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Case Studies and Course Review - Lecture 12 - Information Visualisation (4019538FNR)

Case Studies and Course Review - Lecture 12 - Information Visualisation (4019538FNR)

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

Beat Signer
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May 25, 2023
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  1. 2 December 2005
    Information Visualisation
    Case Studies and Course Review
    Prof. Beat Signer
    Department of Computer Science
    Vrije Universiteit Brussel
    beatsigner.com

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  2. Beat Signer - Department of Computer Science - [email protected] 2
    May 25, 2023
    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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  3. Beat Signer - Department of Computer Science - [email protected] 3
    May 25, 2023
    Scagnostics SPLOM
    ▪ Scalable idiom for the exploration of scatterplot
    matrices (SPLOMs)
    ▪ scagnostics = scatterplot computer-guided diagnostics

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  4. Beat Signer - Department of Computer Science - [email protected] 4
    May 25, 2023
    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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  5. Beat Signer - Department of Computer Science - [email protected] 5
    May 25, 2023
    Scagnostics SPLOM …

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  6. Beat Signer - Department of Computer Science - [email protected] 6
    May 25, 2023
    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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  7. Beat Signer - Department of Computer Science - [email protected] 7
    May 25, 2023
    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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  8. Beat Signer - Department of Computer Science - [email protected] 8
    May 25, 2023
    Hierarchical Clustering Explorer (HCE) …

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  9. Beat Signer - Department of Computer Science - [email protected] 9
    May 25, 2023
    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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  10. Beat Signer - Department of Computer Science - [email protected] 10
    May 25, 2023
    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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  11. Beat Signer - Department of Computer Science - [email protected] 11
    May 25, 2023
    PivotGraph …

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  12. Beat Signer - Department of Computer Science - [email protected] 12
    May 25, 2023
    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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  13. Beat Signer - Department of Computer Science - [email protected] 13
    May 25, 2023
    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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  14. Beat Signer - Department of Computer Science - [email protected] 14
    May 25, 2023
    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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  15. Beat Signer - Department of Computer Science - [email protected] 15
    May 25, 2023
    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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  16. Beat Signer - Department of Computer Science - [email protected] 16
    May 25, 2023
    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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  17. Beat Signer - Department of Computer Science - [email protected] 17
    May 25, 2023
    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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  18. Beat Signer - Department of Computer Science - [email protected] 18
    May 25, 2023
    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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  19. Beat Signer - Department of Computer Science - [email protected] 19
    May 25, 2023
    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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  20. Beat Signer - Department of Computer Science - [email protected] 20
    May 25, 2023
    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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  21. Beat Signer - Department of Computer Science - [email protected] 21
    May 25, 2023
    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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  22. Beat Signer - Department of Computer Science - [email protected] 22
    May 25, 2023
    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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  23. Beat Signer - Department of Computer Science - [email protected] 23
    May 25, 2023
    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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  24. Beat Signer - Department of Computer Science - [email protected] 24
    May 25, 2023
    Exam
    ▪ Exams take place on June 26/27/28, 2023
    ▪ Oral exam in English (20 mins slot)
    ▪ covers content of lectures and exercises
    ▪ counts 60% for the overall grade
    ▪ 5 mins questions about the assignment
    ▪ 15 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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  25. Beat Signer - Department of Computer Science - [email protected] 25
    May 25, 2023
    Exam …
    ▪ Submission of the assignment and video via
    Canvas
    ▪ deadline: May 29, 23:59 (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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  26. Beat Signer - Department of Computer Science - [email protected] 26
    May 25, 2023
    Are You Interested in a Thesis?
    ▪ Various possibilities for BA, MA and PhD theses
    ▪ Data Physicalisation
    - extensible dynamic data physicalisation platform and framework
    ▪ Innovative Mixed Reality Interfaces
    - augmented realty board, museum guides, …
    ▪ Personal Information Management (PIM)
    ▪ Technology-enhanced Learning
    ▪ End-User Development and Human-AI Interaction
    ▪ Hybrid Positioning and Implicit Human-Computer Interaction
    ▪ Smart Environments and Cross-Domain Internet of Things (IoT)
    ▪ Next Generation Presentation Solutions (e.g. MindXpres)
    ▪ ...
    ▪ Do you have your own ideas? Come along to discuss them ...
    - https://wise.vub.ac.be/thesis-proposals

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  27. Beat Signer - Department of Computer Science - [email protected] 27
    May 25, 2023
    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)
    Maxim Van de Wynckel
    Hybrid Positioning, Implicit
    Human-Computer Interaction
    Yoshi Malaise
    Technology-enhanced Learning,
    Content-driven Presentations

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  28. Beat Signer - Department of Computer Science - [email protected] 28
    May 25, 2023
    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
    Isaac Valadez
    Knowledge Physicalisation and
    Augmentation, Tangible UIs
    Xuyao Zhang
    Extensible Platform for Dynamic
    Data Physicalisation
    Ingela Rossing
    Dynamic Data Physicalisation
    Framework and Guidelines

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  29. Beat Signer - Department of Computer Science - [email protected] 29
    May 25, 2023
    WEB & INFORMATION
    SYSTEMS ENGINEERING
    CISA
    Human-Machine &
    Human-Information
    Interaction
    Information
    Systems &
    Management
    Information
    Visualisation
    & Navigation
    CROSS-MEDIA INFORMATION SPACES
    AND ARCHITECTURES (CISA)
    Arun Sojan
    Physicalisation for Digital Twins
    Piet Van Der Paelt
    Julia-based Framework for
    Simulation and Optimisation
    Evan Cole
    Technology-enhanced Learning,
    Study Lenses
    Migdeily Cantera
    End-User Development, Mixed
    Reality IoT UIs, Intelligibility

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  30. Beat Signer - Department of Computer Science - [email protected] 30
    May 25, 2023
    Final Project Presentations
    ▪ Each team will have 12 minutes to present
    their work
    ▪ dataset
    ▪ preprocessing
    ▪ visualisation and demo
    ▪ evaluation

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  31. Beat Signer - Department of Computer Science - [email protected] 31
    May 25, 2023
    Further Reading
    ▪ This lecture is mainly based on the
    book Visualization Analysis & Design
    ▪ chapter 15
    - Analysis Case Studies

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  32. Beat Signer - Department of Computer Science - [email protected] 32
    May 25, 2023
    References
    ▪ Visualization Analysis & Design, Tamara
    Munzner, Taylor & Francis Inc, (Har/Psc edition),
    May, November 2014,
    ISBN-13: 978-1466508910

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  33. 2 December 2005
    Information Visualisation
    The End
    Good Luck with the Exam!

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