In the context of increasingly fierce global competition for talents, the mobility and agglomeration of international talent have become a key focus for governments and academia. This paper examines the spatio-temporal evolution and strategic mechanisms of global talent agglomeration in the United States since the 21st century, using international students as a case study, aiming to uncover the underlying logic of U.S. global talent competition. The findings reveal that: ① The ambivalent attitude of American society toward international talent—both reliant and exclusionary—has led to fluctuations in the overall scale of international talent concentration. However, the proportion of foreign doctorate holders in high-end fields continues to grow, and the rate of those remaining in the United States for work remains consistently high, reflecting a significant structural dependence on international talent. ② The U.S. demonstrates distinct disciplinary lock-in effects, primarily concentrating on applied basic research in the "Pasteur's Quadrant" with engineering, mathematics & computer science, and business & management remaining the three core fields for international student agglomeration, while humanities attract a shrinking minority. ③ The funding structure for talent agglomeration has been reconfigured, shifting from universities to non-governmental entities—including corporations, foundations, and industry associations,enhancing the opacity and diversity of U.S. global talent competition. ④ The U.S. strategic mechanisms for talent agglomeration are characterized by decentralization and concealment, deeply embedded in complex policy networks spanning immigration, education, innovation, and industrial development, and implemented through "altruistic" international engagements such as academic exchanges, talent cultivation, scientific collaboration, and humanitarian aid. This study expands theoretical understandings of global talent competition, offering insights into China's talent agglomeration strategies.

The impact of various factors on crime differs in scale and is closely related to the spatial heterogeneity effect. The research takes Chicago, USA as the study area and focuses on the spatial scales and spatial differentiation of the impact of urban built environment factors on theft crime by employing Multiscale Geographically Weighted Regression (MGWR). The results show that: ① Compared to classical OLS regression and traditional GWR models, MGWR not only offers higher model accuracy but also quantitatively demonstrates the differing scales of impact that various urban built environment factors have on theft crimes. ② The scales of impact that different urban built environment factors have on theft crimes show significant variations. Restaurants, schools, and shopping centres have the smallest scales of impact, which are considered local-scale variables; banks, bus stops, grocery stores, and bars are also local-scale variables, with their scales of impact increasing in that order; parks have the largest scale of impact, which is a global-scale variable; ③ The influence of urban built environment factors on theft crimes exhibits varying degrees of spatial heterogeneity. The presence of restaurants, shopping centres, bus stops, bars, and schools tends to promote theft crimes, but there are differences across regions; banks and grocery stores show both positive and negative effects on theft crimes depending on the region, while parks have a globally homogeneous inhibitory effect on theft crimes. The city's land use patterns affect the distribution of different built environment facilities, leading to diverse spatial distribution characteristics of the local-scale variables' impact on theft crimes. The research has shifted from previous global analysis focusing on variables or local analysis under a single scale to the analysis of heterogeneity of multiple variables across different spatial scales, which has positive significance in promoting the application of MGWR model in the field of geography of crime, and can provide a decision-making basis for Police and law enforcement agencies to plan different precise crime prevention strategies according to the differentiated impact space of influencing factors.

With the significant shifts in the geopolitical landscape of the Pacific region, the strategic importance of Pacific Island Countries (PICs) has grown considerably. In recent years, China's support for enhancing the agency of PICs in regional affairs has attracted widespread international attention. The period from 2014 to 2023 marks a golden decade for building a comprehensive strategic partnership between China and PICs, as well as for the rapid development of PIC studies in China. Based on a knowledge mapping analysis of research literatures from 2014 to 2023, this study systematically outlines the evolving trends in Chinese research on PICs, offers a deeper understanding of the historical intersection between Pacific geopolitics and the emerging "Pacific Era," and helps identify future research directions and hotspots. Findings indicate that regionalism and regional mechanisms, climate change and ocean governance, geopolitics and major-power competition, China-Pacific relations and international cooperation, as well as nationalism and identity construction, are key focus areas in China's PIC research. Both research content and methodologies have shown multidimensional and diversified developmental trend. Future studies should adopt a global perspective, recognizing the global engagement of PICs; value the indigenous knowledge traditions in PIC studies; construct a Chinese academic discourse on PIC studies.

