As a computational design summer associate on the Sidewalk Labs' Generative Design team, I was primarily concerned with two tasks. Exploring ways of expanding the existing use cases for the generative design tool for urban prototyping, and streamlining the process of including spatial data into this tool. The initial research was focused on establishing potential users and use cases for the generative design tool, and the spatial data needed for this.
The complicated process of importing spatial data limited the scope of its application in the generative design tool's objective functions. The inclusion of standardized data in the tool's spatial analyses also provided the opportunity to improve their accuracy. As a result, the process of importing spatial data's replicability and availability of data were important factors in narrowing down this research.
The final product was a replicable process for automating the import and analysis of urban spatial data into the generative design pipeline.
The ingestion of spatial data into the pipeline was streamlined through a series of automated processes. A disaggregation of the resident and workforce population datasets from larger census geographies into buildings was also used to estimate pedestrian activity at the street level. This process was conducted using a test site, but validated through replication in other cities and over a larger area.
This process could be integrated into the existing generative design workflow to generate building-level population, workforce and land use among other data as well as a 'buzz metric', which visualizes pedestrian activity.