| name | geo-infer-sim |
| description | Agent-based simulation for geospatial environments. Use when building spatial simulations, modeling agent interactions in geographic space, running Monte Carlo spatial experiments, or comparing spatial planning scenarios. |
| prerequisites | {"required":["geo-infer-space","geo-infer-time"],"recommended":["geo-infer-bayes","geo-infer-act"]} |
| difficulty | advanced |
| estimated_time | 60min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-SIM
Instructions
Core Capabilities
- Agent-based modeling: Spatial agents on grids, networks, and continuous space
- Environment simulation: Geographic environment state management, land use dynamics
- Monte Carlo: Stochastic spatial experiments with ensemble statistics
- Scenario analysis: What-if spatial scenario comparison and sensitivity analysis
- Visualization: Simulation playback, spatial animation, time-step rendering
Key Imports
from geo_infer_sim.core.simulation import SpatialSimulation
from geo_infer_sim.core.environment import GeoEnvironment
from geo_infer_sim.core.scenario import ScenarioManager
from geo_infer_sim.core.monte_carlo import MonteCarloRunner
Examples
from geo_infer_sim.core.simulation import SpatialSimulation
sim = SpatialSimulation(
grid_size=(100, 100),
n_agents=50,
time_steps=200
)
sim.add_rule("diffusion", rate=0.1)
results = sim.run()
final_state = results.get_snapshot(t=200)
Guidelines
- Mesa integration in development (Alpha)
Integrations
- Integrates with AGENT for Active Inference agent behavior
- Integrates with ANT for swarm simulation
- Test:
uv run python -m pytest GEO-INFER-SIM/tests/ -v
