Whitney Huang '19, Mihika Kapoor '18, Danny Wilson '18
In a world of ever increasing accessibility to data, we visualized a dynamic heat map of people on Princeton’s campus. We have chosen not to label or explain any of them. Not to highlight any observations of interest, hypotheses about trends, or to postulate what the data means.
Part of the inherent value of this piece is its abstraction; however, we struggled to represent data in a way that it still remained sensical. An 18x18 LED grid is limiting in the era of the Retina Display. We explored disguising high resolution displays as simpler surfaces but ultimately settled on the aesthetic of the RGB LEDs. Additionally, we needed to determine whether to present live data or historical data—which dictated our hardware requirements.
Our primary challenge was coherently consolidating data from a series of email reports from OIT detailing population movement across campus via wifi router feedback. Other explorations included visualizing energy usage across campus and Uber movement in NYC. Apart from the visualization itself, factors that informed our final design included budget, practicality of soldering hundreds of LED nodes, using an Arduino—or a Raspberry Pi for more complicated computation, and the scale limitations imposed by our tools (primarily our 12”x24” laser cutter). If the project is well received, we may consider a full-scale installation.
Transformation is a central tenet of our project’s theme in that we strive to transform the data into an interpretable form—without imposing our own biases upon it. This presented some unique difficulties and we worked hard to find a balance between data that was understandable and data that was completely analyzed ahead of time for the viewer.
Our collective experience with modeling and electrical hardware design was rather limited prior to this project. Now we find ourselves confident, in vector graphics, computer aided design, electrical signal transmission, microcontroller programming, laser cutter operation, and custom graphical representation of data with visualization constraints.