A spatial-driven urban pattern language framework for design and planning
Cai Wu is a PhD student in the Department of Geo-information Processing. (Co)Promotors are prof.dr. M.J. Kraak and dr. J. Wang from the Faculty ITC and dr. M. Wang from the University of Glasgow.
This thesis explores the evolution of urban design from traditional, empirical methods to a digitalised, data-informed approach centred on a novel multi-scale pattern framework inspired by Christopher Alexander’s concept of a pattern language. This new framework seeks to integrate abstract urban planning theories with tangible urban development realities and emerging technologies, using quantitative urban patterns to simplify the analysis of complex urban environments, thereby enhancing their design and analysis.
The study is grounded in the philosophy of urban morphology, which considers the physical forms of urban elements as significant indicators of social and economic processes. Employing advanced quantitative methods such as network science and machine learning, the thesis addresses urban analysis across three scales: Macro, Meso, and Micro.
At the macro level, it examines urban planners’ use of spatial and master plans focusing on sustainable development elements like density and compactness. This includes a case study of Singapore's master plan which effectively reinforces its sub-centres while maintaining a dominant central business district, illustrating polycentric development.
The meso scale investigates district and neighbourhood planning, with an emphasis on street layouts and connectivity. Machine learning techniques classify complex street networks into patterns such as Gridiron, Organic, Hybrid, and Cul-de-sacs, revealing insights into urban development stages and socioeconomic conditions.
At the micro scale, the study applies the SpaceMatrix method and clustering techniques to individual plots and structures, analysing changes in planning ideologies through a detailed case study of Singapore. This reveals diverse town clusters, each embodying unique density patterns and planning strategies geared towards sustainability and quality of living.
The thesis culminates in a detailed urban morphological framework that uses a pattern language approach to cohesively understand complex urban forms and their interrelationships. It assesses predefined urban patterns in different cities, examining their unique, rule-based arrangements and interrelations. The thesis suggests that the evolving pattern language approach could further incorporate dynamic urban elements, offering a comprehensive view of urban morphology. This foundational work promotes a data-centric, digitally forward methodology poised to enhance future urban planning for sustainable and vibrant environments.
