why does this suck?
Information Visualization
Jeffrey HeerUC Berkeley | PARC, Inc.
CS160 – 2004.11.22
(includes numerous slides from Marti Hearst, Ed Chi, Stuart Card, and Peter Pirolli)
Basic Problem
We live in a new ecology.
Scientific JournalsJournals/person increases 10X every 50 yearsJournals/person increases 10X every 50 years
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1750 1800 1850 1900 1950 2000
JournalsJournals
Journals/People x10Journals/People x1066
DarwinDarwin V. BushV. Bush YouYou
Web Ecologies
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10000000
Aug-92 Feb-93 Aug-93 Feb-94 Aug-94 Feb-95 Aug-95 Feb-96 Aug-96 Feb-97 Aug-97 Feb-98 Aug-98
Serv
ers
Source: World Wide Web Consortium, Mark Gray, Netcraft Server Survey
1 new server every 2 seconds7.5 new pages per second
Human Capacity
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1750 1800 1850 1900 1950 2000DarwinDarwin V. BushV. Bush YouYou
Attentional Processes“What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
~Herb Simonas quoted by Hal Varian
Scientific AmericanSeptember 1995
Human-Information Interaction
The real design problem is not increased access to information, but greater efficiency in finding useful information.Increasing the rate at which people can find and use relevant information improves human intelligence.
Amount ofAccessibleKnowledge
Amount ofAmount ofAccessibleAccessibleKnowledgeKnowledge
Cost [Time]Cost [Time]Cost [Time]
Amount ofAccessibleKnowledge
Amount ofAmount ofAccessibleAccessibleKnowledgeKnowledge
Cost [Time]Cost [Time]Cost [Time]
Information Visualization
Leverage highly-developed human visual system to achieve rapid understanding of abstract information.
1.2 b/s (Reading)2.3 b/s (Pictures)
Information Visualization“Transformation of the symbolic into the geometric” (McCormick et al., 1987)
“... finding the artificial memory that best supports our natural means of perception.'‘ (Bertin, 1983)
The depiction of information using spatial or graphical representations, to facilitate comparison, pattern recognition, change detection, and other cognitive skills by making use of the visual system. (Hearst, 2003)
Why Visualization?Use the eye for pattern recognition; people good at
scanningrecognizingremembering images
Graphical elements facilitate comparisons vialengthshapeorientationtexture
Animation shows changes across timeColor helps make distinctionsAesthetics make the process appealing
VisualizationSuccess Stories
Visualization Success Story
Mystery: what is causing a cholera epidemic in London in 1854?
Visualization Success Story
From Visual Explanations by Edward Tufte, Graphics Press, 1997
Illustration of John Snow’sdeduction that a cholera epidemic was caused by a bad water pump, circa 1854.
Horizontal lines indicate location of deaths.
Visualization Success Story
From Visual Explanations by Edward Tufte, Graphics Press, 1997
Illustration of John Snow’s deduction that a cholera epidemic was caused by a bad water pump, circa 1854.
Horizontal lines indicate location of deaths.
A Visualization Expedition
(a tour through past and present)
Perspective Wall
Slide adapted from Chris North 18
Starfield Displays
Film Finder
Table Lens
Distortion Techniques
Indented Hierarchy Layout
Places all items along vertically spaced rows
Uses indentation to show parent child relationships
Breadth and depth end up fighting for space resources
Top-down layout
Uses separate dimensions for breadth and depth
Reingold-Tilford Layout
tidier drawing of trees - reingold, tilford
TreeMaps
Space-filling technique that divides space recursively
Segments space according to ‘size’ of children nodes
map of the market – smartmoney.com
SpaceTree
Cone Trees
Tree layout in three dimensions
Shadows provide 2D structure
Can also make “Balloon Trees” – 2D version of ConeTree
cone tree – robertson, mackinlay, and card
Degree-of-Interest Trees
Hyperbolic Trees
Network visualization
Often uses physics models (e.g., edges as springs) to perform layout.
Can be animated and interacted with.
Network Visualization
Skitter, www.caida.org
WebBook
Web Forager
Document Lens
Data Mountain
Supports document organization in a 2.5 dimensional environment.
