A Density-Based Spatial Cluster Analysis Supporting the Building Stock Analysis in Historical Towns
A Density-Based Spatial Cluster Analysis Supporting the Building Stock Analysis in Historical Towns
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DOI：
10.26868/25222708.2019.210346
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
E. Lucchi;V. D’Alonzo;D. Exner;P. Zambelli;G. Garegnani
中科院分区：
文献类型：
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作者：
E. Lucchi;V. D’Alonzo;D. Exner;P. Zambelli;G. Garegnani
本文介绍了支持历史城镇建筑群分析的空间聚类方法的应用。这种方法被应用于马德鲁佐市内的卡拉维诺镇，这是位于特伦托省的历史悠久的定居点，具有很高的自然和遗产价值。所提出的数据挖掘方法表明，当物理特征与其他变量（例如建筑功能、所有者、年龄等级、形状和物理特征、遗产价值和保护状态等）相结合时，无法将卡拉维诺历史中心的多个建筑物分类为集群。经过分析后，可以针对特定的“建筑类型”进行详细的能源审计和动态模拟。
The paper presents the application of a spatial cluster approach supporting the building-stock analysis of historic towns. This method was applied in the town of Calavino, within the Municipality of Madruzzo, an historic settlement located in the Province of Trento and characterized by high natural and heritage values. The proposed data mining approach shows as several buildings can not be classified in clusters for the historic centre of Calavino when physical features are combined with other variables (e.g. building function, owner, age class, shape and physical features, heritage values and conservation state, etc.). After this analysis, detailed energy audits and dynamic simulations can be addressed on specific “building typologies”.