Visualizing, Clustering, and Predicting the Behavior of Museum Visitors

Claudio Martella, Armando Miraglia, Jeanna Frost, Marco Cattani, Martinus Richardus van Steen

Research output: Contribution to journalArticleAcademicpeer-review

44 Citations (Scopus)
18 Downloads (Pure)


Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.
Original languageEnglish
Pages (from-to)430-443
JournalPervasive and Mobile Computing
Publication statusPublished - 2017


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