GAMA

GAMA enables construction and execution of spatially explicit agent-based simulations that integrate GIS and multi-source spatiotemporal data to model complex systems such as marine activities.

Key Features:

• GIS integration: Integrates open-source Geographical Information Systems (notably GRASS GIS) with agent-based models for geographical data preprocessing and use in simulations.
• Spatially explicit ABM: Supports construction and execution of spatially explicit agent-based models (ABM) representing agents and environments over geographic space.
• Multi-source, multi-scale spatiotemporal constraints: Handles multi-source and multi-scale spatiotemporal constraints and incorporates them as forcing variables in simulations.
• Batch preprocessing: Executes a batch preprocessing step within GRASS GIS to calculate and prepare necessary spatial data for model initialization.
• Batch simulation execution: Launches agent-based simulations on the GAMA platform in batch mode.
• Python orchestration: Orchestrates preprocessing and simulation operations using Python scripts.

Scientific Applications:

• SIMARIS case study: Simulation of fishing activities in the Iroise Sea (Brittany, France) as demonstrated in the SIMARIS project.
• Marine biology: Modeling marine activities and spatial dynamics to analyze intensity and variability of ecological processes.
• Environmental science and resource management: Assessing environmental scenarios and resource-management strategies using geographically explicit, spatiotemporal forcing variables.

Methodology:

Perform batch preprocessing in GRASS GIS to compute required spatial data, launch the agent-based simulation on GAMA in batch mode, and orchestrate these steps via a Python script.