Information Visualization Antvision is humanitys usual fate but
![Information Visualization • “Ant-vision is humanity’s usual fate; but seeing the whole is every Information Visualization • “Ant-vision is humanity’s usual fate; but seeing the whole is every](https://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-1.jpg)
Information Visualization • “Ant-vision is humanity’s usual fate; but seeing the whole is every thinking person’s aspiration” - David Gelernter • “Visualization … transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations” - Mc. Cormick et al
![Information Visualization and Computer Science • The use of computer-supported, interactive, visual representations of Information Visualization and Computer Science • The use of computer-supported, interactive, visual representations of](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-2.jpg)
Information Visualization and Computer Science • The use of computer-supported, interactive, visual representations of abstract data to amplify cognition
![The Problem • Internet and World Wide Web provide information options • Users have The Problem • Internet and World Wide Web provide information options • Users have](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-3.jpg)
The Problem • Internet and World Wide Web provide information options • Users have difficulty maintaining awareness of changes
![Examples Examples](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-4.jpg)
Examples
![Human Vision • • • Highest bandwidth sense (100 MB/sec) Parallel processing Pre-attentive Pattern Human Vision • • • Highest bandwidth sense (100 MB/sec) Parallel processing Pre-attentive Pattern](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-5.jpg)
Human Vision • • • Highest bandwidth sense (100 MB/sec) Parallel processing Pre-attentive Pattern recognition Extends memory and cognitive capacity People think visually
![Napoleon’s March Napoleon’s March](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-6.jpg)
Napoleon’s March
![• Which state has highest Income? • Relationship between Income and Education? • • Which state has highest Income? • Relationship between Income and Education? •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-7.jpg)
• Which state has highest Income? • Relationship between Income and Education? • Outliers?
![College Degree % Per Capita Income College Degree % Per Capita Income](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-8.jpg)
College Degree % Per Capita Income
![Find the green square? Find the green square?](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-9.jpg)
Find the green square?
![How to Lie with IV • Only show data ranges that accentuate your argument How to Lie with IV • Only show data ranges that accentuate your argument](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-10.jpg)
How to Lie with IV • Only show data ranges that accentuate your argument (chop off bottoms) • Choose time spans appropriate for you • Compare logarithmic data on a nonlogarithmic graph • Use multiple dimensions to show onedimensional data • Change scale in the middle of your graph
![Bottoms Up, Life is Great (? ) Bottoms Up, Life is Great (? )](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-11.jpg)
Bottoms Up, Life is Great (? )
![Another View Another View](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-12.jpg)
Another View
![Good News or Bad News? Good News or Bad News?](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-13.jpg)
Good News or Bad News?
![Logarithmic Scale Logarithmic Scale](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-14.jpg)
Logarithmic Scale
![Dimensional Pictures Dimensional Pictures](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-15.jpg)
Dimensional Pictures
![Changing Scale Changing Scale](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-16.jpg)
Changing Scale
![How NOT to Lie with IV • Show complete data ranges • Choose representative How NOT to Lie with IV • Show complete data ranges • Choose representative](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-17.jpg)
How NOT to Lie with IV • Show complete data ranges • Choose representative time spans • Use appropriate scales in displaying information • Use dimensions in an appropriate manner • Maintain a common scale throughout your graph
![IV Mantra Overview first, zoom and filter, details on demand IV Mantra Overview first, zoom and filter, details on demand](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-18.jpg)
IV Mantra Overview first, zoom and filter, details on demand
![IV Tasks • • Overview Zoom Filter Details-on-demand Relate History Extract IV Tasks • • Overview Zoom Filter Details-on-demand Relate History Extract](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-19.jpg)
IV Tasks • • Overview Zoom Filter Details-on-demand Relate History Extract
![Data Types • • 1 -D Linear (document lens, See. Soft, IM) 2 -D Data Types • • 1 -D Linear (document lens, See. Soft, IM) 2 -D](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-20.jpg)
Data Types • • 1 -D Linear (document lens, See. Soft, IM) 2 -D Map (GIS, Theme. Scape) 3 -D World (CAD, Visible Human) Temporal (Perspective Wall, Life. Lines) Multi-dimensional (Spot. Fire, Home. Finder) Tree (Cone trees, Hyperbolic trees) Network (Netmap, Sem. Net) Documents (Digital Library)
![1 -D Linear Data • Examples include text documents, source code listings, and other 1 -D Linear Data • Examples include text documents, source code listings, and other](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-21.jpg)
1 -D Linear Data • Examples include text documents, source code listings, and other sequential lists • Simple visualizations include font, color, and size highlighting • More complex visualizations attempt to show an overview and facilitate scrolling and selection
![An Interactive IV: Scrollbars • View relative size and position of visible portion • An Interactive IV: Scrollbars • View relative size and position of visible portion •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-22.jpg)
An Interactive IV: Scrollbars • View relative size and position of visible portion • Control visible contents in several ways • Can we increase available information related to content of non-visible space?
