geo-infer

name	geo-infer
description	Geospatial Active Inference framework with 44 modules for ecological, civic, and commercial spatial analysis. Use when working with geospatial data, Active Inference, Bayesian modeling, H3 hexagonal indexing, spatial statistics, or any domain-specific geographic analysis (agriculture, health, economics, risk, climate, energy, transport, marine, forestry, water).

GEO-INFER

A 44-module Python monorepo implementing Active Inference principles for geospatial analysis.

Instructions

Module Discovery

Modules are in GEO-INFER-{NAME}/ directories. Each has its own SKILL.md for detailed guidance.

Analytical Core

Module	Import	Purpose
MATH	geo_infer_math	Spatial statistics, topology, graph theory
ACT	geo_infer_act	Active Inference (free energy, belief updating)
BAYES	geo_infer_bayes	Bayesian inference (PyMC, TFP, variational)
AI	geo_infer_ai	ML pipelines, model selection, spatial features
COG	geo_infer_cog	Cognitive modeling (attention, memory, trust)
SPM	geo_infer_spm	Statistical Parametric Mapping (GLM, RFT)

Spatial-Temporal & Infrastructure

Module	Import	Purpose
SPACE	geo_infer_space	H3 v4 hexagonal indexing, spatial backends
TIME	geo_infer_time	Time series, temporal analysis, forecasting
IOT	geo_infer_iot	IoT sensor ingestion (MQTT, streaming)
DATA	geo_infer_data	ETL pipelines, connectors, format loaders
API	geo_infer_api	FastAPI REST/GraphQL endpoints
SEC	geo_infer_sec	Security, threat detection, access control
OPS	geo_infer_ops	Monitoring, alerting, observability

Domain Modules

Module	Import	Purpose
AG	geo_infer_ag	Agriculture, soil health, carbon (IPCC Tier 1)
HEALTH	geo_infer_health	Epidemiology, health access, SaTScan
ECON	geo_infer_econ	Bioregional markets, call auctions
RISK	geo_infer_risk	Catastrophe models, Monte Carlo loss
LOG	geo_infer_log	Logistics, routing, supply chain (PuLP)
BIO	geo_infer_bio	Biodiversity, habitat connectivity
CLIMATE	geo_infer_climate	Climate projections, SPI/PDSI indices
ENERGY	geo_infer_energy	LCOE, renewable siting, grid analysis
FOREST	geo_infer_forest	Forest cover, carbon stocks, wildfire
MARINE	geo_infer_marine	Ocean currents, coastal, MPA planning
EMERGENCY	geo_infer_emergency	SAR, disaster response, evacuation
TRANSPORT	geo_infer_transport	Traffic (BPR), EWMA forecasting, emissions
WATER	geo_infer_water	Watershed, hydrology, flood risk

Agents & Simulation

Module	Import	Purpose
AGENT	geo_infer_agent	Multi-agent systems, telemetry
ANT	geo_infer_ant	ACO, PSO, ABC swarm optimization
SIM	geo_infer_sim	Agent-based simulation, Monte Carlo

Community & Governance

Module	Import	Purpose
CIV	geo_infer_civ	Civic engagement, STEW-MAP
PEP	geo_infer_pep	Public engagement, CRM
ORG	geo_infer_org	Organizational modeling
COMMS	geo_infer_comms	Messaging, notifications, spatial routing
METAGOV	geo_infer_metagov	Polycentric governance, Ostrom
NORMS	geo_infer_norms	Compliance, normative inference (Jaccard)
REQ	geo_infer_req	Requirements, traceability, P3IF

Presentation & Operations

Module	Import	Purpose
APP	geo_infer_app	Dashboards, agent widgets, map views
ART	geo_infer_art	Generative geo-art, cartographic design
EDU	geo_infer_edu	Curricula, exercises, assessment
PLACE	geo_infer_place	Geocoding, catchment analysis, H3
GIT	geo_infer_git	Spatial data versioning, lineage
TEST	geo_infer_test	Unified test runner, fixtures, markers
EXAMPLES	geo_infer_examples	Orchestration patterns, workflows
INTRA	geo_infer_intra	Documentation hub, integration guides

