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 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
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 𝜔 𝑆, 𝑇 > 0, subsets S and T overlap
Partitioning
• INIT: First create partition K containing a
singleton {o} for each 𝑜 ∈ 𝑂
• MERGE: While there exists pairs of subsets S
and T in K where 𝜔 𝑆, 𝑇 > 0, merge the pair
with maximum overlap.
1. Subsets as small as possible
2. 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 types
B. Direction of objects
C. 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
•
•
•
•
4.
Green,Red&Blue
100,1
200,2
300,4
When a quadrant no longer
contains both blue and red
objects, press its number.
Results
Results
1. Which square contains more points?
2. Which square contains more blue points?
3. Which square contains more blue points heading approximately North-East?
n, number of points
ns, number of special points
p, percent difference special points left & right
ps, percentage special points
Results
1. Which square contains more points?
2. Which square contains more blue points?
3. Which square contains more blue points heading approximately North-East?
n, number of points
ns, number of special points
p, percent difference special points left & right
ps, percentage special points
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.