China's urban development is currently in the new stage of upgrading existing resources. In response to the insufficient innovation vitality of existing spaces in current urban renewal practices, major cities have successively implemented the "City Silicon Alley" project, attempting to explore new paths for the innovative development of existing spaces in old urban areas. This study used the Nanjing Jinchuan Silicon Alley as a typical case, combined with the Actor-network theory, through qualitative methods, such as on-site observation and in-depth interviews. The study analyzed the spatial evolution process of the Jinchuan Silicon Alley and conducted a dialectical assessment of its spatial effectiveness, summarizing the multidimensional promoting effects of urban renewal on material and social spaces, thereby providing experience references for the in-depth construction of urban silicon alleys. The research concludes the following: 1) The Jinchuan Silicon Alley realizes spatial innovation through the combined force of market capital and policy systems. As a key actor, the Silicon Alley operator takes on capital power and completes the identity transformation. Through various means, such as creating cultural landscapes and developing industrial chains, it promotes the reconfiguration of the action network and shapes a material space with a prominent innovative vitality atmosphere, significant scientific and technological clusters, and diverse social group integration. 2) The development mechanism of the Jinchuan Silicon Alley is based on the leading role of superior innovation policies, actively exploring potential key actors and practical places and requiring the government and operator to coordinate the diverse needs of internal action subjects, leveraging the feedback effect of human and non-human action subjects on the action network, while adopting community-based operation governance ideas to ensure the completion and long-term stable development of the Jinchuan Silicon Alley. 3) The success of the Jinchuan Silicon Alley relies on government guidance, emphasizing the use of market mechanisms to promote urban renewal. Through the "up and down linkage" role of the operator in innovation policies, capital markets, and community residents, it builds an operation model of "industry recruitment + diversified sharing," achieving old city industrial renewal and coexistence of diverse populations, and weakening the spatial isolation induced by gentrification. This study supplements the empirical research on the transformation of existing spaces into innovative spaces from a micro perspective, proving that as a type of alternative path for urban renewal, the internal operation logic of "City Silicon Alley" must possess the traditional renewal "government-market-society" operation mechanism, rely on the overall planning of the market operator, and actively consider the spatial rights of disadvantaged groups, such as community residents. Future research should focus on internal population differences and the social promotion paths of innovative space renovation, promoting the high-quality development of urban silicon alleys.

The south China Sea, a crucial passage for international shipping, features a complex and hazardous navigational environment; therefore, effective emergency response mechanisms are vital. Numerous islands and reefs distributed around shipping routes have the potential to serve as rapid-response locations for handling maritime emergencies. Therefore, evaluating the search and rescue (SAR) reachability of South China Sea islands and reefs from shipping routes is crucial. In this study, a Voronoi diagram was used to spatially configure the South China Sea islands and reefs and indicate their functional areas. Subsequently, multilevel buffer zones were utilized to grade their SAR ranges. The shipping routes located closer to islands and reefs had higher SAR intensity weights for the buffer zone. We then performed a quantitative analysis on the relationship between the SAR intensities of 138 South China Sea islands and reefs and shipping routes to assess the SAR reachability and strategic values of above-water inhabited, above-water uninhabited, and submerged islands and reefs. The results indicate that the SAR reachability of islands and reefs to shipping routes largely depends on their spatial relationships with other islands and reefs. Specifically, the distance from an island or reef to a shipping route is a key determinant of its SAR reachability. Islands/reefs located close to the shipping routes had higher SAR intensities and higher SAR reachability. However, in regions containing relatively dense distribution of islands and reefs, the SAR buffer zones may be reduced owing to competition and squeezing by other islands and reefs, which resulted in weakened SAR intensity. Conducting effective SAR operations on shipping routes may be impossible in such cases, thereby lowering the SAR reachability. Among the above-water inhabited islands and reefs, Dongdao Island, Swallow Reef, Zhongjian Island, and Beizi Island exhibited more prominent SAR reachability for different shipping routes and had the highest values. Among the above-water uninhabited islands and reefs, Huangyan and Quanfu islands had relatively high SAR reachability and the highest development potentials. Among the submerged islands and reefs, the comprehensive advantages of Commodore Reef, Haima Shoal, Investigator Shoal, Wood Bank, Langhua Reef, and Royal Charlotte Reef were clear, with relatively high potential development values.

The spatial effect of tourism-led growth has long been confirmed by academia; however, the scope of its spatial effect, whether there is a boundary, and its regularity urgently need to be further studied. Based on the Tourism Economic Growth Hypothesis and the "First Law of Geography" hypothesis, using panel data from 88 county units in Guizhou Province from 2010 to 2020, this study tested the spatial spillover effect of county tourism economic growth on economic development based on the spatial panel Durbin model double fixed effect model and detected its spatial attenuation boundary. The research findings are as follows: (1) From 2010 to 2020, the growth of county-level tourism economy in 88 counties and districts in Guizhou Province had a significant positive spatial correlation with the level of economic development and showed stage differences. Based on the spatial panel Durbin model double fixed effects test results, it was evident that the growth of county-level tourism economy had a positive effect on the local economic development level. The growth of county-level tourism economy had significantly improved local economic development. Simultaneously, it achieved spillover and radiation to surrounding areas through spatial spillover, ultimately leading to an overall improvement in regional economic development. In addition, affected by the stage of development, the spillover coefficient of the indirect effects was lower than that of the direct effects, and the direct effects passed more stringent significance tests. (2) The spatial spillover effect of county-level tourism economic growth in Guizhou Province on the level of economic development had a spatial spillover attenuation boundary, and the spatial spillover effect formed an "inverted V-shaped" curve as the geographical distance changed. The minimum spatial spillover boundary for the spatial spillover of county-level tourism economic growth in Guizhou Province was 90 km. The optimal and maximum spatial spillover boundaries were 350and 450 km, respectively. This study further infers that administrative boundary constraints affect the economic growth of counties in mountainous provinces. The contributions of this study are as follows. First, it revealed the spatial decay law of the spatial spillover effect of county-level tourism economic growth on the level of economic development and detected its decay boundary. Thisstudy verified the hypothesis of economic externalities and the first law of geography, effectively enriching the explanatory power of the "LTGH hypothesis" at the provincial and county levels and addressing the deficiencies in county-level research. Second, by studying this heterogeneity law from the perspective of spatial spillover distance, this study further explored the minimum, optimal, and maximum spatial spillover distance, aiming to facilitate regional spatial collaborative development in different areas, reduce the significant constraints of high time and transportation costs caused by geographical barriers, and provide theoretical and data references for the formulation of policies for high-quality regional collaborative development.

