XClose

The Bartlett Centre for Advanced Spatial Analysis

Home
Menu

Content

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

This module provides a cross-disciplinary introduction to urban theory and science.  We will discuss fundamental concepts and models developed by urban geographers, planners and social thinkers.  Topics include urbanisation, systems and complexity theory, urban form and function, mobility, socio-spatial differentiation and urban governance.

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

This Master's level module introduces students to a range of statistical and mathematical tools for analysing and interpreting data.  The module also focuses on key skills, such as communicating data, writing technical reports, and approaching quantitative problems.  Applications and examples concentrate on the field of cities research.  Explanations are intended to develop conceptual understanding rather than technical mathematical frameworks.  Little to no prior knowledge is assumed.  This module is of most relevance to students from the social sciences or the field of cities research specifically who wish to develop their quantitative skills.  It is not appropriate for students from outside the Centre for Advanced Spatial Analysis who already have significant tehcnial training (e.g. a background in mathematics or the natural sciences).  Content covered includes: Fermi Estimations, Linear Regression, Hypothesis Testing, Clustering, Linear Programming, Statistical Fallacies, Systems Dynamics Models.

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

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

The purpose of this module is to equip students with an understanding of the principles underlying the conception, representation/measurement and analysis of spatial phenomena. As such, 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 spatial analysis. It is concerned with unearthing and understanding the importance of spatial data in a range of contexts. The module is designed to have a large practical component in order that students can use the latest software and techniques to analyse and infer from contemporary datasets. The module is taught predominantly in R but also covers basic concepts in QGIS and ArcMap.  The intention is that students will complete the course with a broad knowledge of spatial analysis which they can draw on for their dissertation and further study or employment.

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

This module focuses on the societal and technological context of smart urbanism and develops a critical perspective on emerging smart city notions, key players and their practices, partnerships, urban digital strategies, infrastructure and analytics.  Part of this will be an examination of how novel information technologies function and relate to social life and institutions in cities.

NB: Students wishing to take this module should have taken CASA0001 Urban Systems Theory also.

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

The module aims to introduce students to the mathematical modelling of urban systems within the framework of complexity science. Two main methodologies will be used: network science and spatial interaction models. In detail, the student will learn to represent processes in urban systems, and to encode the connectivity, relationships between agents, locations or services, leading to the prediction of the emergent flows. The skills learnt in this course can be applied to a wide range of systems. During the course we will look at transport systems, centrality measures and community detection in social systems; and the modelling of flows related to commuting or migration patterns. There are no prerequisites for the course, nevertheless, the students possessing programming skills and basic linear algebra knowledge will be at an advantage. Codes needed for the practical workshops and assessment will be provided.

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]

This module gives students an introduction to the basics of computer programming through simple material related to spatial analysis.  It covers the theory of computing, giving an easy introduction to two different languages in order to highlight their similarities and differences.  The course focuses on how to make use of the tools presented here in a larger workflow.  Throughout, students will discover how they can use what they learn here to support their research and studies.

OR:

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

This module will introduce students to the design and use of agent-based models (ABMs) in a variety of urban analytical contexts. The course will employ a hands-on approach, with practical sessions guiding students through the construction of example models, building up to producing their own simulations and using them to conduct experiments.

Themes covered in this module will include:

  • Cellular Automata
  • Presenting ABMs using the Overview Design Detail (ODD) Protocol
  • Modelling competition among agents
  • Optimisation of ABM performance
  • Using ABM to make forecasts and predictions
  • Integration of ABM and GIS

The module will further cover the history and development of the field, as well as current trends, focusing particularly on the evaluation of model outcomes. It will address applications such as pedestrian movement, traffic and transportation, land use, evacuations and crises, and basic epidemiology.

Models will be built using the specialist agent-modelling software NetLogo (or an appropriate alternative – adaptations may be made to keep the course current).

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.