In this paper, proximity-based Bluetooth tracking is postulated as an efficient and effective methodology for analysing the complex spatiotemporal dynamics of visitor movements at mass events. A case study of the Ghent Festivities event (1.5 million visitors over 10 days) is described in detail and preliminary results are shown to give an indication of the added value of the methodology for stakeholders of the event. By covering 22 locations in the study area with Bluetooth scanners, we were able to extract 152,487 trajectories generated by 80,828 detected visitors. Apart from generating clear statistics such as visitor counts, the share of returning visitors, and visitor flow maps, the analyses also reveal the complex nature of this event by hinting at the existence of several mutually different visitor profiles. We conclude by arguing why Bluetooth tracking offers significant advantages for tracking mass event visitors with respect to other and more prominent technologies, and outline some of its remaining deficiencies. Bluetooth, tracking, mass event, geographical information technology, geographical information systems In the last few years the representation and analysis of large volumes of trajectory information of objects moving through geographical space has become a major topic of interest in research domains such as geographical information science (Ahlqvist et al., 2010; Shaw, Yu, & Bombom, 2008) , computer science (Bogorny, Kuijpers, & Alvares, 2009; Orlando et al., 2007 ), visual analytics (Andrienko & Andrienko, 2007 and urbanism (Van Schaick & Van der Spek, 2008). This burgeoning academic interest has emerged as a result of the increased feasibility and affordability of collecting detailed data about spatiotemporal phenomena triggered by the widespread adoption of location-aware technologies. Past studies have focused on the movements of various kinds of objects including vehicles (Quiroga & Bullock, 1998 ), animals (Laube et al., 2007, bank notes (Brockmann, Hufnagel, & Geisel, 2006) and typhoons (Terry & Feng, 2010) , but the majority of research has been devoted to human movement in different contexts and at various scales. Some examples are the movement of athletes on a pitch (Laube, Imfeld, & Weibel, 2005) , tourists on a regional (Ahas et al., 2008) and local scale (Kemperman, Borgers, & Timmermans, 2009; O'Connor, Zerger, & Itami, 2005; Shoval & Isaacson, 2007a) , and customers in a supermarket (Hui, Fader, & Bradlow, 2009 ). In these contexts, advanced tracking technologies complement more traditional qualitative methods, such as shadowing (Quinlan, 2008) and collecting travel diaries (Axhausen et al., 2002) . Within research on human behaviour, particular attention has been devoted to the collective behaviour of crowds at mass events such as street parades, festivals, public assemblies, sporting events, and exhibitions (Batty, Desyllas, & Duxbury, 2003; Helbing, Johansson, & Al-Abideen, 2007; Zeitz et al., 2009) . Tragic events such as the recent stampede during the 2010 edition of the Love Parade in Duisburg show that it is vital to have accurate information on the spatiotemporal flow of visitors at mass events. However, the collection of quantitative movement data by the use of advanced tracking technologies, with GPS (global positioning system) as the main example, in these contexts raises serious issues concerning feasibility. Distributing and recollecting tracking units to a sufficient number of individuals within the crowd is indeed a labour-intensive and expensive process. Additionally, GPS usage in dense urban settings and inside buildings engenders problems due to signal distortion. To date, video surveillance on the basis of closed circuit television (CCTV) has been the customary approach to capture human motion in crowded environments. Technological advancement in the last decade has led to a large number of distinct research topics related to video surveillance including crowd density estimation, crowd behaviour monitoring, and face detection and recognition in crowds (Saxena et al., 2008) . Despite substantial progress made in recent years, the use of video data to track individual movements within crowds remains a challenging task. First, dense packing and constant interactions among individuals make it difficult to unambiguously distinguish between individuals in a crowd. Second, limited viewing angles and changes of illumination and weather conditions highly complicate the visual recognition of individual spatial patterns from imagery. Finally, difficulties also arise with respect to the reconstruction of individual movements across multiple camera views, which is a necessity for larger study areas. Hence, current applications of video surveillance have achieved to record the