Urban commercial banks are a primary force that supports local economies and alleviates the financing challenges faced by small- and medium-sized enterprises. However, these banks have experienced substantial changes in their overall strengths and diversified management capabilities over the past two decades of development. Previous studies have indicated that numerous issues persist amid these ongoing changes and expansions. It is therefore crucial to investigate the expansion pathways and associated effects to ensure the sustainable development of urban commercial banks in China. Therefore, we used unbalanced panel data from 46 urban commercial banks in the Yangtze River Economic Belt from 1996 to 2023 with spatial network analysis and econometric modeling to delineate the spatiotemporal changes related to both endogenous and exogenous expansions. We also examined how remote expansion influenced the efficiency levels of urban commercial banks. The endogenous expansion of urban commercial banks exhibited notable fluctuations in its intensity and geographical reach. Under the combined influence of mechanisms that favor optimal selection and neighboring connections, inter-city, inter-provincial, and regional administrative barriers were dismantled. Consequently, some expansion-path heterogeneity existed across different regions and scales. Nevertheless, these expansion networks were consistently concentrated within the Yangtze River Economic Belt, which is characterized by concentrated activity in its eastern segments, and exhibited sporadic distributions beyond this belt. Notably, radial expansions within provinces were predominant. The intensity of exogenous expansion was relatively limited. Rural banks acted as the primary focus of their expansion efforts. Urban commercial banks tend to favor out-of-province expansions, particularly among eastern urban commercial banks (e.g., Chouzhou and Mintai banks). The expanded numbers and geographic reaches of urban commercial banks significantly enhanced their efficiency levels at the 1% significance level. Scale and time heterogeneity analyses indicate that small-sized urban commercial banks have a greater propensity to increase their efficiencies. There was a pronounced contribution to the asset scale of these banks during the early stages of urban commercial banking development. The robust expansion capacity during this initial phase facilitated more effective improvements in market share and capital adequacy ratios, thereby consolidating foundational strength. However, owing to data limitations, this study did not conduct a more comprehensive analysis of the effects. Future research should conduct more detailed comparative studies of the expansion and contraction dynamics within urban business networks by utilizing richer datasets and broader methodological approaches.

Recreational spaces play a pivotal role in enhancing the quality of national life, fostering physical and mental health, and promoting social interaction. While previous research has predominantly examined the supply side of urban recreational spaces, there is a noticeable gap in the characterization of these spaces based on societal perceptions. Using Guangzhou as a case study, this study harnesses big data sourced from Ma Feng Wo and Ctrip. Based on machine learning techniques, it discerns recreational emotions and synergizes Geographic Information System (GIS) spatial analysis with heat-emotion matching analysis. This approach facilitates a nuanced and precise examination of the structural and experiential characteristics of urban recreational spaces from the perspective of social perception. The objective of this study is to offer informed references for the strategic planning and management of urban recreational spaces. The findings indicate that: (1) The distribution of recreational spaces in Guangzhou exhibits a "core agglomeration, edge dispersion" pattern, characterized by a "one core, multiple centers" configuration. Notably, the central urban region and its proximate suburbs—specifically, the Panyu, Baiyun, and Huangpu Districts—show a marked concentration in both the number and popularity of recreational spaces. In contrast, peripheral urban zones primarily feature government residences, premium ecological recreational spaces, and hot spring resources. These areas—including the Huadu, Conghua, and Zengcheng urban areas, Conghua District's hot spring town, and Paitan town—stand out as secondary hubs in terms of recreational space concentration and popularity. Moreover, recreational spaces situated along waterfronts and in areas with dense road networks tend to follow a "point-axis" distribution model, with a staggering 90.45% of these spaces located within a 1 km radius of the road network. (2) The overall approval rate of urban recreational spaces in Guangzhou is significantly high, evidenced by an average positive emotion ratio of 86.99%. However, there is a marked polarization in terms of popularity, manifesting as a "core-edge" decline in spatial distribution. Recreational spaces that evoke predominantly positive emotions are primarily located in the city center and are proximate to the administrative seats of the Panyu, Baiyun, Huangpu, and Nansha Districts. These areas also record a low ratio of negative emotions. In contrast, neutral and negative emotions are more prevalent in the commercial streets and pedestrian zones of the central city, as well as the suburbs of the Conghua, Huadu, and Zengcheng Districts. A considerable emphasis on "cost performance" is observed across all emotional categories related to Guangzhou's urban recreational spaces, indicating a widespread concern among tourists/residents regarding the balance between the costs of these spaces and their service quality. (3) The congruence between popularity and emotional response in Guangzhou's urban recreational spaces is suboptimal. Spatially, this disconnect can be categorized into three distinct types: the "polarized" pattern in the central city, the "experience enhancement" pattern in the Huadu, Panyu, Baiyun, and Conghua Districts, and the "insufficient supply" pattern in the Zengcheng, Huangpu, and Nansha Districts. These findings provide valuable insights for metropolitan areas aiming to refine the layout and management of recreational spaces. By understanding social demands and experiences, cities can craft a more optimized group perception of urban recreational spaces, thereby enhancing tourists' satisfaction and elevating the well-being of residents.

