Approaching location-based services from a place-based perspective: from data to services?

Despite the seemingly obvious importance of a link between notions of place and the provision of context in location-based services (LBS), truly place-based LBS remain rare. Place is attractive as a concept for designing services as it focuses on ways in which people, rather than machines, represent and talk about places. We review papers which have extracted place-relevant information from a variety of sources, examining their rationales, the data sources used, the characteristics of the data under study and the ways in which place is represented. Although the data sources used are subject to a wide range of biases, we find that existing methods and data sources are capable of extracting a wide range of place-related information. We suggest categories of LBS which could profit from such information, for example, by using place-related natural language (e.g. vernacular placenames) in tracking and routing services and moving the focus from geometry to place semantics in location-based retrieval. A key future challenge will be to integrate data derived from multiple sources if we are to advance from individual case studies focusing on a single aspect of place to services which can deal with multiple aspects of place

Real time movement labelling of mobile event data

International audienceIn this paper we introduce an advanced platform to label mobile event data with significant subscriber locations in real time. The presented platform is divided into two sections – the learning section and the real-time processing section. During the real-time processing step, we enrich live event streams with significant locations calculated in the learning step using stream and call detail record data. We validate our system by comparing a sample of subscribers' calculated locations with actual locations and give state benchmarks for minimum event counts. The validation confirms that the platform works within desired deviation levels from real locations. The accuracy strongly depends on the event count that we can take into account. Finally, we simulate a real-world scenario and measure the real-time labelling performance of our system. The results of this simulation confirm that our event labelling platform performs sufficiently well to process real event streams for millions of subscribers in real time