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The PG Dip Smart Cities and Urban Analytics comprises 120 credits which can be taken full-time over 12 months. part-time over two years, or on a flexible modular basis of up to 5 years duration.

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.

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 the Smart Cities reading list.

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

The purpose of this module is to provide students with both the technical and the critical skills required for the treatment, analysis and presentation of spatial datasets. In line with this objective, the course is divided into three main themes. In the first, database concepts and techniques are introduced, providing the students with the skills required for manipulating databases. SQL syntax will be taught in depth at this stage, with a strong emphasis on practical application. The second phase of the course moves towards covering the practical skills required in data handling and analysis. Students will first learn how to manage and validate raw, unprocessed data, before moving onto exploring methods for deriving deeper insight into trends in data. The third and final element of the course moves into the presentation of data, introducing web development practices that enable the interactive visualisation and interrogation of complex datasets. In this section of the course, students will learn the techniques necessary to take datasets from the database through to interactive analysis and visualisation.

Urban Simulation (15 credits) - Term 2

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.

The indicative reading list for this module can be viewed at the 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).

To successfully complete the programme, you are required to gain a total of 120 credits. None of the study for these modules can be undertaken by distance learning. It is a UCL requirement that your attendance for all study is at least 70%.