As the pace of life accelerates and the demand for tourism quality increases, slow tourism, which emphasizes experiences, relaxation, and sustainability, has emerged. However, slow-tourism behaviors and perceptions differ widely across different urban contexts. We applied the basic framework of landscape perception theory to popular Citywalk routes in Chengdu, Wuhan, and Shanghai, which were obtained from the Xiaohongshu platform. By integrating spatial, multimodal data, and content analyses, as well as other methods, we explored the spatial behavioral patterns, perceptual differences, and the associated mechanisms of tourists during Citywalk activities in different urban contexts. The findings indicate that Citywalk activities mainly occurred within the second rings of cities, representing small-scale urban exploration that emphasizes experiential feelings over conventional mobile tourism. Tourists preferred culturally and artistically vibrant urban destinations. Citywalks are generally free, thereby embodying a subcultural phenomenon that contrasts with the stressful rhythm of life emitomized by "involution" and "996" work culture. Notable differences in cognitive imagery, emotional imagery, and cultural perception were present among the tourists in different cities, which shaped unique urban Citywalk tourism experiences. Based on different models and perceptual differences, Chengdu's Citywalk was defined as "a slow city tour centered around creative cultural districts that blends creative spaces and gourmet exploration," whereas those in Wuhan and Shanghai were defined as "a slow city tour centered around historical architecture, that blends cultural spaces and natural scenery" and "a slow city tour centered around urban landscapes that blends humanities, arts, and modern fashion," respectively. Differing geographical locations, planning concepts, development orientations, and historical backgrounds affected the Citywalk tourism experiences by influencing aspects such as the natural environment, spatial layout, developmental direction, and cultural characteristics of each city, which created different place perceptions. Geographical location affects the natural environment, tourism facilities, and cultural atmosphere of a city, whereas planning concepts influence urban spatial layouts, functional zoning, and the mode of tourism resource development, which affect the form and experiences in slow tourism. Development orientation determines the development direction of a city, thereby crafting unique attractions. Differing historical backgrounds create distinct urban cultural features, lifestyles, and tourism resources, which affect the direction of slow-tourism development. The findings of this study present the differences in Citywalk behaviors and perceptions in various urban contexts, filling a gap in comparative studies of cities within slow-tourism scenes. The findings also provide a new theoretical perspective for understanding the interactions between tourism behavior and urban spaces and offers reference experiences for other cities to develop slow tourism, enhance urban cultural tourism competitiveness, and promote sustainable urban tourism development.

During the current critical stage in China's urbanization transition from rapid expansion to high-quality development, stock planning and renewal strategies have become important topics that drive urban sustainability. Urban villages, as remnants of rapid urbanization, pose major challenges to urban governance owing to their complex spatial forms, socioeconomic structures, and disconnection from the mainstream urban system. The government has implemented multidimensional measures such as policy guidance, environmental remediation, and industrial upgrading, which have achieved certain results in addressing urban villages. However, issues such as a lack of development momentum, insufficient self-renewal capabilities, and low integration with the city remain. The successful transformation of Gaopu Village in Xiamen City is a valuable example of an autonomous development path for urban villages. We focused on the transformation practices used in Gaopu Village to determine the underlying reasons behind the village transformation. An analytical model was constructed that comprehensively considered dynamic policy, market, and culture mechanisms, as well as exploring their impacts on the development of Gaopu Village and the changes in interactive relationships among the three factors. The benign development of Gaopu Village was found to have benefited from synergy between policy, market mechanisms, and local culture. Government policies, as external driving forces, provide directional guidance and resource support to Gaopu Village, injecting new vitality. Market mechanisms, particularly the establishment of the Xinglin Industrial Zone, increased tourism while industrial upgrading enhanced the economic level of Gaopu Village and facilitated the optimization of spatial functions. In addition, the profound local culture in Gaopu Village, including historical memory, community identity, and values, had an implicit stabilizing role in the transformation, which strengthened the villagers' sense of belonging and cohesion and promoted the coordination of multiple interests and consensus formation. When market driving forces weaken or the external environment changes, local culture transformed into an endogenous driving force that supported the sustained development of the village, which allowed it to achieve modern transformation while preserving its unique characteristics. The transformation of Gaopu Village offers important insights for other urban villages: to achieve benign and autonomous development, it is necessary to explore and fully utilize intrinsic resources, particularly cultural resources, and construct a transformation mechanism where internal and external dynamics mutually reinforce and synergize each other. This study not only enriches the theoretical framework of urban village transformation and development but also provides practical guidance for urban planners, policymakers, and community managers. The findings provide new perspectives for exploring the harmonious coexistence of cultural inheritance and modern governance in urban villages, emphasize the importance of self-renewal capabilities in urban villages, and offer valuable lessons for future urban governance.