As the core driving force of global economic activities, the supply and demand pattern and flow pattern in the transformation process of energy have an important impact on the economic growth and social stability of various countries. An in-depth analysis of the structural characteristics of the global energy trade network and its differentiated driving mechanism is helpful to understand the evolution of the global energy trade. Using the social network analysis method, this paper systematically deconstructs the evolution of global fossil energy and renewable energy trade network from 2012 to 2021. This paper empirically examines the influence mechanism of trade networks and its relative importance by means of the Stochastic Actor-Oriented Model. The results show that although fossil energy is still dominant, its demand growth has gradually slowed down. Demand for renewable energy is on the rise. There are differences in the structure of the two types of energy trade networks. The core economies of the fossil energy trade network are relatively stable, but trade relations are volatile. The renewable energy trade network presents a higher reciprocity and small-world characteristics. The main trade core includes the Middle East region, the United States, China, Germany, Japan and other economies, while the import market is highly concentrated in developed economies. Further analysis shows that the fossil energy trade network is mainly driven by exogenous attributes. On the other hand, the renewable energy trade network shows a balance between endogenous structural factors and exogenous attribute factors, in which the output effect, carbon dioxide emission, economic development level and other factors play an important role. Based on this, it is recommended to effectively respond to the challenges of the global energy market by deepening the trade network effect, implementing energy import diversification, accelerating green transformation, and expanding export markets in order to promote the sustainable development of energy trade.

The digitalization and serviceability of international trade have become typical features of the current economic structural transformation. Based on the bilateral digital service trade data of 65 countries along the Belt and Road from 2005 to 2021, this paper applies the complex network analysis method to deconstruct the network effects of digital service trade of countries along the Belt and Road from the "node-path-pattern" multi-level, and uses the time index random graph model to test. The findings are as follows:① From the point of view of primary node, the net flow of digital service trade between China and neighboring countries is relatively stable, while the middle countries of the Black Sea and the Baltic Sea are more volatile, and have "remote" attributes and "community" inertia characteristics.②From the perspective of backbone path, the digital service trade network path of countries along the "One Belt and One Road" has been continuously reconstructed, gradually forming two "community" patterns: China-Singapore-India "three core" and China-India-Singapore, Russia-Ukraine-Cyprus, and the "leader" effect is obvious.③ From the perspective of spatial pattern, it shows the characteristics of sparse east and dense west, directivity and density on the whole, and the geographical distance sensitivity decreases, the communication channels are diversified, and the number of participating regions increases.④Reciprocity and stability affecting the digital service trade network, economic development level, population size, internet penetration rate,common continent, common language and trade agreements are among the most important influencing factors.

China's soybean import has the characteristics of large overall scale and concentrated import countries. Under the background of greater uncertainty in the current economic and trade environment, China's soybean import supply chain is facing greater pressure. In order to ensure the security of soybean supply in China and deal with the possible external crisis in time, this paper measures the node resilience and structure resilience of global soybean trade network and China's soybean import trade network from 1996 to 2022. The results show that the resilience of China's soybean imports is highly dependent on countries with high-outdegree (e.g., the United States, Brazil) and key intermediate countries (e.g., the United States, Canada, Turkey). When countries in the Northern Hemisphere reduce their soybean exports, Brazil plays a central role in balancing supply and restoring network operations. The role of other countries in the Southern Hemisphere needs to be further developed. Based on this, China should sign long-term purchase and sale agreements with Brazil, establish a joint agricultural fund, and strengthen the mechanism for binding interests with the United States, etc., in order to ensure the stability of China's soybean import supply. China should sign trade agreements with Paraguay, Argentina and other countries, optimize the transportation network, cultivate high-yield soybean varieties, and promote regional cooperation alliances, etc., in order to diversify the supply system in South America. China should sign bilateral agreements with intermediate countries, establish regional supply buffers, and invest in modern logistics facilities, etc., in order to enhance the resilience and risk response capabilities of the soybean supply chain.

