A modelling paradigm for field-agent based modelling

Many simulation models needed for instance for hydrological, ecological, or health studies require the combination of fields representing spatially distributed values (e.g. elevation, catchments, land use or contamination) and agents representing individual objects (e.g. trees, agricultural farms, or humans).

This workshop provides hands-on exercises for Campo, a Python modelling environment providing operations accepting both fields and agents as arguments. Campo resembles and extends the map algebra approach to field-agent modelling and allows for the construction of static or dynamic models. Campo builds upon LUE, a recently developed conceptual and physical data model for storage and access of field or agent data.

Recommended reading:

de Bakker, M. P., de Jong, K., Schmitz, O., & Karssenberg, D. (2017). Design and demonstration of a data model to integrate agent-based and field-based modelling. Environmental Modelling & Software, 89, 172–189. https://doi.org/10.1016/j.envsoft.2016.11.016

de Jong, K., & Karssenberg, D. (2019). A physical data model for spatio-temporal objects. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.104553

Note

We strongly recommend to set up your working environment before starting the workshop.

Basic Python knowledge is required for the modelling exercises.

Workshop contents:

• Setting up your workshop environment
  • Installing Python
  • Obtaining the workshop files
  • Setting up the workshop environment
• Static field-agent models with Campo
  • Initializing your model
  • Adding phenomena, property sets and properties