The transformation of livelihood tourism is a key factor for fishermen in optimizing their livelihood strategies and achieving sustainable development. This is an effective strategy for improving the marine ecological environment, protecting marine biodiversity, and supporting the development of destination tourism. Recent research has demonstrated that the transformation of livelihood tourism is one of the most promising strategies for rural tourism destinations. In addition to considering the individual livelihood capital status, livelihood tourism transformation must assess the risks and uncertainties that may exist. Based on sustainable livelihood and prospect theories, this study considered the unique characteristics of coastal fishermen and established a livelihood capital evaluation index system for them. An integrated model was constructed using livelihood capital, risk cognition, and livelihood tourism transformation intention. An empirical test was conducted using 310 valid questionnaires obtained from a survey of fishermen in Tanmen Town, Qionghai City, Hainan Province, China. The results were as follows. (1) The overall level of sustainable livelihood among coastal fishermen was relatively low, with significant differences in its dimensions. The level of natural capital was relatively high, with a livelihood capital value of 0.646. Human and material capital were at moderate levels, with livelihood capital values of 0.555 and 0.510, respectively. Financial and social capital levels were relatively low, with livelihood capital values of 0.280 and 0.192, respectively. The livelihood capital levels of coastal fishermen were generally lower than those of farmers in other regions. (2) Livelihood capital significantly influenced fishermen's willingness to engage in livelihood tourism transformation, with human, material, and social capital showing significant positive effects, consistent with previous research findings. Natural and financial capital had significant negative effects. Owing to the unique attributes of coastal fishermen, the impact of financial capital on their willingness to engage in livelihood tourism transformation differedO from previous research conclusions. (3) Risk cognition had an intermediate effect between human, social, and financial capital on livelihood tourism transformation intention. The mediating effects of natural and material capital on livelihood tourism transformation intention were not significant. This study revealed the relevant factors influencing livelihood tourism transformation intention. It innovatively discusses the mediating role of risk perception in the influence of livelihood capital on livelihood tourism transformation intention by combining it with prospect theory. It deepens the understanding of existing studies on the livelihood level and structure of coastal fishermen and enriches the application of prospect theory in tourism research. It provides theoretical support and a scientific basis for improving the livelihood level of fishermen and the driving force of livelihood tourism transformation, offering new insights for optimizing livelihood strategies and sustainable development of coastal fishermen.

Tourism corridors are a new type of activity carrier with the advent of self-driving tours and transportation integration in tourism. Tourism corridors can help to reconstruct spatial tourism patterns and optimize regional tourism industry structures. Performing tourism corridor maturity assessments can be used to guide corridor construction. However, few studies have included both linear corridor maturity assessments and obstacle factors. Using the entropy weight method and obstacle degree model, we constructed an evaluation index system for the Sichuan–Tibet tourism corridor maturity based on four aspects: resource endowment, reception level, infrastructure, and security guarantee. The obstacle factors of the corridor were combined with the results obtained from the evaluation system and discussed. The Sichuan–Tibet tourism corridor is currently at a relatively low maturity level and sections with low maturity levels and below account for 89.04% of the total mileage. Owing to the fact that the economic development level of Tibet lags behind that of Sichuan, as well as the geographical complexity, the low maturity sections were mostly concentrated in Tibet. The maturity of the tourism corridor exhibited a "high-low-high" spatial distribution, in which the high-maturity regions were located in Chengdu and Lhasa at both ends of the corridor. In contrast, the low-maturity regions were mainly concentrated in the Bomi–Mangkang and Litang–Yajiang sections in the central part of the corridor. The main obstacles to the maturity of the Sichuan–Tibet tourism corridor had effects that decreased in the order of reception level > infrastructure > security guarantee > resource endowment. Based on the specified indicators, service facilities, tourist catering, medical level, entertainment facilities, and communication conditions were the main obstacles. The main obstacle factors in each section of the tourism corridor differed and were divided into resource obstacle, reception facility obstacle, and security obstacle sections. Among these, security obstacle factors were present in most sections. Sections with differing obstacles should therefore take measures according to local conditions to reduce the impacts of their obstacles. This study addressed the spatial evaluation of linear corridor development and construction, which can be used to evaluate the maturities of other tourism corridors and provide support for the development and construction of the Sichuan–Tibet tourism corridor.

With the trend of the "Internet of Everything" breaking the time and space boundaries of information dissemination, virtual and real interactive activity spaces are gradually replacing physical activity spaces and becoming the dominant form of carrying out human activities. For the tourist town, the "user-generated content" and community sharing platform represented by TikTok not only improves the convenience of tourists in planning trips, booking products, obtaining real-time information, and sharing experiences, but also enhances tourists' perception and interest in the living environment and cultural atmosphere of the ancient town. Under the background of the integration of virtual and real in the digital age, it is very important to explore the spatial characteristics and internal relations of the online and offline heat of the ancient town for understanding the phenomenon of large-scale tourist gathering. From the perspective of environmental behavior and attention economy theory, this study used multi-source heterogeneous data and spatial econometric analysis methods to take Dayan Ancient Town in Lijiang as an example to explore the following: 1) What are the spatial performance characteristics of online and offline popularity in tourist towns? 2) What is the potential relationship between online and offline popularity in tourist towns? The results were as follows. 1) The spatial correlation between online and offline popularity heat in Dayan Ancient Town was high, and the overall distribution was extremely uneven. The top 10% of the space unit's online popularity contributed to more than 90% of the traffic and attendance, showing clear power-law attenuation characteristics. The top 10% of the space units of offline popularity contributed more than 33.6% of the total tourists, showing a tourist gathering mode with Sifang Street as the core and decreasing to the periphery. 2) The spatial and temporal differentiation of tourist volume in ancient towns was the result of the interaction between online and offline environments. In the online dimension, the concentration of tourists, number of digital content punch cards, and number of digital content views formed a positive promotional effect. In the offline dimension, tourists' mobile behavior was positively affected by shopping service facilities, attractions, leisure and entertainment facilities, functional density, building density, and sDNA (spatial Design Network Analysis) proximity (r=400 m), and negatively affected by infrastructure, educational service facilities, and sDNA accessibility (r=n). Tourists' calling behavior was positively affected by accommodation service facilities, infrastructure, and functional density. 3) The online and offline popularity of ancient tourist towns was transformed by the influence of digital media traffic on tourists' punching and mobile behavior. In this process, the environmental characteristics and cultural landscapes of traditional villages were packaged as tourism attractions. Local tourism resources were transformed into digital content through tourists' card-making behavior. High-quality digital content accumulated attention capital through traffic transmission, drove tourist movement and consumption demand gathering, and created a new "net red card" in the physical geographical space.

