1 / 73

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?.

eliot
Download Presentation

INFORMATION VISUALIZATION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related