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.
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.