Geographical Techniques GEOG4015
- Academic Session: 2022-23
- School: School of Geographical and Earth Sciences
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
This course prepares students in techniques and methods suitable for use in Honours or Advanced Ordinary dissertations, and similar research reports.
A one-hour lecture and two-hour practical class per week.
Geographical Thought GEOG4013
2 Assessed laboratory reports (50% each = 100%).
1. To prepare students in techniques and methods suitable for use in Honours dissertations and similar research reports.
2. To develop a critical approach to research methods, and thus to enable students to make reasoned evaluations of the appropriateness of particular methods in particular contexts.
3. To provide training in transferable skills of use in both academic and non-academic contexts.
Intended Learning Outcomes of Course
1. have an understanding of the principles of Geographical Information Systems (GIS), be able to carry out basic analyses using GIS, and have an awareness of the importance of spatial data quality in the results of GIS analysis;
2. be able to analyse and interpret both uni- and multi-variate geographical data sets, using appropriate statistical techniques;
3. have an understanding of the role of numerical and statistical modelling in geographical data analysis, and be able to use and analyse the results from simple models of geographical processes;
4. be able to account for and critically discuss the use of both quantitative and qualitative analytical techniques within geography;
5. be able to carry out data gathering exercises in human or physical geography as appropriate, including the planning, execution, analysis and interpretation of data.
Transferable skill ILOs:
6. be capable of using a widely used GIS package for a range of purposes;
7. be able to use both spreadsheet (Excel) and statistical (Minitab) packages for the analysis and presentation of data;
8. be able to explain some of the sources of error and uncertainty in 'real-world' data, and to evaluate the limitations that these create for data analysis.
Minimum Requirement for Award of Credits