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New for 2022/2023, the Urban Spatial Science MRes programme explores the theoretical, social and scientific foundations of the modern built environment through a geo-spatial, data-oriented lens.
The new MRes Urban Spatial Science replaces the previous CASA MRes programme, Spatial Data Science and Visualisation.
The Urban Spatial Science MRes programme equips students with a multi-disciplinary and critical perspective on approaches to understanding, monitoring and improving global urban resilience and sustainability through the use of data and spatial analysis. Taught content explores the theoretical, social and scientific foundations of the modern built environment through a geo-spatial, data-oriented lens. We also cultivate a practical appreciation of the technical and methodological ‘state-of-the-art’ associated with urban analytics and data-driven decision making, including: mathematical, statistical and simulation modelling, computer programming, spatial analysis and visualisation. Importantly, these practical skills are underpinned by broad theoretical perspectives on demographics, economics, form and function, network interactions and complexity, governance and policy, planning and, crucially, urban science.
The programme is composed of three pathway* options: (1) Smart Cities and Urban Policy, (2) Modelling and Simulation, and (3) Data Visualisation. Core concepts applicable to all programme pathways provide a foundation in urban spatial science, with pathways supporting optional thematic specialisation. The programme is deliberately cross-disciplinary, drawing on staff with backgrounds in geography, planning, computer science, physics, as well as the arts and humanities.
We do not require a specific undergraduate degree, only a 2:1 classification or equivalent according to the classifications in your country of study (including equivalent professional experience), and seek to encourage creative engagement with the ideas of urban spatial science while recognising the wide range of degrees and prior employment that could be developed with an Urban Spatial Science MRes. Core modules do not presume prior knowledge and the practical application of taught materials informs the programme aims and learning outcomes.
Through learning what is possible with code, about the benefits of data-informed urban analytics, and (as importantly) about the limitations technology-led solutionism, our graduates are distinguished as being simultaneously technically-capable and critically reflective, able to look past the hype that accompanies the buzz around smart cities, urban data science, and urban science.
data science visualisation geography cities smart cities Quantitative Social Science modelling urban