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The “Map Trap”? An evaluation of map versus text-based interfaces for location-based mobile search services. International World Wide Web Conference, 2010 Session: Visual interfaces Karen Church, Joachim Neumann, Mauro Cherubini and Nuria Oliver Telefonica Research, Barcelona, Spain
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The “Map Trap”? An evaluation of map versus text-based interfaces for location-based mobile search services International World Wide Web Conference, 2010 Session: Visual interfaces Karen Church, Joachim Neumann, Mauro Cherubini and Nuria Oliver Telefonica Research, Barcelona, Spain 2010.7.2 Presented by Seunghun Ok, IDB
Outline • Introduction • The SSB Prototypes • Evaluation • Results • Discussion & Implications • Conclusions
Introduction (1/2) • Users of super-powered mobile handsets tend to use the Web more heavily than users of simpler devices • Such as iPhone • The world of mobile information access is evolving • Investing mobile version of standard Web services • The interface design of mobile Web services display information • According to which it refers • Geographical • Based on some order or ranking • Time or search engine ranking
Introduction (2/2) • The most important concept to consider when designing mobile interfaces is “context” • Where an application is used • How information is entered and interacted with • SocialSearchBrowser (SSB), mobile search prototype • SSB gives users the ability to connect with other users while on-the-go and ask them geo-located questions • The goal of this paper • To analyze the impact that the type of user interface has on the search and information discovery experience of mobile users
The SSB Prototype (1/4) • To enhance the search and information discovery experience of mobile users • By pro-actively displaying what other users have been searching for in a particular location • SSB presents the users with a view of evolving search activities • That is sensitive to their context • Two core interfaces: SSBmap and SSBtext
The SSB Prototype (2/4) • The software architecture consists of two components • An iPhone application allows users to • Issue queries • Browse existing queries and their answers • Answer other people’s queries • A server • Synchronizes and stores the queries • Answers from both application in a common database • Difference between SSBmap and SSBtext • Representation of user’s location, location of queries and answers • SSBmap • Represents visually with a map • SSBtext • Represents as textual addresses arranged in list format
The SSB Prototype (3/4) • SSBmap • Map-based interface • Provides users with a sense of place at a glance • SSBtext • Text-based interface
The SSB Prototype (4/4) • Two interactive filters • Time filter • Enables selective display of queries based on time • Query similarity filter • Enables users to limit the queries to those that overlap with the queries that have been previously entered by the user him/herself • Query details • Header • Original query string • Answers • Human generated answers • Local search results • A set of localized search results extracted from Google’s local search service • Event search results • A set of localized event listings
Outline • Introduction • The SSB Prototypes • Evaluation • Participants • Procedure • Resolving Locations via Wizard-of-Oz • Results • Discussion & Implications • Conclusions
Participants • Participants are required to own an iPhone or iPod Touch • 34 users take part in and complete the live field study • 32 users with an iPhone • 2 users with an iPod Touch • 31 males, 3 females • Ranged in age between 20 and 55 (avg 32.2) • Lived in various counties in Ireland • Worked in a wide range of employment sectors • Including IT, Accountancy, Banking, Healthcare, Construction, Public • They used Internet and mobile phone every day
Procedure • Each participant was required to install the SSBmap and SSBtexton their personal iPhone or iPod Touch device • The live field study ran for a period of 27 days during September 2009 • Participants were asked to complete a post-study survey to gather subjective information on their experiences with the two applications
Resolving Locations via Wizard-of-Oz • The location is manually resolved using a Wizard-of-Oz (WoZ) approach • Instead of relying on automatic geo-coding • Would fail in cases like “at the Temple Bar side to the Ha’Penny Bridge” • Wizard of Oz experiment is a research experiment in which subjects interact with a computer system that subjects believe to be autonomous, but which is actually being operated or partially operated by an unseen human being • Employ 3 mechanical turks • Resolve the textual locations of queries and answers to physical latitude/longitude values • Mechanical turks means a fake chess-playing machine
Outline • Introduction • The SSB Prototypes • Evaluation • Results • Basic Usage Patterns • Experience Samples • Content Classification: Queries & Answers • Query Classification • Answer Classification • Location Precision • Discussion & Implications • Conclusions
Basic Usage Patterns • The 34 participants generated 1266 interactions in total • 236 queries, 835 query look-ups, 195 answers • Conducts an independent samples t-test • Participants produce more queries through the map interface than through the text interface • (t[34, 66] = 2.60, p < .05) • Participants retrieved content more often through the text interface than through the map interface • (t[34, 66] = -3.35, p < .05) • Participants answered queries more often through the text interface than through the map interface • (t[34, 66] = -1.66, p < .05)
