Interpreting core forms of urban morphology linked to urban functions with explainable graph neural network
This work addresses urban planning and sustainability by providing an interpretable method to analyze urban morphology, though it is incremental as it builds on existing deep learning and graph neural network techniques.
The study tackled the challenge of representing complex urban forms in an explainable way by proposing CoMo, an explainable deep learning framework that achieved a stable weighted F1-score of 89.14% and revealed links between urban form and function, such as a correlation with land use efficiency (R2=0.721).
Understanding the high-order relationship between urban form and function is essential for modeling the underlying mechanisms of sustainable urban systems. Nevertheless, it is challenging to establish an accurate data representation for complex urban forms that are readily explicable in human terms. This study proposed the concept of core urban morphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call CoMo. By interpretating the well-trained deep learning model with a stable weighted F1-score of 89.14%, CoMo presents a promising approach for revealing links between urban function and urban form in terms of core urban morphology representation. Using Boston as a study area, we analyzed the core urban forms at the individual-building, block, and neighborhood level that are important to corresponding urban functions. The residential core forms follow a gradual morphological pattern along the urban spine, which is consistent with a center-urban-suburban transition. Furthermore, we prove that urban morphology directly affects land use efficiency, which has a significantly strong correlation with the location (R2=0.721, p<0.001). Overall, CoMo can explicably symbolize urban forms, provide evidence for the classic urban location theory, and offer mechanistic insights for digital twins.