With the rapid growth of the digital economy, integrating the cultural and tourism industries has become a key driver of regional economic development and industrial upgrading. As a vital component of the cultural and digital sectors, the gaming industry facilitates integration through digital innovation and creative design. While existing research on cultural-tourism integration is extensive, little attention has been paid to how virtual cultural symbols transform and drive this process in the digital era. Recent advancements in gaming have blurred the boundaries between virtual and real experiences through virtual scene construction, cultural symbol reproduction, immersive interactions, and social media dissemination, accelerating the transformation of cultural resources into tourism assets. Understanding how the gaming industry promotes cultural-tourism integration enhances existing research frameworks, deepens insights into the dissemination and reproduction of cultural symbols in the digital economy, and offers new regional cultural tourism development strategies. Using Black Myth: Wukong as a case study, this research applies the field conversion theory to examine the flow and transformation of cultural symbols between virtual and real-world tourism contexts. It explores two key questions: (1) How does the gaming industry reconstruct traditional cultural symbols through digitalization and integrate them into real-world tourism using field conversion mechanisms? and (2) How does the participation and feedback of different groups influence the effectiveness of this integration, shaping the gaming industry's role in regional cultural tourism development? The findings indicate that digital technologies not only overcome spatial constraints on cultural resources but also enhance interactivity and dissemination, promoting the transformation of symbolic capital into cultural, social, and economic capital. However, engagement levels varied across groups. Players deeply immersed in virtual cultural symbols strengthened the connection between gaming and real-world tourism through social media, offline activities, and digital communities. In contrast, non-players rely on traditional tourism information sources and respond passively and indirectly to game-driven cultural symbols. This study identifies capital accumulation, habit migration, and stakeholder collaboration as the core mechanisms facilitating cultural-tourism integration. While gaming fosters cultural identity, tourism consumption, and economic diversification, it also presents challenges, such as infrastructure strain and tourism industry homogenization due to sudden visitor influxes. This research expands the scope of the theory's application by integrating the field conversion theory into the study of gaming and cultural-tourism integration. It examines how cultural symbols gain value through cross-field transformations. Furthermore, it highlights how digital games that leverage virtual reality, short videos, and social media facilitate cultural symbols' cross-regional flow and reproduction. Moving beyond static cultural transmission models, this study reveals the dynamic evolution of virtual culture and offers fresh perspectives on the development of the cultural industry in the digital economy.

The homestay industry, an essential component of rural tourism, plays a crucial role in promoting the rural industries and realizing rural revitalization strategies. Female homestay owners, as important participants in this industry, have driven the development of rural tourism and the homestay sector and benefited from it. Therefore, further research is necessary to explore how female homestay owners leverage their feminine traits to become rural tourism elites, thus becoming a significant force in developing and revitalizing rural homestays. This study considerd 30 female homestay owners in Guangdong Province as research subjects, combines the four core principles of life course theory, and employs interviews and thematic analysis to explore the bidirectional interaction mechanism between the growth of female homestay owners and the development of rural homestays. This study divides the driving forces of female homestay owner growth into personal agency, accumulation of past resources, socio-historical conditions, and social relationship networks, thereby analyzing the growth path of female homestay owners. The results indicate that female homestay owners are key in promoting rural tourism development. Under certain socio-historical conditions, they actively exercise personal agency; integrate accumulated resources with family, social, and governmental relationship networks; obtain elite status; respond to rural development needs at different stages; and contribute to developing rural tourism and the homestay industry while achieving personal growth. In the exploration phase, they rely on developing rural tourism, seizing opportunities using social relationship networks, promoting the number of homestays, improving rural living environments, and responding actively to rural homestay development needs. In the foundation-building phase, they use accumulated resources and business experience to weave social relationship networks, drive outstanding homestay practitioners, and meet the need for standardization and branding in rural homestays. In the formation phase, they return to rural tourism, shape social images, pursue social recognition, address homestay clustering and branding issues, and promote exemplary development in the rural homestay industry. The feminine traits of female homestay owners play a vital role in this process; they use traits such as affinity, delicacy, and sensitivity to enhance homestays' competitiveness and customer satisfaction, create a warm accommodation environment, and keenly capture market changes. The traditional role of women in family structures influences homestay owners' career choices. They achieved economic independence through homestay businesses, enhanced their say in the family, and supported their families. In terms of care ethics, they focus on women's development, improving the employment situation of rural women, offering training and support, promoting economic independence and self-development of rural women, and contributing to rural revitalization. This study, from the perspective of integrating micro-individuals with macro-society, provides a reference for clarifying the formation and evolution mechanism of the positive interaction between local elites and local development, offers a new perspective for understanding the role of female homestay owners in rural revitalization, and provides a rich set of empirical data and a theoretical framework for future research.

