Discovering urban block typologies in Seoul: Combining planning knowledge and unsupervised machine learning

Abstract

Urban blocks are fundamental elements of urban form and play an important role in urban development. Identification of their typologies has been a long-standing challenge in urban planning and design. Traditionally, expert-based classification has been used, but recent studies have shifted towards data-driven clustering to achieve better results. However, most studies tend to use only one of these approaches and few integrate both. To address this gap, this study proposes a framework that combines planning knowledge and machine learning to identify urban block typologies in complex urban environments. The framework first classifies blocks based on their size types according to planning regulations and development types. Then, a combined hierarchical and Gaussian mixture model is used to discover clusters with complex feature data distributions. These clusters are translated into block typologies using a semantic naming system and illustrated with representative blocks. Finally, the validity of the results is tested by comparing them with other building features, such as building age. The proposed integrated framework was applied to Seoul, resulting in the discovery of 27 residential block typologies across the entire city. These typologies show significant correlations with the average building age in the blocks. The application of expert knowledge in the clustering method demonstrates higher accuracy compared to conventional clustering approaches. Overall, this framework holds significant potential for discovering urban block typologies that are consistent with planning domain knowledge and provide urban planners and designers with valuable insights into the characteristics of urban block typologies, thereby helping decision-making regarding zoning regulations, urban design, and urban development strategies.