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
Led by Dr Martin Zaltz Austwick, BENVGACH 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.
GI 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) and exam. Plus an optional module selected from any other relevant 15 credit M-level module from UCL.
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 course 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.
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.
Data Science for Spatial Systems [15 credits] - Term 2
Led by Dr Ed Manley Data Science for Spatial Systems will equip you with the skills and knowledge required to handle, process, and analyse large datasets. The aim of the module is to not only introduce the technical skills required to process data, but also the thought process required in approaching a data analysis problem.
Through the course you’ll understand how to use databases to store large datasets, and how SQL scripting can be used to access and process this data. Building on the lessons learnt during Quantitative Methods, this module will extend your skills in Python, using relevant libraries to clean, explore and analyse complex data. Through the course you’ll learn how and where to apply regression, classification and clustering machine learning methods to gain maximum insight into your data.
Group Project: Digital Visualisation [30 credits] - Terms 2 & 3
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. Project assessment will be on a group basis.
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.
Check back soon for our suggested two-year route.