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.

Transcript

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 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 - …
3. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 3 May 25, 2023 Scagnostics SPLOM ▪ Scalable idiom for the exploration of scatterplot matrices (SPLOMs) ▪ scagnostics = scatterplot computer-guided diagnostics
4. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
5. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 5 May 25, 2023 Scagnostics SPLOM …
6. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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.
7. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
8. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 8 May 25, 2023 Hierarchical Clustering Explorer (HCE) …
9. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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.
10. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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)
11. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 11 May 25, 2023 PivotGraph …
12. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
13. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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.
14. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
15. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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.
16. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
17. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
18. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
19. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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)
20. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
21. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
22. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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, …
23. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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?
24. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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!
25. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
26. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
27. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
28. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
29. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 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
30. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 30 May 25, 2023 Final Project Presentations ▪ Each team will have 12 minutes to present their work ▪ dataset ▪ preprocessing ▪ visualisation and demo ▪ evaluation
31. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 31 May 25, 2023 Further Reading ▪ This lecture is mainly based on the book Visualization Analysis & Design ▪ chapter 15 - Analysis Case Studies
32. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 32 May 25, 2023 References ▪ Visualization Analysis & Design, Tamara Munzner, Taylor & Francis Inc, (Har/Psc edition), May, November 2014, ISBN-13: 978-1466508910
33. 2 December 2005 Information Visualisation The End Good Luck with the Exam!