spatiotemporal paths of only few objects in limited spatial environments (Dee & Velastin, 2007) , severely limiting its use as a tracking technology in the context of mass events. In response to these issues and given the ubiquity of Bluetooth-enabled devices such as mobile phones and personal digital assistants (PDA) carried around by their owners, Bluetooth technology has increasingly been suggested as a simple and low-cost alternative for the reconstruction of spatial behaviour ( Designed as an open and wireless communication technology by Ericsson in 1994, Bluetooth has become a well-known and implemented standard for wireless exchange of data between devices. If devices are set to be discoverable, the movement of the devices -and by extension their users -can be reconstructed by means of a unique Media Access Control (MAC) address that gets broadcasted in the discovery process. Because this fixed MAC address cannot be linked to any personal information such as names or phone numbers (contrary to the friendly name of the device), tracked individuals remain anonymous avoiding potential privacy infringements. Applications of Bluetooth tracking include the estimation of travel times on highway segments (Haghani et al., 2009; Wasson et al., 2008) , public transport usage in Graz (Weinzerl & Hagemann, 2007) , movement behaviour in a shopping mall (Millonig & Gartner, 2008) , and functioning as an extension on social data gathered from Facebook in the Cityware project (Kostakos & O'Neill, 2008) . Because Bluetooth allows for non-participatory, unannounced and simultaneous tracking of a large number of individuals, it is particularly useful to study visitor flows at mass events. Despite its potential, only pilot studies using Bluetooth tracking at mass events have been reported (Leitinger et al., 2010; Van Londersele et al., 2009 ). This paper aims to significantly augment the current knowledge by reporting on a recent and comprehensive experiment using Bluetooth as a tracking technology. The experiment was carried out at the Ghent Festivities, one of the largest outdoor cultural events in Europe which lasts for 10 days and attracts around 1.5 million visitors. This setting offers a challenging test bed in terms of crowd size, duration of the event and spatial extent of the study area. The aim of this case study was to explore the potential of Bluetooth tracking for studying the spatiotemporal dynamics of visitors at mass events by highlighting a selection of analytical possibilities with the gathered data and showing the corresponding results. The remainder of the paper is organized as follows. Section 2 gives a brief discussion of Bluetooth as a tracking technology. Section 3 describes the background and experimental design of the case study, and in section 4 we describe the pre-processing of the tracking data. In section 5, we present the results of this study. Finally, we contextualize these results, argue why Bluetooth tracking has the potential to become a valuable methodology for studying the dynamics associated with mass events, and outline some of its remaining deficiencies in section 6. Bluetooth as a tracking technology Bluetooth is a short-range, low-power and open protocol for implementing Wireless Personal Area Networks (WPAN) between mobile devices. It operates on the Industrial, Scientific and Medical (ISM) frequency band (2.4 GHz). To minimize potential interference with other technologies using this crowded radio band (Wi-Fi, microwave ovens, etc.), Bluetooth employs a frequency hopping technique (Bensky, 2007) . For technical details about the Bluetooth protocol and the discovery and communication process, the reader is referred to (Peterson, Baldwin, & Kharoufeh, 2006) . In brief, the process can be summarized as follows. To set up a Bluetooth network (piconet) in which all devices follow the same hopping frequency to communicate with each other, a Bluetooth device first needs to be discovered by a master device during an inquiry phase. This master device enters the inquiry substate and starts transmitting inquiry packets, which triggers unknown devices within the detection range of the master device that are in the inquiry scan substate ('discoverable') to respond by transmitting an identification message containing their MAC address and class of device (COD) code. In a next phase, called the page phase, the master and slave devices synchronize their internal clocks to align their frequency hopping schemes and start communicating.
Versichele et al. REF developed a method for tracking people at mass events which uses Bluetooth signals sent out by mobile phones to detect a person's location.
The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: a case study of the Ghent Festivities.
Applied Geography
Geography