The spatial-temporal evolution characteristics of innovation network and its influence mechanism have become the focus of innovation economic geography, while the research on the dynamic evolution mechanism of industrial innovation network by internal and external factors urgently needs to be further deepened. Rare earth elements, often referred to as the "lifeline of high technology," play a crucial supporting role in technological innovation. This study analyzes the spatial structural evolution characteristics of China's rare earth industry innovation network, utilizing patent cooperation data from 2005 to 2021 and employing methodologies such as social network analysis and weighted index random graph models. It comprehensively explores the driving mechanisms of proximity, subject attributes, and endogenous dynamics in network evolution. The findings indicate that: ① The scale of China's rare earth industry innovation network continues to expand, with Beijing and Shanghai serving as core nodes, which catalyze the development of sub-core and peripheral cities, gradually forming a pyramid-like hierarchical network structure predominantly centered around eastern coastal cities; ② The network evolution demonstrates a transition from a "single-core multi-axis" structure to a "quadrilateral" model, characterized by a shift from core cities leading to a pattern where both core cities and regional centers drive development; ③ The evolution of the innovation network of China's rare earth industry is subjected to the common role of exogenous factors and endogenous momentum. Proximity dynamics and interactions play a role in different stages of innovation networks; cities with high innovation capacity promote network generation through 'strong alliances'; and the influence of path dependence gradually diminishes with the development of the industry.

Food security is the greatest thing of the country. Food trade is one of the important measures to ensure food security. Based on the production and trade data of maize, rice, soybeans and wheat, combining the measurement of data indicators such as food production, food self-sufficiency rate, food import and export quantity and value, concentration rate of import, and Shannon entropy index, the article comprehensively and systematically sorts out the evolution of China's food security and trade pattern from 2000 to 2022. The results show that: ① The productions of maize, rice, soybeans and wheat are relatively stable and have increased to varying degrees in China. The self-sufficiency rates of maize, rice and wheat have always remained above 90%, which is at a safe level. However, the self-sufficiency rate of soybeans has declined rapidly, and the supply and demand risks are relatively large. ② The import of maize, rice, soybeans and wheat increased with varying degrees, while the corresponding exports have continued to decline. The concentration rate CR5 of maize, rice, soybeans and wheat imports are all higher, while the concentration rate CR3 and concentration rate CR1 fluctuate greatly. The main food importing countries of China are highly concentrated in a few countries, including the United States, Ukraine, Vietnam, Thailand, Brazil, Australia and Canada. China's dependence on the imports of maize and soybeans is higher than the imports of rice and wheat. ③ The security of China's import trade structure of main food is lower than that of its export trade structure. The import trade structures of maize and rice are primarily characterized as extremely insecure, while soybeans are moderately insecure, and wheat is mainly moderately insecure. The export trade structure of maize is mainly extremely insecure and moderately insecure, rice moderately secure and secure, soybeans slightly insecure and slightly secure, and wheat exhibits the greatest volatility in export trade structure security, with most years showing extremely insecure or slightly secure. ④ The food imports of China are restricted by key channels such as the Panama Canal, the Suez Canal and the Strait of Malacca, and are also facing threats such as monopoly control by world grain traders.

