The high separation of crude oil supply and demand markets has led to the formation of a global crude oil trading system. This paper constructs global crude oil trade networks, integrates macro, meso, and micro network analysis methods, combines geospatial visualization techniques, and then portrays the spatiotemporal patterns and topological evolution of the global crude oil trade networks. Thus, it attempts to dig deeper into the world crude oil competition and cooperation links and evolution laws and provides a scientific reference for a comprehensive understanding of the global crude oil market dynamics. The results show that: (1) After three fluctuations of increase and decrease since 2000, the global crude oil trade volume is entering the adjustment period, and the scale of the crude oil market is rising slowly. (2) The international crude oil trade has formed trade network patterns with complex structures, clear hierarchy and unbalanced distribution. The “rich club” phenomenon is significant, with large trading countries dominating the trade network. (3) The scale and density of the global crude oil trade network show a trend of increasing and then decreasing, the network agglomeration pattern becoming more obvious, the inter-nodal links continuously strengthening, and the network connectivity improving. (4) The global crude oil trade networks are characterized by core–periphery structures, and the polarization effect is significant. The US, Russia, China, Japan, the Netherlands, and South Korea hold the core positions in the crude oil trade network, and the major importing countries have become the dominant forces in the trade network. In addition, we present policy suggestions for different types of countries for energy transformation and security in the global trade market system, which can be used as a reference for policymakers.
The multi‐scale characteristic of online car‐hailing traffic volume can reflect the implied distribution pattern, which is crucial for traffic management and even urban planning. Nevertheless, the spatio‐temporal heterogeneity of online car‐hailing traffic volume makes it challenging to analyze its multi‐scale characteristics effectively. Here, a method named multi‐scale characteristic analysis for online car‐hailing traffic volume with quantum walk (MCATV‐QW) is proposed. MCATV‐QW adopts quantum walks to generate multi‐scale probability patterns that online car‐hailing appears at different locations over time. Then stepwise regression is applied to screen the generated multi‐scale probability patterns, to further analyze the multi‐scale characteristic. We validate MCATV‐QW with online car‐hailing traffic volume in the northeast of Chengdu, China. MCATV‐QW not only achieves better simulation performance, but also reveals the distribution pattern that the influence degree of multi‐scale probability patterns weakens from southwest to northeast of study area. MCATV‐QW also reflects the traffic spatial pattern that is dominated by gradual traffic (48%), with both abrupt (26%) and uniform traffic (26%).
Trade connectivity is a crucial component in the implementation of the Belt and Road Initiative (BRI). Based on the BRI trade network database, this study integrated three mesoscale analysis methods, including community detection, core-periphery profile, and disparity filter, to build an analytical framework for exploring trade network connectivity and further investigated the spatiotemporal patterns, topological relationship, and structural evolution of "the Belt and Road" trade network from 2000 to 2020 in two dimensions, including nodes and edges. This study aimed to provide scientific references for a comprehensive and in-depth understanding of "the Belt and Road" trade network connectivity. The results show that: 1) The BRI trade network connections and density continuously increased, and the size distribution in trade volumes showed spatial heterogeneity, gradually forming patterns with an apparent hierarchical structure, unbalanced spatial distribution, and increasingly close trade ties. 2) The BRI trade network included five trade blocs with significant geographical proximity, and geographical distance still played an essential role in the evolution of the international trade division of labor system at the global and regional scales. 3) The core-periphery structures of the BRI trade network are undergoing structural adjustments, with the core and peripheral structures showing apparent differentiation and the core-peripheral polarization effect emerging. 4) The backbone structures of the BRI trade network have been continuously expanded and enriched, showing a trend of convergence to the core countries, forming backbone network patterns with China as the absolute core, radiating outward and linking the whole region while India, Russia, and Turkey have also formed their backbone networks in local regions. From the perspective of network science, examining trade network connectivity is crucial for understanding the trade network structure, optimizing the trade development pattern, and enhancing the trade network resilience in "the Belt and Road" regions.
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