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Mobile crowd sensing (MCS) is as a promising people-centric sensing paradigm which allows ordinary citizens to contribute sensing data using mobile communication devices. In this paper we study correlation between users' mobility and their role as contributors in MCS applications. We propose a new trajectory-based approach for task allocation in MCS environments and model participants' spatio-temporal...
In this paper, we show how we can use Foursquare check-ins to understand the behavior of tourists that would be hard using traditional methods, such as surveys. For that, we analyze the behavior of tourists and residents in four popular cities around the world in four continents: London, New York, Rio de Janeiro, and Tokyo. We perform a spatio-temporal study of properties of the behavior of these...
There are different functional regions in cities such as tourist attractions, shopping centers, workplaces and residential places. The human mobility patterns for different functional regions are different, e.g., people usually go to work during daytime on weekdays, and visit shopping centers after work. In this paper, we analyse urban human mobility patterns and infer the functions of the regions...
Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) GPS data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinspointing where people live at scale. We revisit this research topic and infer home location within 100 by 100 meter squares at 70% accuracy...
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