Designing Visualizations
(some tricks of the trade)
Graphical Excellence [Tufte]
the well-designed presentation of interesting data – a matter of substance, of statistics, and of designconsists of complex ideas communicated with clarity, precision and efficiencyis that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest spacerequires telling the truth about the data.
Interactive Tasks [Shneiderman]
1. Overview: Get an overview of the collection2. Zoom: Zoom in on items of interest3. Filter: Remove uninteresting items4. Details on demand: Select items and get
details5. Relate: View relationships between items6. History: Keep a history of actions for undo,
replay, refinement7. Extract: Make subcollections
Proposed Data Types
1. 1D: timelines,…2. 2D: maps,…3. 3D: volumes,…4. Multi-dimensional: databases,…5. Hierarchies/Trees: directories,…6. Networks/Graphs: web,…7. Document collections: digital libraries,…
This is useful, but what’s wrong here?
Basic Types of Data
Nominal (qualitative)(no inherent order)city names, types of diseases, ...
Ordinal (qualitative)(ordered, but not at measurable intervals)first, second, third, …cold, warm, hotMon, Tue, Wed, Thu …
Interval (quantitative)integers or reals
QUANT ORDINAL NOMINAL
Position Position PositionLength Density Color HueAngle Color Saturation TextureSlope Color Hue ConnectionArea Texture ContainmentVolume Connection DensityDensity Containment Color SaturationColor Saturation Length ShapeColor Hue Angle Length
Ranking of Applicability of Properties for Different Data Types(Mackinlay 88, Not Empirically Verified)
Visualization Design PatternsPre-Attentive Patterns
Leverage things that automatically “pop-out” to human attentionStark contrast in color, shape, size, orientation
Gestalt PropertiesUse psychological theories of visual groupingproximity, similarity, continuity, connectedness, closure, symmetry, common fate, figure/ground separation
High Data DensityMaximize number of items/area of graphicThis is controversial! Whitespace may contribute to good visual design… so balance appropriately.
Small MultiplesShow varying visualizations/patterns adjacent to one anotherEnable Comparisons
Visualization Design PatternsFocus+Context
Highlight regions of current interest, while de-emphasizing but keeping visible surrounding context.Can visually distort space, or use degree-of-interest function to control what is and isn’t visualized.
Dynamic QueriesAllow rapid refinement of visualization criteriaRange sliders, Query sliders
Panning and ZoomingNavigate large spaces using a camera metaphor
Semantic ZoomingChange content presentation based on zooming levelHide/reveal additional data in accordance with available space
Software Architectures
The Information Visualization Reference Model [Chi, Card, Mackinlay, Shneiderman]
Evaluating Visualizations
Evaluating VisualizationsVisualizations are user interfaces, too…established methodologies can be used.Questions to ask
What tasks do you expect people to perform with the visualization?What interfaces currently exist for this task? In what ways do you expect different visualizations to help or hurt aspects of these tasks?
Metrics: task time, success rate, information gained (e.g., test the user, or exploit priming effects), eye tracking.
Evaluating Hyperbolic Trees
The Great CHI’97 Browse-Off: Individual browsers race against the clock to perform various retrieval and comparison tasks.Hyperbolic Tree won against M$ File Explorer and others.Can we conclude that it is the better browser?
vs.
Evaluating Hyperbolic TreesNo!
Different people operating each browser.Tasks were not ecologically valid.
Can’t say what is better for what.PARC researchers did extensive eye-tracking studies uncovering very nuanced visual psychology.Found Hyperbolic Tree is better when underlying information design (e.g., tree structure and labeling) is better.
In case of CHI Browse Off, the Hyperbolic Tree had a quicker human user “behind the wheel”.
Moral: Exercise judicious study design, but also don’t feel let down if task times are not being radically improved… subtleties abound.
