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Multi-Scale Food Flow Prediction using Graph Neural Networks

A project leveraging Graph Neural Networks (GNNs) to predict food flows between counties and FAF zones for economic planning, infrastructure development, and policy-making.

License GitHub Repo

References​

Data Sources​

  • Trade Data: FAF5 SCTG1 commodity flow data (code/data/FAF5_SCTG1.csv)
  • Geographic Information:
    • County shapefiles (code/data/shapefiles/cb_2017_us_county_500k/cb_2017_us_county_500k.shp)
    • State shapefiles (code/data/shapefiles/cb_2018_us_state_20m/cb_2018_us_state_20m.shp)
    • FAF zones shapefiles (code/data/shapefiles/2017_CFS_Metro_Areas_with_FAF/2017_CFS_Metro_Areas_with_FAF.shp)
  • Economic Indicators: County-level economic data (code/data/faf_features.csv)
  • Distance Information: FAF Distance Matrix (code/data/FAF_Distance_Matrix.csv)

Acknowledgements​

National Science Foundation (NSF) funded AI institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) (OAC 2112606)

Future Work​

  • Extending the model to handle inter-county trade flow predictions
  • Refining the model to capture more granular trade patterns
  • Implementing visualization tools for inter-county trade networks