Last night Liz and I presented our final project in Sculpting Data Into Everyday Objects, and got some great feedback from our guest critics. As I’ve previously described, we we’re looking into the impact of Hurricane Sandy on the NYC Subway System using the MTA turnstile data. The ultimate expression of our research is a sink containing a topographical print of the subway system showing how long it took each station to come back online after the shutdown of October 28, 2012. Here’s a brief video describing a little bit of our process, and the final sculpture in action:
I was recently tipped off to Delaunay Triangulation, which connects an arbitrary set of points with a minimum number of triangles. This is exactly what we needed to create planar surfaces between our subway station nodes, and I was able to do that with the Edgy triangulation library for iOS. It was quite a nice feeling to see this rendered as a 3D topography for the first time.
Once we had the triangles, it was trivial to manually dump the data into an .obj file and open it in Rhino.
Liz Khoo and I have been working with the MTA Turnstile data for our semester-long project in Sculpting Data into Everyday Objects. Over the past few weeks we’ve been able to parse the data and visually investigate what we’ve got.
After paging through a few weeks of data it became clear that the days and weeks after Hurricane Sandy would be our focus. The system was dramatically brought offline just prior to landfall, and the data indicates just how long it took for stations to resume operation after the storm passed.
This was our initial sketch showing the volume of riders entering the system. This screen represents 24 “control units” at 1 station over 1 week.
Here we’re parsing all stations over the course of a week. The bright white activity on the left is the flow prior to Sandy, and the right is just starting to show the first stations coming back online.
We mapped the station data to their geographic coordinates to create this map. Scrubbing the mouse horizontally changes the brightness of the dots correspondant to the traffic shown in the screenshot above. This sketch is parsing 3 weeks of data, which gives us a better indication of how long it took for the stations to recover.
In this sketch we looked at how long an individual station took to come back online. Darker stations took longer. We’re tentatively going to use this data as the basis for our X, Y and Z values for 3D modeling.
Here are some screenshots of an iPad app that lets us browse the data in 3 dimensions. The slider changes the Z depth.