Optimizing land-use policies to enhance carbon storage has become a strategic priority in reaching the dual carbon goals in China. Among these policies, the Demolition and Reclamation (D&R) initiative for rural construction land provides a substantial opportunity to increase carbon sequestration through ecological restoration. Guangdong Province was selected as a representative case study for developing a comprehensive multi-stage evaluation framework for carbon storage, structured across three sequential stages: System Design (SD), Construction (CO), and Quality Effect (QE). The primary objectives were to quantify the differences in carbon storage capacity before and after reclamation and to analyze the key influencing factors at each stage to increase policy effectiveness in supporting the carbon neutrality goal. A stage-wise quantitative model was constructed to evaluate the carbon storage changes associated with reclaiming rural land. Data were collected from multiple counties using satellite imagery, soil surveys, vegetation coverage assessments, and policy implementation records. The carbon storage levels were calculated using established biomass and soil carbon estimation methods. Performance metrics such as carbon realization ratios and contribution rates were used to determine the effectiveness of the implementation of the policy in different stages and regions. The results showed the following: (1) The carbon storage markedly increased after reclamation, especially in counties located at provincial boundaries, with a nearly nearly five-fold increase in the per mu carbon storage. The average province-wide carbon storage contribution rate was 0.16%, with the highest single-county contribution reaching 5.25% (Maonan). (2) The carbon storage realization ratio was 5.81% in the SD stage, indicating a considerable potential gap of approximately 36.27 million tons in unrealized carbon storage. (3) The realization ratio was higher at 24.71% in the CO phase but with wide variation among counties, ranging from 1.26% to 97.55% (Jinping and Yingde, respectively). (4) The realization ratio was 65.03% in the QE stage, being spatially higher in the east and lower in the west. The carbon performance was stronger in the eastern counties, which was largely attributed to the adoption of professional land management and post-reclamation care practices. This study contributes to the literature by offering a multi-stage evaluation framework that links the SD, CO, and QE stages to measurable carbon storage performance. The results of the analysis revealed that the D&R policy has the potential for enhancing carbon sequestration; however, each stage has experienced specific institutional and operational challenges. The carbon realization ratio was low in the SD stage, reflecting an insufficient alignment between the top-down policy and the carbon neutrality goal. This finding could be attributed to the lack of effective bottom-up feedback mechanisms, low public acceptance, and limited access to localized, reliable data, which were factors that strictly constrained the operational relevance of carbon-related policy objectives. The wide disparities in the carbon storage realization in the CO stage across counties were primarily due to differences in organizational capacity. High-performing counties typically had clear task assignments, defined project timelines, integrated project management, and strong inter-program coordination. In contrast, low-performing counties tended to adopt vague and informal approaches, such as verbal agreements, which hindered implementation consistency and effectiveness. The carbon realization was highest in the QE stage among the three stages; however, room for optimization remains. Counties with superior performance often established designated maintenance personnel and enforced regular management practices. Issues such as weed overgrowth, stagnant water, and restricted tree growth emerged in areas where post-reclamation maintenance was absent, which directly inhibited carbon accumulation. Several policy implications are proposed based on these findings to enhance the effectiveness of the D&R policy in achieving carbon neutrality. First, county-specific implementation guidelines should be developed to bridge fiscal and technical disparities, especially in underdeveloped regions, such as western Guangdong, to increase the regional adaptability of policy instruments. Second, professional post-reclamation stewardship must be institutionalized through long-term ecological management funds to ensure continuous carbon sequestration and ecological stability. Third, an integrated SD-CO-QE approach should be prioritized in high-carbon-potential areas and supported by remote-sensing-based dynamic monitoring to enable timely feedback, adaptive management, and precise interventions. These strategies collectively offer a coherent and targeted policy pathway for maximizing the carbon storage potential of D&R and reinforcing their strategic role in achieving the carbon neutrality agenda in China.

Against the backdrop of rapid urbanization and escalating risks posed by extreme rainstorms, the complexity of urban hydrological systems and limitations of fragmented data-driven approaches underscore the necessity of constructing integrated frameworks to enhance rainstorm situation awareness. Traditional methodologies typically rely on isolated physical monitoring, digital modeling, or social response mechanisms and fail to address the interdependencies among physical infrastructure, informational technologies, and social systems. This study aims to deepen our understanding of how digital and intelligent technologies can be configured across a physical–informational–social ternary space to achieve robust urban rainstorm governance by identifying context-specific empowerment paths and their applicability to diverse urban typologies. Guided by the theoretical framework of the physical–informational–social ternary space, this study employs a mixed-method approach combining fuzzy-set qualitative comparative analysis (fsQCA) and Latent Dirichlet Allocation (LDA) modeling to investigate the pathways through which digital and intelligent tools empower urban rainstorm perception and to explore the disaster-affected characteristics of cities under different configurational paths. By tracking 35 typical Chinese cities, the fsQCA analysis reveals three differentiated empowerment configurations: (1) Balanced ternary space empowerment (G1), which achieves high-efficiency empowerment through three-dimensional collaboration among physical space data integration (including real-time sensor networks for hydrological monitoring), informational space intelligent analysis (including machine-learning-based risk prediction models), and social space emergency response (including interagency coordination systems), relying on dynamic interactions across the three domains. (2) Physically–socially dominant ternary space empowerment (G2): Grounded in core conditions of multisource data integration (combining meteorological, topographical, and citizen-generated data) and high disaster perception efficiency, this configuration incorporates peripheral conditions of server-side intelligence (including cloud-based data analytics) and user-side participation (including mobile application-driven hazard reporting), emphasizing data diversity and user-centric empowerment. (3) Physically–socially interactive binary space empowerment (G3): Empowerment is realized through the binary coupling of multisource data integration and high perception efficiency as the core conditions, prioritizing the technical synergy between physical monitoring and informational processing. Concurrently, a single-dimensional, low-empowerment configuration, which relies on isolated spatial data or technologies, is found to be insufficient for comprehensive disaster perception, thus empirically validating the necessity of ternary space configurational intersections. LDA topic modeling further demonstrates that different digital-intelligent empowerment patterns align with distinct disaster-sensitive city types: G3 suits hazard-sensitive cities (including Guangzhou), G2 matches vulnerable cities (including Xi'an), and G1 benefits exposure-sensitive megacities (including Beijing and Shanghai). Theoretical contributions of this study include constructing a "ternary space for urban rainstorm situation awareness" framework, which systematically analyzes the effects of digital-intelligent empowerment through the coupling mechanism of real-time physical space perception, intelligent informational space processing, and optimized social space decision-making—thereby transcending the limitations of traditional technological determinism. Methodologically, the research overcomes the constraints of single-method approaches by retaining fsQCA's strength in causal necessity analysis and integrating LDA's capability for semantic theme identification, forming a complete explanatory chain of "causal mechanisms-adaptive paths-type characteristics." At a practical level, this study proposes differentiated implementation strategies that provide both theoretical foundations and practical guidance for the digital and intelligent enhancement of urban rainstorm situation awareness.

