Spatial Simulation

Everything is related to everything else… This core principle of spatial analysis is equally true for the temporal domain: history matters! Together, the two dimensions of space and time build the spatio-temporal context of our environment. Whereas GIS has focussed primarily on the spatial perspective, there is a clear trend towards the incorporation of time. Dynamic models on the other side have for a long time ignored space. Only since a few decades a new theory of “complex systems” explicitly includes spatial heterogeneity. Therefore, spatial simulation models are fundamentally new tools to study systems from a truly spatio-temporal perspective.
Topics that have been successfully addressed with spatial simulation models include biological applications (e.g. predator-prey models, habitat models, emergence of territories), geomorphological applications (e.g. hydrological models and fluvial erosion), heuristic algorithms (e.g. ant algorithm to solve shortest-path problems) and transport models.

06 April 2020
27 July 2020

3 months



The module is free of charge for UNIGIS students working to meet their elective subject requirements. Included are:

  • all related study materials
  • supervision and assessment
  • module accreditation according to the curriculum

ClubUNIGIS members can register at a price of € 300,-. Included are:

  • all related study materials
  • supervision and assessment
  • course certificate upon completion of the module

The module will provide a broad overview on existing theories and methods in the domain of spatio-temporal models. The focus is given to hands-on experience with adapting, designing and coding agent-based simulation models. Upon successful completion of this module, you will be equipped with the competences to design, implement, analyse and valdiate your own models to solve applied problems and conduct research with simulation models.

Core UNIGIS modules 1-3 or equivalent understanding of GIS, data modeling, data structures and data acquisition. Additionally, you need to have interest in application development, as you will have to program your own models in the practical parts of this module. While it is no strict prerequisite to have prior experience with programming, it certainly helps. See also the information under “software”. If you are not sure whether you qualify, please contact (UNIGIS DE) or (UNIGIS INT).

This is an exclusively english language module, hence any kind of communication with the module lecturer should be in English. A discussion forum is maintained in Moodle in order to support efficient module instruction. You are requested to submit all your questions related to this module to this forum only. The lecturer will check all incoming comments on a regular basis. She will answer your questions or provide you with pointers for solving your problems. The module is delivered in form of an instructed self-study that is based on explorative learning process and process. Theoretical concepts are complemented with practice oriented examples demonstrated with help of multimedia elements. Upon completion of the module students are requested to evaluate the module, which is a part of our quality assurance policy and practice.

The software that will be used for model development is GAMA. GAMA is an open source programming framework that was specifically developed for the design and the implementation of spatially explicit simulation models. The particular strongpoint of GAMA is its great capability in simulating spatial data. It makes use of geospatial open source libraries for handling, analysing and visualising spatial data. The platform further supports typical modelling tasks like loading data, conducting simulation experiments, and visualising results.
Models in GAMA are developed with the programming language GAML. This is a domain-specific language (DSL), which is targeted at domain experts that do not necessarily have any background in computer science. During the course of this module, you will – step by step – learn how to code with GAML. Thus, for taking this module, you need to have interest in programming. If you have already taken an application development module in UNIGIS, or have acquired prior experience in coding another way: even better!

Lecturer assessment reflects student’s achievements in this module and is conducted through assessing module assignments. Assignments (English language!) must be submitted in written format (.PDF) within the required time period. Exercises are designed to enforce students’ knowledge and skills. These should be completed to allow students to assess their own progress and are not included in the module assessment.

Lesson 1 – Simulation Modelling
This lesson introduces to Simulation Modelling, for which type of problems it can be used, common application areas and different methodological approaches.

Lesson 2 – Theory of (complex) systems
This lesson provides the theory-based background of systems sciences: the nature of systems and how systems have been conceptualised in science from the General System Theory to Complex Systems.

Lesson 3 – Thinking in systems
This lesson discusses the core properties of systems: its structure made of elements and connections, and its behaviour that is governed by flows, feedback and equilibria.

Lesson 4 – System Dynamics
This lesson guides through the workflow of modelling simple systems in a way that it can be implemented in System Dynamics software.

Lesson 5 – Models of spatial systems
This lesson focuses on the spatial perspective of simulation models. Several way of how to include the spatial dimension are discussed, including Spatial System Dynamics, Finite Elements, probability-based spatial models and bottom-up models of Complex Systems.

Lesson 6 – Cellular Automata
This lesson introduces Cellular Automata as a commonly used method to model (continuous) spatial processes.

Lesson 7 – Agent-based models
This lesson focuses on Agent-based models, their distinct characteristics and typical problems that can be addressed by ABMs.

Lesson 8 – Spatial processes
This lesson presents a number of alternative algorithms how to model the three spatial processes with which are the building blocks of virtually any spatial process: diffusion & growth, movement, and aggregation / segregation.

Lesson 9 – Space and Time
This lesson gives an integrated overview of the spatial and the temporal dimension in simulation modelling, with respect of scale and resolution, hierarchy, neighbourhood, topology / scheduling and system boundaries.

Lesson 10 – Developing a model
 This lesson presents the workflow of designing and building (or modifying) a simulation model.

Lesson 11 – Parameterisation
This lesson is dedicated to input parameters of a model: how they are assigned (parameterisation), assessed (calibration) and analysed with respect to their impact on the uncertainty of a model.

Lesson 12 – Validation and POM
This lesson links back from the ‘model world’ to the ‘real world’. Different levels and strategies of validation are discussed to assess whether a model is an adequate tool for its purpose.

Lesson 13 – Reporting a model -ODD protocol
This lesson presents the ODD strategy of reporting a model. The added value of following the ODD protocol lies in a structured way to write about – and above all to think about – a model.

Lesson 14 – Scenario-based research
This lesson draws everything together and walks the reader through the steps of a ‘doing research’ by means of simulation modelling by the example of a run-off model.