Article-Journal

Theory-informed and interpretable graph learning for urban commuting flows
PIG-GNN combines graph learning with spatial interaction theory to model commuting flows. It distinguishes origins from destinations …
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Understanding multi-perspective urban green space patterns under urban expansion: Evidence from Guangzhou
This study combines satellite-based and street-level measures of greenness to examine urban expansion in Guangzhou. Comparing these …
Qiming Liu, Shaoqing Dai, Guosheng Yang, Cai Wu
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Beyond fidelity: Rethinking realism through human behavior in VR city modeling LOD design
This study uses gaze behaviour to examine realism in virtual urban environments. It assesses how geometric complexity, appearance, and …
Yunlei Su, Guosheng Yang, Wufan Zhao, Cai Wu
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Beyond single snapshots: Quantifying multi-scale heterogeneity from street-view imagery and what it reveals about perception
This study examines how variation within and between street views relates to urban perception. Using imagery and geotagged social media …
Guosheng Yang, Yunlei Su, Minwei Zhao, Chaosu Li, Cai Wu
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UST-GNN: A unified spatial–topological graph neural network framework for urban analytics demonstrated through a case study on urban health prediction
UST-GNN brings neighbourhood connections, urban attributes, and spatial embeddings together in a graph learning framework. A study of …
Minwei Zhao, Sanja Šćepanović, Stephen Law, Ivica Obadić, Cai Wu, Daniele Quercia
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Advancing graph-supported machine learning in generative design for architectural engineering
This review maps how graph-supported machine learning is used in generative design for architectural engineering. It examines data, …
Hanmo Wang, Zhuyin Lu, Shawn Owyong, Huan Ting Chen, Cai Wu, Tam H. Nguyen, Alexander Lin
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'Local Hubs and Global Gateways': Understanding the impact of Singapore's master plan on urban polycentricity
This paper examines how Singapore’s master plan reshapes urban polycentricity under the “Local hubs and global …
Cai Wu, Mingshu Wang, Jiong Wang, Duncan Smith, Menno-Jan Kraak
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Formalising the urban pattern language: A morphological paradigm towards understanding the multi-scalar spatial structure of cities
This paper formalises urban pattern language as a multi-scalar analytical framework for understanding city structure. It shows how …
Cai Wu, Jiong Wang, Mingshu Wang, Filip Biljecki, Menno-Jan Kraak
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Machine learning-based characterisation of urban morphology with the street pattern
Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a …
Cai Wu, Jiong Wang, Mingshu Wang, Menno-Jan Kraak
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Mapping Street Patterns with Network Science and Supervised Machine Learning
This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, …
Cai Wu, Yanwen Wang, Jiong Wang, Menno-Jan Kraak, Mingshu Wang
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