Rice holds a significant position as one of the world’s most important food crops, and international trade plays a crucial role in regulating rice supply and demand. Analyzing the structural evolution of the global rice trade from a network perspective is paramount for understanding the global rice-trade supply chain and ensuring global food security. This study utilizes international rice-trade data from 2000 to 2021 and employs various network analysis methods to depict the spatial and temporal patterns of the global rice trade, examines the network topologies of the global rice trade, and reveals the impacts of its evolution on food security. The research findings are as follows: (1) Global rice-trade scale has increased over time, indicating a relatively stable development with the gradual formation of complex rice-trade networks. Since 2000, the global rice-trade networks have shown increasing density characterized by Asia as the primary export source and Africa as an important import market. (2) Network analysis indicators demonstrate a growing trend in the size and density of the global rice-trade networks, along with increasingly optimized network structures and improved network connectivity efficiency. Core positions in the networks are occupied by Thailand, Vietnam, India, China, Pakistan, and the United States, while import partners in European and American countries, such as Germany, France, UK, Canada, The Netherlands, and Belgium, show greater diversification. Asia, Europe, and North America form agglomeration regions for rice-exporting countries. Additionally, importing and exporting countries in the global rice-trade networks exhibit certain geographical concentrations. (3) The network backbones of the global rice trade are continuously evolving and being refined, characterized by dominant large rice-exporting countries in Asia and prominent developed countries in Europe and North America. The backbone structures revolve around India as the core, Thailand and Pakistan as the second cores, and critical nodes represented by Italy, the United States, China, and Vietnam. Regional backbone networks have also formed in Asia and Europe. Based on these findings, this paper clarifies the complex network characteristics of the global rice trade and offers insights to promote international rice-trade cooperation and safeguard global food security.

Cereal trade is essential for economic and commercial cooperation among countries along the “Belt and Road” (BRI). It helps ensure food security and contributes to building a community of interests and destinies for the BRI countries. Based on the UN Comtrade database, this study, using a network analysis approach, investigates the structural characteristics and spatiotemporal dynamics of cereal trade networks among the “Belt and Road” countries. Results show that: (1) The cereal trade among the BRI countries has formed well-connected and complex trade networks, and the “Belt and Road” initiative has significantly promoted cereal trade networks among the BRI countries. (2) The backbone structures of cereal trade networks along the BRI are in geographical proximity. India, Russia, and Ukraine are the most important trading partners and absolute core nodes in the trade networks, influencing the entire cereal trade networks. (3) The BRI cereal trade networks exhibit significant core-periphery structures, with considerable power asymmetries between the countries reflecting food supply and demand differences. In general, the BRI cereal trade networks have developed from relatively diversified to polarized. Supply chains in the cereal trade network are dominated by a few large countries and are fragile, with weak resilience and low resistance to risk. Therefore, governments should continue to strengthen regional cooperation, optimize cereal trade network structure, enhance their reserve capacity, and build a stronger system to guarantee food security and prevent risk. All these measures will support the food security of the “Belt and Road” countries.

Globalization and informatization have significantly reshaped the map of the global economy. Mega cities and regions have become the battlegrounds in the interplay between globalization and localization, with megaregions becoming the most globally significant spatial configurations in this regard. However, academics and government departments disagree on how to define the spatial boundaries of megaregions. In this study, on the basis of highway traffic flow data between cities, we integrate the community detection and core‐periphery profile algorithms to characterize the city networks in China and identify the city groups and delineate the core structures of city groups, which are the underlying megaregional structures in China. Based on this, we identify 21 megaregions among city groups in China, including the Yangtze River Delta, Pearl River Delta, Beijing‐Tianjin‐Hebei, and Chengdu‐Chongqing megaregions, and preliminarily delineate their spatial boundaries. On the whole, there are spatial differences among China’s megaregions to a certain extent. Central and eastern China have numerous, large, and a high density of megaregions, while the western region has relatively few megaregions. The latter also differs notably from mature megaregions in terms of rank sizes, urban systems, and functional divisions of labor. Generally, this study develops a novel analytical framework for identifying the functional regions of megaregional space in China from a perspective of relational geography, with methodological implications for other fields of inquiry.

On the basis of the “space of flows” theory, city networks emphasizing factor connectivity and spatial connection have become a core perspective for regional spatial relationships. They provide a context for discussing the spatial structures of city networks and a scientific basis for making regional development policies. Relying on expressway traffic flow data from Jiangsu Province in 2014, this study describes macro-spatial patterns and hierarchical structures of city networks, and uses the Walktrap algorithm of community detection to execute space divisions and elucidate potential spatial connection structures in the city networks. In comparison with other algorithms, the Walktrap algorithm demonstrates significant adaptation and stability to the short expressway traffic flows with the characteristics of strong network density and sparse node distribution. The results indicate that the macro-spatial patterns of Jiangsu’s city networks have clear regional differences. The cities with relatively dense spatial linkages are distributed along the Yangtze River banks, and many different sub-network systems have also developed internally. Cities and linkage axles have clear hierarchical structures. City hierarchies have certain spatial couplings with the cities’ intrinsic social economic attributes and geographical locations. The linkage axles hierarchy has clear spatial interactions and superposition with geographical distance. The community detection algorithm identified six spatially connected “city communities”, the Suzhou-Wuxi-Changzhou Community (SWCC), the Nanjing-Zhenjiang-Yangzhou-Taizhou Community (NZYTC), the Nantong-Yancheng Community (NYC), the Lianyungang Community (LYGC), the Huai’an-Suqian Community (HSC), and the Xuzhou Community (XZC). The community spatial metaphors had four aspects. First, trans-prefectural linkages were formed through spatially integrated effects of metropolis regions. Second, some communities share the same boundaries with their prefecture level administrative units; this reveals that significant administrative regional economies still exist in contemporary Jiangsu. Third, several cities located in the marginal areas of prefectures and captured by the powerful center cities in neighboring prefectures have been absorbed into the communities of neighboring prefectures. Fourth, the two cities Jiangyin and Jingjiang, divided into different administrative districts, have switched status opposite city community divisions.