data visualization visualization: the use of computer-supported, interactive, visual representations...
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Data VisualizationVisualization: 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
Data VisualizationBrief History Key TechniquesScience versus Aesthetics
Data Visualization: Brief History
Literature Overview:
Jacques Bertin•Semiology of Graphics: Diagrams, Networks, Maps, 1983
coined the term “using vision to think”
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
Data Visualization: Brief History
Literature Overview:maximization of useful information on a limited display
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
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
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 BioGOMS
Data Visualization: Brief History
Literature Overview:
More on HCI:
Affordances
Hick’s Law
Fitts’ Law
Five Hat Racks
Usability Engineering
Evaluation
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)
Data Visualization: Scientific
Literature Overview:
Network / Graph Visualization
•Peter Eades•Thomas Fruchterman, Edward Reingold•Tomihisa Kamada, Satoru KawaiGraph Drawing: Algorithms for the Visualization of Graphs
•Stephen Eick•Kenneth Cox•Richard Becker (Visualizing Network Data)
•Tamara Munzner (H3viewer)•John Lamping, Ramana Rao (Focus+Context)•George Furnas (Fisheye View)
Graph Drawing Survey Paper
Data Visualization: Aesthetics
Data Visualization & Aesthetics:
Martin WattenbergDirector of Visual Communication Lab at IBM Watson Center
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
Data Visualization: Aesthetics
Data Visualization & Aesthetics:
Golan Levin
Golan Levin Home
Lisa Jevbratt
Lisa Jevbratt Projects
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, •Focus+context views,•Cartograms…
Data Visualization: Algorithms
Force-Directed Placement
Proposed to achieve several aesthetic criteria about graph layouts
i) Uniform distribution of nodes
ii) Uniform edge lengths
iii) Minimum edge crossings
iv) Symmetry
demo
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
Data Visualization: Algorithms
Force-Directed Placement
• Ms thesis, Basak Alper
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 λd , where λ is a constant for all elements
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 λd , where λ is a constant for all elements
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
jiij
jiijij
g
gd
Stress2
2)(
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
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 < λ (total number of iterations)
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
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)
Data Visualization: Algorithms
SOM
• Processing demo
• Model Tunes
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