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

1. 2 December 2005
Information Visualisation
Interaction
Prof. Beat Signer
Department of Computer Science
Vrije Universiteit Brussel
beatsigner.com

2. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 2
April 29, 2021
Interaction
▪ Interactivity is necessary for vis tools handling complexity
▪ limitations of people and displays make it impossible to show a
large dataset at once
▪ change level of details
▪ show different aspects of a dataset
▪ different representations and summaries of data
▪ different presentations of data

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Handling Visual Complexity
▪ There a 5 major approaches for dealing with
visual complexity
▪ deriving new data to be shown in a view
▪ view manipulation (single view)
▪ reduction (items or attributes)
▪ faceting into multiple views
▪ embed: focus+context (single view)

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Facet Into Multiple Views

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Facet Into Multiple Views …

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Facet Into Multiple Views …
▪ Faceting information into multiple views to deal with
visual complexity
▪ Juxtapose views side by side
▪ coordination of views to create linked views (multiple views)
- sharing of encoding
- sharing of data
- synchronisation of navigation
▪ arrangement of views
▪ when to show views
▪ Superimpose views as layers
▪ partitioning of elements between layers
▪ number of layers
▪ …

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Sharing of Encoding
▪ Shared encoding views
▪ all channels handled the same for an identical visual encoding
▪ Multiform views (multiple views)
▪ some aspects of the visual encoding different between views
▪ each view might show a subset of attributes to avoid visual clutter
▪ Linked highlighting (brushing)
▪ items interactively selected in one view are highlighted with the
same colour in all other views
- see how region that is continous in one view is distributed in another

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Exploratory Data Visualizer Example
▪ Linked highlighting (brushing) between views

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Sharing of Data
▪ Shared data
▪ each view shows all the data
▪ Overview-detail (subset)
▪ one view shows entire dataset (overview) and another view
shows detailed information about a selected subset of the data
▪ different possible combinations of views
- subset data and shared encoding (e.g. bird's-eye map)
- multiform views (e.g. details-on-demand view)
▪ Small multiples
▪ multiple views with same visual encoding but different partitions of
the data
- often aligned in matrix to support comparison between datasets
- limited screen estate to show many views next to each other

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Bird's-Eye Maps Example
Bird's-Eye Maps
What(Data) Geographic.
How(Encode) Use given geographic information.
How(Facet) Partition into two views with same encoding, overview-detail.
How (Reduce) Navigate.

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Improvise Example

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Partition Into Views
▪ Divide data up between views based on attributes
▪ partitioning attribute typically a categorical variable with limited
number of values (levels)
▪ List alignment or matrix alignment
▪ Recursive subdivision
▪ partitioning used in an exploratory way
▪ user can reconfigure the display to see different choices of
partitioning variables

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Partitioning Bar Charts Example
Single bar chart with grouped bars Aligned small-multiple bar chart views

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Hierarchical Visual Expression (HiVE)

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Hierarchical Visual Expression (HiVE) …

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Hierarchical Visual Expression (HiVE) …

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Hierarchical Visual Expression (HiVE) …

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Superimpose Views as Layers
▪ Design choices when superimposing multiple layers
▪ number of layers
- often just two layers (e.g. background and foreground layer)
▪ distinction of layers
- use different and non-overlapping range of the visual channels
▪ static vs. dynamic layers
▪ partitioning of items into layers

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Cartographic Layering Example
Cartographic Layering
What(Data) Geographic.
How(Encode) Area marks for regions (water, parks, other land), line marks for roads,
categorical colourmap.
How(Facet) Superimpose: static layers distinguished with colour saturation, colour
luminance, and size channels.

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Superimposed Line Charts Example
Superimposed Line Charts
What(Data) Multidimensional table: one ordered key attribute (time), one
categorical key attribute (machine), one quantitative value attribute
(CPU utilisation).
How(Encode) Line charts, coloured by machine attribute.
How(Facet) Superimpose: static layers, distinguished with colour.
Scale Ordered key attribute: hundreds. Categorical key attribute: one dozen.

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Hierarchical Edge Bundles Example

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Hierarchical Edge Bundles Example …
Hierarchical Edge Bundles
What(Data) Compound graph: network, hierarchy whose leaves are nodes in
network.
How(Encode) Back layer shows hierarchy with containment marks coloured grey,
middle layer shows network links coloured red-green, front layer shows
nodes coloured grey.
How(Facet) Superimpose static layers, distinguished with colour.

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Embed: Focus+Context

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Embed: Focus+Context
▪ Single view embedding detailed information (focus) and
overview information (context)
▪ avoid potential for disorientation that might result from navigation
techniques such as geometric zooming
▪ Focus changes dynamically based on user interaction

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Elide and Superimpose Data
▪ Some items are completely omitted from the view while
others are summarised (context)
▪ dynamic aggregation for context
▪ only focus items shown in detail
▪ Superimposed layers can also be used for focus and
context
▪ e.g. toolglass and magic lens example

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DOITrees Revisited Example
DOITrees Revisited
What(Data) Tree.
How(Encode) Node-link layout.
How(Reduce) Embed: elide, multiple foci.
Scale Nodes: hundreds of thousands.

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Toolglass and Magic Lenses Example
Toolglass and Magic Lenses
What(Data) Spatial, quantitative curvature attribute across surface.
How(Encode) Use given, colour by curvature.
How(Reduce) Embed: superimpose.

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Distortion
▪ Geometric distortion of contextual regions to make more
space for details in focus region
▪ Design choices
▪ number of focus regions
▪ shape of focus region (e.g. radial or rectangular)
▪ global or local focus region
▪ interaction metaphor (e.g. movable lenses)
▪ Costs and benefits
▪ good for topological network structures
▪ length judgements severely impaired
▪ users might not be aware of distortion (potential misunderstanding)

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Fisheye Lens Example
Fisheye Lens
What(Data) Any data.
How(Encode) Any layout.
How(Reduce) Embed: distort with fisheye; single focus, local radial region,
moveable lens interaction metaphor.

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Hyperbolic Geometry Example
Hyperbolic Geometry
What(Data) Tree or network.
How(Encode) Hyperbolic layout.
How(Reduce) Embed: distort by projecting from hyperbolic to Euclidean space;
single global radial focus; hyperbolic translation interaction metaphor.

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Nonlinear Magnification Fields Example
Nonlinear Magnification Fields
What(Data) Any data.
How(Encode) Any layout.
How(Reduce) Embed: distort with magnification fields; multiple foci, local arbitrary
regions, lens or stretch or data-driven interaction metaphors.

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Graph Exploration Example
Fisheye lens Magnifying lens
Neighbourhood highlighting with layering
Neighbourhood highlighting
with both layering and Bring
and Go interaction

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Exercise 8
▪ Interaction and Design Guidelines
with Bokeh and Plotly

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Further Reading
▪ This lecture is mainly based on the
book Visualization Analysis & Design
▪ chapter 12
- Facet Into Multiple Views
▪ chapter 14
- Embed: Focus+Context

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

36. 2 December 2005
Next Lecture
Dashboards