This lecture forms part of an Information Visualisation course given at the Vrije Universiteit Brussel.

1. 2 December 2005
Information Visualisation
View Manipulation and Reduction
Prof. Beat Signer
Department of Computer Science
Vrije Universiteit Brussel
beatsigner.com

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April 22, 2021
View Manipulation

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April 22, 2021
View Manipulation …

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View Manipulation
▪ Why to manipulate and change the view?
▪ datasets might be too large to show everything at once
- reduce complexity of single view
▪ single static view might lead to visual clutter
▪ How to manipulate/change a view over time?
▪ select specific elements (items or attributes)
▪ reordering (sorting) of items
- find patterns by ordering based on different attributes
▪ change parameters of a particular idiom
- e.g. range of possible mark sizes
▪ semantic zooming
▪ switch between idioms
▪ …

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Change Between Visual Encoding Idioms

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LineUp Example With Reordering
▪ Slope graphs (bump charts) with connecting line marks
linking the same items together

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LineUp
LineUp
What(Data) Table.
What(Derived) Ordered attribute: weighted combination of selected attributes.
Why(Task) Compare rankings, distributions.
How(Encode) Stacked bar charts, slope graphs.
How (Manipulate) Reorder, realign, animated transitions.

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Animated Transitions Example
▪ Maintain a sense of context between two states
Animated Transitions
What(Data) Compound network.
How (Manipulate) Change with animated transition. Navigation between aggregation
levels.

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Element Selection
▪ Different design choices for element selection
▪ which elements can be selection targets?
- data items, links, data attributes, levels within a data attribute, …
▪ one kind of selection vs. multiple kinds of selection (e.g.via hover)
- multiple mouse buttons or combination with key presses for more advanced
types of selections
▪ selection of single elements vs. selection of many elements
▪ selection of primary and secondary target
- e.g. for path traversal from source to target in a directed graph
▪ Selection often defines the target of a next action

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Selection Highlighting
▪ Provide immediate visual feedback to users about
element selection
▪ different possibilities for highlighting of data items
- changing colour (hue, luminance or saturation) for visual popout
- add or change existing outline
- change the size of a data item
- motion coding (e.g. slightly moving items of moving pattern)
▪ different possibilities for highlighting link marks
- changing colour
- changing linewidth, shape (e.g. dashed)
- …
▪ multiple highlighting design choices can be combined
▪ selected items might be connected via explicit visual links
(connection marks)

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Context-preserving Visual Links Example

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Context-preserving Visual Links
Context-preserving Visual Links
What(Data) Any data.
How(Encode) Any encoding. Highlight with link marks connecting items across
views.
How (Manipulate) Select any element.
How (Coordinate) Juxtaposed multiple views.

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Navigate: Changing Viewpoint
▪ Navigation can help to see a large and complex dataset
from different points of view
▪ changing viewpoint of virtual camera changes the set of items
visible in the camera frame
▪ often leads to a combination of filtering and aggregation
▪ Three main aspects of navigation
▪ zooming
- moves camera closer (less items but with more details) or further away
(more items but less details) from the image plane
- geometric zooming vs. semantic zooming
▪ panning (translating)
- moves camera parallel to the image plane (up and down or from side to side)
▪ rotating
- spins camera around its axis (rarely used in 2D navigation)

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Semantic Zooming Example

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Semantic Zooming
▪ In contrast to geometric zooming, the fundamental
appearance of objects is no longer fixed
▪ object visualisation changes based on number of available pixels
▪ details added or removed based on the semantic zoom level
▪ different idioms might be used at different semantic zooms levels
▪ Constrained navigation limits the possible motion of the
virtual camera
▪ avoids that user get lost by for example pointing the camera to an
empty space or zooming out too much
▪ systems might also automatically compute the best viewpoint to
view a selected item
- smooth animated transition to the new viewpoint
- powerful when combined with linked navigation between multiple views

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Navigate: Reduce Attributes
▪ Number of attributes can be reduced in three different
ways
▪ slice
- single attribute value defines which items should be extracted
- e.g. intuitive metaphor when reducing spatial data from 3D to 2D
- possible to have higher dimensional slicing planes (hyperplanes)
▪ cut
- plane dividing the viewing volume and everything on the side of the plane
closer to camera viewpoint is not shown
▪ project
- all items are shown but without the information for specific attributes
- projections often used via multiple views
• e.g. 2D views of a 3D XYZ scene (XY floor plan, YZ side view and XZ front view)
• e.g. Mercator map projections from the surface of the earth to 2D maps

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3D Scan Slice Example
Axis-aligned slice
Axis-aligned cut

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Reducing Items and Attributes

