This project generates architectural design directly from data. It correlates two sets of data. One representing the amount of trees in certain neighborhoods in New York City, the other representing bicycle routes in these same neighborhoods.
We found that the bicycle routes that were more popular for users (based on the amount of city bikes dropped and picked in these locations), had more trees. The goal was to generate a network that represented this correlation. The extracted network of information is used as a foot print for the creation of architectural design.
Through the 3-dimensional connection of the data abstraction, and the rules created, we are able to begin forming space, while using the established language from the chosen studies.