3. Geoinformatics content (mainly computer labs)

3.1. Python programming

3.1.1. Key topics

  • Principles of computer programming
  • Python programming
  • Introduction to object orientation

3.1.2. Literature for exam

Think Python, An introduction to software design, A. Downey, 2008, Green Tea Press, Needham, 234 pp. Chapters 1, 2, 3, 5, 6, 7, 8, 10, and 14. Soon available at ‘verkoopruimte’ (fourth floor W.C. van Unnikbuilding, appr. 11 Euro), online at http://www.greenteapress.com/thinkpython/thinkpython.html

3.1.4. Computer lab

Available at the PCRaster site, please ask your tutor for the password.

3.2. Map Algebra

3.2.1. Key topics

  • Static modelling with PCRaster
  • Point operations and neighbourhood operations

3.2.2. e-Lectures

e-Lecture Introduction to Map Algebra

e-Lecture Map Algebra Operations

e-Lecture slides Map Algebra, pdf

3.2.3. Computer lab

Map algebra course, available in Blackboard. In Blackboard, go to ‘Communities’, select the community ‘PCRaster Python - Map Algebra’ and go to ‘My Tasks’, ‘Assignment’.

3.2.4. Literature for exam

Burrough, P.A. & McDonnel, R., Principles of Geographical Information Systems, Oxford University press, Chapter 7, The analysis of discrete entities in space, p. 162-170, and Chapter 8, Spatial analysis using continuous fields, p. 183-209. (Note: this is the same literature that also need to be studied for Model Theory, Spatial Models)

3.3. Dynamic modelling with PCRaster Python

3.3.1. Key topics

  • Forward modelling
  • Importing / reporting to the database
  • Point models

Spatial models with neighbourhood interaction

3.3.2. Computer lab

Available in Blackboard.

3.3.3. Reading material (not for exam)

Karssenberg, D., De Jong, K. and Van der Kwast, J., 2007, Modelling landscape dynamics with Python. International Journal of Geographical Information Science, 21, pp. 483-495. Link. This article explains how you can construct dynamic models using the PCRaster Python framework.

3.4. Stochastic modelling with PCRaster Python

3.4.1. Key topics

  • Defining probability distributions as inputs to models
  • Monte Carlo simulation

3.4.2. Computer lab

Available in Blackboard.