in collaboration with Maider Llaguno
This research utilizes dynamic data collection techniques to map the air quality of the University of Southern California campus with the use of a dynamic visualization machines. The collected high-resolution climatic data - CO2, temperature and humidity concentration - are dictating the behavior of a 4-axis visualization machines that moves and behaves in simulated three-dimensional physical space. By separating various climatic variables and assigning them to different appendages of the visualization machine, the rhythmic differences are amplified in a way that one becomes aware of the concentrations and durations. The goal is to amplify the reading of the climatic data and increase sensitivity towards the built environment.