Category Archives: Research

Visualizing Hive NYC – Part 2

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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:

Hive NYC TreeMap

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Visualizing Hive NYC – Part 1

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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-funded projects and partnerships and see what sorts of interesting patterns might emerge through various visualization techniques. The data was self-reported and derived from project proposals–the resulting prototypes offer new ways to visualize the types of activities and collaborations that take place within Hive NYC. 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.

In the first post of this two part series, we spoke with Team Buzz Buzz, made up of Simon Duff, Camaal Moten, John Patterson, Ann Priestley and Sarah Webber.

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

Camaal Moten (CM), Team Buzz Buzz (TBB): We began the process by identifying our research questions, thinking about potential Hive NYC needs, and hand-sketching some ideas to explore the various visualization techniques we were learning each week. Our low-fidelity sketches allowed us to quickly problem-solve and be creative, while providing a basis for discussions between the team and Hive Research Lab. We used components of the exemplary visualizations shared during class as a starting point, and then worked within the team provide feedback on each other’s ideas. After a few rounds of discussion, we decided upon two visualization techniques and began adding more detail to each sketch to match the dataset.

We then began cleaning the dataset and made a normalized version to maintain consistency throughout the team and began appending the unique data needed to create our proposed visualizations. For example, John used the member locations to append the latitude and longitude coordinates to the dataset for our geospatial visualization. We also gathered background information on each organization and looked for new ways to interpret the data or additional data points that could be added.

As the project progressed, we used a shared Google+ community page to post examples of preliminary results from the dataset and provided each other with feedback. We continued this process until we created a high-fidelity visualization that matched our sketch. This iterative process of cleaning, parsing, and visualizing the data continued throughout the entire project. Each cycle of feedback inspired new visualization ideas and expanded the final results. We spent most of our time transforming data, so one of the highlights was when one of our team members created a script that could automatically transform our excel data into the Graph Exchange XML Format (GEFX) used in Gephi (an open-source data visualization application). In the end, we added even more visualizations that were not included in the original scope. We were having too much fun!

John Patterson (JP), Team Buzz Buzz (TBB): I think Camaal covers it well. Interestingly, the majority of the time visualizing Hive NYC was spent on data organization and data transformation and not on the visualization itself. What felt different about data visualization compared to some other data analysis approaches is that we constantly faced new challenges requiring a mix of skills. For example we wanted to show Hive NYC as it changed over time, so we needed to get the data into GEXF format. There was a “Wait how do we do that?” moment and Simon (the programmer in our team) was able to solve that challenge and write a short script. This meant Camaal (our designer/social network analyst) could then get back to visualizing. So the process required lots of collaboration. Google+ really surprised me in how easy it facilitated this kind of work.

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

  collaboration_network_1

Collaboration Network Visualization (click for hi-res version) Continue reading →

Exploring How Organizations “Interface” with Hive NYC

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

Screen Shot 2013-08-31 at 1.02.10 PM

As we’re coming towards the end of our preliminary fieldwork phase to get a snapshot of Hive NYC, we’re starting to see that member organizations “interface” with the Hive in very distinct ways. For example, we’re noticing that in a number of larger organizations, the relationship to the network and its associated opportunities is managed by one individual within a specific programmatic department, with development (i.e., fund raising) folks coming into the picture when the organization responds to a Hive RFP. In some smaller ones, we’ve seen a range of set ups, from executive directors being the only one in the organization that even knows what Hive is (common with some of the newer small organizations), to others where teams that span leadership and programmatic roles attend Hive community meetings together. In still other cases, we’ve seen “hand-offs”‘- an instance where someone moves on from a position and was a “point person” to many Hive-affiliated relationships and activities, and then moves out of that role, explicitly giving it to another person in the organization.

This brings up the fact that there are so many things that we might count as an “interface” with the network (and what the network actually *is* from an analytic perspective is a whole other post). Any of the following might qualify:

  • being on monthly community calls
  • attending monthly in person Hive meet-ups
  • participating (or just lurking) on Hive’s mingroup email list
  • running an activity station at a one-day Hive-affiliated pop-up event
  • submitting an application with other Hive members to the bi-annual Hive RFP
  • running a Hive funded program or partnership
  • taking part in “learning lab” calls that occur for each cohort of funded Hive projects
  • …and probably a whole bunch of things we’re either forgetting or don’t know about yet.

So why does this all matter? Well, as a project that’s studying the way that Hive NYC can improve its ability to be an infrastructure for innovation, knowing how each member organization interfaces with the Hive becomes really important because it gives us insight into a range of related questions. Who’s bringing ideas and technologies into this community? How does organizational interface mediate who participates in the broader Hive NYC community and who doesn’t? How do innovations travel within and across organizations based on the nature of that organization’s interface? How does an organization’s knowledge and understanding of the Hive NYC community and its ethos, values and educational approaches change over time depending on how it interfaces with network activities? All of these questions are consequential to the broader goal of supporting Hive as a context for educational innovation, and so we’re paying close attention to these issues in the ground.

One of the questions we’ve asked some older Hive NYC members is if there are things they’d recommend to new Hive member organizations. One member recently spoke directly to this point of organizational interface, saying that he felt it was critical that an organization find a Hive point person (or people) who is both really interested in the network and the ideas that are associated with it and at the same time has some degree of power to capitalize on that participation and the opportunities that stem from it in a way that benefits the broader organization. Adding to that point, another member recently mentioned that while she’s the active liaison now, that’s a role that someone else (originally from their development department) used to occupy, and it gradually shifted as it became clearer to the organization that Hive NYC was not just another funding opportunity but rather largely about educational practice, and that from that perspective having a programmatic-oriented staff member engaging made good sense.

There’s plenty more than we’re finding about this issue, but I thought I’d take the opportunity to open this question up for any Hive members that might be reading (folks from other Hives aside from NYC are welcome!). How does your organization “interface” with the Hive? Are there recommendations you have for other orgs about what’s worked for you, or what hasn’t? And what are the things you consider most as you’re making these sorts of decisions?