Rural areal system is a multidimensional object of rural revitalization in the new era, and its complexity and dynamics require a breakthrough in single-domain thinking. Through the in-depth fusion of system science theories, multi-source data-driven technical methods and sustainable transformation paths, the full-chain governance of system diagnosis-scenario simulation-precise policy implementation should be carried out. Based on the scientific theory of human-land system and the multi-scale analysis paradigm of geography, this paper explores the "element-structure-function" cascade analysis system of rural areal system, analyzes the scientific connotation of rural areal system and rural revitalization, clarifies the multi-scale evolution law and transformation mechanism of rural areal system, develops rural areal system development status assessment and scenario simulation technology, and sorts out and proposes a "three-device" (diagnostic device, detector, simulator) platform framework for the transformation mechanism and simulation of rural areal system. By innovating multimodal spatiotemporal data fusion and intelligent interpretation methods, it can break through the technical bottleneck of quantitative inversion of rural areal system elements, establish a multi-factor collaborative observation indicator system and a data sharing application platform, reveal the spatial pattern of rural transformation under different development paths, and explore and create "three major systems", including a theoretical system of rural areal system with multi-dimensional interaction of nature-society-technology, a technical system of cross-platform coordinated observation of rural areal system, and a system of standards and norms for the scientific path of rural revitalization and its management. The findings will help to achieve the systematization of coordinated observation and scenario simulation of multi-source heterogeneous data in rural areal system, and provide theoretical guidance and technical support for the scientific formulation of rural revitalization plans and the comprehensive promotion of rural revitalization strategic decisions. Additionally, focusing on the current trend of technological progress, modern geography should connect with the international scientific frontier and national strategic needs, and take the system integration of geographic science-technology-engineering-practice (Geo-STEP) as the core to promote the transition of rural areal system research from "problem explanation" to "future design" and "scientific governance".

By tracing the theoretical evolution of new geopolitics, this paper constructs a methodological framework for new geopolitics studies, trying to bridge the paradigmatic gap between new geopolitics and traditional geopolitics, thus enhancing the cohesion of geopolitics on the discipline level in a completely different background of social transformation. The methodological construction of the new geopolitics studies in this paper is mainly reflected in the multi-level analysis and the diversity of inter-state exchanges in the wide geopolitical discussions. From the perspective of multi-level analysis, new geopolitics has expanded in terms of scale and agency dimensions, including multi-scale spatial units such as cities and communities, as well as multiple actors such as grassroot groups into the discussion of geopolitics. While from the perspective of inter-state exchange, new geopolitics depicts how the details of people's everyday life such as culture and tourism become key factors in shaping global geopolitics. Moreover, this paper also proposes to procedurally incorporate qualitative research methods such as ethnography and textual analysis, which is based on the discussions of the multi-level analysis and the diversity of inter-state exchanges, into the methodology of geopolitics, in an attempt to provide a different perspective from traditional geopolitics to re-understanding and studying geopolitics in the new era context. Under the backdrop of the revolutionary impact of globalization and informatization upon the inter-state geopolitical relationship, the methodological construction of new geopolitics in this paper can help to describe the transformation of factors affecting the evolution of the global geopolitical pattern and its occurrence process and mechanism, and can also widely guide the processing of new geopolitics studies. Furthermore, in the context of China's increasingly active overseas socio-economic activities and the increasingly obvious geopolitical risks from the "bottom-up" perspective, the methodological construction of new geopolitics also has important practical value for systematically guiding the studies on geopolitics related to China's overseas socio-economic activities.

