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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
| name | geo-infer-econ |
| description | 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. |
| prerequisites | {"required":["geo-infer-space","geo-infer-data"],"recommended":["geo-infer-time","geo-infer-bayes"]} |
| difficulty | intermediate |
| estimated_time | 45min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
from geo_infer_econ.bioregional.bioregional_markets import BioregionalMarket
from geo_infer_econ.core.spatial_economics import SpatialEconomicModel
from geo_infer_econ.integrations.logistics_integration import LogisticsEconomicAnalyzer
from geo_infer_econ.bioregional.bioregional_markets import BioregionalMarket
market = BioregionalMarket(region="pacific_northwest")
market.add_bid(buyer="co_op_a", price=12.50, quantity=100, location=(45.5, -122.6))
market.add_ask(seller="farm_b", price=11.00, quantity=80, location=(45.3, -122.8))
result = market.clear_auction()
print(f"Clearing price: ${result.price:.2f}, Volume: {result.volume}")
from geo_infer_econ.integrations.logistics_integration import LogisticsEconomicAnalyzer
analyzer = LogisticsEconomicAnalyzer()
cost = analyzer.compute_transport_cost(
origin=(45.5, -122.6), destination=(47.6, -122.3),
cargo_tonnes=50
)
print(f"Transport cost: ${cost.total:.2f}")
uv run python -m pytest GEO-INFER-ECON/tests/ -v