The Bartlett Centre for Advanced Spatial Analysis



The MSc Smart Cities and Urban Analytics comprises 180 credits which can be taken full-time over 12 months or on a flexible modular basis of up to 5 years duration.

If taken full time over one year, the following structure would be followed:

Term 1 – 60 credits to be taken

Urban Systems Theory (15 credits) - Term 1

Urban Systems Theory will provide you a comprehensive introduction to a theory and science of cities. Many different perspectives developed by urban researchers, systems theorists, complexity theorists, urban planners, geographers and transport engineers will be considered, such as spatial interactions and transport models, urban economic theories, scaling laws and the central place theory for systems of cities, growth, migration, etc., to name a few. The course will also focus on physical planning and urban policy analysis as has been developed in western countries during the last 100 years.

This class runs during term one, for two hours per week. Assessment is by coursework (2,500 – 3,000 words).

The indicative reading list for this module can be viewed at Urban Systems Theory reading list.

Quantitative Methods (15 credits) - Term 1

Quantitative Methods aims to equip students with essential mathematical techniques to be able to describe quantitatively many aspects of a city. You will learn various methodologies, from traditional statistical techniques, to more novel approaches, such as complex networks. These techniques will focus on different scales and hierarchies, from the micro-level, e.g. individual interactions, to the macro-level, e.g. regional properties, taking into account both discrete and continuous variables, and using probabilistic and deterministic approaches. All these tools will be developed within the context of real world applications.

This class runs during term one, for two hours per week. 
Assessment is by a mix of presentations and coursework.

The indicative reading list for this module can be viewed at Quantitative Methods reading list.

Geographic Information Systems and Science (15 credits) - Term 1

GI Systems and Science aims to equip students with an understanding of the principles underlying the conception, representation/measurement and analysis of spatial phenomena. It presents an overview of the core organising concepts and techniques of Geographic Information Systems, and the software and analysis systems that are integral to their effective deployment in advanced spatial analysis.

The practical sessions in the course will introduce students to both traditional and emerging technologies in geographical information science through the use of desktop GIS software like Arc GIS and Quantum GIS, and the powerful statistical software environment, R.

In developing technical expertise in these software tools, students will be introduced to real-world geographical analysis problems and, by the end of the course, will be able to identify, evaluate and process geographic data from a variety of different sources, analyse these data and present the results of the analysis using different cartographic techniques. 

This class runs during term one, for three hours per week (one hour lecture followed by two a hour practical). Assessment is by coursework (2,500 – 3,000 words).

The indicative reading list for this module can be viewed at Geographic Information Systems and Science reading list.

Term 2 – 60 credits to be taken

Smart Cities: Context, Policy and Government (15 credits) - Term 2

Smart Cities: Context, Policy and Government will provide you a perspective of smart cities from the viewpoint of technology. It will provide a context for the development of smart cities through a history of computing, networks and communications, of applications of smart technologies, ranging from science parks and technopoles to transport based on ICT. The course will cover a wide range of approaches, from concepts of The Universal Machine, to Wired Cities and sensing techniques, spatio-temporal real time data applications, smart energy, virtual reality and social media in the smart city, to name a few. 

This class runs during term two, for one and a half hours per week.
 Assessment is by coursework (2,500 – 3,000 words).

The indicative reading list for this module can be viewed at Smart Cities reading list.

Spatial Data Capture, Storage & Analysis (30 credits) - Term 2

Spatial Data Capture, Storage & Analysis will teach you the basic tools you will need to manipulate large datasets derived from Smart Cities data, from sensing, through storage and approaches to analysis. You will be able to capture and build data structures, perform SQL and basic queries in order to extract key metrics. In addition, you will learn how to use external software tools, such as R, Python, etc., in order to visualise and analyse the data. These database statistic tools will be complemented by artificial intelligence and pattern detection techniques, in addition to new technologies for big data.

This class runs during term two, for two hours per week.

Assessment is by project output (5,000 – 6,000 words).

Urban Simulation (15 credits) - Term 2

On this module you will learn to construct and apply models in order to simulate urban systems. These are key in the development of smart cities technologies. You will learn different approaches, such as land-use transport interaction models, cellular automata, agent-based modelling, etc., and realise how these are fashioned into tools that are applicable in planning support systems, and how they are linked to big data and integrated data systems.  These models will be considered at different time scales, such as short-term modelling, e.g. diurnal patterns in cities, and long term models for exploring change through strategic planning. 

This class runs during term two, for two hours per week.
 Assessment is by coursework (2,500 – 3,000 words).

The indicative reading list for this module can be viewed at Urban Simulation reading list.

Optional modules

Students may choose any UCL Masters 15 credit module; however, the following two modules are highly recommended:

CASA0013 Introduction to Programming for Spatial Analysis (T1) [If you are not an experienced programmer]

Introduction to Processing for Architecture and Design covers the rudiments of programming using Processing, a Java-based language created for visual designers, architects and artists. Through the course, students learn how to use core Processing methods, and transferable programming techniques, to create programming solutions to visualisation and analysis problems.

The course begins with the elements of a Processing sketch, through variables, methods, classes, loops and conditionals, into applications in data visualisation, 3D environments, image processing and user interaction. The module is designed to take beginners through to intermediate programmers, learning about Java syntax and Processing’s powerful capabilities.


CASA0011 Agent Based Modelling for Spatial Systems (T2) [if you have some experience programming]

Available to MSc students, the Agent Based Model (ABM) module teaches students to build ABMs in a Java framework, building on the Java/Processing skills students learn in the Introduction to Programming for Architecture and Design module. By the end of the module, students will be able to program ABMs in Java, creating object-oriented code to address spatial systems.

If you are interested in studying CASA0011 and are comfortable learning a new programming language for the class, please contact the module convenors to discuss it.

Term 3

Dissertation (60 credits) - Term 3

This module marks the culmination of your studies and gives you the opportunity to produce an original piece of research making use of the knowledge gathered in the lectures. You will be guided throughout this challenge by your supervisor and with the support of the Course Director, and together you will decide the subject of research. This enterprise will enable you to create a unique, individual piece of work with an emphasis on data collection; analysis and visualisation linked to policy and social science oriented applications.

Assessment is by 10,000-12,000 words dissertation.

Part-time study

Part-time study is completed within two years. Students on this mode of study should aim to follow the study pattern below:


Part-time Year One


Part-time Year Two

•    CASA0001 Urban Systems Theory•    CASA0005 Geographic Information Systems and Science
•    CASA0007 Quantitative Methods•    CASA0008 Smart Cities: Context, Policy and Government
•    CASA0009 Spatial Data Capture, Storage and Analysis•    CASA0002 Urban Simulation
RTPI Pathway: BPLN0042 Urban Design: Place making
•    N/A•    Any UCL Masters 15 credit module
RTPI Pathway: BPLN0055 Planning Practice
•    N/A•    CASA0010 Dissertation

Modular/Flexible study

•    These students can take up to five years to gain 180 credits to complete the programme
•    The student registers at the start of every academic year, and chooses (via Portico) which modules to study in that academic year. The action of making a module choice generates an invoice from the Fees Office.
•    It is possible for a modular/flexible student to enrol onto the programme at the start of the academic year but decide not to take any modules in that year and defer study to another year. They will pay no fees for that year, but will remain an enrolled registered student of UCL.

Modular/flexible students must complete the module CASA0007 before CASA0009.

All taught modules must be completed before completing the Dissertation.

NB: All Modular/Flexible students MUST re-enrol every autumn until they complete their studies, regardless of whether they intend to actively study that year or not. Student registration must remain current, otherwise it will be assumed the student has left the programme.