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Understanding Visualization: Types, Techniques, and Applications in Data Representation

Visualization is the cognitive ability to form mental images from data, enabling better understanding and insight. This guide explores various data types, including numerical, ordinal, and categorical, and discusses visualization techniques like tree maps, clustering, and geographic maps. We delve into historical examples, such as the London Underground map and John Snow's cholera visualization, illustrating the significance of effective data representation. Additionally, the advantages of using virtual reality for interactive visualization are examined, enhancing users' ability to perceive complex datasets.

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Understanding Visualization: Types, Techniques, and Applications in Data Representation

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Presentation Transcript

  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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