Experience Samples • Collects 94 samples throughout the 1 month period • 41 via SSBmap and 53 via SSBtext • Samples via SSBmap • Definite visual and location-specific aspect • Easier mechanism to look at different streets • Better visual overview and works well when attempting to pinpoint “local” queries • Samples via SSBtext • Accessing a query • Viewing an answer submitted to a query • Seeing if there were any new queries that need to be answered • Quick and easy • Enable a more efficient means of looking up the details of a query
Query Classification • 1. General queries: Focus on finding an answer to a particular question • 1.1 Business / Service • 1.2 Other queries • 2. Location explicit queries: Describe a query in which the user’s current location has a definite impact on the information need and the answer expected • 2.1 Addresses / directions • 2.2 Business / services • 2.3 Recommendation / opinion • 3. Location implicit queries: Describe needs in which the user is searching for a physical location either directly or indirectly • 3.1 Businesses / services • 3.2 Recommendations • 4. Misc queries: All queries that could not be classified into one of the other types
Answer Classification • 1. General Answers: Describe a non location-specific answer • 1.1 Business / service • 1.2 Recommendation / opinion • 1.3 Other • 2. Location explicit answers: Describe an answer that includes an explicit location cue • 2.1 Address / directions • 2.2 Business / service • 2.3 Recommendation / opinion • 3. Conversational Answers: Are probes for additional details or statements that appear to be motivated by the desire to chat • 4. Application Related • 5. Miscellaneous Answers
Location Precision (1/2) • In SSBtext, users can choose to enter a location in free text form • Manually classified the locations into one of five types based on their geographical precision • 1. Precise: Locations refer to very specific places • 2. Street-level: Locations list a specific street name but no exact street number is provided • 3. Neighborhood: Refer to a small area or borough within a city • 4. City / county: Refer to a particular city or county within Ireland • 5. Imprecise: Do not provide the user with any relevant location details
Outline • Introduction • The SSB Prototypes • Evaluation • Results • Discussion & Implications • Choice of interface • Personal Preferences • Situational Context • Information Need • Location precision • Hybrid Interface ≠ Text + Map • Conclusions
Choice of interface • The choice of user interface depends on three factors • Personal preferences • Situational context • Information need
Personal Preferences • Hypothesis 1 - Gender affects to the choice of user interface • Men tend to have better spatial awareness skills than women • Men tend to orientate themselves more easily • The 3 women who took part in our user study indicated that they preferred SSBtext • But, imbalance in gender exists • Hypothesis 2 – Users’ past experiences with similar applications will also have an impact • Users who preferred SSBmap rated their experiences and knowledge of mapping services more highly than users who preferred SSBtext • Take-away message 1: Track the application usage/behavior of their users
Situational Context • Maps are a useful interface when trying to understand one’s surroundings or to visualize a physical area • Take-away message 2: Infer the situational context of the end-user automatically
Information Need • The participants’ information need had a strong influence on the preferred interface • Participants seeking information related to a specific address had a strong preference for SSBmap • Participants preferred SSBtext when answering queries from other users • Take-away message 3: Automatically determining the intent of the user would allow designers to present the most appropriate interface type
Location precision (1/2) • SSBtext allowed users to specify the location of both queries and answers in more vague terms • Participants were more inclined to choose SSBtext when answering a query • The effort required to submit an answer and its location via SSBtext was lower than the effort required to accomplish the same task via SSBmap • We, as human-beings, often do not need exact locations to orientate ourselves and locate items of interest
Location precision (2/2) • High-level location details are probably sufficient in many circumstances • Such as “around the corner” or “down that street” • Precise locations are not necessarily needed or desired at all times • Providing support for fuzzy or vague locations is important from a privacy perspective • UI perspective should provide users with more control in specifying vague or ambiguous locations • Take-away message 4: Mobile search and information access tools should provide support for users to specify fuzzy or vague locations in order to address • Growing privacy concerns of mobile users • Increasing desires for ambiguous locations
Hybrid Interface ≠ Text + Map • The user interface that is solely based on a map visualization is not optimal • However, an ideal hybrid solution is not a simple parallel implementation of two interface but rather a smart mix • Take-away message 5: Location-based search tools should support both text-based and map-based interface modalities. However, the integration of the two modalities in a single hybrid application should involve a mash-up that supports users’ interactions and intentions while on-the-move.
Conclusions • The majority of existing mobile location based services are built on top of a map-based visualiztion • The choice of mobile interface depends on a range of factors • Including the user’s personal references, their information need, their situational context, their need/desire • Hybrid solution that considers each of five take away messages is the way forward in terms of providing useful mobile information access services