Architecture & Data Flow

┌──────────────────────────────────────────────────────────┐
│                      Data Sources                        │
│  (files, DBs, APIs, MQTT sensors)                        │
└───────────────────────┬──────────────────────────────────┘
                        ▼
┌──────────────────────────────────────────────────────────┐
│              DATA / IOT (Ingestion Layer)                 │
└───────────────────────┬──────────────────────────────────┘
                        ▼
┌──────────────────────────────────────────────────────────┐
│         SPACE / TIME / PLACE (Indexing Layer)             │
│  H3 v4 tessellation, CRS transforms, temporal indexing   │
└───────────────────────┬──────────────────────────────────┘
                        ▼
┌──────────────────────────────────────────────────────────┐
│     MATH / BAYES / ACT / SPM (Analysis Layer)            │
│  Spatial statistics, Bayesian models, Active Inference    │
└───────────────────────┬──────────────────────────────────┘
                        ▼
┌──────────────────────────────────────────────────────────┐
│    AI / AGENT / ANT / SIM (Intelligence Layer)           │
│  ML pipelines, agent systems, swarm optimization         │
└───────────────────────┬──────────────────────────────────┘
                        ▼
┌──────────────────────────────────────────────────────────┐
│   Domain Modules (Application Layer)                     │
│  AG, HEALTH, ECON, RISK, LOG, BIO, CLIMATE, ENERGY,     │
│  FOREST, MARINE, EMERGENCY, TRANSPORT, WATER             │
└───────────────────────┬──────────────────────────────────┘
                        ▼
┌──────────────────────────────────────────────────────────┐
│      API / APP / ART (Presentation Layer)                │
│  REST endpoints, dashboards, visualizations              │
└──────────────────────────────────────────────────────────┘

Common Patterns

# Graceful dependency imports (every __init__.py)
try:
    from .core.engine import Engine
    HAS_ENGINE = True
except ImportError:
    HAS_ENGINE = False

# H3 v4 API (never use legacy h3.geo_to_h3)
import h3
cell = h3.latlng_to_cell(lat, lng, resolution)
lat, lng = h3.cell_to_latlng(cell)

# Module installation
# uv pip install -e ./GEO-INFER-MATH

Testing

# All modules
uv run python GEO-INFER-TEST/run_unified_tests.py

# Single module
uv run python -m pytest GEO-INFER-MATH/tests/ -v

# With coverage
uv run python -m pytest GEO-INFER-MATH/tests/ --cov=GEO-INFER-MATH/src

Modular Hygiene

uv sync --all-packages --all-extras
uv run python GEO-INFER-TEST/validate_repo_contracts.py --strict-source-language --skip-import-smoke

• Root pyproject.toml, uv.lock, and .python-version define the uv workspace.
• Each module keeps importable behavior under src/ and at least four pytest files under tests/.
• Planned work belongs in root TODO.md or a tracked issue, not source or test task markers.
• Importable modules use logging.getLogger(__name__); CLI entrypoints configure process-wide logging.

Examples

Cross-Module Pipeline

from geo_infer_data.formats.geojson import GeoJSONLoader
from geo_infer_space.backends.h3 import H3Backend
from geo_infer_math.core.spatial_statistics import MoranI
from geo_infer_bayes.core.bayesian_inference import BayesianModel

# 1. Load data
features = GeoJSONLoader().load("observations.geojson")

# 2. Index to H3 cells
backend = H3Backend()
cells = backend.tessellate(features.bounds, resolution=7)

# 3. Compute spatial autocorrelation
moran = MoranI(values, weight_matrix)
result = moran.compute()

# 4. Bayesian parameter estimation
model = BayesianModel(prior="normal", likelihood="normal")
posterior = model.fit(data)

Domain Workflow: Agricultural Risk

from geo_infer_ag.models.soil_health import SoilHealthModel
from geo_infer_risk.core.risk_engine import RiskEngine
from geo_infer_space.backends.h3 import H3Backend

cells = H3Backend().tessellate(farm_polygon, resolution=9)
soil = SoilHealthModel()
health = {cell: soil.assess(cell) for cell in cells}
risk = RiskEngine().assess(hazard=drought_index, exposure=health)

Active Inference Agent

from geo_infer_agent.core.active_inference import ActiveInferenceAgent
import numpy as np

agent = ActiveInferenceAgent(n_states=8, n_observations=5, n_actions=4)
obs = np.random.dirichlet(np.ones(5))
action = agent.act(obs)
print(f"Action: {action}, Free energy: {agent.free_energy:.4f}")

Guidelines

• No mock/stub/placeholder code — every function must have real logic
• Active Inference first — ground implementations in free energy minimization
• Type hints everywhere — full annotations on all parameters and returns
• H3 v4 only — use latlng_to_cell, not legacy geo_to_h3
• Graceful degradation — optional deps via try/except ImportError
• Logger, not print — use structured logging in all modules
• Root task ledger — use TODO.md or issues for future work; keep source and tests free of task markers
• uv workspace hygiene — keep root pyproject.toml, .python-version, and uv.lock as the shared environment contract
• Check each module's SKILL.md for domain-specific rules and integrations