Perceiving Data with Color Theory
Data collection is a continuous process which uses the philosophical framework of essentialism to represent our identities in the digital sphere. However, though we are constantly being represented as data, existing metaphors for understanding data are rooted heavily in the language of mathematics and probability theory, eluding the grasp of the general public. This project asks: what new metaphors for data can we devise that are immediately intuitive to everyone? What innate cognitive frameworks do we already have for perceiving data?
In Light Waterfall, student ID numbers are read off of a magnetic card swipe and mapped to a unique falling light pattern on strips of RGB LEDs using color theory, underscoring the extraordinary uniqueness attributed to our existing data identities.
In Personal Rave, participants are asked to perceive the world from the perspective of a light diode. Light diode sensors are mapped to the color spectrum within a mask with a color theory algorithm, shifting colors from one eye to the other as the participant’s head pans the landscape.