Resilient Communities: A Novel Workflow

Abstract

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.

Measuring Resilient Communities: an Analytical and Predictive Tool

“…The satellite images from the first dataset (all of which were at the same altitude and resolution) have been labelled one by one using Microsoft VoTT: Visual Object Tagging Tool (Microsoft, 2019). Our previous work on the city of Copenhagen (Carta et al, 2021) showed that resilience values calculated on proximity and density of physical elements are mostly affected by green areas, natural elements and entertainment venues. Based on these previous findings, we focused on the 4 visually recognisable typologies below to identify relevant classes for our training: 1) Green areas, 2) Buildings (built areas vs unbuilt), 3) Large infrastructures (train stations, stadiums etc.)…”

“…Firstly, we used data from existing literate and rankings, using data from the 2013 Grosvenor report (Barkham et al, 2013) and the Quality of Life global ranking from Numbeo (2021). Secondly, we evaluate the resilience values for the same cities using the method we developed in previous work (Carta et al, 2021), as detailed in Section 3.1. We then compared the three sets of values to estimate the precision of our model.…”

“…In order to test our model, we calculated the resilience value of each neighbourhood using the method developed in Carta et al 2021 which can be summarised in (1):…”

“…In previous work (Carta et al, 2021), we developed a quantitative method to evaluate the resilience of net-zero communities, based on position, density and proximity of physical resources. In this paper we present further findings where we apply our method to generate an online tool to automatically evaluate the level of S. CARTA, T. TURCHI AND L. PINTACUDA resilience of any neighbour and urban area based on their urban configuration.…”

Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1]

“…In previous work (Carta et al, 2021), we developed a quantitative method to evaluate the resilience of net-zero communities, based on position, density and proximity of physical resources. In this paper we present further findings where we apply our method to generate an online tool to automatically evaluate the level of resilience of any neighbour and urban area based on their urban configuration.…”