1. Non-Overlapping Aggregated Multivariate Glyphs for Moving Objects Roeland Scheepens, Huub van de Wetering, Jarke J. 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 1.No overlap or occlusion between visual representations of the subsets 2.Subsets as small as possible 3.User can estimate point density of areas 4.User can recognize patterns in the attributes 5.User can see more detail by zooming in 6.Areas of influence of different subsets do not overlap 7.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 {S 1,…,S m } of non-empty disjoint subsets S i that span O. Each subset has a circular area of influence defined by the centroid c S and radius r S =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

12. 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 A.Distribution of object types B.Direction of objects C.Proportion of objects that are stationary

16. Visualization Variations:

17. Visualization A.Mouseover a glyph shows the spatial distribution of objects represented by the glyph B.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 (M part ) KDE (M dens ) Single Point (M single )

21. Evaluation Proposed (M part ) KDE (M dens ) Single Point (M single )

22. Evaluation Proposed (M part ) KDE (M dens ) Single Point (M single )

23. Evaluation Proposed (M part ) KDE (M dens ) Single Point (M single )

24. Evaluation Tested – Static Visualization Ability of subjects to recognize density & patterns – Dynamic Visualization Situational awareness Tasks (3 static, 1 dynamic) 1.Which square contains more points? 2.Which square contains more blue points? 3.Which square contains more blue points heading approximately North-East? 4.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%) 1.Which square contains more points? 2.Which square contains more blue points? 3.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 4.When a quadrant no longer contains both blue and red objects, press its number.

27. Results

28. n, number of points n s, number of special points p, percent difference special points left & right p s, percentage special points 1.Which square contains more points? 2.Which square contains more blue points? 3.Which square contains more blue points heading approximately North-East?

29. Results n, number of points n s, number of special points p, percent difference special points left & right p s, percentage special points 1.Which square contains more points? 2.Which square contains more blue points? 3.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.