Data Visualization

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Data Visualization

• Visualization The use of computer-supported,
  interactive, visual representations of data to
  amplify cognition.
• Information Visualization The use of
  computer-supported, interactive visual
  representations of abstract data to amplify
  cognition.
• S. Card

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Data Visualization

• Brief History
• Key Techniques
• Science versus Aesthetics

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Data Visualization Brief History

• Literature Overview

• Jacques Bertin
• Semiology of Graphics Diagrams, Networks, Maps,
  1983
• coined the term using vision to think

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Data Visualization Brief History

• Literature Overview

• Edward Tufte
• The Visual Display of Quantitative Information,
  2001
• Envisioning Information, 1990
• Visual Explanations Images and Quantities,
  Evidence and Narrative, 1997
• The Cognitive Style of PowerPoint, 2006
• Keywords visualization of statistical data,
  cartograms, history of information visualization,
  visualization displays, micro and macro readings,
  small multiples, escaping flatland
• Tufte Home Page
• Tufte Article on Stanford Alumni Magazine

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Data Visualization Brief History

• Literature Overview

maximization of useful information on a limited
display
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Data Visualization Brief History

• Literature Overview

• Chaomei Chen
• Information Visualization Beyond the Horizon,
  2004
• Mapping Scientific Frontiers The Quest for
  Knowledge Visualization, 2003
• Information Visualisation and Virtual
  Environments, 1999
• CiteSpace II Detecting and visualizing emerging
  trends and transient patterns in scientific
  literature, 2006
• Top 10 unsolved information visualization
  problems, 2005 (pdf)
• Keywords domain visualization, network/graph
  visualizations, visualization in virtual
  (collaborative) environments, social networks
• Chaomei Chen Home Page

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Data Visualization Brief History

• Literature Overview

• Ben Shneiderman
• The Craft of Information Visualization Readings
  and Reflections, 2003
• Readings in Information Visualization Using
  Vision to Think, 1999
• Keywords human factors, HCIL, visual dynamic
  query tools, social networks
• Film Finder
• Ben Shneiderman on Social Networks

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Data Visualization Brief History

• Literature Overview

• Stuart Card
• A Framework for Visualization, 2002
• The Internet Edge Social, Technical, and Legal
  Challenges for a Networked World, 2000
• Readings in Information Visualization Using
  Vision to Think, 1999
• The Structure of the Information Visualization
  Design Space (survey paper on evaluation)
• Keywords HCI, Model Human Processor, GOMS
  (goals, operators, methods, and selection rules)
  theory of user interaction, information foraging
  theory, statistical descriptions of Internet use
• Stuart Card Bio
• GOMS

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Data Visualization Brief History

• Literature Overview

More on HCI Affordances Hicks Law Fitts
Law Five Hat Racks Usability Engineering Evaluatio
n
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Data Visualization Scientific

• Literature Overview

• GIS, Geographic Data Visualizations
• Therese-Marie Rhyne
• Daniel Keim
• Alan MacEachren
• Waldo Tobler (cartograms survey paper)
• Andre Skupin (cartograms perception)

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Data Visualization Scientific

• Literature Overview

• Network / Graph Visualization
• Peter Eades
• Thomas Fruchterman, Edward Reingold
• Tomihisa Kamada, Satoru Kawai
• Graph Drawing Algorithms for the Visualization
  of Graphs
• Stephen Eick
• Kenneth Cox
• Richard Becker (Visualizing Network Data)
• Tamara Munzner (H3viewer)
• John Lamping, Ramana Rao (FocusContext)
• George Furnas (Fisheye View)
• Graph Drawing Survey Paper

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Data Visualization Aesthetics

• Data Visualization Aesthetics

• Martin Wattenberg
• Director of Visual Communication Lab at IBM
  Watson Center

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Data Visualization Aesthetics

