This paper presents a taxonomy of glyph placement strategies for multidimensional data visualization. It discusses issues with glyph layout, such as overlaps and screen utilization. Glyph placement can be data-driven, using raw or derived data dimensions, or structure-driven, using explicit or implicit relationships. Data-driven strategies map data dimensions to glyph properties, while structure-driven uses relationships. Both approaches may involve post-processing to reduce clutter. The paper outlines different techniques under each category and discusses factors to consider when selecting a placement strategy. It concludes by motivating the need for customizable glyphs and usability testing of placement methods.

1. A Taxonomy of Glyph Placement Strategies for
Multidimensional Data Visualization
Jitendra Kumar Patel
Friday, November 6,

2. This paper presents an overview of
multivariate glyphs, a list of issues
regarding the layout of glyphs,
and a comprehensive taxonomy of
placement strategies to assist the
visualization designer in selecting the
technique most suitable to his or her
data and
task.

3. Paperin Nutshell...?
Problem: Analyzing large, complex, multivariate data sets
Solution: Draw a picture!
Visualization provides a qualitative tool to facilitate analysis,
identification of patterns, clusters, and outliers.

4. What is a Glyph...?
Glyphs are graphical entities that convey one or more data values via attributes
such as shape, size, color, and position.
A glyphconsists of a graphical entity with p components, each of which may
have rgeometric attributes and s appearance attributes.
Geometric attributes : shape, size, orientation, position,
direction/magnitude of motion
Appearance attributes : color, texture, and transparency

5. Example of a Glyph...?

6. Multivariate Data...?
Multivariate data, also called multidimensional or n-
dimensional, consists of some number of points m,
each of which is defined by an n-vector of values.
Data can be viewed as an mxn matrix, where each
row represents a data point and each column
represents an observation, variable or dimension.
An observation may be scalar or vector, nominal or
ordinal, and may or may not have a distance metric,
ordering relation, or absolute zero. Each
variable/dimension may be independent or
dependent

7. Glyph placement issues...?
Data Driven or Structure Driven ?
Whether overlaps between glyphs are allowed ?
The trade-off between optimized screen utilization algorithms versus the use of
white space to reinforce distances between data points.
Whether the glyph positions can be adjusted after initial placement to improve
visibility at the cost of distorting the computed position ?

8. Glyph Placement Strategies....?

9. Data-Driven Glyph Placement...?
In data-driven placement, the data are used to compute or specify the location
parameters for the glyph.
The two categories of this strategy class are raw and derived.
The placement algorithm can be characterized as a function that maps D to L,
and may involve either local knowledge, such as when li is computed based
solely on di ), or global knowledge, as when li is computed using the entirety of
D or both D and L.
Each category of data-driven placement strategies can be further divided
based on whether or not the computed positions are subjected to a
modification/distortion process in order to reduce visual clutter or
overlap

10. RAWDDGP...?
 Conveys detailed relationships between the dimensions selected
 There are N(N-1) possible mappings
 An ineffective mapping can result in substantial cluttering and poor screen utilization.
 Some mappings may be more meaningful to the person interpreting the display than
others.
 Bias is given to the dimensions involved in the mapping, and thus conveys only
pairwise (or three-way, for 3-D) relations between the selected dimensions.
 Most useful when two or more of the data dimensions are spatial in nature.

11. RAWDDGPExample...?
One, two or three of the
data dimensions are
used as positional
components

12. Derived DDGP...?
 Reflects some combination of all the dimensions in an attempt to convey N-dimensional
relational information in a smaller number of display dimensions.
 Common dimensionality reduction techniques include Principal Component Analysis (PCA)
Multidimensional Scaling (MDS) , and Self-Organizing Maps (SOMs) .
 PCA attempts to find linear combinations of the dimensions which explain the largest variation in
the multivariate data set.
 SOMs and MDS are iterative refinement/optimization processes which attempt to adjust weights
or positions until a certain criteria is met
 Resulting display coordinates have no semantic meaning.
 PCA assumes that the majority of the variation in a data set will be well embodied in the first few
principal components
 MDS and SOMs are not guaranteed to be optimal, and the results are generally not unique.

13. Issues: Clutter& Overlap...?
Post-processing can involve distorting positions to reduce clutter and overlap.
 Random jitter has been employed in statistical graphics when data-
driven positioning is being used
 Alternatively, shift positions to minimize or avoid overlaps.
 Concern is the level of distortion that is introduced.
 Can selectively vary the level of detail shown in the visualization.
 Need to provide users interactive control of the transformation to
facilitate maintenance of context.

14. Structure-Driven Glyph Placement...?
Structure implies relationships or connectivity
Explicit structure: one or more data dimensions drive structure
Implicit structure: structure derived from analyzing data
Common structures: ordered, hierarchical, network/graph
Each kind of structure can help drive placement algorithm in distinct ways

15. Ordered SDGP...?
Ordered structure may be linear (1-D) or grid-based (N-D).
Good for detection of changes in the dimensions used in the sorting
May provide clues into potential dependencies in the other variables
Common linear ordering include raster scan, circular , and recursive space-
filling patterns .