Accuracy Ranking of Quantitative Perceptual TasksEstimated; only pairwise comparisons have been validated
(Mackinlay 88 from Cleveland & McGill)
Interpretations of Visual Properties
Some properties can be discriminated more accurately but don’t have intrinsic meaning
Density (Greyscale)Darker -> More
Size / Length / AreaLarger -> More
PositionLeftmost -> first, Topmost -> first
Hue??? no intrinsic meaning
Slope??? no intrinsic meaning
Micro-Aspects of Visualization Design
(aka fun with visual psychology)
Preattentive Processing
A limited set of visual properties are processed preattentively
(without need for focusing attention).This is important for design of visualizations
what can be perceived immediatelywhat properties are good discriminatorswhat can mislead viewers
All Preattentive Processing figures from Healey 97http://www.csc.ncsu.edu/faculty/healey/PP/PP.html
Example: Color Selection
Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in color.
Example: Shape Selection
Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in form (curvature)
Pre-attentive Processing
< 200 - 250ms qualifies as pre-attentiveeye movements take at least 200msyet certain processing can be done very quickly, implying low-level processing in parallel
If a decision takes a fixed amount of time regardless of the number of distractors, it is considered to be preattentive.
Example: Conjunction of Features
Viewer cannot rapidly and accurately determinewhether the target (red circle) is present or absent when target has two or more features, each of which arepresent in the distractors. Viewer must search sequentially.
All Preattentive Processing figures from Healey 97http://www.csc.ncsu.edu/faculty/healey/PP/PP.html
Example: Emergent Features
Target has a unique feature with respect to distractors (open sides) and so the groupcan be detected preattentively.
Example: Emergent Features
Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.
Asymmetric and Graded Preattentive Properties
Some properties are asymmetrica sloped line among vertical lines is preattentivea vertical line among sloped ones is not
Some properties have a gradationsome more easily discriminated among than others
Use Grouping of Well-Chosen Shapes for Displaying Multivariate Data
SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
Text NOT Text NOT PreattentivePreattentive
Preattentive Visual Properties(Healey 97)
length Triesman & Gormican [1988]width Julesz [1985]size Triesman & Gelade [1980]curvature Triesman & Gormican [1988]number Julesz [1985]; Trick & Pylyshyn [1994]terminators Julesz & Bergen [1983]intersection Julesz & Bergen [1983]closure Enns [1986]; Triesman & Souther [1985]colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991]
Kawai et al. [1995]; Bauer et al. [1996]intensity Beck et al. [1983]; Triesman & Gormican [1988]flicker Julesz [1971]direction of motion Nakayama & Silverman [1986]; Driver & McLeod
[1992]binocular lustre Wolfe & Franzel [1988]stereoscopic depth Nakayama & Silverman [1986]3-D depth cues Enns [1990]lighting direction Enns [1990]
Gestalt Principles
Idea: forms or patterns transcend the stimuli used to create them.
Why do patterns emerge?Under what circumstances?
Principles of Pattern Recognition“gestalt” German for “pattern” or “form, configuration”Original proposed mechanisms turned out to be wrongR les themsel es are still sef l
Gestalt PropertiesProximity
Why perceive pairs vs. triplets?
Gestalt PropertiesSimilarity
Slide adapted from Tamara Munzner
Gestalt PropertiesContinuity
Slide adapted from Tamara Munzner
Gestalt PropertiesConnectedness
Slide adapted from Tamara Munzner
Gestalt PropertiesClosure
Slide adapted from Tamara Munzner
Gestalt PropertiesSymmetry
Slide adapted from Tamara Munzner
Gestalt Laws of Perceptual Organization (Kaufman 74)
Figure and GroundEscher illustrations are good examplesVase/Face contrast
Subjective Contour
More Gestalt Laws
Law of Common Fatelike preattentive motion property
move a subset of objects among similar ones and they will be perceived as a group
Colors for LabelingWare recommends to take into account:
DistinctnessUnique hues
Component process modelContrast with backgroundColor blindnessNumber
Only a small number of codes can be rapidly perceivedField Size
Small changes in color are difficult to perceiveConventions
Ware’s Recommended Colors for Labeling
Red, Green, Yellow, Blue, Black, White, Pink, Cyan, Gray, Orange, Brown, Purple.The top six colors are chosen because they are the unique colors that mark the ends of the opponent color axes. The entire set corresponds to the eleven color names found to be the most common in a cross-cultural study, plus cyan (Berlin and Kay)