The Perception of Data

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The Perception of Data

• CMSC 120 Visualizing Information
• 2/5/08

Lecture adapted in part from materials by
Benjamin Bederson
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What is Visual Design?

• Representations
• Perception

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Visual Design

• Semiotics study of symbols and how they convey
  meaning
• Semantics relationship between signs and the
  things they represents
• Syntactics relationship of signs to each other
• Pragmatics impact of signs on those who use them

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Visual Design

• Psychophysics
• Level of intensity at which a sense can detect a
  stimulus
• Pitch frequency of sound a human can hear
• Cognitive Psychology
• How humans perceive and process information

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Low-level Perception

• Two stage process
• Parallel extraction of basic properties
• Sequential, goal-directed processing

Detection of color, texture, shape,
spatial attributes
Serial processing of object identification
(using memory) and spatial layout, action
(after Ware 2000)
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Low-Level Perception

• Neurons in eye brain
• Arrays of neurons work in parallel
• Occurs automatically
• Rapid
• Information is transitory
• Bottom-up data-driven processing
• Often called pre-attentive processing

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Goal-Directed Perception

• Working and long-term memory
• Serial processing
• Slow
• Information is stored
• Semiotics
• Top-down processing

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Goal-Directed Perception
Top-down formulate overview of system and then
refine until reduced to basic elements
Bottom-up specify basic elements in great detail
and link together to formulate system
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How do we analyze images?

• Pre-attentive processing
• No need for focused attention
• 200-250 milliseconds (msec)

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How Many 3s?
1281768756138976546984506985604982826762 980985845
8224509856458945098450980943585 909103020990595959
5772564675050678904567 884578980982167765487636490
8560912949686
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How Many 3s?
1281768756138976546984506985604982826762 980985845
8224509856458945098450980943585 909103020990595959
5772564675050678904567 884578980982167765487636490
8560912949686
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How do we analyze images?

• Pre-attentive processing
• No need for focused attention
• 200-250 milliseconds (msec)
• Target detection
• Is something there?
• Boundary detection
• Can the elements be grouped?
• Counting
• How many elements of a certain type are present?

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Target Detection Hue

• Detect red circle target
• Presence/Absence of a Feature
• Non-targets are distractors

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Target Detection Shape

• Detect a circle
• Presence/Absence of a Feature
• Non-targets are distractors

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Target Detection Conjunction

• Detect a red circle
• Cannot be detected pre-attentively
• Serial search 1) hue, 2) shape

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Boundary Detection Fill and Shape

• Detect a texture boundary between 2 groups of
  elements
• All elements of group have a common visual
  property
• Pre-attentive on left and right

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Boundary Detection Hue and Shape

• Detect a texture boundary between 2 groups of
  elements
• Pre-attentive on left

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Boundary Detection Hue and Brightness

• Detect a texture boundary between 2 groups of
  elements
• Pre-attentive on right

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Pre-attentive Features
length width size curvature number terminators int
ersection closure
hue intensity flicker direction of
motion lustre stereoscopic depth 3-D depth
cues lighting direction

• Brightness
• Color
• Texture
• Shape

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Brightness

• Perceived amount of light coming from asource
• Luminance
• Measured amount of light coming from a source

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Color

• The quality of an object with respect to the
  light reflected by the object
• Determined visually by
• Hue color
• Saturation purity
• Brightness black

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Color Purposes

• Emphasis
• Appeal
• Dimensionality

Hello, here is some text. Can you read what it
says?
Hello, here is some text. Can you read what it
says?
Hello, here is some text. Can you read what it
says?
Hello, here is some text. Can you read what it
says?
Hello, here is some text. Can you read what it
says?
Hello, here is some text. Can you read what it
says?
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Texture

• Physical composition or structure
• Size
• Shape
• Arrangement of Parts
• Feel
• Complex attribute

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Shape

• Distinct object or body defined by an outline
• An orderly arrangement
• Symbol a shape used to represent something else
• May have brightness, color, and texture
• Rapid Perception
• Rapid Differentiation
• Create Targets and Boundaries

