Data Visualization

1. 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

2. Data Visualization Brief History Key Techniques Science versus Aesthetics

3. Data Visualization: Brief History • Jacques Bertin • Semiology of Graphics: Diagrams, Networks, Maps, 1983 • coined the term “using vision to think” Literature Overview:

4. Data Visualization: Brief History • 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 Literature Overview:

5. Data Visualization: Brief History maximization of useful information on a limited display Literature Overview:

6. Data Visualization: Brief History • 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 Literature Overview:

7. Data Visualization: Brief History • 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 Literature Overview:

8. Data Visualization: Brief History • 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 Literature Overview:

9. Data Visualization: Brief History More on HCI: Affordances Hick’s Law Fitts’ Law Five Hat Racks Usability Engineering Evaluation Literature Overview:

10. Data Visualization: Scientific • GIS, Geographic Data Visualizations • Therese-Marie Rhyne • Daniel Keim • Alan MacEachren • Waldo Tobler (cartograms survey paper) • Andre Skupin (cartograms & perception) Literature Overview:

11. Data Visualization: Scientific • 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 (Focus+Context) • George Furnas (Fisheye View) • Graph Drawing Survey Paper Literature Overview:

12. Data Visualization: Aesthetics • Martin Wattenberg • Director of Visual Communication Lab at IBM Watson Center Data Visualization & Aesthetics:

13. Data Visualization: Aesthetics • Ben Fry • Organic information design (Anemone) • Software visualization • (Dismap) • Keywords: Qualitative versus quantitative representation of data, • algorithmic design, processing, genetic algorithms Data Visualization & Aesthetics: Haplotypes

14. Data Visualization: Aesthetics • Golan Levin • Golan Levin Home • Lisa Jevbratt • Lisa Jevbratt Projects Data Visualization & Aesthetics:

15. Data Visualization: Methods • 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

16. Data Visualization: Algorithms • Proposed to achieve several aesthetic criteria about graph layouts • Uniform distribution of nodes • Uniform edge lengths • Minimum edge crossings • Symmetry • demo Force-Directed Placement

17. Data Visualization: Algorithms • 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 Force-Directed Placement

18. Data Visualization: Algorithms • Ms thesis, Basak Alper Force-Directed Placement

19. Data Visualization: Algorithms • 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 λis a constant for all elements MDS

20. Data Visualization: Algorithms • 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 λis a constant for all elements MDS

21. Data Visualization: Algorithms • 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 MDS 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

22. Data Visualization: Algorithms • 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) SOM

23. Data Visualization: Algorithms • 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 SOM

24. Data Visualization: Algorithms • 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) SOM

25. Data Visualization: Algorithms • Processing demo • Model Tunes SOM