Visual Attributes
1. Position
2. Length
3. Angle, Slope
4. Size
5. Color
Increased accuracy for quantitative data
-W.S. Cleveland
Color better for categorical data
-J. Mackinlay
Basic Charts…
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1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
East
West
North
0102030405060708090
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0 2 4 6
East
West
North
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
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1st Qtr 2nd Qtr 3rd Qtr 4th QtrEast
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Factors in Visualization Design
• User tasks
• Data
• Data scale:• # recs
• # attrs
• # possible data values
Data Scale
• # of attributes (dimensionality)
• # of items
• # of possible values (e.g. bits/value)
Spotfire
• Multiple views: brushing and linking
• Dynamic Queries
• Details window
TableLens (Eureka by Inxight)
• Visual encoding of cell values, sorting
• Details expand within context
Parallel Coordinates
• Bag cartesian orthogonal layout
• Parallel axes
• Data point = connected line segment
• (0, 1, -1, 2) =
0
x
0
y
0
z
0
w
Parallel Coordinates (XmdvTool)
Parallel Coordinates
Info. Vis. Topics
• Information types:• Multi-dimensional: databases,…
• 1D, 2D, 3D
• Trees, Graphs
• Text, document collections
• Interaction strategies:• Overview+Detail
• Focus+Context
• Zooming
• How (not) to lie with visualization
Homework #2: Info. Vis. Tools
• Get some data:• Tabular, >=5 attributes (columns), >=500 items (rows)
• Use 2 visualization tools + Excel:• Spotfire, TableLens, Parallel Coordinates
• Mcbryde 104c
• 2 page report:• Discoveries in data
• Comparison of tools
• Due:• Feb 19: A-K
• Feb 21: L-Z
Project 2: Java
• 3 students per team
• Ambitious project
• 0: form team (feb 14)
• 1: design (feb 28)
• 2: initial implementation (mid march)
• 3: final implementation (end march)
Next
Presentations: proj1 design, UI critique
• Thurs: john randal, tom shultz
• Next Tues: mohamed hassoun, aaron dalton
• Next Thurs: nadine edwards, steve terhar