Visitor behavior and experience are among the most important factors informing museum management, with proven value in enhancing operations – yet data is traditionally generated by observations and surveys. These means ascertain visitors’ mediated perspectives on personal experience. The recent emergence of ubiquitous technologies has revolutionized the process of collecting data on human behavior, yet new digital means of data collection do not preclude
Consequently, the availability of large datasets based on quantified museum visitation patterns provides new opportunities to apply computational and comparative analytical techniques. In this pioneering study, we attempt to analyze visitors’ behavior in one of the world’s largest museums – The Louvre Museum – from anonymized longitudinal datasets generated by noninvasive Bluetooth sensors.Our analysis discovered that the visiting style of short-stay (less than 1:30 min) and long-stay (more than 6 hours) visitors are not significantly different. Visitors in both categories tend to visit a similar number of key locations in the museum, yet long-stay visitors tend to do so more extensively. This disproves our initial hypothesis that short-stay visitors explore fewer of the popular places due to the time constraints.
Finally, when visitors are offered a greater number of spaces, they tend to follow more selective paths. That is, when the number of the rooms with exhibits increases, visitors seem not to visit all of the exhibits, but target only a few of them selectively. Most importantly, this pattern is almost independent from the duration of a visitor’s stay – regardless of how long they spend in the museum, visitors to use the same trajectories.» Read the paper
The research team deployed 7 Bluetooth sensors, with sufficient coverage to measure visiting sequences and duration at key representative locations. Sensors were arrayed along the main path from the entrance (Pyramid) to the Venus de Milo statue. Two were positioned on floor -1 (0 or Hall, 1 or Denon access); five were on floor 0 (2 or Denon 0, 3 or Samothrace 0, 4 or Venus de Milo, o 5 or Caryatides), and 3 on floor 1 (7 or Great Gallery, 8 or Samothrace 1, 9 or Glass).
The sensors recorded a unique encrypted identifier that distinguishes each Bluetooth-enabled mobile device within its range, as well as time stamps for entry and exit times. Assuming that a mobile device belongs to a person, we can relate the movement of the device to that of the visitor.
The study was conducted over a 24-day period with a high volume of visitor traffic. During this period, the array of sensors recorded the presence of 24,452 unique devices. A data cleaning process has removed security and museum staff traces by matching recurrence with the time of presence (e.g., outsider visiting time). In addition, two sensors were found to have an faulty time synchronization system and their logs were discarded.
This study asks whether a visitors’ length of stay at an exhibit increase or decrease depending on the density of other visitors in the same space. It also asks how might this impact the length of time a visitor spends at the Louvre, as a whole. For example, a visitor may rush to see a famous piece of artwork but then experience discomfort at the number of other visitors also occupying the space, and as a consequence, shorten their visit. The study revealed that higher the density, more visitors are attracted to specific museum locations (known as “attraction power”) - but up until a certain point. If a museum room becomes too overcrowded, other visitors will be dissuaded from staying and admiring that piece of art for long, or even visiting it at all (“crowding-out effect”).
On average, 8.2% of visitors activated Bluetooth on their mobile device in the Louvre Museum. Based on the paths derived from Bluetooth-enabled mobile devices and the number of ticket sales, we extrapolated visiting sequences and dwell times at key representative locations for the whole number of visitors. These data sets were integrated into microscopic crowd simulations which allow to examine visitors’ movement behavior in greater detail. This simulation-based prediction and analysis of visitor flows reveals valuable information such as crowd density, local congestions and capacity estimations.
The fastest recorded trip through the Louvre Museum is by Swiss artist Beat Lippert in 2010, clocked at 9 minutes and 14 seconds and recorded as an art piece La Sprezzatura. Yet he was hardly the first – his run was inspired by the three heroes of Jean-Luc Godard’s 1964 film Bande à part, who, with a bit of extra time on their hands, run through the museum in an attempt to break the previously standing record set by American Jimmy Johnson. Their pace certainly would skew our study – pulling down the average with a time of 9 minutes, 43 seconds.
Watch their careening visit here.
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