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Optional modules

Details of the optional modules available at CASA, all of which are open to existing UCL students.

CASA0005 Geographic Information Systems and Science (15 credits)

Geographic 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.

CASA0007 Quantitative Methods (15 credits)

Quantitative Methods will empower you 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.

CASA0001 Urban Systems Theory (15 credits)

This course will give 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.

CASA0008 Smart Cities: Context, Policy & Government (15 credits)

This course will give 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.

CASA0002 Urban Simulation (15 credits)

In this course 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.

CASA0003 Group Mini Project: Digital Visualisation (30 credits)

This module introduces students to methods of visualisation and data mining within the geospatial domain. 

Developed as a group project, the module aims to provide an understanding of the juxtaposition between research, data capture and data display methodologies. It is designed to build upon the taught sections of the course to develop initial research questions for the dissertation.

CASA0009 Spatial Data Capture, Storage & Analysis (30 credits)

This course will teach you the basic tools needed 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.

CASA0011 Agent Based Modelling for Spatial Systems (15 credits)

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.

CASA0013 CASA Introduction to Programming for Spatial Analysts (15 credits)

This course 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 material covered here will help and support the student in their work throughout the rest of the program, building a solid foundation upon which they can draw in the rest of their computational studies.
 
Specifically, the student will be instructed in Python and JavaScript, with particular focus on how these tools can be utilised together as part of a coherent workflow. Particular emphasis will be placed on understanding how to think about programming across different languages, as well as how to go about learning a new programming language. Throughout, students will learn how they can use what they learn here to support their research and studies.