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geo-infer-risk

geo-infer-risk

Geospatial risk modeling including catastrophe models, exposure analysis, and underwriting. Use when assessing spatial risk, building catastrophe models, analyzing exposure/hazard/vulnerability, or computing portfolio risk metrics.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: geo-infer-risk description: Geospatial risk modeling including catastrophe models, exposure analysis, and underwriting. Use when assessing spatial risk, building catastrophe models, analyzing exposure/hazard/vulnerability, or computing portfolio risk metrics. prerequisites: required: - geo-infer-space - geo-infer-data recommended: - geo-infer-bayes - geo-infer-math difficulty: advanced estimated_time: 60min examples_dir: ../GEO-INFER-EXAMPLES/examples/

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
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