Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network
This work addresses food security issues by providing a method to assess resilience in multi-commodity flow networks, but it is incremental as it applies an existing geospatial knowledge graph approach to a specific domain.
The authors tackled the problem of quantifying resilience in food supply chains by developing a geospatial knowledge graph-based method to measure node-level and network-level resilience in a multi-commodity flow network, applied to a US state-level agricultural case study, which helped discover concentration patterns of agricultural resources across different geographic scales.
Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics of a multi-commodity flow network comprehensively, and design resilience metrics that measure the node-level and network-level dependence of single-sourcing, distant, or non-adjacent suppliers/customers in food supply chains. We conduct a case study of the US state-level agricultural multi-commodity flow network with hierarchical commodity types. The results indicate that, by leveraging GeoKG, our method supports measuring both node-level and network-level resilience across space and over time and also helps discover concentration patterns of agricultural resources in the spatial network at different geographic scales.