Space of flows research provides a novel analytical perspective in the domain urban and regional development, facilitating the elucidation and comprehension of dynamic interactions within urban systems and inter-regional networks. This research not only offers scientific analytical tools for urban governance and spatial planning but also provides theoretical guidance for promoting sustainable urban and regional development by optimizing resource allocation and improving spatial efficiency. This paper systematically reviews and analyzes the connotation of the space of flows in terms of its concept, attributes, characteristics, and fundamental elements. Based on this, the paper proposes and examines the evolutionary framework of space of flows research. Through theoretical tracing, research trend analysis, and hotspots identification, the study reveals that space of flows research exhibits a development pattern characterized by "super-core, polycentric, and pan-thematic" tendencies. Furthermore, this paper explores the analytical paradigm of space of flows research and its significant shifts: the research domain has expanded from singular economic linkages to multi-dimensional connections encompassing social, cultural, ecological, and geographical spheres; research data have evolved from traditional statistical surveys to an integration of conventional data and multi-source big data; and research methodologies have transitioned from qualitative studies to dynamic quantitative research. Future directions for deepening space of flows research include innovating data acquisition and integration technologies, developing advanced analytical tools, expanding cross-scale research methods, enhancing the synthesis of theoretical and empirical studies, and promoting interdisciplinary collaboration in the context of spatial dynamics and urban-regional systems.

Border ports serve as critical frontlines against potential threats from neighboring countries, with their risk status directly impacting national security and the livelihoods of local residents and businesses. Despite this importance, there remains a lack of quantitative assessments of geo-risk at these border ports. This study addresses this gap by constructing a geo-risk assessment model based on the concepts of probability and potential loss. By integrating multi-source spatio-temporal data, the geo-risk at border ports is estimated for type classification and feature analysis. The results reveal several key findings: geo-risk stress is gradually shifting westward, with significant accumulation at southwestern border ports, and comprehensive border ports are experiencing heightened risk. Geo-risk probability is high at China-Myanmar and China-Vietnam border ports, with notable risks at northern transit node ports, and Ruili ranks highest in geo-risk probability among all ports. Potential losses are concentrated along the southern border, especially at critical nodes of external transit routes and ports along the Belt and Road. The overall spatial distribution of geo-risk at border ports follows a pattern of "dense in the south, sparse in the north, and balanced across multiple points,"with risks categorized into four types: high risk, high probability, high loss, and low risk, with approximately one-quarter of ports categorized as high-risk. This study enhances the understanding of border risk, and the constructed geo-risk assessment model provides a valuable reference for risk quantification in other countries or regions while offering a scientific basis for decision-making related to border security.

Intercity competitive and cooperative relationships provide crucial lenses for understanding the structure, processes, and dynamics of urban systems. While existing studies have explored the network structure of cooperative intercity relationships and offered case analyses of competition between cities, there remains a relative scarcity of quantitative research on the competitive dimension. Building on a review of domestic and international research on competitive intercity dynamics, this study adopts a firm-centric approach to propose a quantitative framework for analyzing competitive intercity relationships. Drawing on multidisciplinary measures of competitive relationships, this framework aims to quantify intercity competition through "structure + scale" similarities and market niche overlap while accounting for the asymmetry of competition. Theoretically, this framework integrates place-based perspectives, which focus on similarities in city attributes, with network-based perspectives that emphasize positional similarities within flow structures. This captures intercity competition in both resource aggregation and external linkages. Methodologically, a bipartite "firm-city" matrix is constructed, applying distance and similarity measures from ecology and management sciences, enabling the identification of both "structure + scale" and market competition within the same dataset while ensuring measurement reliability. Finally, the study applies this method to measure the competitive relationships among cities in the Yangtze River Delta region, using data from innovative enterprises, which effectively validates the proposed method. Overall, the study provides a methodological framework for deeper exploration of intercity competition and encourages urban systems and urban network research to place greater emphasis on this critical dimension.

As the most basic unit of society, family is a key object for understanding urban society, activity, and space. However, the systematic exploration of interaction pattern between family activity and urban environment was not sufficient, and they were always in an imbalanced state, which raised a few concerns. Therefore, it is essential to introduce the concept of "human-environment relationship" into family research, exploring the relationship between family and urban environment, that is the study of "human-environment relationship" of urban families (here, we call it "family-environment relationship"). To this end, we firstly propose a brand-new identification method for family and its spatiotemporal behaviors based on mobile phone location big data. Subsequently, taking the case of Wuhan, we verify the effectiveness of this method by using China Unicom's mobile signal data, which proves that the family identification samples can be regarded as large-scale sampling of realistic families. Finally, based on the concept of "human-environment relationship", we attempt to build a theoretical framework for "family-environment relationship" at the urban micro scale, and summarize how family big data should be appropriately applied in this framework. Our study provides a theoretical framework and a series of methods for exploring the interaction pattern between family and urban environment, which contribute to accurately diagnose the imbalance phenomenon of family-environment relationship and effectively formulate "family oriented" environmental optimization strategies, thus formulate a strong push for improving the quality of family life, constructing a family friendly city, and promoting sustainability of urban society.