![The Pile Metaphor • Use pile metaphor (Rose et al 93) to show objects The Pile Metaphor • Use pile metaphor (Rose et al 93) to show objects](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-23.jpg)
The Pile Metaphor • Use pile metaphor (Rose et al 93) to show objects in the information space • Properties of information reflected in block size • Stacking order provides additional organization method
![The Pile Metaphor in a Scrollbar • Make use of familiar scrollbar feaures • The Pile Metaphor in a Scrollbar • Make use of familiar scrollbar feaures •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-24.jpg)
The Pile Metaphor in a Scrollbar • Make use of familiar scrollbar feaures • Use trough area to graphically describe information space
![Pile Bar Example • Highlights show information properties • Stacking order based on access Pile Bar Example • Highlights show information properties • Stacking order based on access](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-25.jpg)
Pile Bar Example • Highlights show information properties • Stacking order based on access times
![Information Murals • Information mural provides graphical description of contents of information space • Information Murals • Information mural provides graphical description of contents of information space •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-26.jpg)
Information Murals • Information mural provides graphical description of contents of information space • Indentation and length of line reflected in mural • Highlights indicate key elements in information
![Mural Bar Example • Useful for identifying features in code Mural Bar Example • Useful for identifying features in code](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-27.jpg)
Mural Bar Example • Useful for identifying features in code
![Other Infomural Uses • Text editors • Sunspot data Other Infomural Uses • Text editors • Sunspot data](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-28.jpg)
Other Infomural Uses • Text editors • Sunspot data
![2 -D Map Data • Examples include geographic maps, newspaper layouts, other 2 D 2 -D Map Data • Examples include geographic maps, newspaper layouts, other 2 D](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-29.jpg)
2 -D Map Data • Examples include geographic maps, newspaper layouts, other 2 D data • Zooming is the most common method for viewing this data • Other techniques combine overview of space with focus on points of interest
![Zooming and Loss of Overview • Zooming allows users to start with overview then Zooming and Loss of Overview • Zooming allows users to start with overview then](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-30.jpg)
Zooming and Loss of Overview • Zooming allows users to start with overview then zoom in on portions of interest • Difficult to maintain sense of context • Confluent zoom reduces this, but consumes space
![Solution: Fisheye Views • Attempts to provide overview (context) and detail (focus) at the Solution: Fisheye Views • Attempts to provide overview (context) and detail (focus) at the](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-31.jpg)
Solution: Fisheye Views • Attempts to provide overview (context) and detail (focus) at the same time • Also known as focus+context views, non-linear magnification, distortionoriented presentations • Focus area magnified to show detail while preserving context
![Fisheye Camera Lenses • Provide distorted view of large amount of information Fisheye Camera Lenses • Provide distorted view of large amount of information](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-32.jpg)
Fisheye Camera Lenses • Provide distorted view of large amount of information
![Fisheye Views in IV • Area of interest is magnified • All information shown Fisheye Views in IV • Area of interest is magnified • All information shown](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-33.jpg)
Fisheye Views in IV • Area of interest is magnified • All information shown • Continuity preserved to edges • Distortion can be disorienting • Information in transition area lost • Zoom factor minimal
![Another Use of Fisheye Another Use of Fisheye](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-34.jpg)
Another Use of Fisheye
![Fisheye Experiment (Video) Fisheye Experiment (Video)](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-35.jpg)
Fisheye Experiment (Video)
![Trees • Examples include directory structures, office hierarchies, categorization systems • Common examples include Trees • Examples include directory structures, office hierarchies, categorization systems • Common examples include](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-36.jpg)
Trees • Examples include directory structures, office hierarchies, categorization systems • Common examples include typical file browsers in Windows • Other examples show tree structure and relationships in greater detail while preserving context
