Machine learning-based characterisation of urban morphology with the street pattern

Wu, C., Wang, J., Wang, M. and Kraak, M.-J. (2024) Machine learning-based characterisation of urban morphology with the street pattern. Computers, Environment and Urban Systems, 102078. (doi: 10.1016/j.compenvurbsys.2024.102078) (Early Online Publication)

Abstract

Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Dr Mingshu

Creator Roles

Wang, M.: Supervision, Writing – review and editing

• Authors: Wu, C., Wang, J., Wang, M., and Kraak, M.-J.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Computers, Environment and Urban Systems
• Publisher: Elsevier
• ISSN: 0198-9715
• ISSN (Online): 1873-7587
• Published Online: 15 February 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in Computers, Environment and Urban Systems 2024
• Publisher Policy: Reproduced under a Creative Commons licence