Learning outcomes

  • Understand the role of a model and its construction
  • Be able to distinguish the different modelling approaches and to choose the most appropriate
  • Be able to measure and interpret different spatial statistics
  • Be able to produce and use statistical surfaces, including the DEM 

Goals

The course is an introduction to spatial modelling, to the different types of models and to spatial statistics that allows students to deepen these different aspects in their Master Cursus. It is based on a lot of different application examples mainly coming from the fields of geography and geology. The course favours the interpretation rather than the technique.

Content

  1. Introduction : what is a model ?
  2. Spatial modelling
  3. Spatial statistics
  4. Statistical surfaces

Exercices

Practical sessions include exercices related to simulation models, empirical statistical models (spatial regressions), spatial autocorrelation and spatial interpolation (statistical surfaces).

Assessment method

Written exam (theory; 50% of final mark) and practical assignment (50% of final mark). The practical assignment consists of a written report (10%) and a practical exam on computer (40%).

Theoretical and practical parts are two different learning activities. Each part can therefore be validated once the mark of 10/20 is reached.

The evaluation is similar for the second session.

Sources, references and any support material

  • Burrough, P., and R. McDonnell. 2005. Principles of Geographical Information Systems. Oxford: Oxford University Press.
  • Demers, M. N. s. 2000. Fundamentals of GIS. 2nd ed. New York: Wiley & Sons.
  • Mulligan J., Wainwright M., 2003, Environmental modelling : finding simplicity in complexity. Chichester : Wiley.
  • Zaninetti J.-M., 2005, Statistique spatiale. Méthode et application géomatique. Paris : Lavoisier.

Language of instruction

Français