The document presents Polyzoom, a multiscale and multifocus navigation technique for 2D visual spaces, aimed to improve user interaction in large datasets like Google Maps. Two user studies demonstrate that Polyzoom significantly outperforms traditional pan and zoom techniques in terms of completion time for both multiscale visual searches and multifocus comparisons. The design goals of Polyzoom include maintaining multiscale and multifocus awareness without distortion or overlap.

1. PolyZoom: Multiscale and
Multifocus Exploration
in 2D Visual Spaces
Waqas Javed, Sohaib Ghani, Niklas Elmqvist
Purdue University
West Lafayette, IN
USA
Presented By: Sohaib Ghani
ACM CHI 2012
May 5-8, 2012 ▪ Austin, Texas

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5. Outline
• Motivation
• Demo
• Related Work
• The PolyZoom Technique
– Design Decisions
– Layout
– PolyZoom: System
• User Studies
• Conclusion
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6. Motivation
• Multiscale visual space
– Example: Google Maps – geospatial dataset
• Large (entire world) and two-dimensional
• Multiscale (different data at different levels of detail)
• Navigation in such visual spaces is difficult
– Desert fog
– Multifocus interaction
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7. PolyZoom
• PolyZoom is a multiscale multifocus technique for navigating in 2D
visual spaces
– Allows users to iteratively build a hierarchy of focus regions
– Allows maintaining awareness of multiple scales of the visual space
• [Video]
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8. Related Work
• Common Navigation Techniques
– Scrolling (Igarashi and Hinckley 2000)
– Pan & Zoom ( Furnas and Bederson 2005)
– Overview + Detail (Hornbæk and Frøkjær 2001)
– Focus + Context (Furnas 1986)
• Multifocus Interaction
– Split-screen techniques (Shoemaker and Gutwin 2007)
– Stack zooming (Javed and Elmqvist 2010)
• Multiscale Navigation
– Pad (Perlin and Fox 1993)
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9. PolyZoom: Design Goals
• Multiscale awareness
• Multifocus awareness
• No distortion
• No overlap
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10. PolyZoom: Layout
• Layout
• Viewport size management
• Correlation graphics
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11. PolyZoom: System
• Web-based implementation
– ActionScript3, Flash, and HTML5
• Google Maps dataset, NASA Universe, a Lunar
dataset, and a Martian dataset
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12. User Studies
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13. Study 1: Multiscale Visual Search
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14. Study 1: Multiscale Visual Search
• Participants: 12
• Navigation Techniques: 2
– P – PolyZoom
– S – Simple Pan & Zoom
• Hierarchy Levels L : 3 (3,4,5)
• Repetitions: 4
• Task
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15. Study 1: Multiscale Visual Search
• Hypothesis: P will be faster than S
• Result: significant main effect of Technique T on completion time
• Also significant main effect of Hierarchy Level L on completion time
• Completion time was roughly linear with number of hierarchy levels
• Levels were significantly different with completion times ordered
3 < 4 < 5 (Tukey HSD, p < .05)
• No significant interaction between T and L
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16. Study 2: Multifocus Comparison
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17. Study 2: Multifocus Comparison
• Participants: 12
• Navigation Techniques: 2
– P – PolyZoom
– S – Simple Pan & Zoom
• Discovery Order D : 5 (1,2,3,4,5)
• Repetitions: 2
• Task
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18. Study 2: Multifocus Comparison
• Hypothesis: P will be faster than S
• Result: significant main effect of technique T on completion time
• Discovery order D had a significant main effect on completion time
• Roughly linear (as expected)
• Pairwise differences between orders were significant (Tukey HSD, p < .05)
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19. Subjective Ratings
• Differences in ratings significant (Friedman test, p < .05)
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20. PolyZoom for 20 Years of UIST
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21. Conclusion
• Design of a multiscale and multifocus
interaction technique called PolyZoom
• Evaluation with 2 user studies
– Study 1 for multiscale visual search
– Study 2 for multifocus comparisons
• PolyZoom performs better than pan & zoom
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22. Thank You!
Online demo available:
http://web.ics.purdue.edu/~wjaved/projects/stackZoom/
Contact Information:
Sohaib Ghani
Purdue University, West Lafayette, IN, USA
E-mail: sghani@purdue.edu
Partially funded by NSF Grant #1123108.
22 http://engineering.purdue.edu/pivot/