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Information Visualization

Information Visualization. UI lab. 이 석 재. Goal. Data. Data transfer. Insight (learning, knowledge extraction). Method. Data. Data transfer. Insight. Map -1 visual → data insight. Map: data → visual. Visualization. Visual transfer. (communication bandwidth). Visual Mappings.

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Information Visualization

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  1. Information Visualization UI lab. 이 석 재

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

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

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

  5. Effectiveness • User learnability: • Learning time • Retention time • User performance: *** • Performance time • Success rates • Error rates, recovery • Clicks, actions • User satisfaction: • Surveys

  6. To understand something is called “seeing” it. Visual metaphors – a nexus of relationships between what we see and what we think. How have we increased memory, thought, and reasoning? -By the invention of external aids. Introduction

  7. Visual and manipulative use of the external world amplifies cognitive performance. Why does using pencil and paper make such a difference? What is hard is holding the partial results in memory until they can be used. ->The visual representation, by holding partial results outside the mind, extends a person’s working memory. Multiplication Aids

  8. slider rule 1. an analogue interactive visual device that represents quantities as scales with length proportional to their logarithms 2. actually does the visual computation Multiplication Aids

  9. The map is not just a calculator, but also a storage device, storing for access enormous amounts of information naturally located near where they are needed for calculation. Navigation charts

  10. Diagrams can lead to great insight, but they canalso lead to the lack of same. The decision depended on whether the temperature would make the O-rings that sealed the sections of the booster rockets unsafe. Diagrams

  11. Diagrams

  12. Diagrams

  13. INFORMATION VISUALIZATION • VISUALIZATION Definition : The use of computer-supported, interactive, visualrepresentations of data to amplify cognition. • Purpose : insight, notpictures • Both of these visualizations show abstractions, but the abstractions are based on physical space=>SCIENTIFIC VISUALIZATION

  14. ORIGINS OF INFORMATION VISUALIZATION • Workin data graphics dates from about the time of Playfair(1750), who seems to be among the earliest touse abstract visual properties such as line and areato represent data visually (Tufte, 1983) • Tukey (1977) began a movement from within statistics with his work on Exploratory Data Analysis. (Box plot) • The first use of the term information visualization to our knowledge was in Robertson, 1989.

  15. Active Diagrams • The PeriodicTable, originally developed by Mendeleyev, isan important diagrams in the development ofchemistry. • Figure 1.12 shows an information visualization based on the Periodic Table (Ahlberg, 1992) The user can set sliders that control which of the elements in the table will be highlighted.

  16. LARGE-SCALE DATA MONITORING • Information visualization to monitor and make sense of large amounts of dynamic, real-time data (decision-support application)

  17. Information Chromatography Visualization is used to detect telephone fraud Information chromatography : Patterns in the data are revealed by laying them out on a particular visual substrate.

  18. Knowledge Crystallization We have said that the purpose of information visualization is to use perception to amplify cognition

  19. Knowledge Crystallization

  20. Visualization on four levels of use Visualization of the infosphere Visualization of an information workspace Visualization Levels of Use

  21. Visualization on four levels of use (3) Visual knowledge tools (4) Visual Objects Visualization Levels of Use

  22. Cost Structure

  23. Cost-of-Knowledge Characteristic Function Cost Structure

  24. By grouping. About a single element. Easy for human How Visualization Amplifies Cognition

  25. Mapping Data to Visual Form We can think of visualization as adjustable mapping from data to visual form to the human perceiver.

  26. Data Table The usual strategy is to transform this data into relation that are mare structured and thus easier to map to visual form. Mathematical treatment omits descriptive information that is important for visualizationDATA TABLE(case by variables arrays)

  27. Bertin(1977/1981)- cases -> objects- variables -> characteristics- function, input variable, output variable Data Table

  28. Data table can undergo data transformations that affect their structure. Data Table

  29. Data table can describe hierarchical and network data. Data Table

  30. N = nominal variableO = ordinal variableQ = quantitative variable Elementary choices for data transformations derive from the variables types(Q->O, O->N, N->O) Subtypes that represent important properties(Qt = Quantitative Time) Variable Type

  31. Metadata is descriptive information about data. Metadata can be important in choosing visualization An important form of metadata is the structure of a Data Table. Additional metadata could be explicitly to the Data Table by adding Metadata

  32. Data Transformations • Data Transformations • Concatenated to form chains of aggregation and classing • as part of the knowledge crystallization • Can be used to detect more patterns

  33. Data Transformations • Aggregation cycle

  34. Data Transformations • Visual Structures • Data tables  Visual Structures • Mapped with Marks and Graphical properties • Effectiveness

  35. Visual Stuctures  Perception • Level of the visual system • 1st level : Retina - Retina is good at detecting movement or other changes • 2nd level : foveola (황반) - preattentive and stereoscopic processing • 3rd level : within the foveola - 황반의 중심부에 움푹 패인 부분 (중심와)

  36. Visual Stuctures • Visual information processing • Controlled processing : Textual description - detailed, serial, low capacity, slow • Automatic processing : Poping out during search - Parallel, high capacity, fast • Interaction among the visual codings of information - Produce patterns

  37. Visual Stuctures  Spatial Substrate • The most fundamental aspect of a Visual Structure is its use of space : Spatial position is a good visual coding of data

  38. Visual Stuctures • Several techniques to increase the amount of information • Composition : orthogonal placement of axes, creating a 2D space • Alignment : Repetition of an axes at a different position in the space

  39. Visual Stuctures • Folding : continuation of an axis in an orthogonal dimension

  40. Visual Stuctures • Recursion and Overloading

  41. View structure Connection and Encloser 1. Connection Link  connection 2. Encloser Link  encloser

  42. View structure Retinal Properties - retina properties - 자동적으로 process되는 visual feature - relative effectiveness of different retinal properties Temporal encoding - Some variable  time

  43. View Transformation Location Probe Viewpoint control - zoom, pan - overview + detail Distortion - focus + context view - bifcoal lens

  44. View Transformation Distortion - focus + context view - bifocal lens

  45. Hyperbolic tree 에서 node 는 마우스를 이용하여 display의 중앙으로 드래그 할 수 있음. Interaction and transformation controls

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