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Interaction - Lecture 10 - Information Visualisation (4019538FNR)

Interaction - Lecture 10 - 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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April 29, 2021
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  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
  3. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 3

    April 29, 2021 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)
  4. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 4

    April 29, 2021 Facet Into Multiple Views
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    April 29, 2021 Facet Into Multiple Views …
  6. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 6

    April 29, 2021 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 ▪ …
  7. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 7

    April 29, 2021 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
  8. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 8

    April 29, 2021 Exploratory Data Visualizer Example ▪ Linked highlighting (brushing) between views
  9. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 9

    April 29, 2021 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
  10. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 10

    April 29, 2021 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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    April 29, 2021 Improvise Example
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    April 29, 2021 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
  13. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 13

    April 29, 2021 Partitioning Bar Charts Example Single bar chart with grouped bars Aligned small-multiple bar chart views
  14. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 14

    April 29, 2021 Hierarchical Visual Expression (HiVE)
  15. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 15

    April 29, 2021 Hierarchical Visual Expression (HiVE) …
  16. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 16

    April 29, 2021 Hierarchical Visual Expression (HiVE) …
  17. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 17

    April 29, 2021 Hierarchical Visual Expression (HiVE) …
  18. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 18

    April 29, 2021 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
  19. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 19

    April 29, 2021 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.
  20. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 20

    April 29, 2021 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.
  21. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 21

    April 29, 2021 Hierarchical Edge Bundles Example
  22. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 22

    April 29, 2021 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.
  23. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 23

    April 29, 2021 Embed: Focus+Context
  24. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 24

    April 29, 2021 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
  25. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 25

    April 29, 2021 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
  26. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 26

    April 29, 2021 DOITrees Revisited Example DOITrees Revisited What(Data) Tree. How(Encode) Node-link layout. How(Reduce) Embed: elide, multiple foci. Scale Nodes: hundreds of thousands.
  27. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 27

    April 29, 2021 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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    April 29, 2021 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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    April 29, 2021 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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    April 29, 2021 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.
  31. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 31

    April 29, 2021 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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    April 29, 2021 Graph Exploration Example Fisheye lens Magnifying lens Neighbourhood highlighting with layering Neighbourhood highlighting with both layering and Bring and Go interaction
  33. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 33

    April 29, 2021 Exercise 8 ▪ Interaction and Design Guidelines with Bokeh and Plotly
  34. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 34

    April 29, 2021 Further Reading ▪ This lecture is mainly based on the book Visualization Analysis & Design ▪ chapter 12 - Facet Into Multiple Views ▪ chapter 14 - Embed: Focus+Context
  35. Beat Signer - Department of Computer Science - bsigner@vub.ac.be 35

    April 29, 2021 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