With rapid societal development, economic growth, industry, agriculture, and human activities, large amounts of sewage rich in nitrogen, phosphorus, and other nutrients are produced. This causes eutrophication in lakes and reservoirs, leading to frequent algal blooms that pose a serious threat to aquatic ecosystem stability and drinking water safety. Chlorophyll-a (Chl-a) concentration, a core indicator of algal biomass and the degree of eutrophication in water bodies, correlates positively with algal bloom outbreaks. However, Chl-a concentration variance under the influence of multiple factors displays highly nonlinear characteristics. Traditional prediction models are unsuitable for relationship analyses between environmental factors, generally producing low prediction accuracy with weak applicability. To address this challenge, we proposed a short-term chlorophyll a concentration prediction method based on multi-scene segmentation, and constructed a prediction model with enhanced adaptability by recognizing the characteristic laws of different environmental scenarios to improve the accuracy of Chl-a concentration prediction. By analyzing the laws influencing various factors, we proposed three partitioning strategies: 1) factor interaction and scene partitioning by analyzing connections between key environmental factors and adopting K-means method to partition these scenes, 2) diurnal difference scene partitioning based on diurnal cyclicity of algal physiological activities, dividing the data into two scenarios, and 3) trophic state scenarios, based on Trophic Level Index. A water body is divided into three trophic categories: anaerobic, mesotrophic, and eutrophic. Three machine learning models (Random Forest, Gradient Boosting Decison Tree, and eXtreme Gradient Boosting) using multi-scenario classification and a linear regression model were each adapted to perform short-term prediction of Chl-a concentration. The multi-scenario partitioning strategy proposed in this study substantially optimized model prediction performance. Factor interaction scenario partitioning yielded the best prediction results, with an overall RMSE(Root mean square error) prediction average of 0.0045, improving prediction accuracy by 4.26% compared to that of the unpartitioned scenarios. Overall improvement in prediction accuracy from diurnal and nocturnal scenario partitioning was limited. Its overall RMSE prediction average was 0.00474, improving prediction accuracy by 0.9%. In eutrophic scenario, the four prediction models (RF, GBDT, XGBoost, and linear regression) exhibited respective RMSEs of 0.0034, 0.0036, 0.0035, and 0.0039, with RF model giving the highest prediction accuracy. In summary, we propose an innovative short-term prediction model to improve the accuracy of low Chl-a concentration predictions in complex situations, providing a new paradigm for intelligent modeling and precise governance. The data obtained using a multi-scenario delineation system revealed the dynamic coupling mechanisms between the diurnal biological rhythms in a water body, nutrient grading, and the interaction of water quality factors. The idea of this study effectively solves the problem that the traditional prediction model can not meet the current prediction needs. This study can provide a systematic analysis for the prediction of cyanobacterial bloom, and can provide technical reference and theoretical support for the multi scenario prediction of Chl-a concentration.This study not only clarified the driving law of Chla concentration change under different scenarios, but also promoted the transformation of simplifying the analysis of complex problems of water environment, providing a new perspective for the mechanism research of complex water environment system.