Foreign aid is a tool of U.S. foreign policy and provides a window into U.S. national strategy. In the era marked by frequent local conflicts and global turmoil, delving the motives of U.S. aid can enhance our comprehension of U.S. national strategic objectives. Utilizing U.S. aid data from 2000 to 2019, this paper examines the spatial evolution patterns in U.S. aid and its driving paths, considering altruistic, egoistic, and utilitarian motives. This analysis is conducted using GIS spatial analysis and Fuzzy-Set Qualitative Comparative Analysis (fsQCA). The results show that: (1) Total U.S. aid exhibits a basic trend of initial growth followed by stabilization, with military security aid and economic development aid as the primary categories of U.S. aid. (2) There are significant spatial and temporal differences in the distribution of U.S. aid. The Middle East and North Africa region has been a long-standing aid priority but is trending downwards, with an emphasis on military security aid, economic development aid, and humanitarian assistance. Sub-Saharan African countries have emerged as new hubs for U.S. aid, with primary focuses on social sector aid, economic development aid, and humanitarian aid. Meanwhile, other regions have seen diminishing amounts of U.S. aid. (3) The key drivers of U.S. aid have expanded from the level of economic development and U.S. exports to geostrategic, political relations and institutional quality factors, with an overall trend of a gradual shift from being driven by economic interests to geopolitical interests. This may be related to the eastward shift of the U.S. strategic centre of gravity. In terms of specific types of aid, economic development aid, social sector aid and humanitarian aid are economic interest-driven, military security aid is geopolitical interest-driven, and democratic political aid is democratic value-driven. The different types of aid serve different foreign policy objectives and together constitute a tool for the U.S. to pursue its political and economic interests. This paper can provide some reference for China's foreign policy making.

Trademarks increasingly serve as indicators of product innovation and broader industry shifts, encompassing a wide range of innovation activities and soft innovations often overlooked by patents. However, research on the relationship between trademarks and innovation remains in its early stages, with a lack of exploration into China's soft innovation from the trademark perspective. This paper addresses this gap by analyzing trademark data from 1980 to 2021. Integrating big data mining, statistical methods, GIS spatial analysis, and econometric models, it explores the spatio-temporal evolution patterns and factors influencing soft innovation in Chinese cities. The results find that: First, the scale of trademark applications exhibits a complex evolution characterized by continuous growth and fluctuation, showing a dual-cycle and three-stage trend. Soft innovation output in cities follows a stepwise spatial distribution pattern, decreasing from the eastern coastal areas to central and western regions. This pattern reflects a regional agglomeration, with hierarchical changes showing both stability and variability. Second, the distribution of trademark categories exhibits significant heterogeneity, notably with a continuous rise in the proportion of service categories. Subcategories are concentrated in the consumer goods sector and maintain steady growth. Spatially, these categories show a mix of clustering and dispersion, predominantly aligning with industrial development trends and innovation-driven features, presenting a pattern congruent with the industrial structure. Finally, factors such as the development of the internet, establishment of new enterprises, and technological innovation have a significant positive impact on trademark applications. Population size, GDP, and urban commodity exports also contribute significantly to increased trademark activity. The influence of industrial composition on trademark application intensity exhibits certain heterogeneity. There is a clear correlation between knowledge-intensive service industries and trademark activity, whereas a negative relationship is observed with government service departments.

