Measuring Network Resilience via Geospatial Knowledge Graph: a Case
Study of the US Multi-Commodity Flow Network
- URL: http://arxiv.org/abs/2210.08042v1
- Date: Sun, 9 Oct 2022 23:12:16 GMT
- Title: Measuring Network Resilience via Geospatial Knowledge Graph: a Case
Study of the US Multi-Commodity Flow Network
- Authors: Jinmeng Rao, Song Gao, Michelle Miller, Alfonso Morales
- Abstract summary: We develop a CFS-GeoKG to describe geospatial semantics of a multi-commodity flow network.
We conduct a case study of the US state-level agricultural multi-commodity flow network with hierarchical commodity types.
- Score: 2.1793134762413437
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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