Visualizing, clustering, and predicting the behavior of museum visitors

Martella, Claudio; Miraglia, Armando; Frost, Jeanna; Cattani, Marco; Steen, Maarten van

doi:10.1016/j.pmcj.2016.08.011

Cited by 51 publications

(31 citation statements)

References 41 publications

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“…In a side‐by‐side view, users can watch the video footage and analyze the recorded movements and occupancy. In recent years there has been a growing interest in visualizations of museum visitor flows [SSNS15; MMF*17]. Kuutti et al [KSS14] proposed a real time activity visualization in addition to a demand‐controlled ventilation system.…”

Section: Related Workmentioning

confidence: 99%

Ocupado: Visualizing Location‐Based Counts Over Time Across Buildings

Oppermann

Munzner

2020

Computer Graphics Forum

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Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi‐year design study that resulted in Ocupado, a set of visual decision‐support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location‐based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location‐based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real‐world data refined through extensive stakeholder feedback, and discussion of its take‐up by our industry partner.

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Section: Related Workmentioning

confidence: 99%

Ocupado: Visualizing Location‐Based Counts Over Time Across Buildings

Oppermann

Munzner

2020

Computer Graphics Forum

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“…Market opportunities have also been explored in the field of museums through improved practices and impact analysis of clusters (Tien, 2010). Museum visitor behaviour may be studied using data produced by mobile sensors (Martella et al, 2017). The sources consulted are oriented towards other areas, e.g.…”

Section: The Development Of Clusters To Optimize the Cultural Tourism Productmentioning

confidence: 99%

Formation of clusters in cultural heritage – strategies for optimizing resources in museums

Mendoza

Talavera

González

2021

JCHMSD

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PurposeThe purpose of this study is to affirm that it is possible to segment visitors of cultural heritage into homogeneous groups according to a series of characteristics to detect the variables that have statistical significance to identify visitor clusters.Design/methodology/approachFour case studies were selected, where a total of 500 questionnaires were made to visitors. The authors proceeded with cluster analysis using SPSS software to differentiate visitor segments. Four groups of visitors were first identified and which have subsequently been reduced to three, according to several factors.FindingsThe main contributions of this paper are: (1) the segment to which each one of the determinants of the cultural tourism product is dedicated; (2) the variable object of the analysis, i.e. the formation of visitor segments; and (3) the inclusion of less studied variables such as type of accommodation contracted, treatment offered in the museums or entrance price.Research limitations/implicationsThe analysis has been developed in different museums, with different management models, in a specific place. However, the results are generalizable to other places and to other institutions that manage cultural heritage. The implications are management strategies for a sustainable cultural development in institutions of tourism and heritage.Practical implicationsFrom a practical point of view, the results are useful for cultural managers, travel agencies, tour operators, tourism companies or political offices, among others, because they generate new ideas and strategies focused on maximizing the use of the resources of cultural institutions.Social implicationsFor both local and non-local agents, the knowledge of the factors that make up the groups of visitors in the heritage sites represents a strategy in aspects of marketing, promotion and distribution, thus generating capacities for the different intermediaries, and the possibility of negotiating lower prices with better benefits. It is also possible to create new products destined for other publics.Originality/valueThe study is original because this has not been published.

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“…However, ge ing accurate results is extremely dicult in the presence of crowds that unpredictably a enuate signals. Fortunately, to understand crowd behaviour, precise location is not strictly necessary and other metrics can be used, such as proximity [8] among people or to de ned points.…”

Section: Examples Of Usagementioning

confidence: 99%

CWI-ADE2016 Dataset

Cabrero

Jansen

Röggla

et al. 2017

Proceedings of the 8th ACM on Multimedia Systems Conference

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ABSTRACTe CWI-ADE2016 Dataset is a collection of more than 40 million Bluetooth Low Energy (BLE) packets and of 14 million accelerometer and temperature samples generated by wristbands that people wore in a nightclub.e data was gathered during Amsterdam Dance Event 2016 in an exclusive club experience curated around human senses, which leveraged technology as a bridge between the club and the guests. Each guest was handed a custom-made wristband with a BLE-enabled device that broadcast movement, temperature and other sensor readings. A network of Raspberry Pi receivers deployed for the occasion captured broadcast packets from wristbands and any other BLE device in the environment.is data provides a full picture of the performance of the real life deployment of a sensing infrastructure and gives insights to designing sensing platforms, understanding networks and crowds behaviour or studying opportunistic sensing. is paper describes an analysis of this dataset and some examples of usage. CCS CONCEPTS•Networks →Network performance analysis; •Applied computing →Performing arts;

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Visualizing, clustering, and predicting the behavior of museum visitors

Cited by 51 publications

References 41 publications

Ocupado: Visualizing Location‐Based Counts Over Time Across Buildings

Ocupado: Visualizing Location‐Based Counts Over Time Across Buildings

Formation of clusters in cultural heritage – strategies for optimizing resources in museums

CWI-ADE2016 Dataset

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