GEO-INFER

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).

Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.

Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.

H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).

Unified test runner and testing infrastructure for the GEO-INFER ecosystem. Use when running cross-module tests, configuring test categories, setting up test fixtures for spatial data, or analyzing test results.

Generative geospatial art and cartographic visualization. Use when creating artistic map visualizations, generative spatial art, interactive cartographic displays, animation sequences, or aesthetically-focused geographic rendering.

Bayesian inference and probabilistic modeling for geospatial data. Use when building hierarchical models, computing posteriors with PyMC or TFP, performing variational inference, model comparison (LOO/WAIC/DIC), or spatial Gaussian processes.

Communication systems for geospatial coordination. Use when implementing spatial messaging, multi-channel notifications (email/SMS/push), event streaming, spatial message routing, or subscriber management for geographic broadcasts.

Geospatial economics and bioregional market modeling. Use when analyzing spatial economic patterns, bioregional markets, location-based pricing, call auctions, or supply-demand modeling with geographic context.

Git-based versioning and collaboration for geospatial datasets. Use when versioning spatial data, managing geospatial dataset lineage, tracking spatial data changes, resolving merge conflicts in geospatial formats, or building reproducible analysis pipelines.

IoT sensor data ingestion and real-time streaming for geospatial monitoring. Use when connecting to MQTT brokers, processing sensor streams, validating spatial sensor data quality, or building real-time geospatial monitoring pipelines.

Logistics optimization including route planning, fleet management, delivery scheduling, and supply chain modeling. Use when optimizing delivery routes, managing fleets, analyzing supply chain resilience, or computing emissions from transportation.

Spatial statistics, topology, and graph theory for geospatial analysis. Use when computing Moran's I, spatial autocorrelation, geodesic distances, graph connectivity, kernel density estimation, or any mathematical operation on geographic data.

Normative inference and compliance tracking for geospatial governance. Use when evaluating spatial policy compliance, tracking governance metrics, computing normative content influence (Jaccard similarity), or managing multi-criteria regulatory frameworks.

Place-based analysis with H3 hexagonal indexing. Use when performing place identification, catchment area analysis, county/region geometry loading, or H3-based place-shedding and geographic boundary operations.

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.

Agent-based simulation for geospatial environments. Use when building spatial simulations, modeling agent interactions in geographic space, running Monte Carlo spatial experiments, or comparing spatial planning scenarios.

Canonical GEO-INFER Active Inference implementation. Use when implementing or reviewing free-energy minimization, belief updating, generative models, policy selection, H3/spatial active inference, or typed ACT diagnostics.

Application framework for geospatial dashboards and web interfaces. Use when building spatial dashboards, map-based UIs, agent control widgets, interactive geospatial web applications, or configuring agent parameters through forms.

Biodiversity analysis and ecological modeling. Use when analyzing species distributions, habitat connectivity, biodiversity indices, ecological networks, conservation planning, or ecosystem health assessment.

Cognitive modeling for geospatial agents including attention, memory, and trust. Use when implementing spatial attention mechanisms, working memory for geographic contexts, or trust dynamics in multi-agent geospatial systems.

Time series analysis and temporal modeling for geospatial data. Use when analyzing temporal patterns, forecasting spatial time series, detecting change points, or working with spatio-temporal datasets.

REST and GraphQL API endpoints for geospatial services. Use when building API routes, defining spatial query endpoints, or exposing GEO-INFER functionality as web services.

Data connectors, ETL pipelines, and data management for geospatial datasets. Use when loading spatial data from databases, APIs, files (GeoJSON, Shapefile, GeoParquet), or building data transformation pipelines.

Working examples and module orchestration patterns. Use when looking for usage examples, cross-module orchestration patterns, end-to-end workflows, or reference implementations of GEO-INFER capabilities.

Operations, monitoring, and observability for geospatial infrastructure. Use when setting up monitoring dashboards, configuring alerts, tracking system health, or managing deployment of spatial services.

Precision agriculture and soil health modeling. Use when analyzing soil health, crop water usage (FAO-56), carbon sequestration (IPCC Tier 1), precision farming, or agricultural land management.

Multi-agent geospatial systems with Active Inference. Use when building spatial agents, implementing perception-action loops, managing agent telemetry, or coordinating multi-agent spatial exploration.

Machine learning pipelines and model selection for geospatial AI. Use when training spatial ML models, building prediction pipelines, performing feature engineering on geographic data, or selecting between ML approaches for spatial problems.

Civic engagement and participatory mapping. Use when building participatory GIS, STEW-MAP implementations, community mapping platforms, citizen science data collection, or democratic spatial planning processes.

Climate modeling and environmental analysis. Use when analyzing climate data, projecting future climate scenarios (RCP/SSP), computing climate indices (SPI, PDSI), performing statistical downscaling, or assessing climate change impacts on geographic regions.

Educational technology for geospatial learning. Use when creating spatial analysis curricula, interactive GIS exercises, learning progression models, competency assessment for geographic concepts, or step-by-step spatial tutorials.

Emergency management and disaster response. Use when planning search and rescue, optimizing emergency resource deployment, modeling disaster scenarios, coordinating evacuation routes, or managing multi-agency response logistics.

Energy systems analysis and renewable energy siting. Use when computing LCOE, analyzing energy grid spatial patterns, optimizing renewable energy placement, assessing energy storage, or performing techno-economic analysis of energy projects.

Forest analysis and forestry management. Use when analyzing forest cover change, timber inventory, deforestation detection, forest carbon stocks, wildfire risk assessment, or canopy structure analysis.

Spatial epidemiology and public health analysis. Use when modeling disease spread, analyzing health disparities, performing spatial health risk assessment, building epidemiological surveillance systems, or assessing healthcare accessibility.

Central documentation hub and cross-module integration guides for GEO-INFER. Use when navigating documentation, finding cross-module integration patterns, understanding data flow architecture, or locating tutorials and API references.

Marine and ocean analysis for coastal and offshore environments. Use when analyzing ocean currents, marine ecosystems, coastal erosion, bathymetry, marine protected area planning, or fisheries management.

Meta-governance frameworks for geospatial decision-making. Use when implementing polycentric governance, multi-level institutional analysis, stakeholder engagement, conflict resolution, or adaptive governance scenarios for spatial resource management.

Organizational modeling for geospatial entities. Use when modeling organizational structures, spatial resource allocation, inter-organizational network analysis, or institutional spatial analysis for governance.