Industrial transfer and diffusion are traditional fields of economic geography and regional economics. They mainly include two main directions: transnational industrial transfer and interregional industrial transfer. Accurately grasping the characteristics and laws of the industrial diffusion process in the new era is of great significance for implementing regional coordination strategies and solving the main problems faced with regional coordinated development at this stage. Using industry investment data from prefecture-level cities and municipalities directly under the central government, this study compares and analyzes the diffusion process of 13 major industries in China. The zero-inflated negative binomial regression model is employed to clarify the different effects of diffusion intensity and diffusion possibility. The results indicate that: (1) The expansion of city size enhances inter-city industry diffusion intensity, with the diffusion intensity being more sensitive to changes in the size of the source city. (2) The 13 industries tend to spread to cities that are closer in distance and larger in scale. Spatial distance primarily affects the possibility of industry diffusion. Long spatial distances lead to a significant increase in the probability of zero investment scale. However, for most industries, hierarchical distance has a greater influence on diffusion intensity. Large hierarchical distances lead to a decrease in investment scale. (3) The 13 industries can be divided into four types based on the differentiated influence of spatial distance and hierarchical distance: double strong constraint type for spatial distance and hierarchical distance, strong spatial distance-medium hierarchical distance constraint type, strong spatial distance-weak hierarchical distance constraint type, and strong hierarchical distance-weak spatial distance constraint type. (4) The producer service industry in the central and western regions should establish a two-level central system at the regional and provincial levels, while the consumer service industry should establish a three-level central system at the regional, provincial, and key city levels.

Narrowing the gap of regional economic development and maintaining the balanced development path is the indispensable goals of China's regional coordinated development. In this study, the nighttime light image data and GDP statistical data are integrated to construct the Genuine Economic Index (GEI). Based on GEI, the Theil index, scale variance model, and the spatial filtering model are combined to analyze the spatial and temporal evolution of China's regional economic discrepancy from a multi-scale perspective. The results show that: (1) China's absolute regional economic differences expanded year by year, while the relative regional economic differences tended to shrink overall from 2000 to 2020. (2) Under different regional divisions, the scale variance of China's regional economy is manifested as municipal scale > county scale > provincial scale > regional scale, with the largest increase and highest contribution rate on the municipal scale. Narrowing inter-municipal gap is the key to decrease China's regional economic differences. (3) The contribution rate of spatial autocorrelation increased from 23.804% to 26.079% during 2000-2020. Regardless of spatial autocorrelation factors will lead to an overestimation of China's regional economic differences. (4) The impact of spatial autocorrelation on regional economic differences is scale sensitive, the smaller the spatial scale is, the stronger the sensitivity is. After spatial filtering, inter-county economic differences have been the largest part of China's regional economic differences. (5) Human capital, industrial structure, consumption scale and fiscal decentralization have a significant positive impact on China's Genuine Economic Index, however these factors show an obvious regional heterogeneity. This study could provide a multi-scale, refined and differentiated reference for the implementation of China's regional coordinated development strategy.

The matching of land-sea multi-elements is crucial for the evolution of coastal resilience and the achievement of sustainable development. Understanding the impact mechanism of this matching on coastal resilience is essential for the healthy development of coastal regions. Based on panel data from 2005 to 2021, this study employs a variable fuzzy recognition model and a rank matching degree to measure coastal resilience and the degree of land-sea multi-element matching in China. A fixed-effects model is used to explore the influence mechanism of land-sea multi-element matching on coastal resilience. Additionally, a system dynamics approach is applied to establish four simulation scenarios, so as to simulate the developmental trends of land-sea multi-element matching and coastal resilience. The results show that: (1) Coastal resilience exhibits a steady growth trend, with Guangdong and Shanghai exhibiting higher resilience levels, while Guangxi and Hainan having lower levels. (2) The rank matching degree of land-sea multi-elements fluctuates in a pattern of "match→mismatch→ match", with Guangdong and Shanghai having higher matching degrees, and Liaoning and Guangxi showing lower degrees. (3) Coordinated matching of land-sea multi-elements enhances coastal resilience, with economic development, openness, and urbanization also positively impacting coastal resilience. (4) Among the four scenarios, Scenario 4, which involves comprehensive regulation of land-sea multi-element matching, proves most effective in maximizing coastal resilience. The conclusions provide valuable scientific support for multi-scenario simulation, assessment, and decision-making in the development planning of China's coastal resilience.

