Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network

TL;DR

This paper introduces a geospatial knowledge graph approach to measure the resilience of multi-commodity food supply networks, enabling detailed analysis of dependence and concentration patterns across space and time.

Contribution

It develops a novel GeoKG-based framework with specific resilience metrics for analyzing food supply chain resilience at multiple geographic scales.

Findings

Supports measuring node-level and network-level resilience.

Discovers concentration patterns of agricultural resources.

Enables analysis over space and time.

Abstract

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.