Physical Geography: Spatial Analysis

<p>The overall aim of the course is to provide basic knowledge and understanding of the principles behind spatial analysis in various physical geography application areas. At the end of the course, you as a student should be able to choose and use an appropriate analytical method for a given problem in spatial analysis. The course is an elective course at advanced level for a master's degree in science in GIS and remote sensing and for a master's degree in physical geography and ecosystem science, all specializations.</p><h3>Course content</h3> <p>The course contains a number of different basic components of spatial analysis, both in terms of theoretical background and analytical methods. Some of the concepts and techniques covered in the course are point pattern analysis, geostatistics, spatial autocorrelation, scale, uncertainty and spatial regression. The student also learns to analyze spatial data using the open source code platform R.</p> <p>After completing the course, you as a student should be able to:</p> <ul> <li>explain how and why knowledge of geography is important when analyzing spatial data</li> <li>describe different methods used for analysis of different types of spatial data</li> <li>analyze different types of geographical data using both descriptive and inferential statistical methods</li> <li>account for how uncertainty and scale entail different limitations and possibilities in the analysis of geographical data and what patterns this may give rise to.</li> <li>independently select relevant spatial analysis methods for different types of geographical data, processes and phenomena</li> <li>independently analyze, interpret and critique results from different spatial analysis methods</li> </ul> <h3>Teaching</h3> <p>The teaching consists of both theoretical and practical elements, where lectures are followed by practical exercises that illustrate, strengthen or further develop the theoretical concepts that are addressed in the lectures. The emphasis is on statistical analysis and interpretation of the results. The exercises are often cumulative, i.e. the knowledge gained in one exercise is used as an introduction to tackle the problem posed in the next exercise. Participation in exercises and related elements is mandatory.</p>