Mapping Street Patterns with Network Science and Supervised Machine Learning

Aug 2, 2026

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This study introduces a machine learning-based framework for mapping street patterns in urban morphology, providing an objective and scalable alternative to traditional methods. Using six diverse cities, it applies supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with street-based local area (SLA) as the analysis unit. Quantitative street metrics and GIS are analyzed with random forest to identify predictive features and reveal spatial structures such as ring formations and urban cores. Results also show that the unit of analysis strongly affects street-pattern identification, supporting future expansion for improved modeling of urban growth and sustainable planning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Introduction

Literature Review

Street Patterns in Urban Morphology

Article Mapping Street Patterns with Network Science and Supervised Machine Learning Cai Wu 1, Yanwen Wang 1, Jiong Wang 1, Menno-Jan Kraak 1 and Mingshu Wang 2, * Citation: Wu, C.; Wang, Y.; Wang, J.; Kraak, M.-J.; Wang, M. Mapping Street Patterns with Network Science and Supervised Machine Learning. ISPRS Int. J. Geo-Inf. 2024, 13, 114 . [https://doi.org/10.3390/ijgi13040114](https://doi.org/10.3390/ijgi13040114) Academic Editors: Maria Antonia Brovelli and Wolfgang Kainz Received: 20 December 2023 Revised: 14 March 2024 Accepted: 17 March 2024 Published: 28 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, 7522 NB Enschede, The Netherlands; [c.wu@utwente.nl](c.wu@utwente.nl) (C.W.); [y.wang-4@utwente.nl](y.wang-4@utwente.nl) (Y.W.); [j.wang-4@utwente.nl](j.wang-4@utwente.nl) (J.W.); [m.j.kraak@utwente.nl](m.j.kraak@utwente.nl) (M.-J.K.) 2 School of Geographical & Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK * [Correspondence: mingshu.wang@glasgow.ac.uk](Correspondence: mingshu.wang@glasgow.ac.uk) 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. Keywords: street pattern; urban spatial structure; urban morphology; machine learning 1. Introduction Studying urban morphology is crucial for a comprehensive understanding of the built environment in our increasingly complex urban landscapes [1,2], including a broad spectrum of elements such as buildings, streets, public spaces, and green spaces [3–6] . Streets, in particular, are the backbone of urban connectivity and accessibility, dictating the flow of people, goods, and information [7–11] . They significantly influence urban planning decisions, impacting everything from public transportation routes to the location of services and amenities. Streets are also a very complex subject to study as countless factors are involved in how a street performs and is perceived by people [7,12] . To ease the studying process, streets are abstracted as a network layout with street junctions as nodes and streets as edges. Street patterns further summarise the types of street network layouts to help scholars and planners ease the understanding of street morphology and provide effective communication tools for stakeholders. Howe

FAQs

What is the main contribution of the proposed framework?

The framework uses supervised machine learning to map street patterns in urban morphology in an objective, scalable way compared with traditional approaches.

How are street patterns classified in the study?

Street networks are classified into gridiron, organic, hybrid, and cul-de-sac patterns using street-based local area (SLA) as the unit of analysis.

Which methods and data are used to perform the analysis?

The study uses quantitative street metrics with GIS and applies the random forest method, leveraging OpenStreetMap (OSM) data from six case study cities.

Aug 2, 2026

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