Marti Hearst SIMS 247 SIMS 247 Lecture 24 Course Recap April 30, 1998.

Marti HearstSIMS 247

SIMS 247 Lecture 24 SIMS 247 Lecture 24 Course RecapCourse Recap

April 30, 1998April 30, 1998


Where Have We Been?Where Have We Been?

• Information VisualizationInformation Visualization– recent surge of interest

• more online information• more computing power

– a developing area• many exciting new ideas, but• little theory• little empirical validation or evaluation

• Tufte’s InfluenceTufte’s Influence


Graphic Display of Abstract DataGraphic Display of Abstract Data

• Data typesData types– nominal, ordered, quantitative

• Anatomy of a graphAnatomy of a graph– framework, content, labels, background– graphs, charts, maps, diagrams

• Conventional graphsConventional graphs– when to use which– how not to mislead


Hypothetical GraphsHypothetical Graphs

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Mapping Types in ChartsMapping Types in Charts

one-to-one

one-to-many many-to-many


How to show link patterns in web How to show link patterns in web access example? access example?

Problem: only shows one stepThink about this for next time.


Graphing Multivariate InformationGraphing Multivariate Information

• How to handle more than 3 How to handle more than 3 variables?variables?– multifunctioning elements– multiple views– brushing and linking– animation


Multiple Views: Star PlotMultiple Views: Star Plot(Discussed in Feinberg 79. Works better with animation. Example (Discussed in Feinberg 79. Works better with animation. Example

taken from Behrans & Yu 95.)taken from Behrans & Yu 95.)


Linked ScatterplotsLinked Scatterplots


Chernoff Experiment Chernoff Experiment (Marchette)(Marchette)


Overlaying Space and TimeOverlaying Space and Time(Minard’s graph of Napolean’s march through Russia)(Minard’s graph of Napolean’s march through Russia)


Multiple Dimensions: Parallel CoordinatesMultiple Dimensions: Parallel Coordinates(earthquake data, color indicates longitude, y axis severity (earthquake data, color indicates longitude, y axis severity

of earthquake, from Schall 95)of earthquake, from Schall 95)


Baseball data:Baseball data:Scatterplots and histograms and barsScatterplots and histograms and bars

(from Wills 95)(from Wills 95)

select highsalaries

avg careerHRs vs avg career hits(batting ability)

avg assists vsavg putouts (fielding ability)

how longin majors

distributionof positionsplayed


Restrict the range of parameter Restrict the range of parameter settings. How many constraints settings. How many constraints

away from success? away from success? (Tweedie et al. 96)(Tweedie et al. 96)

Coding seems complex initially, but suits the designers’ needs and is easily learned.


Dynamic QueriesDynamic Queries

• Instead of a formal database languageInstead of a formal database language• Explore a dataset interactivelyExplore a dataset interactively• Use graphical devices to interactively Use graphical devices to interactively

update a visualizationupdate a visualization– Examples

• Ahlberg & Shneiderman 93 Filmfinder, etc.• Roth et al. 96 VISAGE• Woodruff et al. DataSplash• Fishkin, Stone, Bier et al. Magic Lenses/Toolglass


VISAGE display VISAGE display (Roth et al. 96)(Roth et al. 96)


Click-through operatorsClick-through operatorsExample: change underlying colorExample: change underlying color

(Bier et al. 93)(Bier et al. 93)

Original Change Fill Color

Change Outline Color


Directly View and Change Font Directly View and Change Font CharacteristicsCharacteristics

(Bier et al. 93)(Bier et al. 93)


Viewing Huge DatasetsViewing Huge Datasets

• Problem: Problem: – The computer display is a small window

through which to view huge datasets

• Standard Solution:Standard Solution:– Display a portion at a time

Problems: lose the context, get lost, comparisons are difficult, ...

• Alternative Solution:Alternative Solution:– Focus + Context


Focus + ContextFocus + Context

• Another solution:Another solution:– Use pixels more carefully

• Focus + ContextFocus + Context– Show a lot of information at once

• Details are too small to be visible

– Focus on a subset of interest• Make this subset large enough to be

viewed


Focus + Context Data TypesFocus + Context Data Types

• TablesTables• HierarchiesHierarchies• NetworksNetworks• MapsMaps• Artificial “worlds”Artificial “worlds”


Viewing Huge Tables:Viewing Huge Tables:Table Lens Table Lens (Rao & Card 94)(Rao & Card 94)


Distortion TypesDistortion Types

• Different distortions for different Different distortions for different data types yield different effectsdata types yield different effects– cartesian, polar coordinates,

hyperbolic

• Leung & Apperley’s TaxonomyLeung & Apperley’s Taxonomy– distinguish focus+context from

distortion• f+c requires a POI function


Distortion TechniquesDistortion Techniques

• Computation must take care not to Computation must take care not to let the magnified part overlap or let the magnified part overlap or cover up the de-magnified partcover up the de-magnified part

• The boundary between the magnified The boundary between the magnified and the demagnified parts of the and the demagnified parts of the viewview– Some techniques have an abrupt

boundary– Some are more gradual


Noik’s DemonstrationNoik’s Demonstration


Elements of Fisheye ViewsElements of Fisheye Views

• Focus, or Point of Interest (POI)Focus, or Point of Interest (POI)– user-selected

