Visual Computing

1
Visual Computing

• Lecture 2
• Visualization, Data, and Process

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Pipeline 1High Level Visualization Process

1. Data Modeling
2. Data Selection
3. Data to Visual Mappings
4. Scene Parameter Settings (View Transforms)
5. Rendering

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Pipeline 2Computer Graphics

1. Modeling
2. Viewing
3. Clipping
4. Hidden Surface Removal
5. Projection
6. Rendering

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Pipeline 3Visualization Process
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Pipeline 4Knowledge Discovery(Data Mining)
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A Data Analysis Pipeline
Raw Data
Processed Data
HypothesesModels
Results
D
Cleaning Filtering Transforming
Statistical Analysis Pattern Rec Knowledge Disc
Validation
A
C
B
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Where Does Visualization Come In?

• All stages can benefit from visualization
• A identify bad data, select subsets, help choose
  transforms (exploratory)
• B help choose computational techniques, set
  parameters, use vision to recognize, isolate,
  classify patterns (exploratory)
• C Superimpose derived models on data
  (confirmatory)
• D Present results (presentation)

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What do we need to know to do Information
Visualization?

• Characteristics of data
• Types, size, structure
• Semantics, completeness, accuracy
• Characteristics of user
• Perceptual and cognitive abilities
• Knowledge of domain, data, tasks, tools
• Characteristics of graphical mappings
• What are possibilities
• Which convey data effectively and efficiently
• Characteristics of interactions
• Which support the tasks best
• Which are easy to learn, use, remember

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Visualization Components
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Issues Regarding Data

• Type may indicate which graphical mappings are
  appropriate
• Nominal vs. ordinal
• Discrete vs. continuous
• Ordered vs. unordered
• Univariate vs. multivariate
• Scalar vs. vector vs. tensor
• Static vs. dynamic
• Values vs. relations
• Trade-offs between size and accuracy needs
• Different orders/structures can reveal different
  features/patterns

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Types of Data

• Quantitative (allows arithmetic operations)
• 123, 29.56,
• Categorical (group, identify organize no
  arithmetic)
• Nominal (name only, no ordering)
• Direction North, East, South, West
• Ordinal (ordered, not measurable)
• First, second, third
• Hot, warm, cold
• Interval (starts out as quantitative, but is made
  categorical by subdividing into ordered ranges)
• Time Jan, Feb, Mar
• 0-999, 1000-4999, 5000-9999, 10000-19999,
• Hierarchical (successive inclusion)
• Region Continent gt Country gt State gt City
• Animal gt Mammal gt Horse

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Quantitative Data

• Characterized by its dimensionality and the
  scales over which the data has been measured
• Data scales comprise
• Interval scales - real data values such as
  degrees Celsius, but do not have a natural zero
  point.
• Ratio data scales - like interval scales, but
  have a natural zero point and can be defined in
  terms of arbitrary units.
• Absolute data scales - ratio scales that are
  defined in terms of non-arbitrary units.

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Data Dimensions

• Scalar - single value
• e.g. Speed. It specifies how fast an object is
  traveling.
• Vector multi value
• e.g Velocity. It tells the speed and direction.
• Tensor multi value
• Scalars and vectors are special cases of tensors
  with degree (n) equal to 0 and 1 respectively.
• The number of tensor components is given as dn,
  where d is the dimensionality of the coordinate
  system.
• In a three dimensional coordinate system (d3), a
  scalar (n0) requires three values and a tensor
  (n2) requires 9 values.
• There is a difference between a vector and a
  collection of scalars.
• A multidimensional vector is a unified entity,
  the components of which are physically related.
• The three components of a velocity vector of
  particle moving through three-space are
  coherently linked while a collection scalar
  measurements such a weight, temperature, and
  index of refraction, are not.

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Metadata

• Metadata provides a description of the data and
  the things it represents.
• e.g., a data value of 98.6 oF has two metadata
  attributes temperature and temperature scale.
• The value 98.6 has little meaning without the
  metadata attribute of temperature.
• By adding Fahrenheit the attribute, we know the
  Fahrenheit sale is used.
• Metadata may also include descriptions of
  experimental conditions and documentation of data
  accuracy and precision.

