Non-Overlapping Aggregated Multivariate Glyphs for Moving Objects

Roeland Scheepens, Huub van de Wetering, Jarke J. van Wijk

Presented by: David Sheets

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

Before

After

Requirements

1. No overlap or occlusion between visual representations of the subsets

2. Subsets as small as possible3. User can estimate point density of areas4. User can recognize patterns in the attributes5. User can see more detail by zooming in6. Areas of influence of different subsets do not overlap7. Small changes in object positions have small effects

on the partition

Other Requirements

• Position of objects must be maintained– Or at least close

• Support real-time streams of data

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)

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

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

Measures of Overlap

A. Overlap of the area of influence

B. Penetration depth(easier to calculate)

Measures of Overlap

• If , subsets S and T overlap

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.

1. Subsets as small as possible2. Areas of influence of different subsets do not overlap

• Update addresses each subset in partition K by running INIT and then MERGE

Partitioning

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

Visualization

A. Distribution of object typesB. Direction of objectsC. Proportion of objects that are stationary

Visualization

Variations:

Visualization

A. Mouseover a glyph shows the spatial distribution of objects represented by the glyph

B. Clicking a glyph shows statistics for the glyph

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

Interaction

• Mouseover• Click• Panning and Zooming– Zoom changes screen space and recalculates

merges

Evaluation

• Proposed (Mpart)

• KDE (Mdens)

• Single Point (Msingle)

Evaluation

• Proposed (Mpart)

• KDE (Mdens)

• Single Point (Msingle)

Evaluation

• Proposed (Mpart)

• KDE (Mdens)

• Single Point (Msingle)

Evaluation

• Proposed (Mpart)

• KDE (Mdens)

• Single Point (Msingle)

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.

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?

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.

Results

Resultsn, number of pointsns, number of special pointsp, percent difference special points left & rightps, 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?

Resultsn, number of pointsns, number of special pointsp, percent difference special points left & rightps, 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?

Results

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

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

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

Other Thoughts

• Change the heading ring to triangle instead of bar chart to better represent direction.

• Using domain experts for evaluation.