![Typical Windows Directories Typical Windows Directories](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-37.jpg)
Typical Windows Directories
![Treemaps • Slice-and-dice visualization • Child items are rectangles nested inside parent rectangles • Treemaps • Slice-and-dice visualization • Child items are rectangles nested inside parent rectangles •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-38.jpg)
Treemaps • Slice-and-dice visualization • Child items are rectangles nested inside parent rectangles • Can show many levels of depth (with increasingly less detail) • Difficult to compare sizes unless aspect ratio is close to 1
![Treemap Example: Dewey Decimal System Treemap Example: Dewey Decimal System](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-39.jpg)
Treemap Example: Dewey Decimal System
![New Treemaps: Stock Data New Treemaps: Stock Data](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-40.jpg)
New Treemaps: Stock Data
![Treemap Alternative: Sunburst • Circular, space-filling technique • Items laid out radially with top Treemap Alternative: Sunburst • Circular, space-filling technique • Items laid out radially with top](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-41.jpg)
Treemap Alternative: Sunburst • Circular, space-filling technique • Items laid out radially with top at center and branches extending to edges • Performance better using Sunburst for many overview tasks
![Sunburst Example Sunburst Example](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-42.jpg)
Sunburst Example
![Extra Credit: Last Chance! • • Tuesday 2 -4 PM Mc. Bryde 655 Takes Extra Credit: Last Chance! • • Tuesday 2 -4 PM Mc. Bryde 655 Takes](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-43.jpg)
Extra Credit: Last Chance! • • Tuesday 2 -4 PM Mc. Bryde 655 Takes about 15 minutes Free pizza!
![VTURCS Project Fair • • Friday October 26 at 4 pm in Mc. B VTURCS Project Fair • • Friday October 26 at 4 pm in Mc. B](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-44.jpg)
VTURCS Project Fair • • Friday October 26 at 4 pm in Mc. B 655 Hear about VTURCS projects Eat free food Win cool prizes (digital camera, webcam, handheld, software)
![2 ½ -D Visualizations • Typical WIMP elements are given a 3 D appearance 2 ½ -D Visualizations • Typical WIMP elements are given a 3 D appearance](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-45.jpg)
2 ½ -D Visualizations • Typical WIMP elements are given a 3 D appearance using shading or overlap • Light source typically at their top right • Useful for providing affordances, highlighting priorities in complex trees and graphs
![SQWID Example SQWID Example](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-46.jpg)
SQWID Example
![Data Mountain • Robertson, “Data Mountain” (Microsoft) Data Mountain • Robertson, “Data Mountain” (Microsoft)](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-47.jpg)
Data Mountain • Robertson, “Data Mountain” (Microsoft)
![Hyperbolic Geometric Transformations • Goal: Keep information space within the confines of a circular Hyperbolic Geometric Transformations • Goal: Keep information space within the confines of a circular](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-48.jpg)
Hyperbolic Geometric Transformations • Goal: Keep information space within the confines of a circular area • In a hyperbolic plane, the circumference of a circle grows exponentially with its radius • Hierarchies (which expand exponentially with depth) can be laid out uniformly so that distances between parents, siblings, and children are similar
![Hyperbolic Browser Demo • Layout on hyperbolic plane mapped to unit disk • Smooth Hyperbolic Browser Demo • Layout on hyperbolic plane mapped to unit disk • Smooth](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-49.jpg)
Hyperbolic Browser Demo • Layout on hyperbolic plane mapped to unit disk • Smooth transitions lessen disorientation • Available from Inxight as Star Trees • Won the CHI 97 Great Browse-Off
![3 D Views • Objects often still flat • Displayed in perspective when at 3 D Views • Objects often still flat • Displayed in perspective when at](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-50.jpg)
3 D Views • Objects often still flat • Displayed in perspective when at an angle • Shrink when “further away” • Virtual reality adds head tracking, immersion, stereo vision
![Cone Tree Examples Cone Tree Examples](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-51.jpg)
Cone Tree Examples
![Drawbacks of 3 D/VR • Easy to get lost: Focus+context difficult • 2 D Drawbacks of 3 D/VR • Easy to get lost: Focus+context difficult • 2 D](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-52.jpg)
Drawbacks of 3 D/VR • Easy to get lost: Focus+context difficult • 2 D input (typing, selecting) is unnatural • Practical concerns: expensive, blocky, cartoonish, long setup times • Lesson: Choose application areas carefully