• Importance Function (API)Importance Function (API)– user-specified or pre-determined

• number of railway connections• height in hierarchy• population of city

• Function for measuring distance Function for measuring distance between objectsbetween objects


Properties of GraphsProperties of Graphs

• Edges can be Edges can be directeddirected– can go from A to B, but not from B to

A– use arrows to show directedness

• Graphs can have Graphs can have cyclescycles– can get back to B when starting from

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Perspective Wall Perspective Wall (Mackinlay et al. 91)(Mackinlay et al. 91)


Force-Directed PlacementForce-Directed Placement(Amir 93, based on Fruchterman and Rheingold 90)(Amir 93, based on Fruchterman and Rheingold 90)


All About TreesAll About Trees


Hyperbolic BrowserHyperbolic Browser

• Focus + Context TechniqueFocus + Context Technique– detailed view blended with a global view

• First lay out the hierarchy on Poincare’ First lay out the hierarchy on Poincare’ mapping of the hyperbolic planemapping of the hyperbolic plane

• Then map this plane to a diskThen map this plane to a disk• Use animation to navigate along this Use animation to navigate along this

representation of the planerepresentation of the plane• Start with the tree’s root at the centerStart with the tree’s root at the center


Hyperbolic Tree Browser Hyperbolic Tree Browser (Lamping et al. 95)(Lamping et al. 95)


Cluster-graphs Cluster-graphs (Eades & Qingwen 96)(Eades & Qingwen 96)

tree-like between planes graph-like within planes


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Multi-Trees Multi-Trees (Furnas & Zachs 94)(Furnas & Zachs 94)

• Often we want more than one view on a Often we want more than one view on a treetree

• Multi-trees convert the view of a dag Multi-trees convert the view of a dag (directed acyclic graph) into a set of (directed acyclic graph) into a set of overlapping treesoverlapping trees


Why do Evaluation?Why do Evaluation?

• To tell how good or bad a visualization isTo tell how good or bad a visualization is– People must use it to evaluate it– Must compare against the status quo– Something that looks useful to the designer might

be too complex or superfluous for real users

• For iterative designFor iterative design– Interface might be almost right but require

adjustments– The interactive components might have problems

• To advance our knowledge of how people To advance our knowledge of how people understand and use technologyunderstand and use technology


Visual PropertiesVisual Properties

Hue based boundary determined preattentively regardlessof variation in form (left). However, the converse is not true (right).


Accuracy Ranking of Quantitative Perceptual TasksAccuracy Ranking of Quantitative Perceptual Tasks(Mackinlay 88 from Cleveland & McGill)(Mackinlay 88 from Cleveland & McGill)

Position

Length

Angle Slope

Area

Volume

Color Density

More Accurate

Less Accurate


Visual IllusionsVisual Illusions

• Mueller-Lyon (off by 25-30%)Mueller-Lyon (off by 25-30%)

• Horizontal-VerticalHorizontal-Vertical


Pan and ZoomPan and Zoom


Space-Scale DiagramsSpace-Scale Diagrams(Furnas & Bederson 95)(Furnas & Bederson 95)

• We can think of this in terms of 1D tooWe can think of this in terms of 1D too• When zoomed out, you can see wider When zoomed out, you can see wider

set of pointsset of points


Why Text is ToughWhy Text is Tough

As the man walks the cavorting dog, thoughtsarrive unbidden of the previous spring, so unlikethis one, in which walking was marching anddogs were baleful sentinals outside unjust halls.

How do we visualize this?


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An example layout produced by Bead, seen in over-view,of 831 bibliography entries from CHI, CSCW and UISTconferences. The dimensionality (the number of unique words inthe set) is 6925 and the layout stress is 0.16. After a search for‘cscw or collaborative’ we see the pattern of occurrencescoloured dark blue, mostly to the right. The central rectangle isthe visualiser’s motion control.


Example: ThemescapesExample: Themescapes(Wise et al. 95)(Wise et al. 95)

Themescapes (Wise et al. 95)


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Guest LecturesGuest Lectures

• Color: Maureen StoneColor: Maureen Stone• DB Pan & Zoom: Allison WoodruffDB Pan & Zoom: Allison Woodruff• Design: Delle MaxwellDesign: Delle Maxwell• 3D Interaction: Tamara Munzner3D Interaction: Tamara Munzner• Animation: Bay-wei ChangAnimation: Bay-wei Chang• Interactive Design: Robert ReimannInteractive Design: Robert Reimann• Automated Graph Layout: Mike SchiffAutomated Graph Layout: Mike Schiff• Data Mining and Viz: Ronny KohaviData Mining and Viz: Ronny Kohavi• Texture and Visual Search: Ruth RosenholtzTexture and Visual Search: Ruth Rosenholtz


… … and Finallyand Finally

Class Projects!!!Class Projects!!!

Marti Hearst SIMS 247 SIMS 247 Lecture 24 Course Recap April 30, 1998.

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Transcript of Marti Hearst SIMS 247 SIMS 247 Lecture 24 Course Recap April 30, 1998.