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Visualization of Raw Material Supply Chains using the EU Criticality Datasets
https://publications.jrc.ec.europa.eu/repository/bitstream/JRC111646/20180525_jrc-tech-rep-supply_chain_viewer_final.pdf
This study uses the existing datasets from the 2017 EU criticality assessment to visualize 74 material supply chains and shows interconnections between them. Firstly, the data sets are rearranged into a simple graph with nodes representing the countries, materials, product applications, and sectors involved in materials supply and use. The weighted edges (links) represent relationships between them, i.e., the production of materials by countries and the flow of materials into product applications and subsequent economic sectors. Secondly, because mapping the critical raw materials data considers the links between countries, materials, product applications, and sectors, the resulting graphs can also be analysed using network statistics (based on their connectivity). For this, degree centrality (a count of the number of incoming or outgoing links of a node) is used to highlight more interconnected nodes (key actors) in the supply and use of materials. This allows, e.g., detection of countries providing a large number of different (raw) materials, materials finding widespread downstream uses, or product applications relying on a large number of materials.
https://publications.jrc.ec.europa.eu/repository/bitstream/JRC111646/20180525_jrc-tech-rep-supply_chain_viewer_final.pdf
This study uses the existing datasets from the 2017 EU criticality assessment to visualize 74 material supply chains and shows interconnections between them. Firstly, the data sets are rearranged into a simple graph with nodes representing the countries, materials, product applications, and sectors involved in materials supply and use. The weighted edges (links) represent relationships between them, i.e., the production of materials by countries and the flow of materials into product applications and subsequent economic sectors. Secondly, because mapping the critical raw materials data considers the links between countries, materials, product applications, and sectors, the resulting graphs can also be analysed using network statistics (based on their connectivity). For this, degree centrality (a count of the number of incoming or outgoing links of a node) is used to highlight more interconnected nodes (key actors) in the supply and use of materials. This allows, e.g., detection of countries providing a large number of different (raw) materials, materials finding widespread downstream uses, or product applications relying on a large number of materials.