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How-To Guides

Follow these steps to set up and run the GNN Food Flow Portal locally.

1. Clone the repository​

git clone https://github.com/ICICLE-ai/GNNFoodFlowPortal.git

cd GNNFoodFlowPortal

2. Create a Python environment & install dependencies​

conda create -n gnnfoodflow python=3.10

conda activate gnnfoodflow

pip install -r requirements.txt

3. Prepare the data​

This repo has included all necessary datasets for the portal

4. Run the portal locally​

streamlit run app.py

5. Explore the portal​

Use the filter panel to select:

  • Commodity code (SCTG1-7)

  • Origin/Destination county or state

Click Download Tab to export filtered flows as CSV for your research.

Filtered Results​

Filtered results represent original datasets referenced by the models.

Summary Statistics​

Summary Statistics are the necessary and simplified version for county-level food flows.

Downstream Use​

  • Spatial forecasting of trade changes under policy shifts
  • Identifying critical counties for supply chain resilience

Out-of-Scope Use​

  • Real-time food trade forecasting
  • Non-U.S. geographic settings without retraining

Bias, Risks, and Limitations​

  • Bias: Model predictions depend on historical FAF data and may not reflect unexpected future disruptions (e.g., disasters, pandemics)
  • Limitations: Prediction is limited to predefined commodity codes (SCTG1-7)
  • Data quality: Assumes accuracy of FAF flow data and economic indicators

Data Sources​