The Visualisation Pipeline Interaction.pdf

The Visualisation Pipeline & Interaction Information visualisation pipeline Card, Mackinlay, Shneiderman (1999), “Information Visualization”, Introduction to “Readings in Information Visualization: Using Vision to Think”  Data...

The Visualisation Pipeline & Interaction Information visualisation pipeline Card, Mackinlay, Shneiderman (1999), “Information Visualization”, Introduction to “Readings in Information Visualization: Using Vision to Think”  Data Visual Form Map the data to a Transform the data visualisation Display the visualisation method Data Visual Raw Data Views Tables Structures Interaction  average average number finishing finishing %male of clubs members male fees position members position 0-15 0 113 75 15 5 113 5 15-19 1 183 167 60 13 183 13 20-24 4 175 135 40 6 175 6 25-29 5 56 13 20 16 56 16 30-34 9 86 45 25 4 86 4 35-39 2 200 150 50 16 200 16 40-44 1 59 21 40 3 59 3 45-49 7 Transform the data 48 16 25 13 48 13 50-54 8 107 52 55 91 107 91 55-59 5 34 8 45 45 34 45 60-64 4 155 122 20 76 Data 155 76 65-69 8 Raw Data 92 70 15 22 92 22 70-74 16 Tables 156 125 10 49 156 49 75-79 10 157 130 50 39 157 39 80-84 6 57 16 40 18 57 18 113 61 15 9 85-89 5 113 9 57 11 30 17 90-94 5 57 17 137 101 40 19 95-99 4 137 19 99 49 35 20 100 0 99 20 89 53 30 11 89 11 … 100 clubs … 100 clubs fees min 5 Q1 18.75 median 30 Q2 40 max 60 mean 29.85 std dev 14.02  average number fees finishing %male of clubs min 5 members position 0-15 0 Q1 18.75 113 5 15-19 1 median 30 183 13 20-24 4 Q2 40 175 6 25-29 5 max 60 56 16 30-34 9 mean 29.85 86 4 35-39 2 std dev 14.02 200 16 40-44 1 59 3 45-49 7 48 13 50-54 8 Map the data to a 107 91 34 45 55-59 5 visualisation 60-64 4 155 76 65-69 8 method 92 22 70-74 16 156 49 75-79 10 157 39 80-84 6 Data Visual 57 18 113 9 85-89 5 Tables Structures 57 17 90-94 5 137 19 95-99 4 99 20 100 0 89 11 … 100 clubs Effective: depicts the data well Expressive: all (and only) the data in the data tables are shown  Display the visualisation Visual Views Structures  average finishing %male number of clubs members male fees position 0-15 0 113 75 15 5 15-19 1 183 167 60 13 20-24 4 175 135 40 6 25-29 5 56 13 20 16 30-34 9 86 45 25 4 35-39 2 200 150 50 16 40-44 1 59 21 40 3 45-49 7 48 16 25 13 50-54 8 107 52 55 91 55-59 5 34 8 45 45 60-64 4 155 122 20 76 65-69 8 92 70 15 22 16 70-74 156 125 10 49 75-79 10 157 130 50 39 57 16 40 18 80-84 6 113 61 15 9 85-89 5 57 11 30 17 90-94 5 137 101 40 19 95-99 4 99 49 35 20 100 0 89 53 30 11 … 100 clubs Map the data to a Transform the data visualisation method Display the visualisation Data Visual Raw Data Views Tables Structures  average finishing %male number of clubs members male fees position 0-15 0 113 75 15 5 15-19 1 183 167 60 13 20-24 4 175 135 40 6 25-29 5 56 13 20 16 30-34 9 86 45 25 4 35-39 2 200 150 50 16 40-44 1 59 21 40 3 45-49 7 48 16 25 13 50-54 8 107 52 55 91 55-59 5 34 8 45 45 60-64 4 155 122 20 76 65-69 8 92 70 15 22 16 70-74 156 125 10 49 75-79 10 157 130 50 39 57 16 40 18 80-84 6 113 61 15 9 85-89 5 57 11 30 17 90-94 5 137 101 40 19 95-99 4 99 49 35 20 100 0 89 53 30 11 … 100 clubs Map the data to a Transform the data visualisation method Display the visualisation Data Visual Raw Data Views Tables 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 C. Ware, Information Visualization: Perception for Design. San Diego, Graphics: Principles and Practice in C, 2nd ed: Addison-Wesley CA, USA: Academic Press, 2000. Professional, 1995. 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: Tan Jerome, 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 Max ISO: 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 Loren K. Rhodes, “Presentation”, jcsites.juniata.edu/faculty/rhodes/ida/presentation.html (accessed 26/05/21)  Perspective Wall, for large volumes of Hyperbolic trees, for large hierarchies linear data (e.g. chronological or alphabetical) John Lamping, Ramana Rao, and Peter Pirolli. A George Robertson, Jock D. Mackinlay, Stuart Card. The focus+context technique based on hyperbolic geometry Perspective Wall: Detail And Context Smoothly Integrated, 1991. for visualizing large hierarchies, 1995 Focus & Context: overviews  Visualisation Milestones 1905-1938 1961-1994 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 Multiple-linked and coordinated views: world map colour legend scatter plot table lens parallel coordinates S. Johansson and M. Jern. 2007. GeoAnalytics visual inquiry and filtering tools in parallel coordinates plots. 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 The number of students studying different subjects in a high school left shows the set of popular and unpopular subjects clearly 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), https://www.data-to-viz.com/caveat/spider.html State of the Art of Parallel Coordinates (accessed 26/05/32)  Encode Switch between views of the same data – e.g. scatterplot to clustered bar chart Change visual variables – e.g colour, shape, line width  Data Visual Form Map the data to a Transform the data visualisation Display the visualisation method Data Visual Raw Data Views Tables Structures Interaction Filter, Select, Abstract & Elaborate, Focus & Context, Connect, Reconfigure, Encode The Visualisation Pipeline & Interaction