Carnegie Mellon University: Situationally Appropriate Interaction

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Situationally Appropriate Interaction

Visualizing Road Information

Displays created by location-based software such as GPS mapping applications are often not straightforward to use in the context of driving. Information is crowded and overloaded on the display. Critical information is designed and presented in a way that slows down the rate of uptake, interfering with the process of learning and remembering the route, encoding the information in memory, and making decisions at critical points. This problem is even more relevant in mobile contexts, and when users are performing divided-attention tasks, such as driving.

This research sets out to examine how visualizing complex mapping information might be useful, by displaying an optomized, salient, and personalizable amount of information to the user. We theorize that information needs will change based on a userÕs prior familiarity with a route; whether he prefers navigating by using landmarks, route information, or highly schematized survey information; and his current position and proximity to critical points. We propose the development of abstraction and salience mechanisms, approximately scoring impact on perceptual resources, to optomize and reduce complex information to rankable chunks. Next, we propose to build a display system, and to maximize and test the system in real-world contexts.

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