Advanced Geographical Information Systems

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Pursue knowledge in an in-depth, ordered and motivated way
• The application of remote sensing and geographic information science for the understanding of social and economic problems and sustainable environmental management
• Understand the ways in which geographical data of various types can be combined, interpreted and modelled.
• Confidently use a range of relevant forms of IT software;
• Marshal and retrieve data from library and internet resources
• Produce fluent and comprehensive written reports on complex topics
• The use of concepts of space and spatial variation in geographic analysis
• Analyse and critically interpret secondary geographical data
• Analyse critically literature in human and physical geography;
• Use appropriate techniques, including computer software, to produce clear diagrams and maps
• Analyse and understand data in human and physical geography, using computer techniques
• The theory, acquisition, analysis and interpretation of geographical data across a range of applications
• Abstract and synthesise information from a range of different geographical sources
• Understand the importance of the spatial characteristics of geographical data
• The influence of spatial and temporal scale upon human and physical processes

This module is comprised of two parts. Part I focuses on spatially distributed dynamic models with particular emphasis on environmental modelling. A range of spatially distributed models will be studied from application areas such as forestry, climate change, and land use planning. Subjects such as model calibration and validation, sensitivity analysis and what-if scenarios are covered, and students should be able to recognise the different types of spatially distributed model by the end of the module. In Part II of the module, the focus is on techniques and concepts in spatial data handling. This encompasses issues such as geospatial data systems, accessing and inputting data, measuring accessibility, and issues of temporal representation and uncertainty. There is also some coverage of writing code in Python for GIS applications, though this is not assessed.

It should be emphasised that the methods and techniques used, and the skills developed in both halves of the course, are applicable across the breadth of quantitative geography (whether human or physical) and environmental science.

A combination of teaching and learning methods (as shown in the 'format' column of the module programme) are employed to provide students with the necessary knowledge, structure and

opportunities to develop and practice skills related to the learning outcomes. Lectures provide a sound knowledge base and structure. Computing practicals provide the opportunity to put these concepts and methods into practice and to gain hands-on experience of using two major GIS software packages (the QGIS open source software and ESRI’s ArcGIS). The practical sessions and associated coursework are problem-based to encourage students to develop skills in the context of the solution of real-world problems. In order to achieve the learning outcomes to a high level, students are expected to supplement and reflect on the knowledge gained through lectures and practical sessions with further study. In particular, you should view the practical classes as an opportunity to gain advice and input from teaching staff but recognise that you may not complete all the practical activities within the allotted class time. It is also essential that you build on the lecture materials by reading the literature. You will be provided with directed reading as well as reading lists for more extensive independent study.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.