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

1. 2 December 2005
Information Visualisation
Case Studies and Course Review
Prof. Beat Signer
Department of Computer Science
Vrije Universiteit Brussel
beatsigner.com

2. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 2
May 20, 2021
Analyse Case Studies
▪ Analysis of existing systems provides foundation for
considering all the possibilities when designing new
systems
▪ use analysis framework introduced earlier
- what, why and how?
- four levels of validation
▪ data/task abstraction
- types of data abstraction
- derived data
- …
▪ visual encoding/interaction idioms
- encoding design choices
- faceting between multiple views
- …

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Scagnostics SPLOM
▪ Scalable idiom for the exploration of scatterplot
matrices (SPLOMs)
▪ scagnostics = scatterplot computer-guided diagnostics

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Scagnostics SPLOM …
▪ Use nine measurements
that categorise the point
distribution of scatterplots
▪ monotonic, stringy, skinny,
convex, striated, sparse,
clumpy, skewed and outlying
▪ Show measurements in a
new scagnostics SPLOM
▪ scatterplot of scatterplots
▪ each point in the scagnostics
SPLOM represents an entire
scatterplot of the original
SPLOM

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Scagnostics SPLOM …

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Scagnostics SPLOM …
▪ Linked highlighting between views
▪ Selection of point triggers popup view with full scatterplot
Scagnostics SPLOM
What(Data) Table.
What(Derived) Nine quantitative attributes per scatterplot (pairwise combination of
original attributes).
Why(Tasks) Identify, compare, and summarise; distributions and correlation.
How(Encode) Scatterplot, scatterplot matrix.
How (Manipulate) Select.
How (Facet) Juxtaposed small-multiple views coordinated with linked
highlighting, popup detail view.
Scale Original attributes: dozens.

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Hierarchical Clustering Explorer (HCE)
▪ Systematic exploration of multidimensional table
▪ Originally designed for genomics domain
▪ multidimensional table with two key attributes (genes and
experimental conditions) and a quantitative value attribute (activity
of gene under experimental condition)
▪ derived data is a cluster hierarchy of items based on a similarity
measure between items
▪ scalability target: 100-20'000 gene attributes and 2-80
experimental condition attributes
▪ Scalability through combination of visual encoding and
interaction idioms

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Hierarchical Clustering Explorer (HCE) …

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Hierarchical Clustering Explorer (HCE) …
Hierarchical Clustering Explorer (HCE)
What(Derived) Hierarchical clustering of table rows and columns (for cluster
heatmap); quantitative derived attributes for each attribute and
pairwise attribute combination; quantitative derived attribute for
each ranking criterion and original attribute combination.
Why(Tasks) Find correlation between attributes; find clusters, gaps, outliers,
trends within items.
How(Encode) Cluster heatmap, scatterplots, histograms.
How(Reduce) Dynamic filtering; dynamic aggregation.
How (Manipulate) Navigate with pan/scroll.
How (Facet) Multiform with linked highlighting and shared spatial position;
overview-detail with selection in overview populating detail view
Scale Genes (key attribute): 20'000. Conditions (key attribute): 80. Gene
activity in condition (quantitative value attribute): 20'000 × 80 =
1'600'000.

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PivotGraph
▪ PivotGraph idiom encodes a network derived from the
original network by aggregating groups of nodes and
links into a roll-up
▪ grouping based on categorical attribute values on the nodes
(up to two attributes)

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PivotGraph …

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PivotGraph …
▪ PivotGraph idiom is highly scalable
▪ summarises arbitrarily large number of nodes and links of the
original network
▪ Visual complexity of the derived network depends on the
number of attribute levels for the two roll-up attributes
▪ PivotGraph complements standard encoding idioms for
networks (e.g. node-link and matrix views)
▪ might be used as a linked multiform view
▪ Well suited for comparison across attributes at the
aggregate level
▪ but not good to understand topological network features

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PivotGraph …
PivotGraph
What(Data) Network.
What(Derived) Derived network of aggregate nodes and links by roll-up into two
chosen attributes.
Why(Task) Cross-attribute comparison of node groups.
How(Encode) Nodes linked with connection marks, size.
How (Manipulate) Change: animated transitions.
How (Reduce) Aggregation, filtering.
Scale Nodes/links in original network: unlimited. Rollup attributes: 2.
Levels per roll-up attribute: several, up to one dozen.

