A tool we created for rapidly prototyping location-enhanced apps. The key idea is to use a few basic abstractions at design time to support location features, and then to use a Wizard of Oz approach at run time to help with testing. Location-enhanced applications use the location of people, places, and things to augment or streamline interaction. Location-enhanced applications are just starting to emerge in several different domains, and many people believe that this type of application will experience tremendous growth in the near future. However, it currently requires a high level of technical expertise to build location-enhanced applications, making it hard to iterate on designs. To address this problem we introduce Topiary, a tool for rapidly prototyping location-enhanced applications. Topiary lets designers create a map that models the location of people, places, and things; use this active map to demonstrate scenarios depicting location contexts; use these scenarios in creating storyboards that describe interaction sequences; and then run these storyboards on mobile devices, with a wizard updating the location of people and things on a separate device. We performed an informal evaluation with seven researchers and interface designers and found that they reacted positively to the concept. Authors are Yang Li, Jason Hong, and James Landay

1. Topiary: A Tool forPrototyping
Location-Enhanced Applications
University of California, Berkeley
Carnegie Mellon University
Intel Research Seattle,
DUB Group, University of Washington
Yang Li
Jason Hong
James Landay

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Location-Enhanced Applications
• Provide useful services by leveraging knowledge
about the location of people, places, & things
– Examples: AT&T’s Find Friends service, Tour Guide
AT&T Find Friends Ekahau

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Problems Building Location-Enhanced Apps
• Require a high level of technical expertise to build
– Sensing technologies are complex
– Location-to-place inference necessary to make useful
• Hard to prototype, evaluate, & iterate on designs
– Several toolkits for developers
– No tools for interaction designers
• Cannot be tested with end-users until built

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Goal of Topiary
• Lower the barrier to entry
– For interaction designers
• Speed up iterative design process
– No application or infrastructure
development required
• Get early feedback from users
– Make major changes when inexpensive
Allow interaction designers to quickly prototype & test location-
enhanced applications

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Location Design Tasks & Challenges
• Modeling location contexts
– Enable exploration of a wider input space than keyboard & mouse
– e.g., “when Alice enters the library”, “when Alice is near Bob”
• Specifying location-enhanced behaviors
– Interaction sequences integrating both explicit input (e.g., click) &
implicit input (e.g., location contexts)
– e.g., “Show the library map when Alice is in the library”
• Testing a design in realistic situations
– Settings often in the field & mobile with changing contextual input
– e.g., “Give a user a PDA and let them walk across campus”

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A QuickView of Topiary

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Outline
• Motivation
• Active Map Workspace
• Storyboard Workspace
• Test Workspace
• Evaluation
• Conclusion & Future Work

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ActiveMap Workspace
Mo de ling Lo catio n co nte xts
• Model a geographical area
– Import a map image as background
• Create entities on the map
– People, places & things
• Capture scenarios
– Scenario producer tool captures scenarios describing
contexts

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ActiveMap Workspace
Mo de ling Lo catio n co nte xts

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ActiveMap Workspace
Mo de ling Lo catio n Co nte xts
Contexts Type Examples
near / far Proximity Alice is near the library
moves
near / away
Proximity Alice moves away from Bob
in / out Presence Bob is in the parking lot
enters / exits Presence Alice leaves her office
ActiveMap workspace gives designers a visual language
for specifying these contexts

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Scenarios
• Represent a collection of location contexts
• Used as triggers & conditions for specifying
location-enhanced interactions
Scenario Alicemeets CarolintheParkingLot
Contexts
Alice is in the Parking Lo t
Caro lis in the Parking Lo t
Alice m o ve s ne ar Caro l

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Capturing Scenarios

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Abstracting Scenarios

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Outline
• Motivation
• Active Map Workspace
• Storyboard Workspace
• Test Workspace
• Evaluation
• Conclusion & Future Work

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Storyboard Workspace
Lo catio n-Enhance d Inte ractio ns
ActionsExplicit Input
Location-enhanced interactions
Scenarios
conditions
triggers
Traditional UI Interactions

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Storyboard Workspace
Spe cifying Lo catio n-Enhance d Inte ractio ns
• Similar to traditional storyboards
– Pages & links (e.g., DENIM, SILK & DEMAIS)
• Different from traditional storyboards
– Explicit & implicit links
• scenarios as co nditio ns for explicit links
• scenarios as auto m atic trig g e rs for implicit links
– Can incorporate context components

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Storyboard Workspace
Explicit & Im plicit Links

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Storyboard Workspace
Co nte xt Co m po ne nts
1. Display spatial & temporal information
2. Encapsulate common location-enhanced interactions
3. Enable continuous interactions
Five context components:
Active Map component
Distance component
Nearest Entities component
Location component
Temporal component

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Storyboard Workspace
Co nte xt Co m po ne nts

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Storyboard Workspace
Co nte xt Co m po ne nts – Active Map Co m po ne nt

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Storyboard Workspace
Co nte xt Co m po ne nts

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Storyboard Workspace
Co nte xt Co m po ne nts – Ne are st Entitie s Co m po ne nt

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Outline
• Motivation
• Active Map Workspace
• Storyboard Workspace
• Test Workspace
• Evaluation
• Conclusion and Future Work

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Test Workspace
Te sting & Analyzing a De sig n
End-userUI Wizard UI

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Test Workspace
Te sting & Analyzing a De sig n

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Outline
• Active Map Workspace
• Storyboard Workspace
• Test Workspace
• Evaluation
• Conclusion and Future Work

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Evaluation #1
An info rm ale valuatio n o n an e arly im ple m e ntatio n
• Settings
– an IBM T20 ThinkPad with a 700MHz CPU, 512MB RAM,
14 inch display, & a Wacom Graphire tablet
• Tasks
– create a tour guide for either Berkeley or San Francisco
• Participants
– 2 ubicomp researchers, 3 professional UI designers, & 2
undergraduate students who took a UI design course
• Results
– Gave positive feedback & areas for improvement. Resulted in
• Storyboard Analysis Window
• Zooming to support large designs

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Evaluation #2
O ur Expe rie nce – Applicatio ns we pro to type d using To piary
• Riddle-based “Geo-caching”
• Tour guide & nearest friend finder
• Context-aware reminder
• Searching for available meeting rooms
• In/Out board
• Thing finder
• Car navigation
• Conference Guide

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Evaluation #3
O ur Expe rie nce – Fro m pro to type s to a re alapplicatio n
Made four designs in three hours
Made a new design in 1 hour
Tested with three people in the field
Built the real application in 2 weeks
Tested with three people in the field
Informal Prototyping of Tour Guide Informal prototypeThe real application
Region of possible location
Trajectory
trail
Path to target

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Future Work
• Modeling the ambiguity of sensor data
• Enabling more kinds of contextual information
• Improving the scalability of storyboards
• Further evaluating Topiary
– being used by students in both undergraduate &
graduate courses at UW & CMU this term

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Conclusion
Topiary is the first tool for interface designers to
rapidly prototype location-enhanced applications
– Demonstrate location contexts via an Active Map
– Specify location-enhanced behaviors via enhanced
storyboards
– Test & analyze a design using either Wizard of Oz or
sensor input

35. Topiary: A Tool forPrototyping
Location-Enhanced Applications
Download available at:
http://dub.washington.edu/topiary