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
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 the 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 the Urban Simulation reading list.
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 CASA0011and are comfortable learning a new programming language for the class, please contact the module convenors to discuss it.
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%.