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INFORMATION VISUALIZATION

INFORMATION VISUALIZATION. Visualization. The use of computer-supported, interactive, visual representations of data to amplify cognition. The purpose of visualization is insight not pictures Goals of insight are Decision making, Discovery and Explanation. Why Visualization?.

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INFORMATION VISUALIZATION

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  1. INFORMATION VISUALIZATION

  2. Visualization • The use of computer-supported, interactive, visual representations of data to amplify cognition. • The purpose of visualization is insight not pictures • Goals of insight are Decision making, Discovery and Explanation

  3. Why Visualization? • A picture is worth ten thousand words • Amplify our cognition ability • Cognition: the acquisition or use of knowledge • Specific goals: • Communicating ideas • Create and discover ideas • Use visual perception to solve problems • To get a ‘Ah HA’ response from the viewer

  4. Origin • Data graphics-1786-Playfair(Use lines, areas visually) • Theory of Graphics-1967-Bertin(Plotting Data) • Theory of Data-1983-Tufte(maximising density of useful information) • Exploratory Data Analysis-use of pictures to give statistical insight to Data

  5. Visualization amplifies cognition • Increases resources • Reduces search • Enhanced recognition of patterns • Perceptual inference • Perceptual monitoring • Manipulable medium

  6. Visualization Principles • Expressiveness: • Encode all the facts in the result set. • Encode only the facts in the result set. • Effectiveness: • Depends on the capability of the perceiver. • Encode the more important information more effectively.

  7. Scientific Visualization Visualization of physical data Information Visualization Visualization of abstract data Automobile web site - visualizing links Ozone layer around earth Visualization – Twin Subjects

  8. Focus is on visualizing set of observations that are multi-variate There is no underlying field – it is the data itself we want to visualize The relationship between variables is not well understood Scientific Visualization – Information Visualization Scientific Visualization Information Visualization • Focus is on visualizing an entity measured in a multi-dimensional space • Underlying field is recreated from the sampled data • Relationship between variables well understood

  9. Information Visualization “… is a process of transforming data and information that are not inherently spatial, into a visual form allowing the user to observe and understand the information.” (Source: Gershon and Eick, First Symposium on Information Visualization) “… the use of computer-supported, interactive, visual representations of abstract data to amplify cognition.” Card, Mackinlay, Shneiderman

  10. Basic Visualization Model

  11. Goal Data Data transfer Insight (learning, knowledge extraction)

  12. Method Data Data transfer Insight ~Map-1: visual → data insight Map: data → visual Visualization Visual transfer (communication bandwidth)

  13. Visual Mappings Data • Visual Mappings must be: • Computable (math) • visual = f(data) • Comprehensible (invertible) • data = f-1(visual) • Creative! Map: data → visual Visualization

  14. Visualization Pipeline task Raw data (information) Data tables Visualstructures Visualization(views) Visualmappings Viewtransformations Datatransformations User interaction

  15. Visual Mapping: Step 1 • Map: data items  visual marks Visual marks: • Points • Lines • Areas • Volumes • Glyphs

  16. Visual Mapping: Step 2 • Map: data items  visual marks • Map: data attributes  visual properties of marks Visual properties of marks: • Position, x, y, z • Size, length, area, volume • Orientation, angle, slope • Color, gray scale, texture • Shape • Animation, time, blink, motion

  17. Information Types • Multi-dimensional: databases,… • 1D: timelines,… • 2D: maps,… • 3D: volumes,… • Hierarchies/Trees: directories,… • Networks/Graphs: web, communications,… • Document collections: digital libraries,…

  18. 1-D Data • Linear data: textual document, source code, etc. • User problems: count, find, replace, … • Encoding: fonts, color, size, layout, scrolling, selection capabilities, … • Product example: text editor, browser, …

  19. 2-D Data • Planar or map data: geographical maps, floor plans, newspaper layouts, … • User problems: find adjacent items, search containment, find paths, filtering, details-on-demand, … • Encoding: size, color, layout, arrangement, multiple layers, … • Product example: CAD

  20. 3-D Data • Real-world objects: building, human body • User problems: adjacency in 3-D, inside/outside relationship, position, orientation • Encoding: overviews, landmarks, transparency, color, perspective, stereo display • Product example: CAD

  21. Temporal Data • Time series data: medical records, project management, historical presentation • User problems: finding all events before, after or during some time period or moment. • Encoding: time lines

  22. Multi-dimensional Data • Relational and statistical databases tuples. • User problem: finding patterns, clusters, correlations, gaps, outliers. • Challenge: • Simultaneously display many dimensions of large subsets of data. • Create displays that best encode the data pattern for a particular task. • Rapidly select a subset of tuples or dimensions.