![Harmony Example Harmony Example](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-53.jpg)
Harmony Example
![Overcoming 3 D Limitations • Identify domain that is applicable to 3 D displays Overcoming 3 D Limitations • Identify domain that is applicable to 3 D displays](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-54.jpg)
Overcoming 3 D Limitations • Identify domain that is applicable to 3 D displays • Focus on selected information first, then connected/related information • Provide overview of entire information space with current selection highlighted
![Data Vis vs Info Vis • Data is – structured – numeric – easy Data Vis vs Info Vis • Data is – structured – numeric – easy](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-55.jpg)
Data Vis vs Info Vis • Data is – structured – numeric – easy to map to visual properties – the result of applications and experiments • Information is – – – unstructured free form hierarchical difficult to quantify generated by people
![Visualizing Image Collections • Large amounts of data • Often hierarchical (date, event) • Visualizing Image Collections • Large amounts of data • Often hierarchical (date, event) •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-56.jpg)
Visualizing Image Collections • Large amounts of data • Often hierarchical (date, event) • Difficult for computer to process and understand • Solution: Leverage what humans do well and what computers do well
![Creating Visual Displays • How do basic properties of visualizations affect their ability to Creating Visual Displays • How do basic properties of visualizations affect their ability to](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-57.jpg)
Creating Visual Displays • How do basic properties of visualizations affect their ability to communicate information? • What tasks can be performed using visualizations? • When are visual displays inappropriate or intrusive?
![Visual Properties Position, length, angle, area, volume, color Visual Properties Position, length, angle, area, volume, color](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-58.jpg)
Visual Properties Position, length, angle, area, volume, color
![Cleveland’s Ordering Decoding accuracy (from best to worst): • Position • Length • Angle Cleveland’s Ordering Decoding accuracy (from best to worst): • Position • Length • Angle](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-59.jpg)
Cleveland’s Ordering Decoding accuracy (from best to worst): • Position • Length • Angle (slope/direction) • Area • Volume • Color (hue, saturation, density)
![Example: Pie Charts • Relies on low accuracy decoding skills (angles, irregular areas) • Example: Pie Charts • Relies on low accuracy decoding skills (angles, irregular areas) •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-60.jpg)
Example: Pie Charts • Relies on low accuracy decoding skills (angles, irregular areas) • Use higher accuracy decoding skills when possible (position, length)
![Alternative: Dot Chart Alternative: Dot Chart](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-61.jpg)
Alternative: Dot Chart
![Multiple Pie Charts • Tufte: ". . . the only worse design than a Multiple Pie Charts • Tufte: ". . . the only worse design than a](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-62.jpg)
Multiple Pie Charts • Tufte: ". . . the only worse design than a pie chart is several of them. . Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used. "
![Task Type • Mackinlay claims that ranking of perceptual properties depends on task: – Task Type • Mackinlay claims that ranking of perceptual properties depends on task: –](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-63.jpg)
Task Type • Mackinlay claims that ranking of perceptual properties depends on task: – Quantitative: position, length, angle, slope, area – Ordinal: position, density, color, texture, connection – Nominal: position, color hue, texture, connection, containment
![What is Pre-attentive Processing? • Refers to cognitive operations that can be performed prior What is Pre-attentive Processing? • Refers to cognitive operations that can be performed prior](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-64.jpg)
What is Pre-attentive Processing? • Refers to cognitive operations that can be performed prior to focusing attention on any particular region of an image • Estimation based on viewing displays for <200 -250 ms (qualifies as preattentive)
![Pre-attentive Processing • Target has a unique feature (color) from the distracters. Hence it Pre-attentive Processing • Target has a unique feature (color) from the distracters. Hence it](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-65.jpg)
Pre-attentive Processing • Target has a unique feature (color) from the distracters. Hence it can be detected pre-attentively.
