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# 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?