Geographic Data Science MSc

  • Programme duration: Full-time: 12 months  
  • Programme start: September 2020
  • Entry requirements: You will need a 2:1 Honours degree or better in an appropriate field of study. Individual consideration is given to mature students with significant and relevant experience and with professional qualifications.
Geographic Data Science msc

Module details

Compulsory modules

Semester One

  • ENVS441 Qualitative Research Methodologies (15 credits)
  • ENVS450 Social Survey Analysis (15 credits)
  • ENVS563 Geographic Data Science (15 credits)*
  • ENVS609 Geographic Information Science (15 credits)*

Semester Two

  • ENVS416 Theorising Human Geography (15 credits)
  • ENVS453 Spatial Analysis (15 credits)
  • ENVS456 Web Mapping and Analysis (15 credits)

Summer

  • ENVS492 Dissertation (60 credits)

Optional Modules

Choose 1x15 credit module if taking all the compulsory modules. If exempt from ENVS609 or (and) ENVS563, add an additional 1(2)x15 credit module.

Semester 1

  • COMP518 Database and Information Systems (15 credits)
  • COMP519 Web Programming (15 credits)
  • COMP529 Big Data Analysis (15 credits)

Semester 2

  • ENVS418 Population and Health Analysis and Projection (15 credits)
  • COMP575 Computational Intelligence (15 credits)
  • ENVS557 Social and Spatial Inequalities (15 credits)

* If an equivalent to ENVS609 or ENVS563 has been taken as part of a first degree, then they may be replaced by an additional optional module, subject to the Programme Directors approval.

Notes

  • No more than 75 credits can be taken in one semester.
  • ENVS418 is a pre-requisite for ENVS462
  • Part-time students must take ENVS609 (if required) in year 1, and ENVS492 in year 2. Other modules may be taken in either year, provided the total credits per year (excluding ENVS492) does not exceed 75.

Compulsory modules

Qualitative Research Methods (ENVS441)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

This module aims to introduce students to research methodology, philosophy, strategy and design, and to the ethical and practical considerations associated with conducting research.
This module aims to provide students with an understanding of various qualitative research methods.
This module aims to enable students to develop the skills necessary to analyse qualitative data.

Learning Outcomes

(LO1) Studentswill gain knowledge of ontological and epistemological positions and theirimplications for research

(LO2) Students will gain an understanding of ethical issues in research

(LO3) Students will develop the skills needed to select appropriate qualitative research methods

(LO4) Students will develop the skills to plan and conduct research using qualitative methods

(LO5) Students will develop the skills to analyse qualitative data

(S1) Ethical awareness

(S2) Research management developing a research strategy, project planning and delivery, risk management, formulating questions, selecting literature, using primary/secondary/diverse sources, collecting & using data, applying research methods, applying ethics

Social Survey Analysis (ENVS450)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

To introduce the principles underlying the collection and analysis of social survey data;
To equip students to analysse social survey data;
To provide a basic competence in the use of the 'R' statistical programming language;
To provide a platform to more advanced data analysis techniques covered in the Semester 2 module ENVS453 'Spatial Analysis'

Learning Outcomes

(LO1) The ability to produce descriptive statistics and graphical representations of quantitative variables

(LO2) The ability to address survey bias through the use of survey reweighting

(LO3) The ability to use statistical inference techniques to address the impacts of sampling uncertainty on survey estimates

(LO4) An understanding of the impact of survey design and sample size on the precision of survey estimates

(LO5) The ability to conduct data analysis using correlation and multivariate regression (OLS and logistic) 

(LO6) Competence in the use of a statistical programming language (R) to undertake basic social survey analysis

(S1) Communication skills

(S2) Numeracy

(S3) Problem solving skills

(S4) IT skills

Geographic Data Science (ENVS563)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

The module provides students with core competences in Geographic Data Science (GDS). This includes the following:

Advancing their statistical and numerical literacy;

Introducing basic principles of programming and state-of-the-art computational tools for GDS;

Presenting a comprehensive overview of the main methodologies available to the Geographic Data Scientist, as well as their intuition as to how and when they can be applied;

Focusing on real world applications of these techniques in a geographical and applied context.