• Data Visualization Aesthetics

• Ben Fry
• Organic information design (Anemone)
• Software visualization
• (Dismap)
• Keywords Qualitative versus quantitative
  representation of data,
• algorithmic design, processing, genetic
  algorithms

Haplotypes
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Data Visualization Aesthetics

• Data Visualization Aesthetics

• Golan Levin
• Golan Levin Home
• Lisa Jevbratt
• Lisa Jevbratt Projects

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Data Visualization Methods

• Data Visualization

• Methods and Algorithms
• MDS,
• SOM,
• Force-Directed Placement,
• Grand tour,
• Parallel Planes,
• Glyphs,
• Node and link displays,
• Tree maps,
• Matrix representations,
• Cone trees,
• Fisheye views,
• Focuscontext views,
• Cartograms

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Data Visualization Algorithms

• Force-Directed Placement

• Proposed to achieve several aesthetic criteria
  about graph layouts
• Uniform distribution of nodes
• Uniform edge lengths
• Minimum edge crossings
• Symmetry
• demo

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Data Visualization Algorithms

• Force-Directed Placement

• Peter Eades proposed as a heuristic approach.
• The idea is to calculate attractive forces
  between connected nodes and repulsive forces
  between every pair of nodes.
• Force models varied significantly
• Eades was complex to run in real time
• Fruchterman, Reingold reduced complexity of
  Eades equations
• Kamada Kawai based on Hooks law and
  minimization of energy
• Iterative algorithms

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Data Visualization Algorithms

• Force-Directed Placement

• Ms thesis, Basak Alper

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Data Visualization Algorithms

• MDS

• Brief intro
• A method for dimensionality reduction, enables to
  visualize 40 dimensional data on a 2D display
• The idea is to keep distance relations between
  nodes, proportionally consistent as you reduce
  dimensions of the space
• If distance in 40D space is d, then distance in
  2D space should be ?d , where ? is a constant for
  all elements

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Data Visualization Algorithms

• MDS

• Multi-dimensional scaling
• Metric MDS methods based on eigen value analysis
  of the matrix showing relatedness of every
  element
• Non-iterative and very costly
• If distance in 40D space is d, then distance in
  2D space should be ?d , where ? is a constant for
  all elements

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Data Visualization Algorithms

• MDS

• Non-metric MDS is proposed by Kruskal to overcome
  problems with metric MDS
• Non-metric MDS defines a stress function to place
  data nodes on lower dimensional space
• Nodes are displaced to lower stress and
  iterations are stopped when overall stress
  reaches below a certain threshold
• Stress function

where d ij is the distance in high dimensional
space and g ij is the distance in low dimensional
space distance function is generally the
Euclidian distance
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Data Visualization Algorithms

• SOM

• Kohonen self-organizing maps
• Another way of reducing dimensions of data in a
  neural networks fashion
• Pseudocode for the algorithm
• 1. Initialize Map Randomize the map's nodes'
  weight vectors
• 2. Grab an input vector
• 3. Traverse each node in the map
• 1. Use Euclidean distance to find
  similarity between the input vector and the map's
  node's weight vector
• 2. Track the node that produces the
  smallest distance
• 4. Update the nodes in the neighbourhood by
  pulling them closer to the input vector
  (neighborhood function)
• 5. Increment t and repeat while t lt ? (total
  number of iterations)

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Data Visualization Algorithms

• SOM

• Initialize map Create a matrix of vectors, where
  the size of these vectors is equal to the
  dimensions of data and magnitudes are in the
  range of data
• The initial map can be totally random or
  organized in a certain way, for instance
  magnitudes
• Neighborhood function is generally a Gaussian,
  and the radius is generally reduced over
  iterations

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Data Visualization Algorithms

• SOM

• Resulting arrangement of the vectors on the map
  is based on similarities of input data vectors.
• For each vector in higher dimensions, the
  position of the closest vector on the map is its
  position in lower dimensional space (which is
  generally 2D)

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Data Visualization Algorithms

• SOM

• Processing demo
• Model Tunes