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InterRing
▪ Visual encoding and interaction idioms for tree exploration
▪ space-filling radial layout for encoding the hierarchy
▪ multifocus focus+context distortion approach for interaction
▪ structure-based colouring (redundant)
- useful if shared colour coding used to coordinate with other views
original hierarchy selected blue region enlarged selected tan region enlarged

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InterRing …
▪ Works well in combination with other views
▪ hierarchy view supports selection, navigation and roll-up/drill-
down operations
▪ supports direct editing of the hierarchy
InterRing
What(Data) Tree.
Why(Task) Selection, rollup/drilldown, hierarchy editing.
How(Encode) Radial, space-filling layout. Colour by tree structure.
How(Facet) Linked colouring and highlighting.
How (Reduce) Embed: distort; multiple foci.
Scale Nodes: hundreds if labelled, thousands if dense.
Levels in tree: dozens.

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Constellation
▪ Supports browsing of complex multilevel linguistic
network
▪ reduce perceptual impact of edge crossing
- dynamic highlighting of foreground layer
▪ nodes duplicated in subgraphs to maximise readability
▪ Specialised vis tool designed for computational
linguistics researchers
▪ should support them in developing algorithms

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Constellation …

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Constellation …
dynamic superimposed layers

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Constellation …

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Constellation …
Mid-level constellation path segment layout

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Constellation …
semantic zooming

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Constellation …
Constellation
What(Data) Three-level network of paths, subgraphs (definitions) and nodes
(word senses).
Why(Task) Discover/verify: browse and locate types of paths, identify and
compare.
How(Encode) Containment and connection link marks, horizontal spatial position
for plausibility attribute, vertical spatial position for order within
path, colour links by type.
How (Manipulate) Navigate: semantic zooming. Change: Animated transitions
How(Reduce) Superimpose dynamic layers.
Scale Paths: 10-50. Subgraphs: 1-30 per path. Nodes: several thousand.

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Course Summary
1. Introduction
▪ classical information visualisations
- London cholera map, Rose diagram, March on Moscow, …
▪ what-why-how question
▪ vis design
- search space metaphor
2. Human Perception and Colour Theory
▪ model of perceptual processing
▪ visible light and anatomy of the human eye
▪ brightness and contrast
▪ various guidelines
▪ colour spaces
▪ illusions

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Course Summary …
3. Data Representation
▪ data types
- items, attributes, links, positions, grids
▪ attribute types
- categorical vs. ordinal and quantitative data
- key vs. value semantics, temporal semantics
▪ dataset types
- tables, networks and trees, fields, geometry, clusters, sets, lists
▪ task abstraction (why)
- analyse: consume and produce
- search: lookup, locate, browse and explore
- query: identify, compare and summarise

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Course Summary …
4. Validation
▪ validating four levels of design
- domain validation, abstraction validation (what and why), idiom
validation (how) and algorithm validation
- threats to validity
- downstream validation
▪ use cases

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Course Summary …
5. Data Presentation
▪ marks
- item marks (points, lines, areas) and link marks (containment, connection)
▪ channels
- position, colour, shape, tilt, size, area, volume
- identity vs. magnitude channels
▪ expressiveness principle
▪ channel effectiveness (Steven's psychophysical power law)
- discriminability, separability, popout, grouping
▪ relative vs. absolute judgements (Weber's law)
▪ colour encoding (hue, saturation and luminance)
▪ colourmaps
- categorical or ordered (sequential or diverging)

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Course Summary …
6. Data Processing and Visualisation Toolkits
▪ R, D3.js and Python
▪ various other solutions and toolkits
7. Design Guidelines and Principles
▪ no unjustified 3D (and 2D)
▪ eyes beat memory
▪ resolution over immersion
▪ overview first, zoom and filter, details on demand
▪ responsiveness is required
▪ get it right in black and white
▪ function first, form next

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Course Summary …
8. Visualisation Techniques
▪ tables
- scatterplot, bubble plot, (stacked) bar chart, dot chart, line chart, steamgraph,
heatmap, scatterplot matrix, parallel coordinates, radial bar chart, pie chart,
polar area charts, …
▪ spatial data (geometry, fields)
- choropleth map, topographic terrain map, …
▪ network and trees
- node-link diagram, force-directed placement, adjacency matrix view, enclosure
(containment), treemap, GrouseFlocks, …
9. View Manipulation and Reduction
▪ element selection and selection highlighting
▪ item and attribute reduction (filtering and aggregation)
▪ semantic zooming

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Course Summary …
10.Interaction
▪ faceting into multiple views
- linked highlighting
- share data and navigation
- juxtaposing views vs. superimposing views as layers
▪ embed: focus+context
- DOITrees, fisheye lens, …

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Course Summary …
11.Dashboards
▪ what is a dashboard?
▪ 13 common mistakes in dashboard design
- exceeding the boundaries of a single screen, supplying inadequate context for
the data, displaying excessive detail or precision, …
▪ strategies for effective dashboard design
- condensing information with summaries and exceptions
- maximising the data-ink-ratio
- designing dashboards for usability/UX
12.Case Studies and Course Review
▪ Scagnostics SPLOM, Hierarchical Clustering Explorer,
PivotGraph, InterRing
▪ what-why-how?