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Information Visualization
Symbolic Display

• Graphs
• Charts
• Maps
• Diagrams

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Graphs

• Graph - Show the relationships between variables
  values in a data table
• 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

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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
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Graph Components

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

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Basic Data Types

• Nominal (qualitative)
• (no inherent order)
• city names, types of diseases, ...
• Ordinal (qualitative)
• (ordered, but not at measurable intervals)
• first, second, third,
• cold, warm, hot
• Interval (quantitative)
• list of numbers

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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
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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

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2. Chart

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

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

• Representation of spatial relations
• Locations identified by labels

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Choropleth Map
Areas are filled and colored differently
to indicate some attribute of that region
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Cartography

• Cartographers and map-makers have a wealth of
  knowledge about the design and creation of visual
  information artifacts
• Labeling, color, layout,
• Information visualization researchers should
  learn from this older, existing area

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4. Diagram

• Schematic picture of object or entity
• Parts are symbolic

Examples figures, steps in a manual,
illustrations,...
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Tuftes Design Principles

• 1. Tell the truth
• Graphical integrity
• 2. Do it effectively with clarity, precision
• Design aesthetics

E. Tufte, The Visual Display of Quantitative
Information (1983) E. Tufte, Envisioning
Information (1990) E. Tufte, Visual Explanations
(1997)
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1. Graphical Integrity

• Your graphic should tell the truth about your data

500
Stock market crash?
475
450
2002
2001
2000
1999
1998
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Show entire scale
500
250
0
2002
2001
2000
1999
1998
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Show in context
500
250
0
2000
1990
1980
1970
1960
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Measuring Misrepresentation
Lie factor 2.8

• Visual attribute value should be directly
  proportional to data attribute value
• Height/width vs. area vs. volume

Size of effect shown in graphic Size of effect in
data
Lie factor
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2. Design Principles

• Maximize data-ink ratio

Data ink
Data ink ratio
Total ink used in graphic
proportion of graphics ink devoted to the
non-redundant display of data-information
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Design Principles

• Avoid chartjunk
• Extraneous visual elements that detract from
  message

dont be the duck of architecture
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Design Principles

• Utilize multifunctioning graphical elements
• Graphical elements that convey data information
  and a design function

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Design Principles

• Use small multiples
• Repeat visually similar graphical elements nearby
  rather than spreading far apart

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Design Principles

• Show mechanism, process, dynamics, and causality
• Cause and effect are key

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Design Principles

• Escape flatland
• Data is multivariate
• Doesnt necessarily mean 3D projection

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Design Principles

• Utilize layering and separation

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Design Principles

• Utilize narratives of space and time
• Tell a story of position and chronology through
  visual elements

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Design Principles

• Content is king
• Quality, relevance and integrity of the content
  is fundamental
• Whats the analysis task? Make the visual design
  reflect that
• Integrate text, chart, graphic, map into a
  coherent narrative

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Graph and Chart Tips

• Avoid separate legends and keys Put that in the
  graphic
• Make grids labeling faint so that they recede
  into background

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Proper Color Use

• To label
• To measure
• To represent or imitate reality
• To enliven or decorate

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Color Examples
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Guides for Enhancing Visual Quality

• Attractive displays of statistical info
• have a properly chosen format and design
• use words, numbers and drawing together
• reflect a balance, a proportion, a sense of
  relevant scale
• display an accessible complexity of detail
• often have a narrative quality, a story to tell
  about the data
• are drawn in a professional manner, with the
  technical details of production done with care
• avoid content-free decoration, including chartjunk

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Graphical Displays Should

• Show the data
• Induce the viewer to think about substance rather
  than about methodology, graphic design the
  technology of graphic production, or something
  else
• Avoid distorting what the data have to say
• Present many numbers in a small space
• Make large data sets coherent

• Encourage the eye to compare different pieces of
  data
• Reveal the data at several levels of detail, from
  a broad overview to the fine structure
• Serve a reasonably clear purpose description,
  exploration, tabulation, or decoration
• Be closely integrated with statistical and verbal
  descriptions of a data set