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