|Teaching method||Contact hours|
|Course coordinator(s)||dr. ir. S de Bruin|
|Lecturer(s)||dr. ir. A Ligtenberg|
|dr. ir. S de Bruin|
|dr. ir. GBM Heuvelink|
|Examiner(s)||dr. ir. GBM Heuvelink|
|dr. ir. S de Bruin|
|dr. ir. A Ligtenberg|
Language of instruction:
Assumed knowledge on:
Basic geo-information concepts and methods (for example GRS-10306 Introduction Geo-information Science); Basic statistics (for example MAT-14303 Basic Statistics; MAT-20306 Advanced Statistics.
Contents of chapters 3 (Data description) ,4 (Probability and probability distributions) and 11 (Linear regression and correlation) of the book 'An Introduction to Statistical Methods and Data Analysis' (Ott & Longnecker, 20xx; ISBN: 9780495109143 (edition 6) or 9781305269477 (edition 7)).
SGL-thesis, GRS-thesis (e.g. GRS-80436 MSc Thesis Geo-information Science and Remote Sensing)
Models of spatial processes are important means to support decision making. Such a model, however, is merely a formal and geo-referenced representation of a mental construction about the real world. This course offers knowledge and skills to qualify and implement spatial models and evaluate their results. The course consists of three modules: the first module provides an overview of spatial models and allows students to practice with a cellular automata-based model. The second part is about geo-statistics and includes practicals to generate spatial model input using sparse observations. The third module concerns the propagation of input uncertainty through spatial models. A central case study based on a flood model is used as a connecting link throughout the course.
After successful completion of this course students are expected to be able to:
- explain commonly used dynamic spatial modelling methods and techniques;
- construct a simple dynamic spatial model using Netlogo for a given spatial problem and data set;
- investigate the spatial correlation structure of a data set using semivariogram analysis;
- apply geo-statistical interpolation and spatial stochastic simulation methods;
- construct a stochastic error model to represent uncertainty in spatial data;
- apply fist order Taylor series and Monte Carlo methods for analysing propagation of input uncertainty through spatial models.
- the topics are introduced in lectures and by studying a reader;
- practicals consist of step by step exercises related to each of the topics introduced in the modules, followed by more independent work on the central case study;
- software tools to be used include Netlogo and R (gstat Package).
- individual practical exercises (30% - 15% spatial modelling, 15% uncertainty propagation);
- an individual open book exam (70%).
A pass can only be obtained if the minimum grade for each individual item (spatial modelling, uncertainty propagation, open book exam) is a 5.0.
The course guide gives details on the assessment procedure.
Reader and practical instructions are provided during the course.
|Restricted Optional for:||MBE||Biosystems Engineering||MSc||5MO|