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Issues Regarding Mappings

• Variables include shape, size, orientation,
  color, texture, opacity, position, motion.
• Some of these have an order, others dont
• Some use up significant screen space
• Sensitivity to occlusion
• Domain customs/expectations

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www3.sympatico.ca/blevis/Image10.gif
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Importance of Evaluation

• Easy to design bad visualizations
• Many design rules exist many conflict, many
  routinely violated
• 5 Es of evaluation effective, efficient,
  engaging, error tolerant, easy to learn
• Many styles of evaluation (qualitative and
  quantitative)
• Use/case studies
• Usability testing
• User studies
• Longitudinal studies
• Expert evaluation
• Heuristic evaluation

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Categories of Mappings

• Based on data characteristics
• Numbers, text, graphs, software, .
• Logical groupings of techniques (Keim)
• Standard bars, lines, pie charts, scatterplots
• Geometrically transformed landscapes, parallel
  coordinates
• Icon-based stick figures, faces, profiles
• Dense pixels recursive segments, pixel bar
  charts
• Stacked treemaps, dimensional stacking
• Based on dimension management (Ward)
• Dimension subsetting scatterplots,
  pixel-oriented methods
• Dimension reconfiguring glyphs, parallel
  coordinates
• Dimension reduction PCA, MDS, Self Organizing
  Maps
• Dimension embedding dimensional stacking, worlds
  within worlds

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Scatterplot Matrix

• Each pair of dimensions generates a single
  scatterplot
• All combinations arranged in a grid or matrix,
  each dimension controls a row or column
• Look for clusters, outliers, partial
  correlations, trends

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Parallel Coordinates

• Each variable/dimension is a vertical line
• Bottom of line is low value, top is high
• Each record creates a polyline across all
  dimensions
• Similar records cluster on the screen
• Look for clusters, outliers, line angles,
  crossings

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Star Glyph

• Glyphs are shapes whose attributes are controlled
  by data values
• Star glyph is a set of N rays spaced at equal
  angles
• Length of each ray proportional to value for that
  dimension
• Line connects all endpoints of shape
• Lay glyphs out in rows and columns
• Look for shape similarities and differences,
  trends

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Other Types of Glyphs
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Dimensional Stacking

• Break each dimension range into bins
• Break the screen into a grid using the number of
  bins for 2 dimensions
• Repeat the process for 2 more dimensions within
  the subimages formed by first grid, recurse
  through all dimensions
• Look for repeated patterns, outliers, trends, gaps

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Pixel-Oriented Techniques

• Each dimension creates an image
• Each value controls color of a pixel
• Many organizations of pixels possible (raster,
  spiral, circle segment, space-filling curves)
• Reordering data can reveal interesting features,
  relations between dimensions

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Methods to Cope with Scale

• Many modern datasets contain large number of
  records (millions and billions) and/or dimensions
  (hundreds and thousands)
• Several strategies to handle scale problems
• Sampling
• Filtering
• Clustering/aggregation
• Techniques can be automated or user-controlled

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Examples of Data Clustering
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Example of Dimension Clustering
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Example of Data Sampling
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The Visual Data Analysis (VDA) Process

• Overview
• Filter/cluster/sample
• Scan
• Select interesting
• Details on demand
• Link between different views

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Issues Regarding Users

• What graphical attributes do we perceive
  accurately?
• What graphical attributes do we perceive quickly?
• Which combinations of attributes are separable?
• Coping with change blindness
• How can visuals support the development of
  accurate mental models of the data?
• Relative vs. absolute judgements impact on tasks

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Role of Perception
MC Escher
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Consider the Following
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Role of Perception

• Users interact with visualizations based on what
  they see. (e.g. black spots at intersection of
  white lines)
• Must understand how humans perceive images.
• Primitive image attributes shape, color,
  texture, motion, etc.

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Op Art - Victor Vasarely
Visualization Example
OpGlyph (Marchese)
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Gestalt Psychology

• Rules of Visual Perception
• Proximity
• Similarity
• Continuity
• Closure
• Symmetry
• Foreground Background
• Size

• Principles of Art Design
• Emphasis / Focal Point
• Balance
• Unity
• Contrast
• Symmetry / Asymmetry
• Movement / Rhythm
• Pattern / Repetition

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Issues Regarding Interactions

• Interaction critical component
• Many categories of techniques
• Navigation, selection, filtering, reconfiguring,
  encoding, connecting, and combinations of above
• Many spaces in which interactions can be
  applied
• Screen/pixels, data, data structures, graphical
  objects, graphical attributes, visualization
  structures

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Interface Design and Usability Engineering

• Articulate
• who users are
• their key tasks

Brainstorm designs
Refined designs
Completed designs
Goals
Task centered system design Participatory
design User-centered design
Graphical screen design Interface
guidelines Style guides
Psychology of everyday things User
involvement Representation metaphors
Participatory interaction Task scenario
walk-through
Evaluatetasks
Usability testing Heuristic evaluation
Field testing
Methods
high fidelity prototyping methods
low fidelity prototyping methods
User and task descriptions
Products
Throw-away paper prototypes
Testable prototypes
Alpha/beta systems or complete specification