![Pre-attentive Processing • Conjunction of features: No unique feature distinct from its distracters. Hence Pre-attentive Processing • Conjunction of features: No unique feature distinct from its distracters. Hence](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-66.jpg)
Pre-attentive Processing • Conjunction of features: No unique feature distinct from its distracters. Hence difficult to detect. • http: //www. csc. ncsu. edu/faculty/healey/PP/PP. html
![Pre-attentive Processing • Example: Hue Vs Form • Feature Interference: Variation of form did Pre-attentive Processing • Example: Hue Vs Form • Feature Interference: Variation of form did](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-67.jpg)
Pre-attentive Processing • Example: Hue Vs Form • Feature Interference: Variation of form did not interfere with hue segregation, but varying hue within a display region interfered with boundary detection based on form
![Emergent Features • Target in (a) contains no unique feature. • Target in (b) Emergent Features • Target in (a) contains no unique feature. • Target in (b)](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-68.jpg)
Emergent Features • Target in (a) contains no unique feature. • Target in (b) contains non-closure as unique feature.
![2 D or 3 D? • When the cubes appear "three-dimensional", the 2 x 2 D or 3 D? • When the cubes appear "three-dimensional", the 2 x](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-69.jpg)
2 D or 3 D? • When the cubes appear "three-dimensional", the 2 x 2 group with a different orientation is pre-attentively detected • When three-dimensional cubes are removed, the unique 2 x 2 group cannot be pre-attentively detected.
![Chernoff Faces • Use faces with different expressions to represent multidimensional data • Each Chernoff Faces • Use faces with different expressions to represent multidimensional data • Each](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-70.jpg)
Chernoff Faces • Use faces with different expressions to represent multidimensional data • Each data value in a multidimensional data element controls an individual facial characteristic • Examples of these characteristics include the nose, eyes, eyebrows, mouth, and jowls • Can support data with up to eighteen dimensions • Groupings in coherent data will be drawn as groups of icons with similar facial expressions
![Visualizations in the Periphery Visualizations in the Periphery](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-71.jpg)
Visualizations in the Periphery
![Questions • Do peripheral displays interfere with other daily tasks? • What are the Questions • Do peripheral displays interfere with other daily tasks? • What are the](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-72.jpg)
Questions • Do peripheral displays interfere with other daily tasks? • What are the relative advantages of different classes of peripheral displays? • How does changing visual features of the display affect information processing and distraction?
![Experimental Studies Experimental Studies](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-73.jpg)
Experimental Studies
![Example Tasks and Activities • Browsing tasks – In what year did Gautama found Example Tasks and Activities • Browsing tasks – In what year did Gautama found](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-74.jpg)
Example Tasks and Activities • Browsing tasks – In what year did Gautama found Buddhism? – How many miles long is the Mississippi River? • Monitoring activities – When a flash flood warning appears, press Button 1. – When IBM drops below 120, press Button 2. • Awareness questions – What teams were playing in the basketball game? – Which team scored the most points in the game?
