The general interest in sustainable development models has grown enormously over the last 50 years, and architecture and urban planning are certainly two areas in which research on the topic is most advanced. At the same time, the contribution of computer science for a systematic analysis of the territory, both from a morphological point of view and as regards performance, seems to have been underestimated in today's research. In this context, our research aims to joining the two - until now separate - worlds of computer science, and architecture and urban planning. In particular, in this work we present SIMBA: Systematic clusterIng-based Methodology to support Built environment Analysis. SIMBA aims to enhance a consolidated analysis methodology, the Integrated Modification Methodology (IMM), through the integration of advanced analysis methods for the extraction of relevant patterns from built environment data. Using the city of Milan as a case study, we will demonstrate the possibility for SIMBA to be generalised to the analysis of any built environment.

SiMBA: Systematic Clustering-Based Methodology to Support Built Environment Analysis

Biraghi C. A.;Lenzi E.;Matera M.;Tadi M.;Tanca L.
2022

Abstract

The general interest in sustainable development models has grown enormously over the last 50 years, and architecture and urban planning are certainly two areas in which research on the topic is most advanced. At the same time, the contribution of computer science for a systematic analysis of the territory, both from a morphological point of view and as regards performance, seems to have been underestimated in today's research. In this context, our research aims to joining the two - until now separate - worlds of computer science, and architecture and urban planning. In particular, in this work we present SIMBA: Systematic clusterIng-based Methodology to support Built environment Analysis. SIMBA aims to enhance a consolidated analysis methodology, the Integrated Modification Methodology (IMM), through the integration of advanced analysis methods for the extraction of relevant patterns from built environment data. Using the city of Milan as a case study, we will demonstrate the possibility for SIMBA to be generalised to the analysis of any built environment.
CEUR Workshop Proceedings
Build environment
Clustering
Data mining
Methodology
Multidisciplinary
Sustainability
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1221056
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