Identifying urban form typologies in Seoul using a new Gaussian mixture model-based clustering framework

Abstract

Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and workflows, lacking a systematic framework. This study addressed these issues by developing a new form clustering framework to systematically identify form typologies at a large scale and demonstrated its application in Seoul. With a 500 m × 500 m grid as the basic spatial unit and twelve urban form attributes as learning features, 14 clusters were identified using the Gaussian mixture model. These clusters were further translated into form typologies following a semantic typology naming system, with representative form samples identified. The resulting typologies were then verified and validated through comparisons with previous studies. Their relationships with zoning classes were also examined, emphasizing their role in urban planning and design. Results suggest this new framework is an effective and promising way to identify urban form typologies in complex urban environments to better support urban planning and management.

Exploring the Spatio-Temporal Evolutionary Characteristics of Paomo Restaurants in Xi’an’s Central Urban Area Through POI Data Analysis

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Identifying urban form typology of residential areas in major cities in South Korea using clustering

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Developing a two-level machine-learning approach for classifying urban form for an East Asian mega-city

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