Numerical Methods for Physical Geographers
| Module title | Numerical Methods for Physical Geographers |
|---|---|
| Module code | GEO2332 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Anne Le Brocq (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 70 |
|---|
Module description
This module provides you with a training in numerical methods used by physical geographers, incorporating:
- Scientific computing,
- Environmental modelling,
- Applied statistics.
Accordingly, it is designed to prepare you for undertaking research within and beyond a university context and seeks to equip you with key employability attributes for professional careers. In so doing, the module will explore a range of numerical methods that physical geographers use in research and their applications for wider society.
The module will be taught using lectures and computer practical teaching and is one of the compulsory modules you study as part of the BSc Geography degree programme.
Module aims - intentions of the module
This module aims to provide an introduction to, and critically engaged understanding of, how physical geographers adopt and use numerical methods in a range of contexts. The module has the following objectives:
- To introduce you to different numerical methods, software platforms and languages typically used by Physical Geographers in their research,
- To equip you in the underlying principles of numerical methods to form a base for future modules and dissertations,
- To support you in translating your learning about numerical methods into identifiable and tangible graduate attributes to enhance your employability.
The module will use a mix of software platforms and programming languages, although no prior experience of coding is expected. The module will introduce these skills through the use of lectures, practicals and instructional, supportive resources. The module will provide you with the opportunity to develop your academic and professional skills, including abilities in problem solving, developing ideas with confidence and time management.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Discuss the role of statistics, programming, and modelling in geographical research and evaluate the key concepts and principles underpinning these methods
- 2. Be able to apply knowledge and understanding of statistical, programming, and modelling theory in a practical geographic context
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Explain how statistical, programming, and modelling techniques can be used to address contemporary geographical challenges
- 4. Implement and evaluate commonly employed computer-based approaches to research in geography in a practical context
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. Identify appropriate quantitative methods, and effectively and appropriately interpret and communicate the resulting quantitative information
- 6. Work independently on decision making and problem solving
Syllabus plan
Numerical methods in physical geography: an introduction and context.
Theme 1: An introduction to the concepts and geographical applications of scientific computing.
This theme will provide you with a working understanding of the concepts of scientific computing. You will learn the theory and application of core computing programming concepts using a computer programming language. Teaching is conducted through both lectures and practical sessions.
Theme 2: An introduction to environmental modelling in geographical research.
This section of the module explores the complexity of environmental systems and the ways in which numerical models can be employed to better understand and quantify system behaviour. This section examines the principles of modelling and explores (through a practical exercise) the opportunities and limitations surrounding such methods. The teaching explores the application of modelling theory to real world situations.
Theme 3: An introduction to statistical techniques and how these can be applied in physical science research within geography.
This part of the module provides you with a knowledge and understanding of specific statistical techniques that are commonly employed in the analysis of environmental data. This section of the module will encourage you to think about the appropriate use of statistical methods and introduce you to some core statistical techniques via real world data.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 32 | 118 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 10 | Lecture-based sessions |
| Scheduled Learning and Teaching | 18 | Computer-based practical sessions |
| Scheduled Learning and Teaching | 4 | Drop-in assessment help sessions |
| Guided Independent study | 118 | Background reading, assessment revision and coursework preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Informal demonstration, questioning, discussion | Self-paced, continuous | 1-6 | Verbal and practical feedback support during scheduled sessions |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Scientific computing and environmental modelling practical report | 65 | Equivalent to 1500 words | 1-6 | Written |
| Statistics practical report | 35 | Equivalent to 1000 words | 1-6 | Written |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Scientific computing and environmental modelling practical report | Scientific computing and environmental modelling practical report | 1-6 | Referral/Deferral period |
| Statistics practical report | Statistics practical report | 1-6 | Referral/Deferral period |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to sit a further examination or submit further practical coursework as necessary. If you are successful on referral, your overall module mark will be capped at 40%.
Indicative learning resources - Basic reading
Basic reading:
- For an introduction to programming in Python: https://www.learnpython.org/
- Barnsley, M.J. 2007. Environmental modelling: a practical introduction. CRC Press, London. https://lib.exeter.ac.uk/record=b2594605
- Smith, J. and Smith, P. 2007. Environmental modelling: an introduction. Oxford University Press, Oxford. https://lib.exeter.ac.uk/record=b2594606
- Wainwright, J. and Mulligan, M. 2004. Environmental modelling: finding simplicity in complexity. Wiley, Chichester. https://lib.exeter.ac.uk/record=b2457478
- Clifford, N., French, S., Valentine, G. (2010) Key Methods in Geography. SAGE Publications. eBook link: http://lib.exeter.ac.uk/record=b1840480~S6
- Harris and Jarvis (2014) Statistics in geography and environmental science. Routledge, Taylor & Francis Group. eBook link: http://lib.exeter.ac.uk/record=b2488137~S6
- Rogerson, P.A. (2019) Statistical Methods for Geography. SAGE Publications, London.
- Wheeler, D., Shaw, G., Barr, S. (2013) Statistical Techniques in Geographical Analysis. David Fulton Publishers. eBook link: http://lib.exeter.ac.uk/record=b1754399~S6
- Hui, E.G.M. (2019) Learn R for Applied Statistics. Apress, Singapore. eBook link: http://lib.exeter.ac.uk/record=b4023120~S6
- Field, A. (2021) Discovering Statistics Using R and RStudio. SAGE, London.
- McGuffie, K. and Henderson-Sellers, A. (2014) The Climate Modelling Primer. Wiley Blackwell, eBook link: http://lib.exeter.ac.uk/record=b4099679~S6
Indicative learning resources - Web based and electronic resources
- ELE page
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 5 |
| Available as distance learning? | No |
| Origin date | 18/02/2019 |
| Last revision date | 03/09/2024 |
