[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122837-en":3,"doc-seo-122837-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122837,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based characterisation of urban morphology with the street pattern","Streets are a core component of the built environment, and their spatial layouts are central to quantifying urban morphology. Conventional street-pattern analysis relies on coarse units such as administrative boundaries and grids, limiting the ability to represent the diversity and complexity of street networks. The research introduces a machine learning method that automatically recognizes street patterns using an adaptive analysis unit based on street-based local areas (SLAs). SLAs employ network partitioning to fit distinct street structures, followed by hierarchical clustering on network metrics. A case study across six cities shows diverse, hierarchical street-pattern taxonomies and derives four major morphometrics-based types with eleven sub-types, capturing structural differences such as urban–suburban divisions and the number of urban centres.","Computers, Environment and Urban Systems 109 (2024) 102078  \nContents lists available at ScienceDirect  \nComputers, Environment and Urban Systems  \njournal [homepage: www.elsevier.com/locate/ceus](homepage: www.elsevier.com/locate/ceus)  \n| Machine learning-based characterisation of urban morphology with the street pattern |  |  |  |\n| --- | --- | --- | --- |\n| Cai Wua, *, Jiong Wang a, Mingshu Wang b, Menno-Jan Kraaka a Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the Netherlands b School of Geographical & Earth Sciences, University of Glasgow, Glasgow, United Kingdom |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Urban morphology Street patterns Machine learning Hierarchical clustering Urban patterns |  | 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. |  |\n\n1. Introduction  \nIn the rapidly urbanising world, creating inclusive, safe, resilient, and sustainable cities, as outlined in the United Nations Sustainable Development Goals (SDGs) 9 and 11, is more critical than ever. A key aspect of achieving these goals lies in understanding the urban built environment, which is a reflection of the inherent urban socioeconomic activities of a city (Jacobs, 1961; Li, Li, Zhu, Song, & Wu, 2013; Lynch, 1960; Venerandi, Zanella, Romice, Dibble, & Porta, 2017; Wu, Smith, & Wang, 2021). Many scholars and practitioners believe in a mutual influence between tangible urban space and intangible human activities. Therefore, a better understanding of the urban built environment could inform potential interventions for enhanced urban activities and living (Batty et al., 2013; Çalikan & Marshall, 2011; Cheng & Shaw, 2018). The morphological study of the physical urban environment involves analysing the forms of basic urban elements, such as streets, buildings, and plots (Moudon, 1997). This study chooses the street as the focus to  \ntap into the morphological study of urban forms, as streets are one fundamental element of a city where human activities are concentrated (Wang & Vermeulen, 2021).  \nIn the study of street forms, street patterns are introduced to ease understanding of the complex street network in the urban built environment. Street patterns refer to the types","cbCaijZoQ2vYIrVu","https://ap.wps.com/l/cbCaijZoQ2vYIrVu","pdf",15888142,1,13,"English","en",105,"# Introduction\n## Urban morphology and the role of streets\n## Street patterns and conventional representations\n## Limitations of existing unit-of-analysis choices\n## Toward machine learning and adaptive analysis units","[{\"question\":\"What limitation does the study identify in traditional street pattern analysis?\",\"answer\":\"Traditional approaches use administrative boundaries and grids, which fail to capture the diversity and complexity of street networks and make transferability difficult.\"},{\"question\":\"How does the proposed method automatically recognize street patterns?\",\"answer\":\"It builds an adaptive analysis unit using street-based local areas (SLAs), computes network metrics for streets, and groups streets with similar characteristics via hierarchical clustering.\"},{\"question\":\"What did the case study across six cities reveal about street pattern diversity?\",\"answer\":\"Street pattern types are diverse and hierarchical, making it challenging to categorize them into clearly demarcated taxonomies, leading to a new set of morphometrics-based patterns with major types and sub-types.\"}]","Machine learning-based characterisation of urban morphology with the street pattern | 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limitation does the study identify in traditional street pattern analysis?","Question",{"text":75,"@type":76},"Traditional approaches use administrative boundaries and grids, which fail to capture the diversity and complexity of street networks and make transferability difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method automatically recognize street patterns?",{"text":80,"@type":76},"It builds an adaptive analysis unit using street-based local areas (SLAs), computes network metrics for streets, and groups streets with similar characteristics via hierarchical clustering.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the case study across six cities reveal about street pattern diversity?",{"text":84,"@type":76},"Street pattern types are diverse and hierarchical, making it challenging to categorize them into clearly demarcated taxonomies, leading to a new set of morphometrics-based patterns with major types and 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