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MSc Urban Spatial Science

New for 2022/2023, the MSc Urban Spatial Science programme explores the theoretical, social and scientific foundations of the modern built environment through a geo-spatial, data-oriented lens.

The Urban Spatial Science MSc 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. 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, 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 MSc. 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. 

Programme Aims

  • Experience a broad range of theoretical perspectives on the demographics, economics, form, function, network interactions, governance, policy, planning and, crucially, science of cities across the World.
  • Equip students with qualitative, quantitative and spatial analytic skills for the interpretation of urban data leading to data-informed decisions and policies.
  • Explore emerging technological innovations, methodologies and theories in cities across the Globe to tackle fundamental governance and sustainability problems facing the urbanised World.
  • Empower students to critically engage with and reflect upon commonly used concepts, methods and buzzwords in urban analytics and governance, providing confidence in responding to technological transitions and a robust foundation for future employment. 
  • Combine the taught material, academic expertise and personal research interests to develop an independent and unique research project into a pertinent and applied urban-centric topic. 

Learning Outcomes 

Knowledge and understanding 

  • Identify key urban theories and discuss their relationship to contemporary challenges
  • Outline topical global urban problems alongside recent methodological approaches and establish the validity of spatial analysis in furthering understanding
  • Critically debate and assess urban research in relation to methodological advancement and policy outcomes
  • Build upon module content and wider academic and policy literature in formulating independent, reproducible and original urban related research

Skills, techniques, methods and practical applications of concepts and theories 

  • Identify geo-spatial data sources and critically assess their applicability for urban focused studies
  • Explain and implement relevant geo-spatial analytical and visualisation approaches in relation to relevant theory
  • Propose smart, data-informed and appropriate solutions for contemporary and future urban challenges 
  • Undertake spatial analysis and urban science research to produce outputs — including data visualisation — appropriate to the intended audience
  • Describe and evaluate competing urban data sources, methodologies, workflows and visualisations
  • Compose academically rigorous and robust reports with a flowing narrative, outlining debate, being interspersed with opinion, whilst highlighting research gaps
  • Draw on taught ‘best-practice’ in industry and academia to write reproducible code and analytical workflows to obtain, wrangle, and analyse data

Transferable skills 

  • Work in a team from diverse educational and international backgrounds in achieving a common goal
  • Evaluate and make decisions at all stages of the typical spatial data science workflow in creating meaningful and robust outputs
  • Effectively source, wrangle, and analyse data, and appropriately communicate the results based on the intended audience
  • Realistically solve problems based on the available data, resources, and expertise
  • Write balanced and concise reports that consider available evidence — and its limitations — in reaching recommendations
  • Lead and manage an independent research project, in turn demonstrating time management, critical evaluation, ethical consideration and appropriate statistical methodologies

Structure

Students undertake modules to the value of 180 credits. The programme consists of three compulsory 15 credit modules (45 credits), with 45 credits of pathway specific modules alongside, one 15 credit optional module (within CASA), one 15 credit elective module (within CASA or any other UCL department) or a 30-credit optional module (within CASA) and a 60-credit compulsory dissertation module.

The modules on the programme are delivered through a combination of diverse teaching and learning activities in traditional and ‘flipped’ formats. Lectures feature widely, as do computer-based practical classes, tutorials alongside both student and teacher led discussion groups. Self-study is expected throughout the programme. 

In addition to formal teaching, students can learn directly from experts in the built environment and spatial analysis through the weekly term time CASA seminar series. 


Mode of Study
Full-time: 1 calendar year 
Part-time: 2 calendar years 
Modular flexible: 5 calendar years 

Location of study 
Campus-based, Bloomsbury Campus


Academic Partnerships

As part of the dissertation module students may have opportunities to collaboratively work with external organisations on dissertation projects. This usually involves meeting in the partner’s office (the academic supervisor is typically present as well), but on occasion students may be allocated a workspace. However, this is not a requirement of any dissertation project or the module and is on a case-by-case basis. 

Assessment

Assessment is undertaken via a variety of means, including practical projects, group presentations, written technical coursework reports, essays, workbooks, and a final research dissertation. 

Careers & Alumni

This programme provides students with the skills and knowledge base to embark on a professional or academic path through the highly interdisciplinary field of urban spatial science and the wider urban planning and policy fields (see the summary programme description and learning outcomes for specific skills). 

Since its original inception in 2013, graduates have gone on to pursue a wide variety of careers in local government, urban planning, software development and academic research. This is indicative of the breadth of knowledge and opportunities afforded by our programme. 

Students have access to termly University and departmental career events, that latter of which involve our active alumni network.  

*As defined in the UCL Academic Manual:
Pathway: A Pathway is an informal specialism within a Programme or Route which guides students towards a particular area but which does not lead to a discrete Field of Study. A Pathway is typically defined by the different Option and Elective Modules available within the Programme but this Pathway is not recorded separately in the Student Record System and does not appear on the student’s degree certificate or transcript.

spatial analysis geography visualisation cities smart cities geographic information systems