This is re-posted from the Hive Research Lab blog.

This past Spring, members of Hive Research Lab worked with students in Indiana University’s IVMOOC, an online information visualization course, to take data about Hive NYC-funded projects and partnerships and see what sorts of interesting patterns might emerge through different visualization techniques. In this two part series, we interview each of the two teams of students  that worked with the data to have them share their process, the visualizations they came up with, and reflections on what it was like working with Hive NYC data.

One qualification to note: as the data these visualizations was based on were sometimes incomplete and also self-reported, these should be treated more like prototypes for how we might represent Hive activity, rather than definitive statements of what activity has been.

In this post, we talked with Team EsHkUsNl, made up of Gloria Jimenez, Elwin Koster, Maria Maza, Carmen Ng, Chantal Mesler and Kristina Simacek.

Hive Research Lab (HRL): Tell us a bit about your approach to visualizing Hive NYC. What kind of process did the team go through?

Team EsHkUsNl (TE): With an international team spanning the globe, we were challenged to collaborate across different time zones and to learn and draw on each other’s strengths.  Using social media, including Google+ hangouts, we were able to facilitate regular collaboration. We were inspired by the fun of doing an online course, and as we progressed in the project it became more of a professional endeavor.

As for the work itself, we approached the project through extensive discussion and sharing of different approaches to visualizations, providing examples from each of our backgrounds and trying to pick out the main elements from each visualization to put together a final visualization that took into account both our desire for a clear and useful visualization as well as balancing the limitations of the data.  In analyzing the data, we went through several iterations of visualizations to determine what would best represent the data in a useful way. Each visualization is a multi-layered process, and in the final visualization we attempted to show multiple layers at the same time so that both an overview of the data and specific elements of the data could be shown at once.

We appreciated having the flexibility to come up with what we thought was important to show to key stakeholders, including administrators, donors, organizations, and youth.  This allowed us to think freely about what we wanted to show, and experiment with different kinds of data visualizations.

HRL: Let’s have a look at the visualizations that the team produced. What do you think they show about Hive NYC?

TE: Here’s the first visualization we came up with:

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