Enhancing Parallel Coordinates with Curves

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Various tweaks to parallel coordinates to ease following of lines at crossing points

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Enhancing Parallel Coordinates with Curves

  1. 1. Using Curves to Enhance Parallel Coordinate Visualisations• Martin Graham & Jessie Kennedy • Napier University, Edinburgh
  2. 2. Overview• Background• Using Curves• Spreading and Focus+Context• Conclusions• Future Work
  3. 3. Background• Parallel Coordinates visualise multi- dimensional data across a set of parallel axes – 1 axis per data dimension (Inselberg & Dimsdale, 1990) • Objects represented as poly-lines across the axes, intersecting the axes at the appropriate value X Y Z R 1 1 1 1 (X, Y, Z, R) 2 2 2 2 (1.5, 2, 3, 3.2) 3 3 3 3 4 4 4 4 5 5 5 5
  4. 4. Background• Various refinements made to the basic technique by IV researchers • General Interactivity • Selecting, filtering, re-arranging axes • Angular Brushing – Hauser et al • Pick out polylines with segments of certain Ѳ – helps identify trends between attributes • Hierarchical clustering - Fua et al • Stats-based distortions – G. & N. Andrienko
  5. 5. Background• Exploring Parallel Coordinates as a technique to visualise and filter individual and company CV data • Quantitative data - salary • Categorical data • Ordinal – qualification i.e. Masters > Bachelors • Nominal – sector i.e. Legal, IT, Engineering
  6. 6. Background Q. How do we follow lines after crossing points?
  7. 7. Using Curves Visual properties of curves can aid us
  8. 8. Using Curves Can act in conjunction with colouring and brushing
  9. 9. Using Curves• Curved paths tend to resolve individually • Gives better picture of dataset population • Bad for screen clutter with many curves
  10. 10. Using Curves• We can use curves because in our data sets the lines act as connectors only • In Inselberg’s original work, the intersections of polylines between axes carried information about the higher order object they formed • But with heterogeneous dimensions, the positions of inter-axial line crossings don’t mean anything
  11. 11. Spreading & focus+context• Curves can help differentiate objects that share an attribute value, especially if they are dissimilar in other values • But for categorical data especially, paths can form a number of dense knots • Can we use screen space more effectively to spread these paths out over a distance?
  12. 12. Spreading & focus+context Spreading out points on categorical axes
  13. 13. Spreading & focus+contextCan also be applied to traditional poly-line representations
  14. 14. Spreading & focus+context• Bounding boxes around categories keep objects visually grouped• A curve’s position of intersection in the bounding box is decided by averaging its vertical coordinates in adjacent axes• Impact can be increased if selected values are expanded – i.e. focus+context
  15. 15. Initial User Testing• Simple observation of six representative users using system• Users could track curves across axes for small sets, especially outliers• Users questioned need to draw all objects as curves• Users mostly liked parallel coordinates as a whole
  16. 16. Conclusions• Developed techniques that enable objects to be followed through ‘crossing-points’ in parallel coordinate visualisations• Techniques work best when • …tracking outliers – often the interesting objects • …used on small sets of user selected objects • …used in conjunction with brushing techniques that use colour
  17. 17. Future work• Investigate situations when it is best to use curved representations • Curved paths for brushed and/or selected items only to reduce screen clutter?• Further investigation of focus+context effect • Link the focus effect across axes so selected items get more space on every axis, not just in the axis of selection
  18. 18. Future work• General issues • Implementing undo functions for selections • What if one individual fits multiple values on an axis?• Further User Testing
  19. 19. Acknowledgements• OPAL – EU Project IST-2001-33288• http://www.dcs.napier.ac.uk/~marting

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