The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.

Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.

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In this article, we introduce a novel platform dedicated to the extraction, transformation and visualization of mobility data. This platform was developed in the framework of a French regional project (DA3T project) aiming at improving the management and valorisation of touristic cities via the fine-grained analysis of tourist mobility data. The system is totally modular, and non-computer scientists (such as geographers) can make processing pipelines from a variety of modules belonging to different categories (e.g., extraction, filtering, visualization, etc.). Each pipeline is created in order to help fulfil one or several reporting needs. Indeed, the results of those pipelines aim to be presented to local authorities and decision makers to assist them in improving infrastructure and tourism management. Beyond this main use case, the platform is also generic and aims to work with any kind of mobility data (not strictly limited to the tourism field). It is heavily inspired by traditional ETL (Extract, Transform, Load) software and processes.