Addressing inequality in residential electricity consumption is crucial for achieving the UN Sustainable Development Goal 7 (SDG 7). Guangdong province, one of the most developed areas in China, is a representative case for examining inequality and its changes between urban and rural areas. However, there is currently a lack of high-resolution energy consumption data to conduct urban and rural comparison analysis. To address this gap, this study combined nighttime light remote sensing data and European Union's Global Human Settlement Layer dataset to develop a novel residential electricity consumption dataset (500 meter grid) from 2000 to 2020. The results are as follows: (1) Inequality index indicates a sharp downward trend from 7.57 to 0.83, with the overall Theil index declining to 0.83 and 0.013. (2) Inequality index is the lowest in the Pearl River Delta among all sub-regions, while per capita residential electricity consumption is the highest in urban center and very low density rural clusters among different settlement types. (3) Lorenz curve and Gini coefficient indicate that urban centers show the greatest equality in terms of per capita residential electricity consumption across prefectures, while semi-dense urban clusters and rural clusters show the lowest. This study enriches research on urban and rural energy consumption analysis from a multi-spatial perspective,and overcomes the limitations of traditional statistical survey data featured by high cost and low accuracy. It provides a framework for energy and electricity consumption analysis in urban and rural areas in China and beyond. The findings are useful for a better understanding of urban-rural energy inequality in Guangdong.

As the "Third Pole" of the Earth and a crucial ecological security barrier for China, the sustainable development of the Qinghai-Tibet Plateau is of great significance. Based on the United Nations Sustainable Development Goals, this paper constructs a sustainable development indicator system across four dimensions: economic development, social inclusion, public infrastructure, and environmental protection. Using counties as the unit of analysis, it examines the sustainable development effects of industrialization, urbanization, and transfer payments in the Qinghai-Tibet Plateau for the years 2000, 2010, and 2020, revealing the impacts of the region's industrial development model and urban population aggregation on sustainability. The study found that: (1) The different dimensions of sustainable development in the Qinghai-Tibet Plateau vary significantly. The environmental index is the highest, while the economic index is the lowest, indicating that human activities exert less pressure on the natural environment. From 2000 to 2020, significant improvements were made in the economy, society, and public infrastructure, while the environmental dimension remained relatively stable, resulting in considerable overall progress in sustainable development. (2) There are clear regional disparities in sustainable development across the Plateau. The core areas of Xizang and Qinghai lag behind the periphery. High levels of sustainable development are observed in the Qaidam-Huangshui Valley, the eastern Hengduan Mountains, and the eastern section of Xizang's "Yarlung Zangbo River and its two tributaries" region, while areas such as the Sanjiangyuan region, the Northern Tibetan Plateau, and the upper reaches of the Yarlung Zangbo River are development "lowlands". (3) Industrialization, urbanization, and transfer payments have a significant positive impact on economic development, social inclusion, and public infrastructure, but exert a negative effect on the environment. Overall, they contribute positively to achieving the UN sustainable development goals. The prioritization of ecological protection in the Plateau inevitably restrains economic and social development. In this context, transfer payments play a crucial role in balancing the relationship between people and the environment. (4) An increase in the number of large-scale industrial enterprises at the county level and higher population concentration in the leading towns can effectively reduce the negative impact of industrialization and urbanization on resources and the environment. Therefore, abandoning small-scale and fragmented industrialization in favor of a more centralized urbanization approach will help balance socioeconomic development with resource and environmental protection, leading toward comprehensive sustainable development.

The scale and spatial distribution of travel demand are crucial foundations for the formulation of transportation planning. This paper extracts the travel demand between counties and districts (referred to as counties) within the Beijing-Tianjin-Hebei (BTH) urban agglomeration based on mobile phone signaling data, and constructs travel networks for general and inter-city travels respectively. Using complex network analysis methods, it analyzes and compares the characteristics differences in node centrality, leading connections, and clustering spaces in general travels and inter-city travels within the urban agglomeration. The results indicate that: (1) The spatial distribution of travel intensities is uneven, with higher travel intensities in the center of the city, higher travel intensities for cross-boundary trips in suburban counties, and higher travel intensities for cross-city travel in border counties. The spatial distribution of travel intensities is uneven, with higher travel intensities in the center of the city, higher travel intensities for cross-boundary trips in suburban counties, and higher travel intensities for cross-city travel in border counties. (2) Travel primarily involves close connections between central urban areas and surrounding counties, with a positive correlation between city rank and travel intensity; inter-city travel is concentrated on the spillover boundaries of core cities, forming leading connection characteristics of central encirclement, boundary interaction, and enclave connections. (3) There are clear differences in the travel clusters between general travel and inter-city travel, with general travel clustering involving individual cities forming clusters; central urban areas jointly form spatially jumping inter-city clusters, with the boundary cluster centered on Beijing already in substantial scale. The differentiated regional functional positioning under different travel perspectives reveals that central urban area clusters play a regional connecting role, with the central urban areas of Beijing and Shijiazhuang simultaneously serving inter-city hub functions, yet a large number of peripheral counties participate less in travel connections. Analyzing the differentiated characteristics of general travel and inter-city travel demand among counties can clarify the transportation development positioning of different areas, providing a planning basis for the construction of a comprehensive transportation network in the urban agglomeration, thus promoting urban-rural and inter-city coordinated development.

