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Abstract. Museums often suffer from so-called "hyper-congestion", wherein the number of visitors exceeds the capacity of the physical space of the museum. This can potentially deteriorate the quality of visitor's experience disturbed by other visitors' behaviors and presences. Although this situation can be mitigated by managing visitors' flow between spaces, a detailed analysis of the visitor's movement is required to fully realize and apply a proper solution to the problem. This paper analyzes the visitor's sequential movements, the spatial layout, and the relationship between them in largescale art museums -Louvre Museum -using anonymized data collected through noninvasive Bluetooth sensors. This enables us to unveil some features of visitor's behavior and spatial impact that shed some light on the mechanism of the museum overcrowding. The analysis reveals that the visiting style of short and long stay visitors are not as significantly different as one could expect. Both types of visitors tend to visit a similar number of key locations in the museum while the longer stay type visitors just tend to do so more extensively. In addition, we reveal that some ways of exploring the museum appear frequently for both types of visitors, although long stay type visitors might be expected to diversify much more given the greater time spent in the museum. We suggest that these similarities/dissimilarities make for an uneven distribution of the quantity of visitors in the museum space. The findings increase the understanding of the unknown behaviors of visitors, which is key to improve the museum's environment and visiting experience.

Abstract. In recent years, the large deployment of mobile devices has led to a massive increase in the volume of records of where people have been and when they were there. The analysis of these spatio-temporal data can supply high-level human behavior information valuable to urban planners, local authorities, and designer of location-based services. In this paper, we describe our approach to collect and analyze the history of physical presence of tourists from the digital footprints they publicly disclose on the web. Our work takes place in the Province of Florence in Italy, where the insights on the visitors' flows and on the nationalities of the tourists who do not sleep in town has been limited to information from survey-based hotel and museums frequentation. In fact, most local authorities in the world must face this dearth of data on tourist dynamics. In this case study, we used a corpus of geographically referenced photos taken in the province by 4280 photographers over a period of 2 years. Based on the disclosure of the location of the photos, we design geovisualizations to reveal the tourist concentration and spatiotemporal flows. Our initial results provide insights on the density of tourists, the points of interests they visit as well as the most common trajectories they follow.

In this paper we discuss the exploitation of data originated from Bluetooth-enabled devices to understand visitor's behaviour in the Louvre museum in Paris, France. The collected samples are analysed to examine frequent patterns in visitor's behaviours, their trajectory, length of stay and some relationships, offering new details on behaviour than previously available. Our work reinforces the emergence of a new methodology to study visitors. It is part of recent lines of investigation that exploit the presence of pervasive data networks to complement more traditional methods in tourism studies, such as surveys based on observation or interviews. However, most past experiments have explored quantitative data coming from mobile phones, GPS, or even geotagged user generated content to understand behaviour in a region, or a city, at a larger scale than that of our current work.

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