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Exam
▪ Exams take place online on June 17/18, 2021
▪ Oral online exam in English (25 mins slot)
▪ covers content of lectures and exercises
▪ counts 60% for the overall grade
▪ 5 mins questions about the assignment
▪ 20 mins questions about the course content (no preparation time)
▪ Overall grade = oral exam (60%) + assignment (40%)
▪ assignment is composed out of two grades
- overall grade for project where students have some flexibility in distributing
the grades (±2 points) (70%)
- your contribution/knowledge to the project as checked in oral exam (30%)
▪ note that the grade for the oral exam as well as for the assign-
ment have to be 8/20 or higher in order to pass the exam!

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May 20, 2021
Exam …
▪ Submission of the assignment and video via
Canvas
▪ deadline: May 23, 24:00 (CET)
▪ The exam will cover all the content presented in the
lectures as well as any additional information from the
exercise sessions
▪ includes the videos shown in some of the lectures
▪ Make sure that you understand the basic concepts
▪ however, we might ask questions at any level of detail to evaluate
your knowledge
▪ Make sure that you can report about any aspects of the
assignment

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Are You Interested in a Thesis?
▪ Various possibilities for BA, MA and PhD theses
▪ Data Physicalisation
- big data exploration interfaces
- extensible dynamic data physicalisation platform and framework
▪ Innovative Mixed Reality Interfaces
- augmented concept maps, museum guides, …
▪ Hybrid Positioning and Implicit Human-Computer Interaction
▪ Smart Environments and Cross-Domain Internet of Things (IoT)
▪ Next Generation Presentation Solutions (e.g. MindXpres)
▪ Personal Information Management (PIM)
▪ End-User Development and Human-AI Interaction
▪ ...
▪ Do you have your own ideas? Come along to discuss them ...
- https://beatsigner.com/flyers/ThesesOverview.pdf

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Prof. Dr. Beat Signer
Cross-MediaTechnology, Interac-
tive Paper, Data Physicalisation
Dr. Audrey Sanctorum
User-defined XDI and IoT Inter-
action, Human-AI Interaction
CISA
Human-Machine &
Human-Information
Interaction
Information
Systems &
Management
Information
Visualisation
& Navigation
WEB & INFORMATION
SYSTEMS ENGINEERING
CROSS-MEDIA INFORMATION SPACES
AND ARCHITECTURES (CISA)
Payam Ebrahimi
Dynamic Data Physicalisation,
Real-Time Point Cloud Analysis
Maxim Van de Wynckel
Hybrid Positioning, Implicit
Human-Computer Interaction

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May 20, 2021
Xuyao Zhang
Extensible Platform for Dynamic
Data Physicalisation
WEB & INFORMATION
SYSTEMS ENGINEERING
CISA
Human-Machine &
Human-Information
Interaction
Information
Systems &
Management
Information
Visualisation
& Navigation
CROSS-MEDIA INFORMATION SPACES
AND ARCHITECTURES (CISA)
Ekene Attoh
IoT Middleware, Context-aware
Computing, Implicit HCI
Jan Maushagen
Learning Analytics, Adaptive
Persuasive ICT Tools
Isaac Valadez
Knowledge Physicalisation and
Augmentation, Tangible UIs

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May 20, 2021
Dr. Ahmed A.O. Tayeh
Open Cross-Media Authoring,
Fluid Document Formats
WEB & INFORMATION
SYSTEMS ENGINEERING
CISA
Human-Machine &
Human-Information
Interaction
Information
Systems &
Management
Information
Visualisation
& Navigation
CROSS-MEDIA INFORMATION SPACES
AND ARCHITECTURES (CISA)
Dr. Reinout Roels
MindXpres: Extensible Content-
driven Presentation Tool
Piet Van Der Paelt
Julia-based Framework for
Simulation and Optimisation

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Final Project Presentations
▪ Each team will have 20 minutes to present
their work

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Further Reading
▪ This lecture is mainly based on the
book Visualization Analysis & Design
▪ chapter 15
- Analysis Case Studies

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

40. 2 December 2005
Information Visualisation
The End
Good Luck with the Exam!