Praxis Course: DSCI 310: Data in Action
Semester: Spring 2026
Faculty Advisor/Professor: Jennifer Spohrer
Community Partner: The Barnes Foundation
Praxis Site Supervisor: Marie Edland and Liza Herzog
Praxis Poster:
DSCI 310 Nina_Hamilton_Barnes Poster
Further Context:
Hi! Our names are Nina, Nicole, Krish, and Olivia!
For our Praxis project, we are working with the Barnes Foundation as part of our DSCI B310: Data into Action course. The Barnes Foundation is an art institution based in Philadelphia, whose purpose is to support education in fine arts and horticulture.
For this project, we were given a large set of anonymized data from the Barnes Foundation and were told to conduct analysis of any kind. And so, our first step would be to design a study that:
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- Is impactful and useful for our partners
- Can be completed within a semester (or less in our case)
We determined that our goal would be to analyze visitation trends and conduct additional cool analysis that would provide instructions to improve Barnes programming and engagement.
More specifically, we wanted to identify visitor-to-membership conversion trends, public programming and engagement, and visitation and membership trends with respect to location (zip codes).
One of our goals was to see which of the Barnes events were the most popular among members and non-members. While there were many events between 2022 and 2024, we focused on about 15 types of events present in the data (like group tours, free admission days, talks, etc.) that made up a majority of the data outside of general admission. We found that events which were free to the public (Free First Sundays and Barnes on the Block) had the biggest non-member visitors, which makes sense because it caters to groups that may not want to pay or afford to pay for regular tickets! This analysis was interesting because oftentimes members bought tickets for guests, and there were a lot of individual events that had to be grouped together in order to make these larger event types, so there was some nuance to what the number of people that attended each event meant. While it was difficult to organize the data into these groups, it was a good learning experience and provided interesting insights for our partners.
Another goal of this project was to understand where in the Philadelphia area Barnes visitors are coming from. The map visualizes visitation data across two dimensions, total visitor volume and membership share. Circle size reflects the number of total visitors from each zip code, while color indicates the percentage of those visitors who are members, ranging from warm red for low membership rates to deep blue for high. Unsurprisingly, the largest circles cluster around the zip codes in closest proximity to the museum. More revealing, however, is the color pattern. Several suburban zip codes show the darkest blue, indicating disproportionately high membership rates, while many zip codes within Philadelphia proper, particularly those with lower median incomes, tend to appear smaller and redder. This contrast suggests that Barnes has built strong loyalty among a suburban base but has room to grow both visitation and membership conversion closer to home. The aim of this analysis is twofold: to identify new areas the Barnes can target with outreach to drive first-time visits, and to spotlight communities where membership is already strong so the institution can invest in retention and deeper engagement.
We were interested in identifying patterns that lead to membership applications to the Barnes. And so, reflected by the top chart on the right, we analyzed the visitation count before a visitor decides to become a member, color-coded by the last event they visited before becoming a member. Surprisingly, most individuals who became members enrolled after their first visit.
To dig more deeply into specifics, we decided to study which programming event the members attended last, which led to their membership (chart on the bottom right). This analysis would help Barnes identify which events were most successful in promoting similar events in the future. The data is calculated by finding the percentage of individuals who became members among all those who attended the same program.
Like any project, our work has some limits. It is important to know them.
First, the data only showed if a visitor was a member on the day they visited. It did not show when they bought the membership. So when we say an event led to someone joining, this is just our best guess. A person may have bought a membership online days before. They may also have been moved by a visit many months earlier. We cannot know for sure. Our findings show useful patterns. But they do not prove what caused people to join.
Second, we did not have qualitative data to analyze. All of our data was numbers — ticket records, visit counts, and zip codes. We did not have interviews, survey answers, or visitor comments. For example, we can see which events came before someone joined. But we cannot hear in their own words what made them want to become a member. Qualitative data would help explain the story behind the numbers.
We want to be clear about these limits. This helps the Barnes team trust our findings in the right way. It also shows where the best next questions are.