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What is visualisation ?

What is visualisation ?. Visualise: (vb) to form a mental image or vision of … Cognitive ability Allows us to internalise data Gain insight and understanding Internal Map = Cognitive Model. What are data types ?. Various different types of data Numerical Ordinal

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What is visualisation ?

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  1. What is visualisation ? • Visualise: (vb) to form a mental image or vision of … • Cognitive ability • Allows us to internalise data • Gain insight and understanding • Internal Map = Cognitive Model

  2. What are data types ? • Various different types of data • Numerical • Ordinal • Naturally order ( days of the week ) • Categorical • Not ordered ( animal names )

  3. Basic Visualization Approaches • Clustering • Galaxy of News • ThemeScape • Hot Sauce • Geographic • Floor plans • Street maps • Node-link diagrams • 2D diagrams • SemNet • Cone Tree • Fisheye Cone Tree • Hyperbolic viewer • FSN • XML3D Indentation • Tree control • Fisheye Containment • Treemaps • Pad++

  4. Examples of Visualisation • London Underground – Harry Beck • Connectivity • Deals with connections, not focused on geography • Differs from other maps, as familiar geography was not overriding concern

  5. London Underground Map 1927

  6. London Underground Map 1990s

  7. Dr. John Snow:Statistical Map Visualization Broad StreetPump • 1855 London Cholera Epidemic

  8. Visualising Tree Data 1 • CS use of trees for data storage

  9. Visualising Tree Data 2 • Difficult to visualise large tree structures • Take a company • CEO as the root node • People reporting to him at next level • So on until all the employees are included

  10. Tree Maps 1 – Schneiderman

  11. Tree Maps 2 – Schneiderman • Johnson & Schneiderman, University of Maryland, Vis’91 • Space filling • ~3000 objects • MicroLogic’s DiskMapper

  12. Hyperbolic Browsing - Lamping

  13. H3 - 1997 Munzner, Stanford Univ., InfoVis’97 Projected onto sphere: 20,000 nodes

  14. Information Visualisation in Information Retrieval • on-line information • diversity of users of such resources • potential overload • establish new formats for the presentation and manipulation of electronic data • spatial ability is an important predictor of effectiveness and efficiency when performing common information (i.e. textual) search tasks

  15. Usefulness of Visualisation in IR • Allows semantic relationships to be represented • Use of Metaphors such as • spatial proximity • visual links • Allows users to develop a conceptual map of the information space

  16. Linking IR to real world tasks • Searching & Browsing of information can be related to real world navigation • Complex Datasets can hide trends / information • A well design graph can express shopping trends through the use of Store Card information

  17. IR and Hypermedia • WWW – another information space • Overview Maps & Zooming/Panning • Improve performance and satisfaction • Move ‘load’ from cognitive to perceptual processes • visualising and directly interact with conventional hypermedia and unstructured text

  18. Combing IR and VR – new perceptions of data • Virtual Reality (VR) environments can further enhance visualisations • Allows for • Real Time Interactivity • Viewing of relationships between object from unlimited number of perspectives • Can allow for haptic or non-visual methods of feedback to the user

  19. Visualization Taxonomy - 1994 • Implicit (use of perspective) • Continuous focus and context • Filtered (removing items of low interest) • Discrete focus and context • Distorted (size, shape, position of elements) • Adorned (color, texture) Reference: Noik (Graphics Interface’94)

  20. Approaches to IV • Core approaches - Colebourne et al. (1994) • 'Benediktine' cyberspace • statistical clustering and proximity • hyper-structures • human centred • Categories are not mutually exclusive

  21. 'Benediktine' cyberspace • Benedikt - 1991 • assigns object attributes (e.g. file size, age, key words) on to extrinsic (x,y,z) and intrinsic (e.g. shape) dimensions. • Well suited to data that is explicitly structured

  22. 'Benediktine' cyberspace

  23. Statistical Clustering and Proximity • Applies statistical models to data prior to presenting the visualisation • conveys spatially the underlying semantic structure. • spatial proximity of documents -> reflect their semantic similarity • Various techniques generate these semantic proximities (eg Vector Space Model)

  24. Statistical Clustering and Proximity

  25. Hyper-structures • extend the notion of hypertext directly • use 3-D graph drawing algorithms to create the visualisation • Works well where explicit links exist, eg in hypertext • Various graph visualisation techniques available

  26. Hyper-structure (Cone Tree 1) Robertson, Mackinlay & Card, Xerox PARC, CHI’91 Limits: 10 levels 1000 nodes Up to 10,000

  27. Hyper-structure (Cone Tree 2)

  28. Human centred • Two main areas • Exploit the user's real world experience, by representing information spaces using real world metaphors • Allow the user themselves to organise the information in a manner that they find intuitive

  29. Human centred – Exploit user experience

  30. Human centred – User themselves organise data

  31. Visual Information Seeking 1 • Research by Ben Schneiderman • Direct-manipulation interfaces • Certain tasks a visual presentation is much easier to comprehend than text • Mantra: Overview first, zoom and filter, then details on demand

  32. Visual Information Seeking 2 • Schneiderman – 7 Data Types • 1-, 2-, 3-d data, temporal, multi-dimensional, tree and network data • All items have attributes and simple search task is to find all items which a certain set of attributes

  33. Visual Information Seeking 3 • Overview: of a collection • Zoom: on items of interest • Filter: out uninteresting items • Details-on-Demand: of a item or group of items • Relate: relationship between items • History & Extract

  34. Combining Sound & Visual retrieval • Aural presentation contains addition information not found in visual representations • Omni directional information • Encoding of information, multiple streams • “Cocktail Party Effect” - Arons 1992 • Recognition of sounds, is most often sufficient to hear only 500 ms to 2 seconds of the characteristic or significant part of a sound (Warren 1999)

  35. Further Readings • Chen, C. (1999) Information Visualisation and Virtual Environments • Card, S et al (1999) Readings in Information Visualization: Using Vision to Think • Spence, R. (2001) Information Visualization • http://www.cribbin.co.uk/infovis.htm

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