Learning Outcomes

(LO1) Demonstrate advanced GIS/GDS concepts and be able to use the tools programmaticallyto import, manipulate and analyse data in different formats.

(LO2) Understand the motivation and inner workings of the main methodological approcahes ofGDS, both analytical and visual.

(LO3) Critically evaluate the suitability of a specific technique, what it can offer and how it canhelp answer questions of interest.

(LO4) Apply a number of spatial analysis techniques and how to interpret the results, in theprocess of turning data into information.

(LO5) When faced with a new data-set, work independently using GIS/GDS tools programmatically.

(S1) Numeracy

(S2) Organisational skills

(S3) Problem solving skills

(S4) IT skills

(S5) Ethical awareness

(S6) Communication skills

Geographic Information Science (ENVS609)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

Understand how digital representations of the real world can be created within a GIS including the referencing of geographic features;
To gain familiarity with the unique properties of geographic data including spatial autocorrelation and modifiable areal units;
Appreciate that there are uncertainties in the creation of geographic representations;
Develop skills in the basic use of GIS to create digital representations and understand their constraints within a framework of GIScience

Learning Outcomes

(LO1) Gain a sound understanding of the function, concepts  and features of a Geographic Information System

(LO2) Understand those constraints and considerations that are required when implementing a GIS to build geographic representations

(LO3) Develop practical skills in the application of a GIS to those data types often associated with a student's disciplinary area

(S1) Numeracy

(S2) Problem solving skills

(S3) IT skills

Theorising Human Geography (ENVS416)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

To situate contemporary Human Geography within the wider context of research in the Social Sciences;
To introduce students to some of the major debates of society, environment, space and place and the relationships between them; To introduce students to the different epistemological underpinnings in geographical research.

Learning Outcomes

(LO1) Gain a thorough understanding of the major themes that define human geography;

(LO2) Gain a thorough understanding of the epistemological underpinnings of different approaches to researching human geography.

(S1) Ethical awareness

(S2) Problem solving skills

(S3) Research management developing a research strategy, project planning and delivery, risk management, formulating questions, selecting literature, using primary/secondary/diverse sources, collecting & using data, applying research methods, applying ethics

(S4) Global perspectives demonstrate international perspectives as professionals/citizens; locate, discuss, analyse, evaluate information from international sources; consider issues from a variety of cultural perspectives, consider ethical and social responsibility issues in international settings; value diversity of language and culture

Spatial Analysis (ENVS453)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

Build upon the more general researchtraining delivered via companion modules on Data Collection and Data Analysis, both of which have an aspatial focus;      
Highlight a number of key socialissues that have a spatial dimension;
Explain the specific challenges facedwhen attempting to analyse spatial data;
Introduce a range of analyticaltechniques and approaches suitable for the analysis of spatial data;  
Enhancepractical skills in using software packages to implement a wide range of spatial analytical tools.

Learning Outcomes

(LO1) Identify some key sources of spatial data andresources of spatial analysis and modelling tools

(LO2) Explain the advantages of taking spatial structure intoaccount when analysing spatial data

(LO3) Apply a range of computer-based techniques for theanalysis of spatial data, including mapping, correlation, kernel densityestimation, regression, multi-level models, geographically-weighted regression,spatial interaction models and spatial econometrics

(LO4) Select appropriate analytical tools for analysingspecific spatial data sets to address emerging social issues facing the society

(S1) Problem solving skills

(S2) Numeracy

(S3) IT skills

Web Mapping and Analysis (ENVS456)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

The module has two main aims. First, it seeks to provide hands-on experience and training in the design and generation of web-based mapping and geographical information tools. Second, it seeks to provide hands-on experience and training in the use of software to access, analyse and visualize web-based geographical information.

Learning Outcomes

(LO1) Experience using tile rendering tools to generate content for map-based web sites.