Scientifically identifying the driving forces of non-grain of cultivated land is of great significance to resolutely stop the non-grain of cultivated land and keep the red line of 1.8 billion mu of cultivated land in China. Taking the agricultural divisions of China as the research unit, this paper uses the Dagum Gini coefficient and spatial error model to identify the driving forces of non-grain of cultivated land, and puts forward the control measures for the divisions.The results show that: ① During the study period, nearly 50% of the cultivated land in China was not planted with rice, corn and wheat, and the ratio of non-grain of cultivated land was large; ②There is obvious regional imbalance in the non-grain of cultivated land in China, The Qinghai-Tibet Plateau was the largest spatial disequilibrium, the lowest was South China, and the Northeast Plain, the Sichuan Basin and the Huanghuaihai Plain, etc. increased significantly during the study period, and the differences in ecological environment, rainfall and rural labor force are the main factors of regional disequilibrium. ③The Northeast Plain, the Huanghuaihai Plain and the middle and lower reaches of the Yangtze River are mainly affected by topography, grain income, and urban-rural income gap. South China, the Qinghai-Tibet Plateau and the arid and semi-arid regions in the north are affected by the level of urbanization development and the labor resources of the primary industry. The ecological environment in the Loess Plateau is fragile, and precipitation has a significant impact on the non-grain of cultivated land. The Yunnan-Guizhou Plateau and the Sichuan Basin are greatly affected by the irrigation level of cultivated land. ④ The introduction of rigid control policies and measures has an obvious effect on curbing the non-grain of cultivated land. It is suggested to implement zoning regulation and control, adopt rigid control such as high-standard farmland quality improvement and strengthening cultivated land use control in areas with severe non-grain cultivated land, and implement a flexible control system focusing on prevention and control increment in other regions, so as to effectively curb the non-grain of cultivated land.

The rise of the network paradigm provides a new perspective for the academic community to study high-level talent highland. The high-level talent highland presents different structural characteristics and network forms due to resource endowment and local conditions, and the development models formed by this also have their own characteristics. Analyzing the development models of high-level talent highland can provide guidance for promoting the construction of high-level talent highland according to local conditions in various regions. This article constructs a theoretical analysis framework from three dimensions: external openness, internal collaboration, and talent level. Based on the network perspective, this paper depicts the internal and external talent flow of the seven high-level talent highlands in China in 2020, constructs an indicator system to scientifically calculate the level of each urban agglomeration, and based on these, classifies the development mode of high-level talent highlands. Research shows that: ① The Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei region are the three urban agglomerations with the highest talent levels, with the Yangtze River Delta urban agglomerations ranking first in the three subfields of talent quantity, talent structure, and talent quality; ② The internal connections of high-level talent highlands are influenced by both urban levels and geographical proximity, while external connections are significantly influenced by urban levels. ③ There are significant differences in the development models of high-level talent highlands. The urban agglomerations of the Yangtze River Delta, Beijing-Tianjin-Hebei region, and Pearl River Delta have a good foundation, and the development model of high-level talent highlands is relatively good. The talent level of the Chengdu-Chongqing urban agglomeration and the Middle Reaches of the Yangtze River urban agglomeration is insufficient, with the former having a low level of external openness and the latter having a low level of internal coordination. Both are in the initial stage of high-level talent highland development. The evaluation of various dimensions of the Central Plains urban agglomeration and the Guanzhong Plain urban agglomeration is very low, and the development model is the least ideal.

It is of great significance to clarify the influence of factor flow on industrial spatial efficiency of the digital economy for the optimization of industrial spatial layout in the information age. Existing studies primarily focused on the impact of the scale of factor inputs on a certain aspect of the spatial development of the digital economy industry, neglecting the analysis of spatial benefits and overlooking the pivotal role of factor mobility. Based on the central flow theory and considering both the scale and flow of factors, this paper, which utilized multi-source enterprise big data along with traditional small data, examined the digital economy industrial park in the Yangtze River Delta as a case study to explore, via regression modeling, the key factor flows and their mechanisms that influenced the spatial efficiency of the digital economy industry. The findings are as follows: ① There is a mismatch between the number and benefits of digital economy industrial parks in the Yangtze River Delta. The distribution density of digital economy industrial parks in many cities is high, however a large number of low-benefit parks also exist. ② Technology flow plays a decisive role, independent innovation ability and knowledge transfer can improve the spatial efficiency of the digital economy industry, and the higher the efficiency of the park, the more dependent on knowledge transfer. Capital flow plays a positive role in the middle and low benefit parks and a negative role in the high benefit parks. ③ Public transportation convenience, green space and accessibility, and district and county per capital GDP also have a certain effect on the improvement of industrial spatial efficiency of the park. Finally, the paper put forward some policy suggestions to enhance the spatial benefit of the digital economy industry from three aspects.

