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Visualizing museum visitors’ behavior: Where do they go and what do they do there?

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Abstract

Museum curators and personnel are interested in understanding what is happening at their museum: what exhibitions and exhibits do visitors attend to, what exhibits visitors spend most time at, what hours of the day are most busy at certain areas in the museum and more. We use automatic tracking of visitors’ position, movements and interaction at the museum to log visitor information. Using this information, we provide an interface that visualizes individual and small group movement patterns, presentations watched, and aggregated information of overall visitor engagement at the museum. We utilized a user centered design approach in which we gathered requirements, iteratively designed and implemented a working prototype and evaluated it with the help of domain experts (museum curators and other museum personnel). We describe our efforts and provide insights from the design and evaluation of our system, and outline how it might be generalized for other indoor domains such as supermarkets or shopping malls.

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Notes

  1. http://mushecht.haifa.ac.il/Default_eng.aspx.

  2. http://www.acoustiguide.com/.

  3. http://estimote.com/indoor/.

References

  1. Bollo A, Dal Pozzolo L (2005, July) Analysis of visitor behaviour inside the museum: an empirical study. In: Proceedings of the 8th international conference on arts and cultural management

  2. Klein HJ (1993) Tracking visitor circulation in museum settings. Environ Behav 25(6):782–800

    Article  Google Scholar 

  3. Yalowitz SS, Bronnenkant K (2009) Timing and tracking: unlocking visitor behavior. Visit Stud 12(1):47–64

    Article  Google Scholar 

  4. Chianese A, Marulli F, Piccialli F, Benedusi P, Jung JE (2016) An associative engines based approach supporting collaborative analytics in the internet of cultural things. Futur Gener Comput Syst 66(1):187–198

    Google Scholar 

  5. Chianese A, Piccialli F, Valente I (2015) Smart environments and cultural heritage: a novel approach to create intelligent cultural spaces. J Locat Based Serv 9(3):209–234

    Article  Google Scholar 

  6. Hooper-Greenhill E (2006) Studying visitors. In: Macdonald S (ed) A companion to museum studies. Wiley, pp 362–376

  7. Dickenson V (1992) Museum visitor surveys: an overview, 1930–1990. Cultural economics. Springer, Berlin, pp 141–150

    Chapter  Google Scholar 

  8. Bitgood S (2006) An analysis of visitor circulation: movement patterns and the general value principle. Curator Mus J 49(4):463–475

    Article  Google Scholar 

  9. McManus PM (1989) Oh, yes, they do: How museum visitors read labels and interact with exhibit texts. Curator Mus J 32(3):174–189

    Article  Google Scholar 

  10. Leinhardt G, Knutson K (2004) Listening in on museum conversations. Rowman Altamira, Lanham

    Google Scholar 

  11. Serrell B (1997) Paying attention: the duration and allocation of visitors’ time in museum exhibitions. Curator Mus J 40(2):108–125

    Article  Google Scholar 

  12. Dodge S, Weibel R, Lautenschutz A (2008) Towards a taxonomy of movement patterns. Inf Vis 7(3–4):240

    Article  Google Scholar 

  13. Andrienko G, Andrienko N, Bak P, Keim D, Kisilevich S, Wrobel S (2011) A conceptual framework and taxonomy of techniques for analyzing movement. J Vis Lang Comput 22(3):213–232

    Article  Google Scholar 

  14. Andrienko N, Andrienko G (2012) Visual analytics of movement: an overview of methods, tools and procedures. Inf Vis 12(1):3–24

    Article  MATH  Google Scholar 

  15. Harle Robert (2013) A survey of indoor inertial positioning systems for pedestrians. IEEE Commun Surv Tutor 15(3):1281–1293

    Article  Google Scholar 

  16. Curran K, Furey E, Lunney T, Santos J, Woods D, McCaughey A (2011) An evaluation of indoor location determination technologies. J Locat Based Serv 5(2):61–78

    Article  Google Scholar 

  17. Gu Y, Lo A, Niemegeers I (2009) A survey of indoor positioning systems for wireless personal networks. IEEE Commun Surv Tutor 11(1):13–32

    Article  Google Scholar 

  18. Kuflik T, Lanir J, Dim E, Wecker A, Corra M, Zancanaro M, Stock O (2011) Indoor positioning: challenges and solutions for indoor cultural heritage sites. In: Proceedings of the 16th international conference on intelligent user interfaces. ACM, pp 375–378

  19. Andrienko G, Andrienko N, Rinzivillo S, Nanni M, Pedreschi D, Giannotti F (2009) Interactive visual clustering of large collections of trajectories. In: IEEE symposium on visual analytics science and technology, VAST 2009. IEEE, pp 3–10

  20. Bak P, Mansmann F, Janetzko H, Keim D (2009) Spatiotemporal analysis of sensor logs using growth ring maps. IEEE Trans Vis Comput Graph 15(6):913–920

    Article  Google Scholar 

  21. Girgensohn A, Shipman F, Wilcox L (2008) Determining activity patterns in retail spaces through video analysis. In: Proceedings of the 16th ACM international conference on multimedia. ACM, pp 889–892

  22. Chittaro L, Ranon R, Ieronutti L (2006) VU-flow: a visualization tool for analyzing navigation in virtual environments. Special issue on visual analytics. IEEE Trans Vis Comput Graph 12(6):1475–1485

    Article  Google Scholar 

  23. Börner K, Penumarthy S (2003) Social diffusion patterns in three-dimensional virtual worlds. Inf Vis 2(3):182–198

    Article  Google Scholar 

  24. Hoobler N, Humhpreys G, Agrawala M. (2004) Visualizing competitive behaviors in multi-user virtual environments. In: Proceedings of the IEEE visualization conference 2004. IEEE Press, pp 163–170

  25. Zancanaro M, Kuflik T, Boger Z, Goren-Bar D, Goldwasser D (2007) Analyzing museum visitors’ behavior patterns. User modeling 2007. Springer, Heidelberg, pp 238–246

    Chapter  Google Scholar 

  26. Veron E, Levasseur M (1983) Ethnographie de l’exposition. Centre Georges Pompidou, Paris

    Google Scholar 

  27. Dim E, Kuflik T (2015) Automatic detection of social behavior of museum visitor pairs. ACM Trans Interact Intell Syst 4(4):17

    Google Scholar 

  28. Lanir J, Kuflik T, Dim E, Wecker AJ, Stock O (2013) The influence of a location-aware mobile guide on museum visitors’ behavior. Interact Comput 25(6):443–460

    Article  Google Scholar 

  29. Kanda T, Shiomi M, Perrin L, Nomura T, Ishiguro H, Hagita N (2007) Analysis of people trajectories with ubiquitous sensors in a science museum. In: IEEE international conference on robotics and automation, 2007. IEEE, pp 4846–4853

  30. Lanir J, Bak P, Kuflik T (2014) Visualizing proximity-based spatiotemporal behavior of museum visitors using tangram diagrams. Comput Graph Forum 33(3):261–270

    Article  Google Scholar 

  31. Kuflik T, Wecker AJ, Lanir J, Stock O (2015) An integrative framework for extending the boundaries of the museum visit experience: linking the pre, during and post visit phases. Inf Technol Tour 15(1):17–47

    Article  Google Scholar 

  32. Jeffery SR, Alonso G, Franklin MJ, Hong W, Widom J (2005) A pipelined framework for online cleaning of sensor data streams. Comput Sci

  33. Falk JH, Dierking LD (2012) Museum experience revisited. Left Coast Press, Walnut Creek

    Google Scholar 

  34. Aoki PM, Grinter RE, Hurst A, Szymanski MH, Thornton JD, Woodruff A (2002) Sotto voce: exploring the interplay of conversation and mobile audio spaces. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 431–438

  35. Havre S, Hetzler E, Whitney P, Nowell L (2002) Themeriver: visualizing thematic changes in large document collections. IEEE Trans Vis Comput Graph 8(1):9–20

    Article  Google Scholar 

  36. Sykes ER, Pentland S, Nardi S (2015) Context-aware mobile apps using iBeacons: towards smarter interactions. In: Proceedings of the 25th annual international conference on computer science and software engineering. IBM Corp., pp 120–129

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Authors and Affiliations

  1. University of Haifa, Mount Carmel, Haifa, Israel

    Joel Lanir, Tsvi Kuflik, Julia Sheidin, Nisan Yavin, Kate Leiderman & Michael Segal

Authors
  1. Joel Lanir
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  2. Tsvi Kuflik
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  3. Julia Sheidin
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  4. Nisan Yavin
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  5. Kate Leiderman
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  6. Michael Segal
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Correspondence to Joel Lanir.

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Lanir, J., Kuflik, T., Sheidin, J. et al. Visualizing museum visitors’ behavior: Where do they go and what do they do there?. Pers Ubiquit Comput 21, 313–326 (2017). https://doi.org/10.1007/s00779-016-0994-9

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  • DOI: https://doi.org/10.1007/s00779-016-0994-9

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