16. Hierarchical Structure SDGP...?
Explicit: use partitions of a single dimension to define layer of tree
Implicit: use clustering algorithms which combine influences of dimensions
Goal is to find placement algorithms which best convey structure
May use additional graphics (e.g., edges) to convey relationships

17. Graph/NetworkSDGP...?
Generalization is set of nodes (data points) and relations
Harder to imply relation with just positioning - need explicit links
Many factors to consider
minimizing crossings
uniform node distribution
drawing conventions for links
centering, clustering subgraphs

18. Distortion Techniques forSDGP...?
Distortion a common post-processing step
Emphasize subsets while maintaining context (e.g., lens techniques)
Shape distortion to convey area or other scalar value
Random jitter, shifting to reduce overlap
Add space to emphasize differences
Trade off between screen utilization, clarity, and amount of information conveyed
Some overlap acceptable for some applications
Dynamic mixing an important feature (rarely found)

19. Distortion Techniques forSDGPExample...?

20. Limitations Of Glyphs...?
Mappings introduce biases in the process of interpreting relationships between
dimensions.
Some relations are easier to perceive (e.g., data dimensions mapped to adjacent
components) than others.
Accuracy with which humans perceive different graphical attributes varies tremendously.
Accuracy varies between individuals and for a single observer in different contexts.
Colour perception is extremely sensitive to context.
Screen space and resolution is limited; too many glyphs = overlaps or very small glyphs;
Too many data dimensions can make it hard to discriminate individual dimensions.

21. Related Fields … ?
There are many fields in which the placement or layout of symbolic components is an integral part
Cartography:
The placement of point, line, area, and text features such that spatial relationships mimic those of the depicted
geography. Density and level of detail for the symbols and structures vary based on the map scale. Map design
can be viewed as data-driven placement with some distortion and filtering options.
VLSI Design:
Designing a chip or integrated circuit involves placing components in a (generally) non-overlapping layout (also
called floor planning), where the main goals include minimizing the area and cost of intercommunication. VLSI
design would be categorized as a structure-driven placement task, with space-filling heuristics (and others)
augmenting the process.
Graph Drawing:
Nearly identical to structure-based glyph placement, except for the use of a glyph as a node representation and
that, in graph drawing, the structure (links) is almost always depicted explicitly.
Interface Design:
Strategies for placement of components in a graphical user interface have ranged from highly structured (e.g.,
following a style guide) to haphazard. Proximity and the use of white space can be effective in controlling a
user's attention and making interfaces easier to learn algorithmically.

22. Total Distinct Views ...?
The total number of distinct views are of N-D data on a 2-D data space is shown below.
Data-driven (raw):
, where D is the number of distortion techniques (e.g., random jitter, relaxation)
Data-driven (derived):
$(1+D)sum_ , where k is the number of distinct derivation algorithms and pj is the number of
variations within algorithm j (e.g., different distance metrics).
Structure-driven (linearordered):
$N times FP (1 + SP + OL)$, where FP is the number of filling patterns, SP is the number of
methods which introduce white space for separation (space-padding), and OL is the number of
methods which distort the placement to allow overlaps.

23. How to select ? Factors include ...?
 Characteristics of data (size, distribution)
 The purpose of the visualization (presentation, confirmation, exploration)
 The specific task(s) at hand (e.g., detection, classification, or measurement of patterns or
outliers).
 The skills of the prospective user of the visualization.
 Domain knowledge that can lead to "intuitive" mapping
 Trade off between efficient screen utilization, the degree of occlusion, and distortion of the
values being used to position the glyph
 Whether to impose an implicit structure (what comparison metric to use?)

24. Conclusions… ?
 The visualization of multivariate data is a task of growing importance in a wide range of
disciplines,
 Glyphs are an important method for multivariate visualization.
 Position of glyph very important for communicating values and relations.
 Techniques can be driven by data or by implicit/explicit structure.
 Distortion can alleviate problems at cost of reduced accuracy and increased complexity.
 Future plans involve consolidating the techniques described into a single application to
facilitate a comprehensive analysis
 A wide assortment of customizable glyphs will be supported, similar to the Glyph maker
program .
 Usability and perceptual testing will also be conducted.
 Also interested in exploring the use of animation to help overcome some of the deficiencies
found in the static presentation

25. Critique… ?
+ Offers list of factors to consider when selecting a
placement algorithm
+ Offers suggestions for future work
+ Motivates author’s stated future work
- Figures not labelled, and all located at the end
- Overview paper – details missing, and assumes familiarity with
terms

26. Refrences… ?
[Ward 02] Matthew O.Ward. "A Taxonomy of Glyph Placement Strategies forMultidimensional
Data Visualization."Information Visualization Journal 1:3-4 (2002), 194-210.
http://davis.wpi.edu/~matt/courses/glyphs/tvcg98.html
http://graphicdesign.stackexchange.com/questions/45162/what-is-the-difference-between-glyph-
and-font
http://kidsactivities.about.com/od/Grade-by-Grade/qt/What-Is-A-Glyph.htm
http://www.moserware.com/2008/02/does-your-code-pass-turkey-test.html
http://www.lovethispic.com/uploaded_images/33233-Limitations-Only-Exist-If-You-Believe-They-
Exist.jpg
https://pbs.twimg.com/profile_images/619627670/issues-logo.jpg

27. Jitendra Kumar Patel
www.jitendrapatel.in
jitendra.patel@iiitb.org
@bewithjitendra
facebook.com/bewithjitendrapatel
Friday, November 6,