Under the guidance of the national transportation development plan, the issue of passenger interline transportation has attracted much attention from scholars. This paper analyzes the spatial differentiation characteristics of high-speed rail (HSR) intermodal travel through the lens of geographical theory. It adopts indicators such as intermodal travel rate, transfer efficiency, and hub degree of HSR transfer stations. Furthermore, it employs a modified gravity model to examine the influencing factors of transfer efficiency in HSR intermodal systems and explores the spatial organization patterns of HSR intermodal transportation. The results show: ① The travel rate of high-speed rail connections is closely related to the city's administrative level and the distribution factors of high-speed rail trunk and branch lines, and there are significant regional differences. The travel rate of medium- and long-distance high-speed rail connections in the western region and northeastern region is generally high, and the transfer efficiency in high-speed rail hubs is low. ② A large number of connected trips between mid- and low-level cities need to be completed through high-level city stations, and the travel space of intercity high-speed rail is "hierarchical". The urban agglomeration network structure, the high-speed rail network, the cross-provincial axis and the provincial core spatial structure constitute the "hub-and-spoke" network structure of the country's high-efficiency interline travel. ③ The transfer efficiency of high-speed rail is affected by the size of the transfer city pair, the time and space distance, and the high-speed rail hub degree of the transfer city. The high-speed rail hub degree plays a stronger role in the intercity direct passenger traffic of the "city pair" than the scale factor of the high-speed rail hub city. Under the market mechanism, railway companies can use interline transportation methods to resolve the problem of insufficient travel demand for medium and long-distance intercity high-speed rail and ensure the efficiency of medium- and long-distance intercity high-speed rail transportation. In view of this, in order to solve the problem of my country's high-speed rail passenger interline travel and the mismatch between supply and demand of high-speed rail capacity, we can scientifically and rationally design the "Hub-and-Spoke " model based on the spatial differentiation characteristics of high-speed rail interline travel and with reference to the air transportation " Hub-and-Spoke " model. The high-speed rail interline transportation network improves the efficiency of my country's high-speed rail interline travel and realizes the optimal allocation of high-speed rail transportation capacity resources.

Promoting the synergistic development of new urbanization and basic public services is a key concern of regional integration and development in the new era. The Yangtze River Delta (YRD) region is both an important comprehensive pilot region for new urbanization in China and a typical demonstration region for the development of equalization of basic public services, and the coordinated development of the two is of great significance for boosting the process of regional integration in the YRD region. This study measures the levels of new urbanization and basic public services in 41 cities in the Yangtze River Delta region from 2011 to 2020 by means of a modified coupled coordination model and spatial correlation analysis, explores the spatial and temporal evolution characteristics of the coupled coordination relationship, and finally explores the influencing factors by using a panel double fixed-effects model. The results show that: ① During the study period, both new urbanization and the level of basic public services in the Yangtze River Delta region show an increasing trend, and there is synchronization in the spatio-temporal evolution of the two. ② The degree of coupling and coordination between the two has been increasing during the study period, but the number of cities with low new urbanization and low basic public services still accounts for the majority of cities, and the overall level of coordinated development still needs to be optimized. ③ There are more obvious spatial differences and spatial correlation characteristics in the coupling coordination degree. The spatial pattern shows the trend of "central coordination - north-south dissonance", and in the spatial correlation, there are high - high agglomeration and low - low agglomeration areas, showing obvious spatial club convergence phenomenon. ④ The results of the analysis of driving factors show that the level of economic development and modernization has a positive effect on the coordinated development of the two, science and education investment has a negative effect, and the influence of government capacity and urban construction investment varies in different provinces.