Food flows between counties in the United States
2019 Winter Cyberseminar Series
- Megan Konar / University of Illinois at Urbana-Champaign
Food consumption and production are separated in space through flows of food along complex supply chains. These food supply chains are critical to our food security, making it important to evaluate them. However, detailed spatial information on food flows is rare. To this end, we develop the Food Flow Model, a data-driven methodology to estimate spatially explicit food flows for subnational locations without data. The Food Flow Model integrates machine learning, network properties, production and consumption statistics, mass balance constraints, and linear programming. We use the Food Flow Model to infer food flows between counties within the United States. Specifically, we downscale empirical information on food flows between 132 Freight Analysis Framework (FAF) locations (17,292 potential links) to the 3,142 counties and county-equivalents of the United States (9,869,022 potential links). Subnational food flow estimates can be used to improve our understanding of vulnerabilities within a national food supply chain, determine critical infrastructures, and enable spatially detailed footprint assessments.
CUAHSI's 2019 Winter Cyberseminar Series: The U.S. Food Energy and Water System at the Mesoscale
Hosted by Benjamin Ruddell, Northern Arizona University
The Food, Energy, and Water (FEW) system in the United States is characterized by connections at all scales, but especially by connections at the mesoscale defined by watersheds, cities, irrigation districts, and counties. At these scales transfers of water, flows of goods and services, and socio-economic gradients form the patterns that capture most of the structure in the complete FEW system. This cyberseminar series presents the current work on the mesoscale FEW system in the U.S., including studies of its network structure, its embedded resources and footprints, its boundaries, its stakeholders, its vulnerability and resilience dynamics, and emerging data products and best practices.