Visualization Lecture 2005


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Lecture by Lisa Tweedie at Bath Uni

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Visualization Lecture 2005

  1. 1. 19th April 2005 Advanced Human Computer Interaction (HCI) Week 7 CM30141-S2 Unit Lecturer: Dr Lisa Tweedie Unit Tutor: Chris Middup
  2. 2. 19th April House keeping • Switching weeks 7/8 in the course
  3. 3. 19th April Overview 1. Introduction 2. External Representations and Interactivity 3. Types of Representation 4. Types of Interactivity
  4. 4. 19th April A Killer Application • The Spreadsheet • Why?
  5. 5. 19th April External Representations • Reduce Cognitive Load - tool for thought • Act as a store for our knowledge over time • Organize and structure information for us • However can force us to look at information in certain ways i.e. can limit thinking. Therefore we need to have an appropriate representation for the external representation to be useful.
  6. 6. 19th April Characteristics of graphics Need the right representation for the type of data and the questions the user wishes to ask of it.
  7. 7. 19th April Characteristics of graphics With the right representation inferences often become very obvious Jon Snow 1845
  8. 8. 19th April Characteristics of graphics A representation does not need to be accurate to be useful
  9. 9. 19th April Characteristics of graphics • Finding the correct representation is still something of a black art – Build on representations that have be used for a problem before – Think about the questions that need to be asked. – Think about multiple views of the data
  10. 10. 19th April Interactivity • Adding Interactivity to representations allows a users to proactively ask questions of the data. • In effect an interactive visualisation allows users to scan many hundreds of static representations very quickly - creates a dialog between the user and their problem. • Encourages iterative exploration of the problem space. • The locus of control has switched to the user
  11. 11. 19th April Bertin (1977) A graphic is no longer ‘drawn once and for all it is “constructed” and “reconstructed” until all the relationships that lie within it have been perceived.
  12. 12. 19th April Types of Representation - Bertin 1977 • Representations of Data Values –bottom up • Representations of Data Structure – top down
  13. 13. 19th April Representations of Data values show relations between subsets of the data e.g. histograms, scatterplots etc.
  14. 14. 19th April Dynamic Queries - Ahlberg et al (1992)
  15. 15. 19th April Table Lens - Rao et al 1994 (PARC)
  16. 16. 19th April Brushing - linking attribute views Can take multiple similar representations of all the attributes in a data set. In some ways Bertins distinction disappears - as you can see the structure of the whole set and the subset in context. In effect the representation provides the structure and the interactivity provides the querying of individual values and their relations.
  17. 17. 19th April A scatterplot Matrix
  18. 18. 19th April The Attribute Explorer - Tweedie et al (1994)
  19. 19. 19th April Netmap - (Davidson 1993)
  20. 20. 19th April Net map
  21. 21. 19th April Netmap
  22. 22. 19th April Netmap • It is unlikely that an individual would have more than three applications for a mortgage on a single house . . . . .
  23. 23. 19th April Parrallel Coordinate plots - Inselberg (1985)
  24. 24. 19th April Linking Multiple representations of data values It is often difficult to anticipate the questions a user would want to ask of the data Different representations might be suited for answering different questions. Thus brushing across different representations is a logical extension.
  25. 25. 19th April Multiple representations of data values
  26. 26. 19th April Representations of Data structure Show relations within an entire set Bertin identified five types: – Rectilinear - ordered lists, tables – Circular - Networks – Ordered patterns - Trees – Unordered patterns - networks and Venn diagrams – Stereograms - structure suggests a volume e.g. 3D models
  27. 27. 19th April Representations of Data structure Whereas representations of Data values tend to be used for analysis - representations of data structure are often used for providing overview and navigation around an information space.
  28. 28. 19th April Hyperbolic Browser
  29. 29. 19th April Perspective Wall
  30. 30. 19th April Tree Maps Tree Map construction
  31. 31. 19th April An early tree map
  32. 32. 19th April An early tree map • Too disorderly – What does adjacency mean? – Aspect ratios uncontrolled leads to lots of skinny boxes that clutter • Color not used appropriately – In fact, is meaningless here • Wrong application – Don’t need all this to just see the largest files in the OS
  33. 33. 19th April An early tree map • Too disorderly – What does adjacency mean? – Aspect ratios uncontrolled leads to lots of skinny boxes that clutter • Color not used appropriately – In fact, is meaningless here • Wrong application – Don’t need all this to just see the largest files in the OS
  34. 34. 19th April What would make it more useful? • Think more about the use – Break into meaningful groups – Fix these into a useful aspect ratio • Use visual properties properly – Use color to distinguish meaningfully • Use only two colors: – Can then distinguish one thing from another • Provide excellent interactivity – Access to the real data – Makes it into a useful tool
  35. 35. 19th April Smart Money
  36. 36. 19th April Peets Coffee shop
  37. 37. 19th April Types of interactivity • hiding/ filtering data • labeling e.g. brushing • reordering • providing information scent and other forms of more complex labelling • animated navigation/ algorithmic transformation
  38. 38. 19th April Information Scent • Relates to the issues surrounding query interfaces • How can a user be given appropriate cues to move towards their desired solution in the problem space
  39. 39. 19th April Traditional query languages Problems: 1. The discretionary user must learn a language. Users are often not prepared to do this. Even for simple query languages controlled tests (Borgman 1986) have shown that even after an hours tuition on 25% of University Students could use the library’s online query system. And that queries created tended to be very simple. 2. Errors are not tolerated 3. Too few or too many hits often result from queries. There is no indication how a query might be reformulated to access fewer or more hits. 4. There is a significant time delay between the formulation of a query and the delivery of the result. This definitely slows the problem solving process and probably discourages users from exploring extensively.
  40. 40. 19th April Dynamic Queries - Ahlberg et al (1992)
  41. 41. 19th April Complex colour coding
  42. 42. 19th April The Model Maker First Order Terms X1 X2 X3 X4 X1 X2 X3 X4 X1 X2 X3 X4 X1X2X3 X1X2X4 X1X3X4 X2X3X4 X1 X2 X3 X4 X1 X2 X3 X4 2 2 2 2 Second Order Terms Third Order Terms
  43. 43. 19th April Other forms of scent • Social scent - e.g. recommender systems - This is what others feel is valuable • History (residue) - where have I been before? - e.g. the blue text in the world wide web. • Boolean colour coding and user defined labels
  44. 44. 19th April Combining automation with visualisation Algorithms can support users in performing their task. Simple algorithm animations - where the user watches an algorithm perform (e.g. data mining) - history can then be a starting point for interactivity - ability for user to interact directly with algorithm Algorithmic transformations which sort and order data creating useful metadata.
  45. 45. 19th April Hypergami
  46. 46. 19th April Bead - Chalmers et al (1993)
  47. 47. 19th April Where are the killers apps? • Technology still not quite there • These things are hard to design well - need to keep it simple • Humans take a long time to develop cultures surrounding and learn to use new representations • matching tasks to representations still a black art. • The web is probably the domain where these tools will emerge.
  48. 48. 19th April That’s it • Any questions? • Email contact: