Lanir et al. proposed a very interesting study in which they executed a design that not only recorded museum visitor behavior, but then aggregated this information into useful charts and graphs. Similarly to my own group’s original project idea, Lanir et al. hypothesized that curators want to learn more about their visitors since current museum visitor evaluation methods (e.g. questionnaires) provide insufficient feedback. Data such as visitor engagement and what hours of the day are the busiest can be essential in acquiring a better understanding of these visitors. In regard to visitor engagement, Lanir et al. determined, from previous literature, that there are two primary measures to capture how thoroughly visitors are engaged with an exhibit: attraction power (the relative number of visitors who stop in front of an exhibit) and holding power (the average time spent in front of an exhibit). Additionally, through open-ended interviews with the museum director and other curators from the Hecht museum (medium-sized museum located at the University of Haifa), Lanir et al. determined that there were five pieces of data that they wanted focus on: visitor engagement, circulation and general movement patterns of visitors, temporal data for individuals and small groups, demographic information, and engagement and movement patterns of organized groups/tours. Even though contextual inquiries could have given them an even better understanding of what information could most effectively assist curators, the interviews with curators gave them a fairly strong starting point. They then set up and used a radio-frequency based positioning system to track visitor movement. After 6 months of data collection, they analyzed the feedback and produced multiple graphs that illustrated this information. The graphics included maps of individual visitor paths, a temporal view of small groups, a heat map to show attraction and holding power, and a distribution of visitors per hour at different exhibitions. They followed-up by conducting semi-structured interviews with various museum personnel at five other museums, and found that the heat map and time distribution graphics were the most useful, while the individual visitor and small group data was seen as relatively unimportant. Even though Lanir et al. could have more done a more thorough initial investigation on the type of data museum personnel would have wanted, it appears that the initial interviews gave them a good foundation for ideas, and the final interviews both confirmed and rejected the value of their graphics. Overall, museum personnel found the information beneficial in further understanding their visitors so Lanir et al. succeeded in their goals.

In regard to my group’s project, this paper illustrates some crucial insights that would have been nice to have since the beginning. We actually started with some ideas similar to the ones described above; however, we were not able to synthesize together all of the design aspects as clearly as Lanir et al. Since we started with the idea of qualitative data collection (i.e. collecting emotional data based on visitor facial expressions), one of our biggest problems was that we were trying to collect data without knowing what this data would be useful for. This led us to difficulty in determining our intended user base and where our design was headed. Since the paper was very similar to what we had tried to do, if we had read it during the design process, it could have been used as a guide, allowing us to avoid the many iterations to finally create a coherent design. Furthermore, since we were able to do contextual inquiries with curators, we could have actually built upon the visitor evaluation ideas proposed above. This would have given us the opportunity to expand on their ideas instead of struggling with multiple design direction changes; however, the learning experience of starting from scratch was definitely a valuable lesson in this process. In regard to our current prototype, we can still learn a lot from this paper. Since the beginning, we’ve had problems determining what type of information would be the most useful to curators, but recently we were able to decide upon three graphics that we found to be valuable in understanding visitors. Although Lanir et al. had analyses and graphs of visitor engagement that are better than the single graph proposed in our project, we still offer emotional graphs that go beyond visitor behavior, as this information could indicate the quality of a visitor’s experience. Therefore, by using some of the graphs proposed by Lanir et al. in our own project, we could benefit by having the best of both sets of solutions, allowing curators to have an even better overall understanding of their visitors.

Citations

Lanir et al. (2016, November). “Visualizing museum visitors’ behavior: Where do they go and what do they do there?” Springer-Verlag London 2016. Retrieved from here.

Note: I think the link can timeout over time, so here’s a link to the ACM page: https://dl.acm.org/citation.cfm?id=3069127