Prior research on urban form typologies has largely relied on qualitative classification methods, resulting in subjective and limited analyses. Recently, the emerging data-intensive studies often use a single clustering algorithm and parameter setting, raising concerns about the reliability of the findings. This paper introduces a novel clustering analytical framework for conducting typological studies on urban form that yield stable and reliable results. We employ clustering ensembles, which can combine multiple clustering algorithms to further provide a comprehensible output that facilitates interpretation and knowledge generation. By applying the new framework using 3D building data in Guangzhou, we identify eight typologies of urban built forms and reveal a consistent polycentric pattern across different clustering algorithms and parameter settings. The findings have implications for urban land use planning and regulations by integrating 3D representations of urban form.

中文翻译：

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通过建筑形式类型来表征城市空间结构：使用集群集成的新框架

先前对城市形态类型学的研究很大程度上依赖于定性分类方法，导致分析主观且有限。最近，新兴的数据密集型研究经常使用单一的聚类算法和参数设置，引发了人们对研究结果可靠性的担忧。本文介绍了一种新颖的聚类分析框架，用于对城市形态进行类型学研究，并产生稳定可靠的结果。我们采用聚类集成，它可以组合多种聚类算法，以进一步提供易于理解的输出，从而促进解释和知识生成。通过应用使用广州 3D 建筑数据的新框架，我们识别了城市建筑形式的八种类型，并揭示了不同聚类算法和参数设置之间一致的多中心模式。研究结果通过整合城市形态的 3D 表示对城市土地使用规划和法规产生影响。