Geosimulation using fields and agents

This repository holds a Jupyter notebook demonstrating the Daisyworld model implementation in Campo, a YAML file to create the Python environment required to run the model, and necessary scripts for pre- and postprocessing.

More information will be given in the OpenGeoHub Summer School 2021 lecture on September 2nd at 2pm CEST.

How to install

A few steps are required to run the Jupyter notebook. General information on Jupyter notebooks and manuals can be found here. The user guide and short reference on Conda can be found here.

1. You will need a working Python environment, we recommend to install Miniconda. Follow their instructions given at:

   https://docs.conda.io/en/latest/miniconda.html

2. Open a terminal (Linux/macOS) or Miniconda command prompt (Windows) and browse to a location where you want to store the course contents.

3. Clone this repository, or download and uncompress the zip file. Afterwards change to the opengeohub2021 folder.

4. Create the required Python environment:

   Linux/macOS:

   conda env create -f environment/course_environment.yaml

   Windows:

   conda env create -f environment\course_environment.yaml

The environment file will create a environment named fieldagents using Python 3.9. In case you prefer a different name or Python version you need to edit the environment file.

How to run

Activate the environment in the command prompt:

conda activate fieldagents

Then change to the notebook folder. You can now start the Jupyter notebook from the command prompt. The notebook will open in your browser:

jupyter-notebook course.ipynb

Further reading

Background on DaisyWorld:

https://en.wikipedia.org/wiki/Daisyworld

Scientific literature about Campo and LUE:

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

K. de Jong, D. Karssenberg (2019). A physical data model for spatio-temporal objects. Environmental Modelling & Software, 122, 104553, DOI: 10.1016/j.envsoft.2019.104553.

K. de Jong, D. Panja, M. van Kreveld, D. Karssenberg (2021). An environmental modelling framework based on asynchronous many-tasks: Scalability and usability. Environmental Modelling & Software, 139, 104998, DOI: 10.1016/j.envsoft.2021.104998.