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.
• Analyse and critically interpret secondary geographical data.
• Understand the importance of the geographical characteristics of data.
• The application of geographic information science for the understanding of social and economic problems and environmental management
• Analyse critically literature in human and physical geography.
• 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.
• The use of concepts of space and spatial variation in geographic analysis
• Understand the different types of spatially explicit model and their relative strengths and weaknesses.
• Confidently use a range of relevant forms of IT software.
• The influence of spatial and temporal scale upon human and physical processes.
• Understand the ways in which geographical data of various types can be combined, interpreted and modelled.
• Use appropriate techniques, including computer software, to produce clear diagrams and maps.
• Marshal and retrieve data from library and Internet resources
• Abstract and synthesise information from a range of different geographical sources.
• Produce fluent and comprehensive written reports on complex topics.

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 will be some coverage of Python coding for GIS applications, though this will not be 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.

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 open source Quantum GIS 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

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.