The course ‚Spatial Simulation‘ was focused on applying concepts and methods introduced in the course ‚Modelling Geographical Systems‘ and to experiment with selected types of Spatial Simulation models to get an understanding of the complexity of such modelling processes.

The main focus was put on the implementation of agent-based models and cellular automata, as both types are commonly used in a wide range of applications from crowd simulations to fluid dynamics.

Throughout the course multiple one-page reports were produced, each covering respective weekly tasks which had to be solved. The weekly tasks were structured to gradually introduce new and more complex features and mechanics. The end-goal was to combine the individual reports into one portfolio which covered the models created in the course.

Why do we need models?

Understanding complex and dynamic systems and detecting the driving factors can be challenging. This is especially true for systems that are made of many individuals that have complex behaviours and not only interact with the static environment but with each other as well. This falls true for human societies and ecology. The drive for understanding the underlying processes and relationships in these complex systems makes it necessary to develop respective models which can depict and explain these noticeable phenomena in our environment.

Spatial Simulation is one of these approaches. In contrast to other types of modelling, Spatial Simulation explicitly incorporates the spatial dimension which is necessary when analysing spatial patterns and individuals who interact in a geographic environment. The usage of Spatial Simulation additionally makes it possible for researchers to experiment with these systems in a controlled
environment instead on having to rely on field experiments which can be expensive, unethical, or otherwise impossible to realize in real systems.

Examples for spatial models

Spatial simulations are developed in controlled environments. In this course the spatial modelling software GAMA was used, which allows the users to code and run their own models using the programming language GAML.

The individual modelling results can be viewed in the end-of-term portfolio containing the collection of weekly tasks and models.