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Reducing Items and Attributes …
▪ Reduction is one of the strategies for dealing with
complexity in visualisations
▪ filtering eliminates elements
- challenge: people might forget about the filtered elements
("out of sight, out of mind")
▪ aggregation combines many elements together
- challenge: how and what to summarise (aggregate) in order to support
a task (and match well with the dataset)
▪ filtering and aggregation can be applied to items or attributes
▪ Bidirectional operation
▪ reduce or increase the number of visible elements

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Filtering
▪ Filtering often accomplished through dynamic queries
▪ tightly coupled loop between visual encoding and interaction
▪ e.g. user can interactively chose a range for the values of an
attribute via graphical UI widgets
▪ Item filtering
▪ reduce number of items based on their values for specific
attributes
▪ Attribute filtering
▪ keep number of items but reduce the number of shown attributes
▪ often used with attributes that can be ordered to filter out the low
or high scoring ones
▪ Item filtering and attribute filtering can be combined

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FilmFinder Example
Overview of all movies Filtering the actor 'Sean Connery'
Details after clicking on a movie mark

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DOSFA Example
▪ Dimensional Ordering, Spacing and Filtering Approach
(DOSFA)
▪ 215 attributes (representing word counts) and 298 points
representing documents in the example
Full dataset After filtering

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DOSFA
DOSFA
What(Data) Table: many values and attributes.
How(Encode) Star plots.
How (Facet) Small multiples with matrix alignment.
How (Reduce) Attribute filtering.

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Aggregation
▪ Group of elements represented by a derived element
(aggregation)
▪ elements are merged rather than eliminated as with filtering
▪ challenge: aggregation (summary) might eliminate interesting
signal in the dataset
- e.g. see Anscombe's Quartet example presented earlier
▪ Item aggregation
▪ interactive aggregation and deaggregation of item sets
▪ Attribute aggregation
▪ group attributes by similarity measure and synthesize a new
attribute based on average across the set
▪ dimensionality reduction (DR)

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Histogram Example
Histograms
What(Data) Table: one quantitative value attribute.
What (Derived) Derived table: one derived ordered key attribute (bin), one derived
quantitative value attribute (item count per bin).
How (Encode) Rectilinear Layout. Line mark with aligned position to express
derived value attribute. Position: derived key attribute

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Continous Scatterplot Example

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Continuous Scatterplots
Continuous Scatterplots
What(Data) Table: two quantitative value attributes.
What (Derived) Derived table: two ordered key attributes (x,y pixel locations), one
quantitative attribute (overplot density).
How (Encode) Dense space-filling 2D matrix alignment, sequential categorical
hue and ordered luminance colourmap.
How (Reduce) Item aggregation.

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Boxplot Charts Example
▪ Boxplots show the spread and skew of the distribution
Standard boxplots Vase plots

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Boxplot Charts
Boxplot Charts
What(Data) Table: many quantitative value attributes.
What (Derived) Five quantitative attributes for each original attribute, representing
its distribution.
Why (Tasks) Characterise distribution; find outliers, extremes, averages; identify
skew.
How (Encode) One glyph per original attribute expressing derived attribute values
using vertical spatial position, with 1D list alignment of glyphs into
horizontally separated regions.
How (Reduce) Item aggregation.
Scale Items: unlimited. Attributes: dozens.

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Spatial Aggregation
▪ Challenge in spatial aggregation is to take the spatial
nature of aggregation into account when aggregating it
▪ changing the boundaries can lead to very different
results → modifiable areal unit problem (MAUP)
Central region with high density Central region with medium density Central region with low density

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Dimensionality Reduction (DR)
▪ Preserve the meaningful structure of a dataset while
using fewer attributes to represent the items
▪ assumes that there is hidden structure and redundancy in the
original dataset
▪ multidimensional scaling (MDS) for more complex forms (not just
a straightforward combination) of dimensionality reduction
▪ Dimensionally reduced data can be visualised as
scatterplot (two attributes) or as scatterplot matrix (more
than two attributes)
▪ only large clusters should be considered relevant
▪ fine-grained structure should not be considered reliable

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Dimensionality Reduction (DR) Example
2D scatterplot of large document collection

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DR for Document Collections
Dimensionality Reduction for Document Collections
What(Data) Text document collection.
What (Derived) Table with 10'000 attributes.
What (Derived) Table with two attributes.
How (Encode) Scatterplot, coloured by conjectured clustering.
How (Reduce) Attribute aggregation (dimensionality reduction) with
multidimensional scaling (MDS)
Scale Original attributes: 10'000. Derived attributes: two. Items: 100'000

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Interim Project Presentations
▪ Project presentations in groups (22.4.2020)

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Further Reading
▪ This lecture is mainly based on the
book Visualization Analysis & Design
▪ chapter 11
- Manipulate View
▪ chapter 13
- Reduce Items and Attributes

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References
▪ Visualization Analysis & Design, Tamara
Munzner, Taylor & Francis Inc, (Har/Psc edition),
May, November 2014,
ISBN-13: 978-1466508910

37. 2 December 2005
Next Lecture
Interaction