Readings Covered Further Readings Ware Interaction: Data - PowerPoint PPT Presentation

Readings Covered Further Readings Ware Interaction: Data Manipulation Ware, Chap 10: Interacting with Visualizations. first half, p 317-324 Toolglass and magic lenses: the see-through interface. Eric A. Bier, low-level control loops Maureen C. Stone, Ken Pier, William Buxton, and Tony D. DeRose. choice reaction time Tufte, Chap 4: Small Multiples Proc. SIGGRAPH’93, pp. 73-76. Lecture 7: Multiples/Interaction depends on number of choices Building Highly-Coordinated Visualizations In Improvise. Chris Weaver. State of the Art: Coordinated & Multiple Views in Exploratory selection time: Fitts’ Law Proc. InfoVis 2004 Information Visualization Visualization. Jonathan C. Roberts. Proc. Conference on Coordinated & depends on distance, target size The Visual Design and Control of Trellis Display. R. A. Becker, W. S. Multiple Views in Exploratory Visualization (CMV) 2007. CPSC 533C, Fall 2009 path tracing Cleveland, and M. J. Shyu Journal of Computational and Statistical The cognitive coprocessor architecture for interactive user interfaces depends on width Graphics, 5:123-155. (1996). George Robertson, Stuart K. Card, and Jock D. Mackinlay, Proc. UIST learning: power law of practice Tamara Munzner Exploring High-D Spaces with Multiform Matrices and Small Multiples. ’89, pp 10-18. also subtask chunking Alan MacEachren, Xiping Dai, Frank Hardisty, Diansheng Guo, and Gene Excentric Labeling: Dynamic Neighborhood Labeling for Data UBC Computer Science Lengerich. Proc InfoVis 2003. Visualization. Jean-Daniel Fekete and Catherine Plaisant. Proc. CHI’99, pages 512-519. Wed, 30 September 2009 1 / 36 2 / 36 3 / 36 4 / 36 Ware Interaction Two-Handed Interaction Example Ware Interaction Small Multiples low-level control loops low-level control loops several small windows with toolglass: two-handed interaction: Guiard’s theory two-handed interaction: Guiard’s theory same visual encoding semi-transparent coarse vs. fine control e.g. paper vs. pen positioning click-through tool coarse vs. fine control e.g. paper vs. pen positioning different data vigilance shown side by side difficult, erodes with fatigue magic lens: see-through control compatability tool learning/transfer: adaption time depends hover/mouseover/tooltip faster than explicit click [Toolglass and magic lenses: the see-through interface. Eric A. Bier, Maureen C. Stone, Ken Pier, William Buxton, and Tony D. DeRose. [Edward Tufte. The Visual Display of Quantitative Information, p 172] Proc. SIGGRAPH’93, pp. 73-76.] 5 / 36 6 / 36 7 / 36 8 / 36 CMV Example: Visual Search Engine CMV Example: cdv CMV Example: CommonGIS Coordinated Multiple Views (CMV) more general than small multiples multiple views multiform different visual encodings of same data overview+detail different resolutions of same encoding/data small multiples same visual encodings of different data power of linking linked highlighting (brushing) linked navigation linked parameter changes [VSE from Boukhelfia, Roberts, and Rodgers, Figure 3 of State of the [CommonGIS from Andrienko and Andrienko, Figure 4 of State of the Art: Coordinated & Multiple Views in Exploratory Visualization. Roberts, Art: Coordinated & Multiple Views in Exploratory Visualization. Roberts, [cdv from Dykes, Figure 2 of State of the Art: Coordinated & Multiple Proc. CMV 2007] Proc. CMV 2007] 9 / 36 10 / 36 Views in Exploratory Visualization. Roberts, Proc. CMV 2007] 11 / 36 12 / 36 Replace, Replicate, Overlay Architectural Issues Animated Transitions Improvise must play nicely with other views animated transitions vs. jump cuts tightly integrated coordination approach when to do which rendering, preprocessing, responding to commands object constancy components with many external control capabilities design tradeoffs most issues also true for scalability of single view guaranteed frame rate avoids slowdown with large data live properties always replace: too much reliance on memory always replicate: too many windows guaranteed response time independent of dataset size early PARC architectural solution: Cognitive Co-Processor value slots, ports always overlay: too much clutter in single window change in response to user action loose confederation split work into small chunks naive approaches fall into cycles multithreaded, each component can work in background animation vs. idle states coordinated queries tighter confederation: return control to master regularly governor controls frame rate filters, projections (TJ,H3) [The cognitive coprocessor architecture for interactive user interfaces. George Robertson, Stuart K. Card, and Jock D. Mackinlay, Proc. UIST divide work into pieces, enqueue ’89, pp 10-18.] continue serving queue when control is returned 13 / 36 14 / 36 15 / 36 16 / 36

