FDU-INS GEO-INFER-RISK
Instructions
Core Capabilities
- Catastrophe models: Cholesky-decomposition spatial correlation
- Risk engine: Moran's I, Geary C, Monte Carlo loss calculation
- Exposure modeling: Multi-source data loading (DB, file, stream, API)
- Hazard modeling: Spatial hazard assessment and mapping
- Vulnerability: Bayesian uncertainty quantification
- Underwriting: Rule-based fraud detection, env var API keys
Key Imports
from geo_infer_risk.core.risk_engine import RiskEngine
from geo_infer_risk.core.catastrophe_models import CatastropheModel
from geo_infer_risk.core.exposure_model import ExposureModel
from geo_infer_risk.core.hazard_model import HazardModel
Examples
from geo_infer_risk.core.risk_engine import RiskEngine
engine = RiskEngine()
result = engine.assess(
hazard_raster=flood_depth,
exposure_data=building_footprints,
vulnerability_curve="residential_flood"
)
print(f"Expected loss: ${result.expected_loss:,.0f}")
print(f"Loss exceedance (100yr): ${result.loss_at_return_period(100):,.0f}")
from geo_infer_risk.core.catastrophe_models import CatastropheModel
cat_model = CatastropheModel(peril="earthquake", region="pacific_ring")
simulations = cat_model.run_monte_carlo(n_simulations=10_000)
print(f"Mean annual loss: ${simulations.mean_annual_loss:,.0f}")
print(f"99th percentile: ${simulations.percentile(99):,.0f}")
Guidelines
- All 18 former placeholder references verified clean (0 remaining)
- Spatial correlation uses Cholesky decomposition
- Risk aggregation uses real Moran's I and Monte Carlo
- Test:
uv run python -m pytest GEO-INFER-RISK/tests/ -v
Integrations
- BAYES → Bayesian uncertainty quantification
- ECON → Economic loss and insurance modeling
- CLIMATE → Climate-driven hazard projections
- SPACE → Spatial correlation of hazards
- AG → Crop loss risk assessment