|Teaching method||Contact hours|
|Course coordinator(s)||dr. ir. JP Verbesselt|
|Lecturer(s)||dr. ir. S de Bruin|
|dr. ir. JP Verbesselt|
|dr. ir. A Ligtenberg|
|Examiner(s)||dr. ir. JP Verbesselt|
Language of instruction:
Assumed knowledge on:
Students should have followed the course 'Programming in Python' (INF-22306), know the R language basics and have a good understanding of geo-spatial data (vector, raster) via GRS-10306 (Introduction Geo-information Science), GRS-20306 (Remote Sensing) and GRS-20806 (Geo-information Tools) courses or comparable.
GRS-30306 Spatial Modelling and Statistics; GRS-33306 Advanced Geo-information Science for Earth and Environment; GRS- thesis.
The course is not a pure programming course, but has a rather strong focus on programming and scripting to solve geographical or spatial applied problems. During the course you will learn how to deal with both vector spatial data and raster data; such as satellite imagery used in remote sensing studies. The programming languages dealt with during the course are R and python. Professionals in private companies and research institutes are increasingly using scripting languages for applied spatial analysis. We notice that students still have a kind of 'scripting' barrier to overcome. Some students are afraid of scripting and others stick very closely to the examples given. We like to relax this attitude. From the perspective of an advanced course we like to change the cookbook approach in favor of a more explorative approach. Becoming more relaxed towards scripting and a more explorative approach means that some generic scripting principles must be known to create a 'core' script. On the other hand students have to learn which libraries are available for spatial data analysis and visualization and how these libraries can be used within the 'core' script.
After successful completion of this course students are expected to be able to:
- understand basic concepts of applied scripting for spatial data;
- apply functions from a library while writing a script;
- able to write a clear and documented script;
- find libraries which offer spatial data handling functions;
- read, write, and visualize spatial data (vector/raster) using a script;
- know how to find help related to spatial scripting challenges;
- solve scripting problems by debugging and creating reproducible examples;
- apply learned scripting concepts in a case study with geo-data.
Self-study, discussions with a lecturer and practical, a final assignment during which students have to work in groups of two on a self-defined research question or spatial project that requires the use of scripting and scripting concepts, presentation session of the final assignment.
Daily handed in assignments (20 %); Final week assignment/project (70%); Participation in the course online forum (the goal is that students are stimulated to help and learn from each other's experiences. (10 %); The minimum average of individual components must be 5.5, and the minimum mark for each component is 5.0. Each assignment is scored (1-10).
Applied spatial data analysis with R (2nd ed.). This book can be downloaded via the WUR library as a pdf. Via Springer books link (WUR Library). And Introduction to the raster package.
|Restricted Optional for:||MGI||Geo-Information Science||MSc||3WD|