Numerical Methods for Physical Geographers
| Module title | Numerical Methods for Physical Geographers |
|---|---|
| Module code | GEO2332 |
| Academic year | 2019/0 |
| Credits | 15 |
| Module staff | Dr Anne Le Brocq (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 85 |
|---|
Module description
This module provides you with a training in numerical methods used by physical geographers, incorporating:
- Statistical methods,
- Computer programming skills,
- Environmental modelling.
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 both lecture-based classes and computer practical teaching and is one of the compulsory modules you study as part of the BSc Geography degree programme, as well as being compulsory for FCH students wishing to undertake physical geography field trip in stage 2 and / or dissertation.
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 a range of numerical methods and software packages 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.
By attending the timetabled lectures and practicals/drop-in sessions and completing the formatively and summatively assessed coursework in this module, you will develop your academic and professional skills. These include developing an ability to
- solve problems,
- develop your own ideas with confidence,
- respond to novel and unfamiliar problems,
- manage structure (task management, goal setting, developing strategies), and
- manage your time effectively.
The module will use the statistical package SPSS, and the mathematical programming software MATLAB. No prior experience of programming is expected, the module will introduce the software through the use of practicals and videos.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Discuss the role of statistical methods, programming, and modelling in geographical research and evaluate the key concepts and principles underpinning these methods
- 2. Identify a research problem and be able to apply knowledge and understanding of statistical, programming, and modelling theory in a practical context
- 3. Explain the significance of statistical, programming, and modelling methods in the generation of new knowledges
- 4. Discuss the significance of data type and scale and appreciate the range of techniques available for analysing Big data
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 5. Evaluate the issues involved in research design and its application in the context of physical geography
- 6. Describe the application of a number of specialised techniques and approaches involved in collecting, analysing and presenting quantitative geographical data
- 7. Identify and evaluate commonly employed approaches to problem solving in physical geography
- 8. Explain how statistical, programming, and modelling techniques can be used to address contemporary geographical challenges
- 9. Apply ideas to new situations
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 10. Use C&IT effectively and appropriately to collect, analyse, and present geographical information
- 11. Work independently on problem solving
- 12. Effectively and appropriately interpret quantitative information
Syllabus plan
Numerical methods in physical geography: an introduction and context.
Theme 1: An introduction to statistical techniques and where/when they may be employed 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. As such, this section of the module will encourage you to think about the strength and limitations of employing statistical analysis within specific environmental contexts.
Theme 2: 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 the Matlab programming language. Teaching is conducted through both lectures and drop-in sessions.
Theme 3: An introduction to 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 predict/forecast system behaviour. This section examines the principles of modelling and explores (through a practical exercise) the opportunities and limitations and surrounding such methods. The teaching explores the application of modelling theory to real world situations.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 28 | 122 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 13 | Lecture-based classroom sessions |
| Scheduled Learning and Teaching | 15 | Computer-based practical sessions |
| Guided Independent study | 122 | Background reading, examination revision and coursework preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Self-paced statistics practical exercises | Self-paced | All | Self-assessed using answer sheets |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 60 | 40 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Examination | 40 | 1 hour | 1-9, 11-12 | Written |
| Scientific computing and modelling practical exercise | 60 | Computing exercise (equivalent to 1500 words) | All | 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 |
|---|---|---|---|
| Examination | Examination | 1-9, 11-12 | August Ref/Def period |
| Scientific computing and modelling practical exercise | Scientific computing and modelling practical exercise | All | August Ref/Def 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
- Barnsley, M.J. 2007. Environmental modelling: a practical introduction. CRC Press, London.
- Bryman, A. and Cramer, D. (2011) Quantitative data analysis with IBM SPSS 17, 18 and 19 (Routledge, London).
- Clifford, N., French, S. and Valentine, G., (2010) Key Methods in Geography, 2nd Edition. Sage.
- Downey, A. (2009) Python for Software Design: how to think like a computer scientist. Cambridge.
- Field, Andy (2009) Discovering Statistics using SPSS (Sage, London).
- Griffith, D. and Amrhein, C. (1991) Statistical Analysis for Geographers. Prentice-Hall.
- Harris, R. (2011) Statistics in Geography and Environmental Science. Prentice Hall.
- Rogerson, P. (2010) Statistical Methods for Geography: a student’s guide, 3rd Edition. Sage, 2010.
- Smith, J. and Smith, P. 2007. Environmental modelling: an introduction. Oxford University Press, Oxford.
- Stevens, J. (2002) Applied Multivariate Statistics for the Social Sciences, 4th Edition. Lawrence Erlbaum.
- Tabachnick, B. and Fidell, L. (2001) Using multivariate statistics, 4th Edition. Pearson.
- Wainwright, J. and Mulligan, M. 2004. Environmental modelling: finding simplicity in complexity. Wiley, Chichester.
- Walford, N. (2010) Practical Statistics for Geographers and Earth Scientists. Wiley.
- Wheeler, D. Shaw, G. and Barr, S. (2004) Statistical Techniques in Geographical Analysis, 3rd Edition. David Fulton.
Indicative learning resources - Web based and electronic resources
- University of Exeter SPSS Handbook
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | GEO2331 Research Design in Physical Geography |
| NQF level (module) | 5 |
| Available as distance learning? | No |
| Origin date | 18/02/2019 |
| Last revision date | 18/02/2019 |