  23. Tree Type Data • Exponential data: hierarchies, tree structures. • User problems: find the structural properties • Height of the tree • Number of children • Find nodes with same attributes • Encoding:Node-link diagrams: allowing the encoding of linkage between entities. • Treemap: child rectangles inside parent rectangles • Product example: windows explorer, internet traffic

  24. Network Data • Graph data: multiple paths, cycles, lattices • User problems: • Shortest path • Topology problems • Encoding: • Node-link diagram • Matrix

  25. Basic Visualization Tasks • Overview of a collection of data. • Zoom in/on objects of interest. • Filter out uninterested items. • Details-on-demand: view details. • Relate: View relationship. • History: Undo, Redo, Refinement. • Extract a subset of the data.

  26. User Tasks Excel can do this • Easy stuff: • Min, max, average, % • These only involve 1 data item or value • Hard stuff: • Patterns, trends, distributions, changes over time, • outliers, exceptions, • relationships, correlations, multi-way, • combined min/max, tradeoffs, • clusters, groups, comparisons, context, • anomalies, data errors, • Paths, … Visualization can do this!

  27. data visualize model render Scientific Visualization Model • Visualization represented as pipeline: • Read in data • Build model of underlying entity • Construct a visualization in terms of geometry • Render geometry as image • Data are more spatial

  28. Classification of InfoVis Techniques • Based on the type of information • Visualization of Information Structure • Trees, Networks • Visualization of Multivariate Data • 1D, 2D, 3D, n-D, Temporal • Visualization of Workspace • Windows, web pages, documents, etc

  29. Classification of InfoVis Techniques • Based on how we interact with the data • Overview: fisheye • Zooming: e.g. Table Lens • Interactive filtering: e.g. Magic Lens • Brushing and linking: e.g. XGobi • Details-on-demand: e.g. Spotfire

  30. InfoVis Design Issues • Selection • What data should we choose to visualize? • Representation • How should data be represented? Colors? Locations? … • Presentation • Too much data, too little display space

  31. InfoVis Design Issues (cont’d) • Scale and Dimensionality • What if you have 93 variables to visualize? • Interaction and Exploration • How user interacts with the data? • Hot Topics: network visualization, document visualization, security related problems, etc

  32. InfoVis Applications • Complex Documents • Biography, manuscript, data structure • Histories • Patient histories, student records, etc • Classifications • Table of contents, organization charts, etc • Networks • Telecom connections and usages, highway, etc

  33. InfoVis Example • Stephen Eick’s Seesoft

  34. Escher’s woodcut 2D Hyperbolic Tree 3D Hyperbolic Tree InfoVis Example • Hyperbolic Trees

  35. InfoVis Example • Themescape (Cartia)

  36. Interactive Graphics • Homefinder

  37. Visualization Techniques

  38. Location Probes-eg.Film finder(use location to view additional data) • View point Controls-eg.Information mural(Overview+detail) • Distortion-eg.Perspective Wall(Focus+Context)

  39. Focus + Context

  40. Data Types • Univariate • Bivariate • Trivariate

  41. Univariate • Dot plot • Bar chart (item vs. attribute) • Histogram

  42. Bivariate • Scatterplot

  43. Trivariate • 3D scatterplot, spin plot • 2D plot + size (or color…)

  44. Information Visualization Using 3D Interactive Animation

  45. Visual Abstractions • Hierarchical Structure -> Cone Tree • Linear Structure -> Perspective Wall • Continuous Data -> Data Sculpture • Spatial Data -> Office Floor plan

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