GEO-INFER-LOG

Instructions

Core Capabilities

• Delivery: KMeans clustering, Voronoi tessellation, Haversine service areas
• Transport: Dijkstra routing, betweenness centrality, max-flow, emissions
• Supply chain: PuLP MILP optimization, articulation points, EOQ, Monte Carlo
• Fleet management: Vehicle routing, real-time tracking, ETA calculation
• Observability: Enhanced structured logging with spatial context

Key Imports

from geo_infer_log import LastMileRouter, DeliveryScheduler, ServiceAreaAnalyzer
from geo_infer_log import EmissionsCalculator, TransportationNetworkAnalyzer
from geo_infer_log import SupplyChainModel, FacilityLocator, InventoryManager
from geo_infer_log import EnhancedLogger, PerformanceMetrics

All logistics classes are lazy-loaded via __getattr__ — zero cost until accessed.

Examples

from geo_infer_log import FacilityLocator

locator = FacilityLocator(n_facilities=5)
locations = locator.locate_facilities(demand_points)
coverage = locator.analyze_coverage(locations, demand_points)

Guidelines

• All implementations are real (KMeans, Dijkstra, PuLP) — no placeholders
• Submodules (api, core, models, utils) lazy-loaded on attribute access
• Logger used for all output — no print() statements
• Test: uv run python -m pytest GEO-INFER-LOG/tests/ -v

Integrations

• ECON → Logistics cost feeding economic models
• TRANSPORT → Route optimization and emissions calculation
• RISK → Supply chain risk and disruption modeling
• SPACE → H3-based delivery zone tessellation
• OPS → Logistics operation monitoring