Towards a Better Understanding of Museum Visitors’ Behavior through Indoor Trajectory Analysis

Authors

  • Alexandros Kontarinis ETIS UMR 8051, Université Paris Seine, Université de Cergy-Pontoise, ENSEA, CNRS, F-95000, Cergy, France; DAVID, Université Paris-Saclay, Université de Versailles Saint-Quentin-en-Yvelines, F-78035, Versailles, France
  • Claudia Marinica ETIS UMR 8051, Université Paris Seine, Université de Cergy-Pontoise, ENSEA, CNRS, F-95000, Cergy, France
  • Dan Vodislav ETIS UMR 8051, Université Paris Seine, Université de Cergy-Pontoise, ENSEA, CNRS, F-95000, Cergy, France
  • Karine Zeitouni DAVID, Université Paris-Saclay, Université de Versailles Saint-Quentin-en-Yvelines, F-78035, Versailles, France
  • Anne Krebs Centre Dominique-Vivant Denon, Direction de la recherche et des collections, Musée du Louvre, F-75058, Paris, France
  • Dimitris Kotzinos ETIS UMR 8051, Université Paris Seine, Université de Cergy-Pontoise, ENSEA, CNRS, F-95000, Cergy, France

DOI:

https://doi.org/10.55630/dipp.2017.7.1

Keywords:

Indoor Trajectories, Trajectory Mining, Movement Patterns, Mobility Patterns, Museum Experience

Abstract

Nowadays, electronic museum guides have evolved to a point that can act as navigational and informational devices in the museum context; thus they also enable the collection of large volumes of spatiotemporal visitor movement data, from which individual visitor trajectories can be extracted and analyzed. These trajectories have individual characteristics expressed through unique semantics in each museum context (based on the museum, its exhibits and its visitors) and they are restricted in an indoor environment that provides additional constraints. This work presents the benefits, the challenges, and a direction for studying museum visitor movements through context-aware indoor trajectory modeling, mining and analysis.

References

Afyouni, I., Ray, C., Claramunt, C. (2012). Spatial models for context-aware indoor navigation systems: A survey. J. Spatial Information Science 4(1), 85–123.

Andrienko, G., Andrienko, N., Bak, P., Keim, D., Kisilevich, S., Wrobel, S. (2011). A conceptual framework and taxonomy of techniques for analyzing movement. Journal of Visual Languages & Computing 22(3), 213–232 .

Bogorny, V., Renso, C., de Aquino, A.R., de Lucca Siqueira, F., Alvares, L.O. (2014). CON- STAnT A Conceptual Data Model for Semantic Trajectories of Moving Objects. Transactions in GIS 18(1), 66–88.

Falk, J., Dierking, L. (2016). The Museum Experience Revisited. Taylor & Francis.

Fileto, R., Raffaet, A., Roncato, A., Sacenti, J.A., May, C., Klein, D. (2014). A Semantic Model for Movement Data Warehouses. In: Proceedings of the 17th International Workshop on Data Warehousing and OLAP . pp. 47–56. DOLAP ’14, ACM. http://www.sciences-patrimoine.org/

Fileto, R., Bogorny, V., May, C., Klein, D. (2015). Semantic Enrichment and Analysis of Movement Data: Probably It is Just Starting! SIGSPATIAL Special 7(1), 11–18.

Furtado, A.S., Kopanaki, D., Alvares, L.O., Bogorny, V. (2016). Multidimensional Similarity Measuring for Semantic Trajectories. Transactions in GIS 20(2), 280–298.

GfK, (2014). Publics, usages et réception de laudioguide sur console nintendo 3ds du musée du louvre: Study results synthesis . Tech. Rep.

Güting, R.H., Valds, F., Damiani, M.L. (2015). Symbolic Trajectories. ACM Trans. Spatial Algorithms Syst. 1(2), 7:1–7:51.

Jin, P., Du, J., Huang, C., Wan, S., Yue, L. (2015). Detecting Hotspots from Trajectory Data in Indoor Spaces. In: Database Systems for Advanced Applications . pp. 209–225. Springer.

Kang, H.K., Li, K.J. (2017). A Standard Indoor Spatial Data Model OGC IndoorGML and Implementation Approaches. ISPRS International Journal of Geo-Information 6(4), 116.

Kharrat, A., Popa, I.S., Zeitouni, K., Faiz, S. (2008). Clustering Algorithm for Network Constraint Trajectories. In: Headway in Spatial Data Handling, pp. 631–647. Lecture Notes in Geoinformation and Cartography , Springer.

Lu, H., Guo, C., Yang, B., Jensen, C.S. (2016). Finding frequently visited indoor pois using symbolic indoor tracking data. In: Proceedings of the 19th International Conference on Extending Database Technology . pp. 449–460.

Marty, P.F., Jones, K.B. (2008). Museum Informatics: People, Information, and Technology in Museums . Taylor & Francis.

Mautz, R. (2012). Indoor Positioning Technologies . Ph.D. thesis, ETH Zurich.

Pelekis, N., Sideridis, S., Tampakis, P., Theodoridis, Y. (2016). Simulating Our Life Steps by Example. ACM Trans. Spatial Algorithms Syst. 2(3), 11:1–11:39.

Ruback, L., Casanova, M.A., Raffaet, A., Renso, C., Vidal, V. (2016). Enriching Mobility Data with Linked Open Data. In: Proceedings of the 20th International Database Engineering & Applications Symposium . pp. 173–182. ACM.

Silva, T.C.d., Zeitouni, K., Macedo, J., Casanova, M. (2016). On-Line Mobility Pattern Discovering using Trajectory Data . In: ResearchGate.

Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C. (2008). A Conceptual View on Trajectories. Data Knowl. Eng. 65(1), 126–146.

Thompson, J.M.A. (2015). Manual of Curatorship: A Guide to Museum Practice. Routledge.

Tzortzi, K. (2014). Movement in museums: mediating between museum intent and visitor experience. Museum Management and Curatorship 29(4), 327–348.

Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K. (2011). SeMiTri: A Framework for Semantic Annotation of Heterogeneous Trajectories. In: Proceedings of the 14th International Conference on Extending Database Technology . pp. 259–270. EDBT/ICDT ’11, ACM, New York, NY, USA.

Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J.P., Blat, J., Sinatra, R. (2014). An analysis of visitors’ behavior in the louvre museum: A study using bluetooth data. Environment and Planning B: Planning and Design 41(6), 1113–1131.

Yoshimura, Y., Krebs, A., Ratti, C. (2017). Noninvasive Bluetooth Monitoring of Visitors’ Length of Stay at the Louvre. IEEE Pervasive Computing 16(2), 26–34.

Zheng, Y., (2015). Trajectory Data Mining: An Overview. ACM Trans. Intell. Syst. Technol . 6(3), 29:1–29:41.

Zhu, Q., Li, Y., Xiong, Q., Zlatanova, S., Ding, Y., Zhang, Y., Zhou, Y. (2016). Indoor Multi- Dimensional Location GML and Its Application for Ubiquitous Indoor Location Services. ISPRS International Journal of Geo-Information 5(12).

Downloads

Published

2017-09-10

How to Cite

Kontarinis, A., Marinica, C., Vodislav, D., Zeitouni, K., Krebs, A., & Kotzinos, D. (2017). Towards a Better Understanding of Museum Visitors’ Behavior through Indoor Trajectory Analysis. Digital Presentation and Preservation of Cultural and Scientific Heritage, 7, 19–30. https://doi.org/10.55630/dipp.2017.7.1