GEO-INFER-NORMS

Instructions

Core Capabilities

• Compliance tracking: Threshold, range, and boolean evaluation with weighted scoring
• Normative inference: Content influence measurement via Jaccard similarity
• Policy evaluation: Multi-criteria governance assessment with configurable metrics
• Regulatory frameworks: Spatial regulation management, jurisdiction tracking

Key Imports

from geo_infer_norms.core.compliance_tracking import ComplianceTracker
from geo_infer_norms.core.normative_inference import NormativeInference
from geo_infer_norms.models.compliance_status import ComplianceMetric
from geo_infer_norms.models.legal_entity import LegalEntity
from geo_infer_norms.models.regulation import Regulation

Examples

import datetime
from geo_infer_norms.core.compliance_tracking import ComplianceTracker
from geo_infer_norms.models.compliance_status import ComplianceMetric
from geo_infer_norms.models.legal_entity import LegalEntity
from geo_infer_norms.models.regulation import Regulation

metric = ComplianceMetric.create(
    name="air_quality",
    description="PM2.5 threshold",
    regulation_id="reg-air",
    evaluation_type="threshold",
    primary_field="pm25",
    threshold_value=35,
    comparison="less_than",
)
tracker = ComplianceTracker("environmental", compliance_metrics=[metric])
entity = LegalEntity("facility-1", "Facility 1", "facility")
regulation = Regulation(
    "reg-air",
    "Air Quality",
    "PM2.5 limit",
    "environmental",
    "County",
    datetime.date(2026, 1, 1),
)
status = tracker.evaluate_compliance(entity, regulation, {"pm25": 28})
print(f"Overall compliance: {status.compliance_level:.1%}")

Guidelines

• Content influence uses Jaccard similarity (real implementation)
• Compliance evaluation handles threshold/range/boolean types correctly

Integrations

• Integrates with METAGOV for governance compliance monitoring
• Integrates with REQ for requirements compliance tracking
• Test: uv run python -m pytest GEO-INFER-NORMS/tests/ -v