Cai Wu
Biographic Data
| ID | 4418312 |
|---|---|
| NAME | Cai Wu |
| GIVEN NAMES | Cai |
| FAMILY NAME | Wu |
| SIGNATURE | WU C |
| AFFILIATIONS | University of Twente |
| ORCID | 0000-0002-5578-5525 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Ust-GNN
Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy. Traditional statistical approaches often struggle to capture complex non-linear relationships, while many machine learning methods overlook the joint roles of spatial autocorrelation and network topology in urban systems. Recent advances in GeoAI have addressed these chall…
Understanding multi-perspective urban green space patterns under urban expansion
Theory-informed and interpretable graph learning for urban commuting flows
Beyond single snapshots
‘Local Hubs and Global Gateways’
Master plans are pivotal in strategising urban development, dictating land use, building height, and development intensity. These plans influence the spatial arrangement of urban infrastructure and activities, shaping the morphological and dynamic urban spatial structure. This study evaluates Singapore’s master plan’s effect on urban spatial structure, utilising data extracted from the master plan to project future land use and spatial interactio…
Formalising the urban pattern language
The urban form is a foundational element in urban analytics, planning, and design. However, systematic and consistent depiction of urban form is challenging due to the complexity of urban elements and the variety of scales involved. This paper formalizes the concept of ‘urban pattern language’ as a multi-scalar analytical approach to decode such complexity, drawing on Christopher Alexander's idea that offers solutions for recurrent design problem…
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, 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 …
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 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, th…
Simulating the urban spatial structure with spatial interaction
Polycentric urban development is gaining momentum in both scholarly research and real-life practice. This brings new demand for planning support systems to simulate and analyse the urban spatial structure in terms of polycentricity under various urban policy scenarios. With the help of emerging urban data, urban simulation techniques, and network science, this study proposes a workflow to simulate the urban spatial structure with spatial interact…
Formalising the urban pattern language
The urban form is a foundational element in urban analytics, planning, and design. However, systematic and consistent depiction of urban form is challenging due to the complexity of urban elements and the variety of scales involved. This paper formalizes the concept of ‘urban pattern language’ as a multi-scalar analytical approach to decode such complexity, drawing on Christopher Alexander's idea that offers solutions for recurrent design problem…
Simulating the urban spatial structure with spatial interaction
Polycentric urban development is gaining momentum in both scholarly research and real-life practice. This brings new demand for planning support systems to simulate and analyse the urban spatial structure in terms of polycentricity under various urban policy scenarios. With the help of emerging urban data, urban simulation techniques, and network science, this study proposes a workflow to simulate the urban spatial structure with spatial interact…
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, 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 …
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 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, th…
‘Local Hubs and Global Gateways’
Master plans are pivotal in strategising urban development, dictating land use, building height, and development intensity. These plans influence the spatial arrangement of urban infrastructure and activities, shaping the morphological and dynamic urban spatial structure. This study evaluates Singapore’s master plan’s effect on urban spatial structure, utilising data extracted from the master plan to project future land use and spatial interactio…
Formalising the urban pattern language
The urban form is a foundational element in urban analytics, planning, and design. However, systematic and consistent depiction of urban form is challenging due to the complexity of urban elements and the variety of scales involved. This paper formalizes the concept of ‘urban pattern language’ as a multi-scalar analytical approach to decode such complexity, drawing on Christopher Alexander's idea that offers solutions for recurrent design problem…
Ust-GNN
Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy. Traditional statistical approaches often struggle to capture complex non-linear relationships, while many machine learning methods overlook the joint roles of spatial autocorrelation and network topology in urban systems. Recent advances in GeoAI have addressed these chall…
Understanding multi-perspective urban green space patterns under urban expansion
Theory-informed and interpretable graph learning for urban commuting flows
Beyond single snapshots
Urban Design and Spatial Analysis (6 works) · Geography (4 works) · Land Use and Ecosystem Services (4 works) · Computer Science (3 works) · Economic geography (3 works) · Engineering (3 works) · Human Mobility and Location-Based Analysis (3 works) · Urban Green Space and Health (3 works) · Urban planning (3 works) · Artificial Intelligence (2 works)