(LO2) Web-based data collection techniques

(LO3) Programming skills to enable basic online data manipulation and web mapping

(LO4) Knowledge of web based mapping infrastructure

(S1) Communication skills

(S2) Problem solving skills

(S3) IT skills

(S4) Organisational skills

(S5) Numeracy

Dissertation - Geographic Data Science (ENVS492)
LevelM
Credit level60
SemesterSummer (June-September)
Exam:Coursework weighting0:100
Aims

To give students the opportunity to develop a theme in population studies at length.

Learning Outcomes

(LO1) A complete study, written to publishable standard.

(LO2) Self managed and planned programme.

(S1) Numeracy/computational skills - Numerical methods

(S2) Communication (oral, written and visual) - Presentation skills - written

(S4) Critical thinking and problem solving - Critical analysis

(S5) Global citizenship - Relevant economic/political understanding

Optional modules

Database and Information Systems (COMP518)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting25:75
Aims

To provide a deep, systematic and critical understanding of the nature of information systems and particularly database systems, including web technology for databases.

Learning Outcomes

(LO1) At the end of the module students should have a critical understanding of the nature of relational databases.

(LO2) At the end of the module students should be able to design and implement large-scale database systems.

(LO3) At the end of the modulestudents should be able to develop the ability touse SQL as a data definition and data manipulation language, and to develop acritical understanding of querying a relational database withSQL.

(LO4) At the end of the module students should be able to develop a systematic understanding of transaction management and concurrency control in database systems.

(S1) Skills in using technology - Using common applications (work processing, databases, spreadsheets etc.)

(S2) Critical thinking and problem solving - Creative thinking

(S3) Critical thinking and problem solving - Critical analysis

(S4) Critical thinking and problem solving - Synthesis

(S5) Skills in using technology - Information accessing

(S6) Numeracy/computational skills - Confidence/competence in measuring and using numbers

(S7) Problem solving

(S8) Business and customer awareness

(S9) Information Technology (IT) skills

(S10) Computer science principles

Web Programming (COMP519)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

To provide students with a deep, critical and systematic understanding of the most significant technologies for developing web applications.
To enable students to use these technologies in the development of web applications.
To provide knowledge of the characteristics of good web site design principles.

Learning Outcomes

(LO1) be able to use a range of technologies and programming languages available to organisations and businesses and be able to choose an appropriate architecture for a web application.

(LO2) be able to develop reasonably sophisticated client-side web applications using one or more suitable technologies and to make informed and critical decisions in that context.

(LO3) be able to develop reasonably sophisticated server-side web applications using one or more suitable technologies and to make informed and critical decisions in that context.

(S1) Problem Solving - Numeracy and computational skills

(S2) Problem solving – Analysing facts and situations and applying creative thinking to develop appropriate solutions.

Big Data Analysis (COMP529)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting60:40
Aims

To introduce the student to middleware often used in Big Data analytics.
To introduce the student to implementing algorithms using such middleware.

Learning Outcomes

(LO1) Understanding of algorithmic approaches for handling batch and streaming analysis.

(LO2) Understanding of middleware that can be used to enable algorithms to scale up to analysis of large datasets.

(LO3) Understanding of the impact of the middleware on how algorithms are articulated.

(S1) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S2) Communication (oral, written and visual) - Following instructions/protocols/procedures

Population and Health Analysis and Projection (ENVS418)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

To provide an introduction to basic techniques of population and health analysis and projection. To introduce students to the use of spreadsheets for population and health analysis.

Learning Outcomes

(LO1) An understanding of the basic techniques of population and health analysis and projection.

(LO2) The ability to use spreadsheets for data analysis and presentation.

(LO3) Appreciation of the value of population and health analysis and projection for understanding society.