![Data Collection • Collected user response data – Browsing times – Monitoring times – Data Collection • Collected user response data – Browsing times – Monitoring times –](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-75.jpg)
Data Collection • Collected user response data – Browsing times – Monitoring times – Awareness responses • Correctness rate • Hit rate • Collected user opinion data
![Result: Effect on Browsing • No significant difference in browsing times based on type Result: Effect on Browsing • No significant difference in browsing times based on type](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-76.jpg)
Result: Effect on Browsing • No significant difference in browsing times based on type or presence of animation • Trends do not suggest that presence is a hindrance
![Result: Identifying Changes • Fade and blast result in better performance than ticker at Result: Identifying Changes • Fade and blast result in better performance than ticker at](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-77.jpg)
Result: Identifying Changes • Fade and blast result in better performance than ticker at identifying changes • May be influenced by size, shape, or speed of animations
![Result: Processing and Remembering • Ticker results in higher hit rates than fade or Result: Processing and Remembering • Ticker results in higher hit rates than fade or](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-78.jpg)
Result: Processing and Remembering • Ticker results in higher hit rates than fade or blast on post -experiment questions • May be influenced by size, shape, or speed of animations
![Results • Animated displays can be used without negatively impacting certain other tasks • Results • Animated displays can be used without negatively impacting certain other tasks •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-79.jpg)
Results • Animated displays can be used without negatively impacting certain other tasks • In-place animations better for monitoring • Motion-based animations better for awareness questions • Small displays better for monitoring • Fast displays better for awareness • Balance productivity and preferences
![Visualization in the Periphery – People do not always use information visualizations as their Visualization in the Periphery – People do not always use information visualizations as their](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-80.jpg)
Visualization in the Periphery – People do not always use information visualizations as their sole or even primary task – How could information visualizations intended for multiple-task situations be designed? – It is suspected that such visualizations are distracting, but little is known about the degree to which it distracts users and whether users can overcome these distractions and interpret the peripheral visualizations – Peripheral visualization vs standalone visualization
![Overview – Information visualizations as secondary displays (peripheral visualizations) – How quickly and effectively Overview – Information visualizations as secondary displays (peripheral visualizations) – How quickly and effectively](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-81.jpg)
Overview – Information visualizations as secondary displays (peripheral visualizations) – How quickly and effectively can people interpret information visualizations (Secondary) while busily performing other tasks (Primary)? – How can peripheral visualizations be designed to reduce distraction while maintaining awareness? – Factors that might impact performance evaluated: • Visual density • Visualization presence time • Secondary task type
![Experimental Design – Dual-task setting • Primary task – a video game • Secondary Experimental Design – Dual-task setting • Primary task – a video game • Secondary](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-82.jpg)
Experimental Design – Dual-task setting • Primary task – a video game • Secondary task – answer a question about info in a visualization that appeared while you played game – The experiment included three independent variables: • Time (1 or 8 seconds ) visualization was present • Density (low=20 objects, high=320) of visualization • Question type (find single item or a cluster)
![Experimental Design (cont) – Each round started with the presentation of the question that Experimental Design (cont) – Each round started with the presentation of the question that](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-83.jpg)
Experimental Design (cont) – Each round started with the presentation of the question that the participant would answer using the visualization – The question was then removed and participants then played the game n After 15 seconds of playing the game, the visualization appeared on the screen n Incorporated in the visualization was the answer to the target question n Visualization remained visible for some time period n Game stopped, participant answered question
![Conclusions – Peripheral visualizations can be introduced without hindering primary task performance – Interpreting Conclusions – Peripheral visualizations can be introduced without hindering primary task performance – Interpreting](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-84.jpg)
Conclusions – Peripheral visualizations can be introduced without hindering primary task performance – Interpreting complex visualizations within 1 second in a dual-task scenario can not be done effectively but with relaxed time constraints can be effective – Lower density displays can result in performance that is as good or better than high density displays in a dual-task scenario – Finding clusters of visually similar items is easier than locating a single item
![Ongoing Work • Do typical visualization guidelines (Cleveland, Mackinlay) apply to peripheral displays? • Ongoing Work • Do typical visualization guidelines (Cleveland, Mackinlay) apply to peripheral displays? •](http://slidetodoc.com/presentation_image/6ccb21370385373efc025f6e0733fff3/image-85.jpg)
Ongoing Work • Do typical visualization guidelines (Cleveland, Mackinlay) apply to peripheral displays? • Does conjoining attributes affect our ability to perceive information? • Are observed guidelines applicable to more realistic situations?
- Slides: 85