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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.

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Beat Signer
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May 20, 2021
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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
  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 - …
  3. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 3

    May 20, 2021 Scagnostics SPLOM ▪ Scalable idiom for the exploration of scatterplot matrices (SPLOMs) ▪ scagnostics = scatterplot computer-guided diagnostics
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    May 20, 2021 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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    May 20, 2021 Scagnostics SPLOM …
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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 Hierarchical Clustering Explorer (HCE) …
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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 PivotGraph …
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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 Constellation …
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    May 20, 2021 Constellation … dynamic superimposed layers
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    May 20, 2021 Constellation …
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    May 20, 2021 Constellation … Mid-level constellation path segment layout
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    May 20, 2021 Constellation … semantic zooming
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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 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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    May 20, 2021 Final Project Presentations ▪ Each team will have 20 minutes to present their work
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    May 20, 2021 Further Reading ▪ This lecture is mainly based on the book Visualization Analysis & Design ▪ chapter 15 - Analysis Case Studies
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    May 20, 2021 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!