Resilient Communities: A Novel Workflow

Abstract

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This study presents a novel workflow to define how resilient communities can be analysed and improved through the optimisation of sustainable design principles through quantitative methods. Our model analyses successful sustainable communities extracting information about daily routines (commuting, working, use of buildings etc.). From these routines, we infer a set of key successful aspects based on location, density and proximity. We then model a resilient community and analyse it using a combination of clustering techniques to find patterns and correlations in the success of existing communities. The proposed workflow is applied to the city of Copenhagen as a case study. The aim of the proposed model is to suggest to designers and city-level policy makers improvements (with manipulation of variables like density, proximity and location of urban typologies) to help them to achieve different levels of sustainable goals as set out by the United Nations Global Challenges including integration inclusiveness and resilience. By using a clustering technique, patterns of proximity have been identified along with density and initial correlations in the observed urban typologies. Some of these correlations were used to illustrate the potential of this novel workflow.

Keywords

• resilient communities
• machine learning
• net-zero communities
• resilience
• quantitative methods