论文标题
通过地理空间知识图测量网络弹性图：美国多商品流网络的案例研究
Measuring Network Resilience via Geospatial Knowledge Graph: a Case Study of the US Multi-Commodity Flow Network
论文作者
论文摘要
量化食品系统中的弹性对于粮食安全问题很重要。在这项工作中，我们提出了一种基于地理空间知识图（GEOKG）的方法，用于测量多商品流网络的弹性。具体而言，我们开发了一个CFS-GEOKG本体论，以全面地描述多商品流网络的地理空间语义，并设计弹性指标，以衡量节点级别和网络级别的依赖性，远处或非附属供应商/食品供应商在食品供应链中的供应商。我们对具有层次商品类型的美国州级农业多商品流网络进行了案例研究。结果表明，通过利用Geokg，我们的方法支持在空间和随着时间的流逝之间测量节点级别和网络级别的弹性，也有助于发现不同地理尺度上空间网络中农业资源的集中度。
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.
