The Visualisation Pipeline & Interaction
Information visualisation pipeline
Card, Mackinlay, Shneiderman (1999), “Information Visualization”, Introduction to “Readings in Information Visualization: Using Vision to Think”
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Transform the data
Map the data to a visualisation method
Display the visualisation
Visual Form
Data Tables
Visual Structures
Interaction
average finishing position
… 100 clubs
average finishing position
… 100 clubs
number of clubs
Transform the data
Data Tables
average finishing position
… 100 clubs
number of clubs
Map the data to a visualisation method
Data Tables
Visual Structures
Effective:
depicts the data well
Expressive:
all (and only) the data in the data tables are shown
Display the visualisation
Visual Structures
average finishing position
… 100 clubs
number of clubs
Map the data to a
Transform the data visualisation method Display the visualisation
Data Tables
Visual Structures
average finishing position
… 100 clubs
number of clubs
Map the data to a
Transform the data visualisation method Display the visualisation
Data Tables
Visual Structures
Interaction
• 1996: B. Shneiderman, “The eyes have it: a task by data type taxonomy for information visualizations,” Proceedings 1996 IEEE Symposium on Visual Languages, 1996, pp. 336-343. [Sh]
• 2003: R. Kosara, H. Hauser, and D. Gresh, “An Interaction View on Information Visualization,” 2003 EUROGRAPHICS Conference State of the Art Report, 2003, pp. 123-137. [K]
• 2007: J. S. Yi, Y. a. Kang, J. Stasko and J. A. Jacko, “Toward a Deeper Understanding of the Role of Interaction in Information Visualization,” in IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1224-1231, 2007. [Yi]
• 2015: A. Figueiras, “Towards the Understanding of Interaction in Information Visualization,” 2015 19th International Conference on Information Visualisation, 2015, pp. 140-147. [F]
Interaction: HCI vs InfoViz (Yi et al., 2007)
“Foley et al. (1995) define an interaction technique as a way of using a physical input/output device to perform a generic task in a human-computer dialogue.
The definition of interaction techniques in the context of Infovis should extend Foley’s definition, however, it was grounded in the general context of HCI. As Ware (2000) identifies via the phrase, “asymmetry in data rates”, the amount of data flowing from Infovis systems to users is far greater than from users to systems.
Thus, interaction techniques in Infovis seem more designed for changing and adjusting visual representation than for entering data into systems, which clearly is an important aspect of interaction in HCI.
We view interaction techniques in Infovis as the features that provide users with the ability to directly or indirectly manipulate and interpret representations.”
J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes, Computer Graphics: Principles and Practice in C, 2nd ed: Addison- , 1995.
C. Ware, Information Visualization: Perception for Design. San Diego, CA, USA: Academic Press, 2000.
Shneiderman Mantra
Overview first,
zoom and filter, then details-on-demand
Shneiderman Mantra
Overview first, zoom and filter, then details-on-demand
Shneiderman Mantra
Overview first, zoom and filter, then details-on-demand
Image taken from: , N. et al (2017). WAVE: A 3D Online Previewing Framework for Big Data Archives. Proc. IVAPP, pp152-163
Types of interaction
• Filtering: only show me the data I am interested in [F,Yi,Sh,K]
• Selecting: mark or track items I am interested in [F,Yi]
• Abstract & Elaborate: show me more or less detail [F,Yi,K]
• Overview & Explore/Focus & Context:
overview first, zoom and filter, details on demand [F,Sh,K]
• Connect/Relate: show me how this data is related [F,Yi,Sh,K]
• Reconfigure: show me a different arrangement of the data [F,Yi,K]
• Encode: show me a different representation of the data [F,Yi]
• Extraction of features: allow me to extract data that interests me [F,Sh]
• History: allow me to retrace the steps I take [F,Sh]
• Participation/Collaboration: allow me to contribute to the data [F]
• Gamification: show me the data in a more playful way [F]
Filter: dynamic queries
Camera Filters: • DSLR
• SO: 3200
• Price: £326-£8686 • Canon
Quantitative attributes: sliders e.g. Price: £190 – £3429
Categorical attributes: check boxes e.g. □ Interchangeable Lens
https://www.productchart.co.uk/cameras/ (accessed 26/05/21)
Select: highlighting items
https://www.productchart.co.uk/cameras/ (accessed 26/05/21)
Abstract & Elaborate: zoom “Filter by navigation”
results in loss or gain of information
A. Figueiras, “Towards the Understanding of Interaction in Information Visualization”, 2015.
Location of Banksy’s murals
https://public.tableau.com/en-gb/gallery/banksy-graffiti-around-world (26/05/21)
Details-on-demand
https://www.productchart.co.uk/cameras/ (accessed 26/05/21) A. Figueiras, “Towards the Understanding of Interaction in Information Visualization”, 2015.
Focus & Context: distortion
Fish eye view of scatterplot matrix Linear distortion of metro map
. Rhodes, “Presentation”, jcsites.juniata.edu/faculty/rhodes/ida/presentation.html (accessed 26/05/21)
Perspective Wall, for large volumes of linear data (e.g. chronological or alphabetical)
Hyperbolic trees, for large hierarchies
, . Mackinlay, . The Perspective Wall: Detail And Context Smoothly Integrated, 1991.
, , and . A focus+context technique based on hyperbolic geometry for visualizing large hierarchies, 1995
Focus & Context:
Visualisation Milestones
M. Friendly & D. Denis, Milestones in the History of Thematic Cartography, https://www.datavis.ca/milestones/ (accessed 26/05/21)
Focus & Context: exposing details
Sunburst – for large hierarchies
J. Stasko & E. Zhang. Focus+context display and navigation techniques for enhancing radial, spacefilling hierarchy visualizations, 2000.
Connecting: multiple views
S. Johansson and M. Jern. 2007. GeoAnalytics visual inquiry and filtering tools in parallel coordinates plots.
Multiple-linked and coordinated views:
• world map
• colour legend
• scatter plot
• table lens
• parallel coordinates
https://infovis-wiki.net/wiki/Multiple_Views (primary source unavailable)
Connect: linking and brushing
https://vega.github.io/vega/examples/brushing-scatter-plots/ (accessed 26/05/21)
Reconfigure:
data choice
https://www.theguardian.com/film/interactive/2014/mar/02/oscars-award-nominees-age-best-actress-actor
Reconfigure: dimension order
Census data for 50 US states, showing relationships:
• top: Illiteracy/Frost (negative)
• bottom: Life Expectancy/Murder (negative)
• bottom: Illiteracy/HS Grad (negative)
J. Heinrich, D. Weiskopf (2013),
State of the Art of Parallel Coordinates
left shows the set of popular and unpopular subjects clearly
The number of students studying different subjects in a high school
https://www.data-to-viz.com/caveat/spider.html (accessed 26/05/32)
• Switch between views of the same data – e.g.scatterplottoclusteredbarchart
• Change visual variables
– e.gcolour,shape,linewidth
Transform the data
Map the data to a visualisation method
Display the visualisation
Visual Form
Data Tables
Interaction
Filter, Select, Abstract & Elaborate, Focus & Context, Connect, Reconfigure, Encode
Visual Structures
The Visualisation Pipeline & Interaction
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