Neighbourhood Types as Planning Tool
Partnering with the City of Vancouver elementslab developed and tested a machine-learning approach to future neighbourhood planning by identifying & measuring existing and future neighbourhood types.
This project developed neighbourhood typologies, built a “sandbox” model of future what-if scenarios, ran select indicators from the City of Vancouver’s Resilient Neighbourhood Design Tool (RNDT), and evaluated the methods and results. In partnership with the City of Vancouver, elementslab developed and tested a machine learning-based approach to identification, analysis and comparison of existing and future-state neighbourhood types. This project assists the City to test and evaluate this future planning method for modelling neighbourhood form alternatives and measuring them with the RNDT, the City’s comprehensive assessment framework that connects physical design indicators with resilience objectives.
A machine-learning method identified eight urban form types in Vancouver— a representative set of neighbourhood scale typical patterns, based on physical characteristics. This method facilitates and expedites the dynamic generation and measuring of urban form for rapid iteration of future what-if scenarios generated from the contemplated policy options, then measured against the many categories of urban resiliency- climate action, neighbourhood equity, built form, open space/amenities, housing mixes, and sustainable mobility.
Sponsor: Social Sciences and Humanities Research Council (SSHRC)
elementslab Team: Ronald Kellett, Cynthia Girling, Yuhao Bean Lu, Ruby Barnard, Nicholas Martino
City of Vancouver Collaborators: Christopher Erdman, Kari Dow, Community Planning Division