GraphicalVisual Properties

1
Graphical/Visual Properties

• CS 7450 - Information Visualization
• August 31, 2000

2
Semiotics

• The study of symbols and how they convey meaning
• Classic book
• J. Bertin, 1983, The Semiology of Graphics

3
Related Disciplines

• Psychophysics
• Applying methods of physics to measuring human
  perceptual systems
• How fast must light flicker until we perceive it
  as constant?
• What change in brightness can we perceive?
• Cognitive psychology
• Understanding how people think, here, how it
  relates to perception

4
Basic Symbolic Displays

• Graphs
• Charts
• Maps
• Diagrams

From S. Kosslyn, Understanding chartsand
graphs, Applied CognitivePsychology, 1989.
5
1. Graph
Showing the relationships between
variablesvalues in a data table
6
Properties

• Graph
• Visual display that illustrates one or more
  relationships among entities
• Shorthand way to present information
• Allows a trend, pattern or comparison to be
  easily comprehended

7
Issues

• Critical to remain task-centric
• Why do you need a graph?
• What questions are being answered?
• What data is needed to answer those questions?
• Who is the audience?

money
time
8
Graph Components

• Framework
• Measurement types, scale
• Content
• Marks, lines, points
• Labels
• Title, axes, ticks

9
Common Graph Formats
Line graph
Bar graph
Scatter plot
Y-axis is quantitativevariable Compare relative
pointvalues
Two variables, want tosee relationship Is there
a linear, curved orrandom pattern?
Y-axis is quantitativevariable See changes
overconsecutive values
10
Graphing Guidelines

• Independent vs. dependent variables
• Put independent on x-axis
• See resultant dependent variables along y-axis
• If there are two independent variables, often
  place them along the 2 axes (you choose which)
  and then the mark may encode the dependent
  variable

11
1,2,3 Variables
A B C D E 1 4 1 8 3 5
A B C D E 1 4 1 8 3 5 2 6 3 4 2 1
A B C D E 1 4 1 8 3 5 2 6 3 4 2 1 3 5 7 2 4 3
12
2. Chart

• Structure is important, relates entities to each
  other
• Primarily uses lines, enclosure, position to
  link entities

Examples flowchart, family tree, org chart, ...
13
3. Map

• Representation of spatial relations
• Locations identified by labels

14
4. Diagram

• Schematic picture of object or entity
• Parts are symbolic

Examples figures, steps in a manual,
illustrations,...
15
Details

• What are the constituent pieces of these four
  symbolic displays?
• What are the building blocks?

16
Visual Structures

• Composed of
• Spatial substrate
• Marks
• Graphical properties of marks

17
Space

• Visually dominant
• Often put axes on space to assist
• Use techniques of composition, alignment,
  folding, recursion, overloadingto 1) increase
  use of space 2) do data encodings

18
Marks

• Things that occur in space
• Points
• Lines
• Areas
• Volumes

19
Graphical Properties

• Size, shape, color, orientation...

Spatial properties
Object properties
Position Size
Expressing extent
Grayscale
Color Shape Texture
Differentiating marks
Orientation
20
Preattentive Processing

• How does human visual system analyze images?
• Some things seem to be done preattentively,
  without the need for focused attention
• Generally less than 200-250 msecs (eye movements
  take 200 msecs)
• Seems to be done in parallel by low-level vision
  system

Reference
http//www.csc.ncsu.edu/faculty/healey/PP/PP.html
Image applets
21
What Kinds of Tasks?

• Target detection
• Is something there?
• Boundary detection
• Can the elements be grouped?
• Counting
• How many elements of a certain type are present?

22
Example

• Determine if a red circle is present
• (2 sides of the room)

23
Hue
Can be done rapidly (preattentively) by
people Surrounding objects called distractors
24
Example

• Determine if a red circle is present

25
Shape
Can be done preattentively by people
26
Example

• Determine if a red circle is present

27
Hue and Shape

• Cannot be done preattentively
• Must perform a sequential search
• Conjuction of features (shape and hue) causes it

28
Example

• Is there a boundary in the display?

29
Fill and Shape

• Left can be done preattentively since each
  group contains one unique feature
• Right cannot (there is a boundary!) since the
  two features are mixed (fill and shape)

30
Example

• Is there a boundary in the display?

31
Hue versus Shape
Left Boundary detected preattentively based
on hue regardless of shape Right Cannot do
mixed color shapes preattentively
32
Example

• Is there a boundary?

33
Hue versus brightness
Left Varying brightness seems to
interfere Right Boundary based on brightness can
be done preattentively
34
Preattentive Features

• Certain visual forms lend themselves to
  preattentive processing
• Variety of forms seem to work

35
Textons
1. Elongated blobs 2. Terminators 3. Crossings of
lines
All detected early
36
3-D Figures
3-D visual reality has an influence
37
Emergent Features
38
Potential PA Features
hue intensity flicker direction of
motion binocular lustre stereoscopic depth 3-D
depth cues lighting direction
length width size curvature number terminators int
ersection closure
39
Color

• Sensory response to electromagneticradiation in
  the spectrum betweenwavelengths 0.4 - 0.7
  micrometers

0.5
10-1
10-6
105
108
visible
gamma
ultraviolet
microwave
tv
40
Color Models

• HVS model
• Hue - what people think of color
• Value - light/dark, ranges blacklt--gtwhite
• Saturation - intensity, ranges huelt--gtgray

white
Value
Hue
Saturation
black
41
Color Schemes
Order these (low-gthi)
42
Color Schemes
Gray scale
Single sequence part spectral scale
Full spectral scale
Single sequence single hue scale
Double-ended multiple hue scale
43
Color Purposes

• Call attention to specific data
• Increase appeal, memorability
• Increase number of dimensions for encoding data

44
Using Color

• Modesty! Less is more
• Use blue in large regions, not thin lines
• Use red and green in the center of the field of
  view (edges of retina not sensitive to these)
• Use black, white, yellow in periphery
• Use adjacent colors that vary in hue value

45
Using Color

• Do not use adjacent colors that vary in amount of
  blue
• Dont use high saturation, spectrally extreme
  colors together (causes after images)
• Use color for grouping and search
• Beware effects from adjacent color regions (house
  example)

46
Great Book on Topic
Information Visualization Perception for
Design Colin Ware Academic Press, 2000
47
Sources Used
Card, Mackinlay, Shneiderman, Information
Vis Healey website and article Marti Hearst SIMS
247 lectures Kosslyn 89 article A. Marcus,
Graphic Design for Electronic Documents and
User Interfaces M. Monmonier, How to Lie with
Maps C. Ware, Information Visualization W.
Cleveland, The Elements of Graphing
Data http//www.csc.ncsu.edu/faculty/healey/PP/PP.
html