Visualization 200 A

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

Data Visualization

Brief History Key TechniquesScience versus Aesthetics

Data Visualization: Brief HistoryLiterature Overview:

Jacques BertinSemiology of Graphics: Diagrams, Networks, Maps, 1983

coined the term using vision to think


Edward TufteThe Visual Display of Quantitative Information, 2001Envisioning Information, 1990 Visual Explanations: Images and Quantities, Evidence and Narrative, 1997The 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 PageTufte Article on Stanford Alumni Magazine


maximization of useful information on a limited display


Chaomei ChenInformation Visualization: Beyond the Horizon, 2004Mapping Scientific Frontiers: The Quest for Knowledge Visualization, 2003Information Visualisation and Virtual Environments, 1999CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature, 2006Top 10 unsolved information visualization problems, 2005 (pdf)

Keywords: domain visualization, network/graph visualizations, visualization in virtual (collaborative) environments, social networks

Chaomei Chen Home Page


Ben ShneidermanThe Craft of Information Visualization: Readings and Reflections, 2003Readings in Information Visualization: Using Vision to Think, 1999

Keywords: human factors, HCIL, visual dynamic query tools, social networks

Film FinderBen Shneiderman on Social Networks


Stuart CardA Framework for Visualization, 2002The Internet Edge: Social, Technical, and Legal Challenges for a Networked World, 2000Readings in Information Visualization: Using Vision to Think, 1999The 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


More on HCI:AffordancesHicks LawFitts LawFive Hat RacksUsability EngineeringEvaluation

Data Visualization: ScientificLiterature Overview:

GIS, Geographic Data VisualizationsTherese-Marie RhyneDaniel KeimAlan MacEachrenWaldo Tobler (cartograms survey paper)Andre Skupin (cartograms & perception)

Data Visualization: ScientificLiterature Overview:

Network / Graph VisualizationPeter EadesThomas Fruchterman, Edward ReingoldTomihisa Kamada, Satoru KawaiGraph Drawing: Algorithms for the Visualization of Graphs

Stephen EickKenneth CoxRichard Becker (Visualizing Network Data)

Tamara Munzner (H3viewer)John Lamping, Ramana Rao (Focus+Context)George Furnas (Fisheye View)

Graph Drawing Survey Paper

Data Visualization: AestheticsData Visualization & Aesthetics:

Martin WattenbergDirector of Visual Communication Lab at IBM Watson Center


Ben FryOrganic information design (Anemone)Software visualization(Dismap)

Keywords: Qualitative versus quantitative representation of data, algorithmic design, processing, genetic algorithms

Haplotypes


Golan LevinGolan Levin Home

Lisa JevbrattLisa Jevbratt Projects

Data Visualization: MethodsData Visualization

Methods and AlgorithmsMDS, 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: AlgorithmsForce-Directed Placement

Proposed to achieve several aesthetic criteria about graph layoutsUniform distribution of nodesUniform edge lengthsMinimum edge crossingsSymmetrydemo


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 timeFruchterman, Reingold: reduced complexity of Eades equationsKamada Kawai: based on Hooks law and minimization of energy

Iterative algorithms


Ms thesis, Basak Alper

Data Visualization: AlgorithmsMDS

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


Multi-dimensional scalingMetric MDS methods based on eigen value analysis of the matrix showing relatedness of every elementNon-iterative and very costlyIf distance in 40D space is d, then distance in 2D space should be d , where is a constant for all elements


Non-metric MDS is proposed by Kruskal to overcome problems with metric MDSNon-metric MDS defines a stress function to place data nodes on lower dimensional spaceNodes are displaced to lower stress and iterations are stopped when overall stress reaches below a certain thresholdStress function where d ij is the distance in high dimensional space and g ij is the distance in low dimensional spacedistance function is generally the Euclidian distance

Data Visualization: AlgorithmsSOM

Kohonen self-organizing mapsAnother way of reducing dimensions of data in a neural networks fashionPseudocode 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 < (total number of iterations)


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 dataThe initial map can be totally random or organized in a certain way, for instance magnitudesNeighborhood function: is generally a Gaussian, and the radius is generally reduced over iterations


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)


Processing demoModel Tunes

Visualization 200 A

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