Commodity flow datasets are essential for freight planning, infrastructure investment, and economic analysis. The Freight Analysis Framework (FAF) provides flows between large regions with relatively detailed commodity classifications. The FAF5 experimental county-level dataset offers finer geographic detail but simplifies commodities into broad groups. Analysts must often choose between geographic detail and commodity detail, and disaggregation methods are needed to bridge that gap.

In this workshop, participants will combine these datasets to produce flows with both detailed geography and commodity resolution using open, reproducible disaggregation methods. We will also incorporate additional open data, such as county-level agricultural production, to support more informed and defensible disaggregation.

Data Structure and Constraints

Participants will begin by examining the structure and limitations of aggregated datasets, focusing on how FAF zones, commodity groupings, and modal categories constrain local analysis. Participants will then construct spatial crosswalks that connect FAF zones to counties. The session will demonstrate how to manage one-to-many relationships, handle incomplete mappings, and ensure that flows are consistently distributed across spatial units.

Disaggregation Methods

The core of the workshop focuses on disaggregation methods. Participants will apply proportional and weighted allocation techniques to translate FAF flows into county-level estimates. These methods are implemented separately by transportation mode, allowing participants to treat truck and rail flows differently. Commodity group disaggregation is then introduced, where broad categories such as agriculture are broken into more specific components using additional data sources.

External Data Integration

A major component of the workflow is the integration of external data to improve disaggregation. Participants will use county-level agricultural production data and apply transparent conversion factors to transform production measures into comparable units. These data are used to estimate commodity shares at both local and regional scales. The resulting shares are then applied to disaggregated flows to derive more detailed commodity-specific estimates, such as specific crop flows.

Hands-On Workflow

The hands-on portion of the workshop closely follows a complete working example. Throughout the process, participants will work with open-source tools including Python, Pandas, and GeoPandas in guided Jupyter notebooks. They will clean and standardize raw data, build crosswalks, apply allocation methods, and generate mapped outputs. Visualization steps will demonstrate how disaggregated flows can be interpreted at both FAF and county levels, highlighting differences between total flows, agricultural flows, and commodity-specific estimates.

Outcomes

By the end of the workshop, participants will be able to construct reproducible workflows that transform aggregated commodity flow datasets into detailed, location-specific estimates. They will gain experience in building spatial crosswalks, applying defensible allocation methods, integrating external datasets, and producing outputs that support planning and analysis. The methods presented are broadly transferable to transportation, agriculture, energy, and economic applications where data must be translated across spatial or categorical scales.

This workshop emphasizes transparency and adaptability, giving participants the tools and understanding needed to extend these methods to their own regions, datasets, and analytical questions.