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The MRes Spatial Data Science and Visualisation consists of four core modules (60 credits), a group mini-project (30 credits) and a research dissertation (90 credits).

Introduction to Programming [15 credits] - Term 1 

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.

GI 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.

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.

Data Science for Spatial Systems [15 credits] - Term 2 

The purpose of this module is to provide students with both the technical and the critical skills required for the treatment and advanced analysis of spatial datasets. During the first part of the course, 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 learn how to manage and validate raw, unprocessed data, and derive deeper meaning from raw data. Methods for the advanced analysis and mining of datasets will be explored in some detail, before moving on to examining techniques for enabling the scaling of these approaches to very large datasets.

Group Project: Digital Visualisation [30 credits] - Terms 2 & 3 

This research module introduces students to methods of data visualisation within cities and the built environment. The project is group-based, running as a series of workshops through term two, and group work in term three, culminating in a presentation of group outputs at the end of the final term. The module builds upon the taught sections of the course (CASA0005 Geographic Information Systems and CASA0013 Introduction to Programming for Spatial Analysts), creating connections between mapping, modelling, and visualisation, and giving the students an opportunity to create a powerful and coherent portfolio of visual content.

The indicative reading list for this module can be viewed at Digital Visualisation reading list.

Dissertation  [90 credits] - Term 2, self study during Summer

The dissertation is based around the writing and preparation of an original research project in the form of a master's dissertation. Students will be required to plan the research their dissertation from an early stage, with ongoing development building on both the mini-project and taught courses developed through the year.

The research topic will be defined under the guidance of your dissertation supervisor with the support of the course director. The aim is to produce a unique piece of work with an emphasis on data collection, analysis and visualisation linked to policy and social science orientated applications. 

Teaching and Learning

The programme is delivered through a combination of lectures, seminars, tutorials and practical based workshops and classes. The interlinked laboratory research-based mini project with data collection focuses on ‘remote data mining’ rather than fieldwork in the traditional planning/geographical/architectural sense. Assessment is through group and individual projects and the 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 OnePart-time Year Two
•    CASA0013 Introduction to Programming for Spatial Analysts (15 credits, T1)•    CASA0005 Geographic Information Systems and Science (15 credits, T1)
•    CASA0007 Quantitative Methods (15 credits, T1)•    CASA0006 Data Science for Spatial Systems (15 credits, T2)
•    CASA0003 Digital Visualisation: Group Mini Project (T2 & 3, 30 credits)•    CASA0004 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 CASA007 before CASA0006.

They must also complete CASA0013 before CASA0003 and CASA0006.

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.