Mapping street patterns with network science and supervised machine learning

Wu, C., Wang, Y., Wang, J., Kraak, M.-J. and Wang, M. (2024) Mapping street patterns with network science and supervised machine learning. ISPRS International Journal of Geo-Information, 13(4), 114. (doi: 10.3390/ijgi13040114)

Abstract

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities.

• Item Type: Articles
• Keywords: Street pattern, urban spatial structure, urban morphology, machine learning.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Dr Mingshu

Creator Roles

Wang, M.: Conceptualization, Methodology, Validation, Writing – review and editing, Visualization

• Authors: Wu, C., Wang, Y., Wang, J., Kraak, M.-J., and Wang, M.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: ISPRS International Journal of Geo-Information
• Publisher: MDPI
• ISSN: 2220-9964
• ISSN (Online): 2220-9964
• Published Online: 28 March 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in ISPRS International Journal of Geo-Information 13(4): 114
• Publisher Policy: Reproduced under a Creative Commons License