generative modeling for Rhino
Finalist in the WPA 2.0 competition sponsored by UCLA Citylab, http://wpa2.aud.ucla.edu
ArcGIS and Grasshopper are doing the heavy lifting here, connecting urban data to site-based parametric procedures.
For more on the grasshopper process, see http://www.grasshopper3d.com/video/local-code-site-design
Project description follows:
Proposal Location : Major US Cities with city-owned abandoned lots, including New York, Los Angeles, Chicago and Washington DC. Case study developed for San Francisco.
Local Code : Real Estates uses geospatial analysis to identify thousands of publicly owned abandoned sites in major US cities, imagining this distributed, vacant landscape as a new urban system. Using parametric design, a landscape proposal for each site is tailored to local conditions, optimizing thermal and hydrological performance to enhance the whole city’s ecology—and relieving burdens on existing infrastructure. Local Code’s quantifiable effects on energy usage and stormwater remediation eradicate the need for more expensive, yet invisible, sewer and electrical upgrades. In addition, the project uses citizen participation to conceive a new, more public infrastructure as well —a robust network of urban greenways with tangible benefits to the health and safety of every citizen.
Project Credits:
Nicholas de Monchaux with
Natalia Echeverri
Liz Goodman
Benjamin Golder
Sha Hwang
Sara Jensen
David Lung
Shivang Patwa
Kimiko Ryokai
Thomas Pollman
Matthew Smith
Laurie Spitler
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