From blocks to cities: Morphology structure rooted in 3D patterns and forming clusters at the block level

This article proposes a 35D form index system to quantitatively describe the 3D form of urban blocks. Utilizing the T-distributed stochastic neighbor (TSNE) embedding algorithm for cluster analysis, the visually complex and disordered urban 3D texture is translated into distinct form clusters, enabling the recognition of the overall urban form structure from the block perspective. The research methodology includes experiments conducted in the central area of Nanjing and comparative analysis in three neighboring cities: Shanghai, Hangzhou, and Suzhou. Results demonstrate the efficacy of form parameters and cluster analysis in achieving sound recognition. The four cities differ remarkably in the number and distribution structure of clusters. Shanghai has the fewest types of clusters with a compact distribution, Suzhou has the most types with a dispersed distribution, and Hangzhou and Nanjing exhibit similar characteristics, located between Shanghai and Suzhou. Correlation analysis reveals a negative relationship between the number of cluster types and the level of urban socioeconomic development in similar areas. This research implies that governments and urban planners can exploit neighborhood morphological types to devise customized spatial management and renewal strategies. The overall urban structure can be improved by strategically minimizing the quantity and distribution of neighborhood morphological types, fostering socioeconomic development. (c) 2023 Higher Education Press Limited Company. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This article proposes a 35D form index system for quantitatively describing the 3D form of urban blocks. Using cluster analysis, the visually complex and disordered urban 3D texture is translated into distinct form clusters, allowing for recognition of the overall urban form structure from the block perspective. The research conducted experiments in Nanjing and comparative analysis in Shanghai, Hangzhou, and Suzhou. Results demonstrate the effectiveness of form parameters and cluster analysis in achieving sound recognition. The number and distribution structure of clusters differ significantly among the four cities. Correlation analysis reveals a negative relationship between the number of cluster types and the level of urban socioeconomic development in similar areas. This research suggests that neighborhood morphological types can be utilized by governments and urban planners to devise customized spatial management and renewal strategies, thereby improving the overall urban structure by strategically minimizing the quantity and distribution of neighborhood morphological types.