Characterizing urban spatial structure through built form typologies: A new framework using clustering ensembles

Abstract

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.