Synchronous Objects in the classroom

One of the great delights of having Synchronous Objects out there is that we hear from students who are exploring the site and asking questions. We’re considering developing this part of the project more through participatory learning initiatives. In the meantime we’re enjoying hearing about the emergent grassroots curriculum development that is taking place and we encourage you to get in touch with us with your questions, ideas, or needs. The latest email we received from from a geography student finishing her undergraduate work at a University in Canada.

She is writing her honors paper on our project and as an initial inquiry wanted to know how we gathered our spatial data for the Movement Density object and others. I thought this might be of interest to others so I am posting Maria’s reply here in the blog:

“We gathered our spatial data by tracking one point on video on each dancer throughout the entire dance. We used both the front and top view of the video to track this using special purpose software that we developed in house. This data was then organized as x,y,z locations over time per dancer. Then we provided this data in a number of formats, for use in visualization. We would reformat the data using code, to match whatever the visualization required. The Density surface maps in the Movement Density object were then created by reading in this data and generating it for every second of the dance. The geographers then output the results in a frame by frame basis, at 25fps.” -Maria Palazzi

And a note on interdisciplinary process: it was never this neat or orderly. In our experience, interdisciplinary collaboration requires a high tolerance emergent structure. The real discoveries in interdisciplinary knowledge transfer happen in the subtle moments of understanding that come within the chaos of working together, not unlike counterpoint. Our collaborators in Geography, Hyowon Ban and Ola Ahlqvist made 100s of different kinds of maps and we looked at all of them together and discussed them at length. We discussed the different interpolations and how they either revealed or obscured the data. We looked a issues of color and transparency. We spent time unpacking our terminologies and carefully defining the exact use of words like spatial data and attribute data. We discussed the issue of creating data from qualitative sources and shared differing disciplinary perspectives. But more than anything we came to the room each time with a healthy respect for the differences in our ways of understanding and our ways of making and doing and a sense of confidence that there were opportunities for agreement and interesting new alignments.

Keep your questions and interests coming, we love hearing from you.
Norah