It is an important field for tourism research to reveal structure and mechanisms of the tourist scenic spot network of the large-scale region based on online big data. Previous literature has rarely analyzed the multilayer structure of the tourist scenic spot network, and fail to reveal the combined effects of the endogenous and exogenous mechanisms for formation of the tourist scenic spot network. A breakthrough in analytical methods is urgently needed. This paper takes the Yangtze River Economic Belt (YREB) as a case study, collects online booking data from Ctrip until 2023 to obtain the tourist flow data between tourist scenic spots in the YREB, and finally constructs the tourist scenic spot network. The multilayer analytical methods of complex network and the valued exponential random graph model (ERGM) are used separately for revealing the multilayer structure and the endo-exogenous mechanisms of the tourist scenic spot network of the YREB. The results show that there are 408 nodes and 1464 edges in this network, and each node is connected with 7.176 other nodes on average in terms of tourist flow. On the macro structure, this network has small-world and scale-free features. On the medio structure, this network has a significant community structure. Totally, 13 communities form a triangular closed structure, and 4 communities are distributed across provinces. For the micro structure, a total of 10 3-node network motifs are identified, which represent ten kinds of travel patterns for tourists. The three patterns of uplinked mutual dyad chain, down-linked mutual dyad chain and centralized cycle occupy the dominant position in travel patterns. The formation of this network is influenced by combined effects of the endogenous and exogenous mechanisms. Reciprocity, as an endogenous mechanism, has the most important impact. As for the exogenous mechanisms, quality level, online rating and online search popularity of tourist scenic spots affect connection formation of this network through the Matthew effect, while the influence of ticket price is weak and the number of online reviews is small. The world heritage attribute of tourist scenic spots affects the network connection through the homogeneity effect.

Innovative and low-carbon cities symbolize the ongoing transition and advancement of Chinese and global urban development, significantly impacting sustainable tourism growth. This study uses panel data from 272 Chinese cities and adopts the time-varying difference-in-differences approach to assess the causal influence of urban transition on tourism competitiveness. It emphasizes urban innovation and low-carbon progress. This study delves into the mechanism and regional disparity of such effects. The findings are as follows. First, urban evolution promotes tourism competitiveness. The combined impacts of various urban transitions are greater than those of a single transition. The positive effect of urban transition on tourism competitiveness grows over time. Urban transition notably boosts tourism resource competitiveness, ecological environment support competitiveness, and socio-economic support competitiveness, while diminishing tourism industrial competitiveness. Second, industrial structure rationalization, industrial structure advancement, innovation output, and environmental regulation are crucial mechanisms for enhancing tourism competitiveness. Third, the positive effect of urban transition on tourism competitiveness is significant only in the central region. Low administrative level cities' transition contributes more significantly to tourism competitiveness compared to high administrative level cities. Resource-oriented cities exhibit a stronger promotional effect of urban transition on tourism competitiveness. This study contributes to expanding and intensifying the relationship between urban systems and tourism competitiveness. It places tourism competitiveness within a broader public policy framework, integrating urban transition and tourism development. This approach enriches the interdisciplinary research between urban and tourism geography. Practically, understanding the impacts of urban transition on tourism competitiveness and its mechanisms can assist policymakers in comprehending the comprehensive effects of urban transition and formulating appropriate sustainable tourism development policies.