This interdisciplinary course examines how urban data is collected, analysed and used, and how spatial methods can support research, policy and decision-making. You explore the social, environmental and governance contexts that shape urban systems, applying critical and technical approaches to areas such as housing, transport, climate resilience and urban inequality.
Through a hands-on approach, you learn to apply advanced analytical methods to urban questions. You develop practical expertise in coding, spatial analysis, urban data science and reproducible research, alongside a critical understanding of urban systems, governance and the limits of technology-driven decision-making. The course encourages you to evaluate urban data carefully, recognising both the insights data can provide and the gaps, biases and uncertainties it may contain.
You tailor your studies through specialist pathways connected to the research strengths of the Bartlett Centre for Advanced Spatial Analysis . The four pathways are Big Data and AI, Smart Cities and Urban Policy, Urban Modelling and Simulation, and Data Visualisation, allowing you to shape the MSc around your interests and future direction.
You will develop the technical, analytical and critical skills needed to understand and respond to contemporary urban challenges through data, spatial analysis and urban research. You build expertise in quantitative analysis, coding, data visualisation, machine learning, spatial modelling and reproducible research, while developing understanding of urban systems, governance and the limitations of data-driven decision-making. Through the core modules, you will have developed your skills using Python, R, Git/GitHub, with the chance to enhance skills and knowledge of Google Earth Engine, NetLogo, React, and HTML dependant on the optional modules you choose.
The course strengthens professional skills valued across urban analytics, planning, policy and data-focused sectors, including critical thinking, communication, collaborative working, problem-solving and the ability to present complex information clearly to different audiences via a range of analytical methods.
Urban and spatial data science graduates have pursued and continued in careers across urban analytics, data science, planning, policy, technology and research. Typical roles include data analyst, software developer, urban researcher, spatial analyst, consultant and public-sector strategist.