Coordinating Axes Coordinating Multiple Scatterplots Example: Complex Application Selection scatterplot from components sync horizontal but not vertical scrolling selection decoupled from data selection-dependent loading, filtering, projection highlighting: user-customizeable differentiation of selected vs. unselected items video [ Building Highly-Coordinated Visualizations In Improvise. Chris Weaver. [ Building Highly-Coordinated Visualizations In Improvise. Chris Weaver. Proc. InfoVis 2004] Proc. InfoVis 2004] [ Building Highly-Coordinated Visualizations In Improvise. Chris Weaver. 17 / 36 18 / 36 Proc. InfoVis 2004] 19 / 36 20 / 36 Critique Critique Automatic Dotplot Ordering: Trellis Trellis Structure conditioning/trellising: choose structure sophisticated and powerful approach to coordination alphabetical site,variety use group median pick how to subdivide into panels but very large learning curve to build new apps pick x/y axes for indiv panels explore space with different choices multiple conditioning ordering large-scale: between panels small-scale: within panels main-effects: sort by group median derived space, from categorical to ordered [ Building Highly-Coordinated Visualizations In Improvise. Chris Weaver. [The Visual Design and Control of Trellis Display. Becker, Cleveland, and Shyu. JCSG 5:123-155 1996] Proc. InfoVis 2004] 21 / 36 22 / 36 23 / 36 24 / 36 Confirming Hypothesis Partial Residuals Critique Critique dataset error with Morris switched? fixed dataset, Morris data switched careful attention to statistics and perception explicitly show differences finding signals in noisy data old trellis: yield against variety given take means into account trends, outliers year/site line is 10% trimmed mean (toss outliers) exploratory data analysis (EDA) new trellis: yield against site and year Tukey work fundamental, Cleveland continues given variety [The Visual Design and Control of Trellis Display. Becker, Cleveland, and Shyu. JCSG 5:123-155 1996] exploration suggested by previous main-effects ordering [The Visual Design and Control of Trellis Display. Becker, Cleveland, and Shyu. JCSG 5:123-155 1996] 25 / 36 26 / 36 27 / 36 28 / 36 Multiform Matrices and Small Multiples Multiform Bivariate Small Multiple Multiform Bivariate Matrix Spacefill Form matrices for bivariate exploration (SPLOM and other) scatterplots/maps, histograms along diagonal common variable: per capita income linked highlight of low doctor ratio counties from vs. small multiples for univariate per-column variables: type of cancer mortality per-column vars: mortality, early detection, recent scatterplot screening per-row forms: scatterplot, choropleth/thematic map uniform vs. multiform multiples spacefill shows it’s roughly half the items univariate map var: screening facility availability left bright green: high income, low cervical cancer techniques hypoth: not screened juxtaposition right dark green: low income, high breast cancer sorting/ordering hypoth: late childbearing manipulation linking multiple bivariate views [ Exploring High-D Spaces with Multiform Matrices and Small Multiples. Alan MacEachren, Xiping Dai, Frank Hardisty, Diansheng Guo, and Gene [ Exploring High-D Spaces with Multiform Matrices and Small Multiples. Lengerich. Proc InfoVis 2003. ] MacEachren et al, Proc. InfoVis 2003. ] [ Exploring High-D Spaces with Multiform Matrices and Small Multiples. [ Exploring High-D Spaces with Multiform Matrices and Small Multiples. MacEachren et al, Proc. InfoVis 2003. ] 29 / 36 MacEachren et al, Proc. InfoVis 2003. ] 30 / 36 31 / 36 32 / 36

Sorting and Linking Excentric Labels Critique Critique sorting show labels around mouseover region great previous work taxonomy manual: direct manipulation from user demo great explanation of how vis techniques used with specific automatic: conditional entropy metric data can lead to hypothesis generation automatic: hierarchical clustering to find interesting careful use of color linking highlighting many others background color, subspce, conditioning, ... conditioning: filter in/out of given range on another var video [Excentric Labeling: Dynamic Neighborhood Labeling for Data Visualization. Jean-Daniel Fekete and Catherine Plaisant. Proc. CHI’99, pages 512-519.] [http://www.cs.umd.edu/hcil/excentric/] 33 / 36 34 / 36 35 / 36 36 / 36