Non-Overlapping Aggregated Multivariate Glyphs for Moving Objects

1. Non-Overlapping Aggregated Multivariate Glyphs for Moving Objects Roeland Scheepens, Huubvan de Wetering, JarkeJ. van Wijk Presented by: David Sheets

2. Problem • Address Visual Clutter in… • High density areas • Low resolution screens (e.g. mobile phones) • Clutter makes it difficult to… • Identify points of interest • Find objects that are occluded by other objects

3. Before

4. After

5. Requirements • No overlap or occlusion between visual representations of the subsets • Subsets as small as possible • User can estimate point density of areas • User can recognize patterns in the attributes • User can see more detail by zooming in • Areas of influence of different subsets do not overlap • Small changes in object positions have small effects on the partition

6. Other Requirements • Position of objects must be maintained • Or at least close • Support real-time streams of data

7. Related Work • Clutter Reduction • Resampling to approximate original • Interactions to explore dense regions • Displace objects to prevent overlap • Clustering to reduce clutter • Aggregation (using Multivariate Glyphs)

8. Related Work • Multivariate Glyphs • Replace a large collection of crowded glyphs with a single, larger glyph • Glyphs are stacked to represent multiple objects • Glyphs represent multiple dimensions • x, y, direction, average, variance, etc. • Pie chart glyphs to show distribution

9. Technique • Divide object set O into a partition {S1,…,Sm} of non-empty disjoint subsets Si that span O. • Each subset has a circular area of influence defined by the centroid cS and radius rS=r(|S|) • r is a function of the number of elements in S • Radii are projected into screen space to deal with zoom • Can now define measures of overlap

10. Measures of Overlap A. Overlap of the area of influence B. Penetration depth(easier to calculate)

11. Measures of Overlap • If , subsets S and T overlap

12. Partitioning • INIT: First create partition K containing a singleton {o} for each • MERGE: While there exists pairs of subsets S and T in K where , merge the pair with maximum overlap. • Subsets as small as possible • Areas of influence of different subsets do not overlap • Update addresses each subset in partition K by running INIT and then MERGE

13. Partitioning

14. Visualization • Encoded values • x(t), y(t), hdg(t), vessel type, velocity • Size of glyph represents number of objects it represents • Represent distribution in a pie chart • Heading encoded as oriented bar chart • Velocity reduced to moving | stationary

15. Visualization • Distribution of object types • Direction of objects • Proportion of objects that are stationary

16. Visualization Variations:

17. Visualization • Mouseover a glyph shows the spatial distribution of objects represented by the glyph • Clicking a glyph shows statistics for the glyph

18. Animations • Visualizing moving objects • Objects split from a merged glyph • Objects merge into a glyph • Animation is used to illustrate the change • Linearly interpolated between states

19. Interaction • Mouseover • Click • Panning and Zooming • Zoom changes screen space and recalculates merges

20. Evaluation • Proposed (Mpart) • KDE (Mdens) • Single Point (Msingle)

21. Evaluation • Proposed (Mpart) • KDE (Mdens) • Single Point (Msingle)

22. Evaluation • Proposed (Mpart) • KDE (Mdens) • Single Point (Msingle)

23. Evaluation • Proposed (Mpart) • KDE (Mdens) • Single Point (Msingle)

24. Evaluation • Tested • Static Visualization • Ability of subjects to recognize density & patterns • Dynamic Visualization • Situational awareness • Tasks (3 static, 1 dynamic) • Which square contains more points? • Which square contains more blue points? • Which square contains more blue points heading approximately North-East? • When a quadrant no longer contains both blue and red objects, press its number.

25. Evaluation • Task 1 given with varying number of points • 50, 500, 1000 • Task 2 and 3 use random number of points • Points to identify based on % of points • Small (5%), Medium (10%), Large (15%) • Difference between left and right vary • Small (5%), Medium (10%), Large (15%) Which square contains more points? Which square contains more blue points? Which square contains more blue points heading approximately North-East?

26. Evaluation • Task 4 • Three variations to distribute data in each quadrant • Green,Red&Blue • 100,1 • 200,2 • 300,4 When a quadrant no longer contains both blue and red objects, press its number.

27. Results

28. n, number of points ns, number of special points p, percent difference special points left & right ps, percentage special points Results Which square contains more points? Which square contains more blue points? Which square contains more blue points heading approximately North-East?

29. n, number of points ns, number of special points p, percent difference special points left & right ps, percentage special points Results Which square contains more points? Which square contains more blue points? Which square contains more blue points heading approximately North-East?

30. Results • Tukey’s HSD post hoc at 5% significance level

31. Results • Participant Questionnaire Summary • Mpart • Intuitive and less clutter • Mixed on heading ring • Animation at high speeds is distracting • Msingle • Simple and intuitive at low density • Occlusion is a problem • Easy to visualize moving objects • Mdens • Distribution of objects is easy • Low clutter • Direction is difficult to interpret • Moving objects difficult to interpret

32. Author’s Conclusion • Benefits • Method is comparable and competitive to existing methods. • Clutter is reduced • Positive feedback from users • Future Work • Heading ring needs improved • Aggregation makes comparing individual items more difficult. Additional interactions may improve that. • Animation needs improvement for faster moving objects • Test using domain experts

33. Other Thoughts • Change the heading ring to triangle instead of bar chart to better represent direction. • Using domain experts for evaluation.