(S1) Numeracy/computational skills - Numerical methods

(S2) Communication (oral, written and visual) - Presentation skills - written

Computational Intelligence (COMP575)
Level1
Credit level15
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

Understand the basic structures and the learning mechanisms underlying neural networks within the field of artificial intelligence and examine how synaptic adaptation can facilitate learning and how input to output mapping can be performed by neural networks. Obtain an overview of linear, nonlinear, separable and non separable classification as well as supervised and unsupervised mapping. Understand the benefit of adopting naturally inspired techniques to implement optimisation of complex systems and acquire the fundamental knowledge in various evolutionary techniques. Become familiar with the basic concepts of systems optimisation and its role in natural and biological systems and entities.

Learning Outcomes

(LO1) Learning  the advantages and main characteristics of neural networks in relation to traditional methodologies. Also, familiarity with different neural networks structures and their learning mechanisms.

(LO2) Appreciation of the advantages of evolutionary-related approaches for optimisation problems and their advantages compared to traditional methodologies. Also, understanding the different techniques of evolutionary optimisation for discrete and continuous configurations

(LO3) Understanding of the needs for genetic encoding and modelling for solving optimisation problems and familiarisation with the evolutionary operators and their performance.

(LO4) Understanding of the neural network learning processes and their most popular types, as well as  appreciation of how neural networks can be applied to artificial intelligence problems.

(S1) On successful completion of this module the student should be able to pursue further study in artificial intelligence as well as more advanced types of neural networks and evolutionary optimisation and bio-inspired techniques.

(S2) On successful completion of this module the student should be able to analyse numerically the mathematical properties of most major network types and apply them to artificial intelligence problems. Also, the student should be able to appreciate and understand the suitability of evolutionary optimisation in systems where classical methods cannot be effective.

(S3) On successful completion of this module the student should be able to approach methodologically artificial intelligence problems and bio-inspired algorithms in general and understand the principal mathematics of learning systems and the fundamental principles governing evolutionary optimisation techniques.

Social and Spatial Inequalities (ENVS557)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting0:100
Aims

Gain an understanding of several core areas of social and spatial inequalities and how these inter-relate, and to engage with academic debates about these issues;
Explore evidence for, and interpretations of, social and spatial inequalities, eg labour market, ethnic, spatial aspects of poverty;
Gain an understanding of the geographies of social inequalities, including why inequalities are not equal between places, and what the implications of this unevenness are for individuals and communities;
Consider how and why social inequalities have persisted and/or changed over time, with reference to allied theories and empirical evidence;
Gain acritical understanding of the meaning and measurement of inequalities, poverty and deprivation;
Identify and review the types of data sources that can be used to explore social and spatial inequalities ;
Explore the wider UK context for the development of social and spatial inequalities, including economic restructuring and welfare reform;
Consider representations of inequalities in the media, policy and political debate;
Consider a number of policy developments/responses to problems of social and spatial inequalities, and to highlight their impact.

Learning Outcomes

(LO1) Develop anunderstanding of social and spatial inequalities, how these inter-relate, andhow the terms have been (mis-)used in academic, political, policy and public discourses

(LO2) Develop anunderstanding of how and why social and spatial inequalities might havepersisted over time, and review the empirical evidence for this 

(LO3) Understand how and why social inequalities havespecific geographies and can be concentrated in particular areas orneighbourhoods

(LO4) Understand the difficulties in defining andmeasuring social and spatial inequalities, and how such definitions may relateto broader theories, perspectives or frameworks of relevance

(LO5) Gain insightinto a range of government responses that have been developed to combat socialinequalities and related issues in the UK, at the regional and sub-regionallevel

(S1) Problem solving skills

(S2) Organisational skills

(S3) Communication skills

(S4) International awareness

(S5) Lifelong learning skills

(S6) Ethical awareness


If an equivalent to ENVS563 has been taken as part of a first degree, then ENVS563 may be replaced by an additional optional module, subject to the Programme Directors approval.

No more than 75 credits can be taken in one semester.

ENVS418 is a pre-requisite for ENVS462.

Part-time students must take SOCI501 and ENVS563 (if required) in year 1, and ENVS492 in year 2. Other modules may be taken in either year, provided the total credits per year (excluding ENVS492) does not exceed 75.