The population problem has always been an overall, long-term, and strategic issue facing China; the fertility rate can reflect the population problem, and the identification of the temporal and spatial evolution characteristics and driving factors of fertility rate is of great significance to the long-term balanced development of population and the coordination of human-land relationship. Based on the census data of 2000, 2010, and 2020, the Theil index, spatial autocorrelation analysis, and geographically weighted regression models were used to explore the spatiotemporal evolution characteristics and influencing factors of city fertility in China from 2000 to 2020. The results showed that: (1) Based on the perspective of time series characteristics, from 2000 to 2020, China's fertility rate presented a downward trend, the degree of distribution first increased and then decreased with the passage of time, the discrete trend between cities decreased, and the regional differences in fertility rate have narrowed. (2) Based on the perspective of spatial pattern, the fertility rate is high in the south and west and low in the north and east, whereas heterogeneity is obvious. Specifically, the west side of the Hu Line is higher than that of the east side, but the fertility rate fluctuation on the east side is higher than that on the west side. China's four major economic regions are in the order of Western > the Central> the Eastern > the Northeast, with fertility rates still declining in the Western and Northeast regions. Focusing on the five major urban agglomerations, the urban agglomeration in the middle reaches of the Yangtze River has the highest fertility rate, followed by the Chengdu-Chongqing urban agglomeration, the Beijing-Tianjin-Hebei urban agglomeration, and the Pearl River Delta urban agglomeration, with the Yangtze River Delta urban agglomeration having the lowest fertility rate. Overall, the H-H cluster agglomeration areas are mainly distributed in the southern and western regions of China, while the L-L cluster agglomeration areas are concentrated in the northern and eastern regions. (3) Based on the perspective of influencing factors, economic, policy, demographic, and social factors are always the key factors affecting fertility, with economic and policy factors generally having a greater impact on China's fertility rate. The spatial heterogeneity of economic factors represented by per capita GDP and urbanization rate is significant, the positive impact of policy factors in northern and eastern China is significant, the population quality and fertility rate in the west of the Hu Line are strongly negatively correlated, the population number and fertility rate in southeast China are positively correlated, the negative impact of marriage and childbearing on fertility on the east side of the Hu Line is greater than that on the west side, and the influence of the concept of raising children and preventing old age in some areas in the east is still greater.

Based on the analytical framework of geographical philosophy, this study investigated the production process at different stages, overall laws, ideological inspiration, and future issues of geographical knowledge production and practice in China. The main findings are as follows: First, philosophy is an important foundation supporting the production of geographical knowledge. The production of geographical knowledge should be seen as a process of content division and succession, in which new knowledge is gradually accumulated and there is movements towards wholeness. Second, in geographical knowledge production, geography reflects the actuality of knowledge through manifested features and reflects the reality of knowledge through unexpressed features. Geographical knowledge production exists dynamically in practice. Exploring the driving forces and processes of geographical knowledge production in practice can facilitate the comprehension of the existence and essence of geography. Only by coordinating stage differences in geography to achieve universal awareness can the effective function and ultimate pursuit of geographical philosophy be reflected. Third, in the future, China will inevitably enter a complex stage of development and practice, and "overlapping China" will require even more "overlapping geographical knowledge". Knowledge production and practice of geography require a comprehensive perspective that integrates wisdom from different temporal and spatial dimensions, establishment of clear philosophical ontology, epistemology, and methodology based on the changes and demands of the times, actively promoting the localization of geographical philosophy system construction to serve national strategic needs and high-quality socioeconomic development more effectively.

In the context of climate change, the escalating frequency of extreme weather phenomena has exacerbated the severity of compound floods in the southeastern coastal regions of China. Rising sea levels significantly contribute to the inundation of low-lying coastal urban areas. The quantitative assessment of compound flood risk offers scientific support for disaster prevention and reduction in coastal cities and for coastal management initiatives. Using Haikou City as a case study, the daily precipitation and maximum storm surge tide data from 66 typhoons that affected Haikou between 1960 and 2017 were utilized to construct compound flood combination scenarios. Based on the quantitative method of D-Flow FM (Delft3D-FLOW Flexible Mesh) numerical simulation, the potential risks of extreme rainfall and storm surge compound flood disasters under sea level rise scenarios were thoroughly investigated by integrating various scenarios. The findings revealed the following: 1) Storm surge was the primary factor contributing to compound flooding during typhoons, with the estuary of the Nandu River and the northern coast being the most affected. 2) In the scenario of maximum rainfall and storm surge combination, the inundation area of Haikou is about 148 km², which is approximately 15 times larger than the minimum rainfall and storm surge combination scenario. Moreover, in more than half of the inundated areas, the water depth exceeds 1 meter. 3) Under extreme rainfall and storm surge compound scenarios, the areas encompassing Haidian Island, Xinbu Island, and Jiangdong New Area were significantly affected by sea level rise. By 2100, the total flooding area is projected to reach about 203 km² under the RCP8.5 scenario. Sea level rise significantly amplifies urban flood risks, implying that coastal cities are poised to encounter heightened threats and manage future challenges. Through comprehensive comparisons of multiple rainfall and storm surge compound flooding scenarios under sea level rise, the temporal and spatial characteristics of the compound flooding risk were systematically evaluated. The results provide an important scientific basis for sustainable regional development, effective management, and prevention.

Typhoons are extreme weather phenomena that seriously affect the daily lives of residents and regular functioning of society. As one of the most typhoon-prone countries in the world, China is constantly affected by typhoons and their secondary disasters, which can cause significant casualties and economic losses. The extent of damage caused by typhoons is inversely proportional to the effectiveness of the emergency response. Therefore, accurate and comprehensive access to damage information is critical for rescue and recovery. Social media, which is characterized by low collection costs and rich content, is an important means of collecting disaster information. With the development of social media, it has become increasingly important to accurately and comprehensively identify social media texts related to typhoons. In this study, by combining typhoon attribute data and a multi-label classification method with Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) models, a typhoon damage identification method based on Weibo texts and deep learning is proposed to identify the damage caused by severe and super typhoons that made landfall in Guangdong Province from 2010 to 2019. First, texts related to typhoon damage were identified from the massive Weibo texts and further classified into five damage categories: transportation, public, electricity, forestry, and waterlogging. The typhoon damage characteristics were comparatively analyzed using spatial distribution, time curves, and quantity curves. The results showed that the accuracy of typhoon damage classification was high, with an F1 score of 0.907 for identifying typhoon damage-related texts and 0.814 for further classifying them into five damage categories. Typhoon attribute data and multi-label classification methods have improved the accuracy and comprehensiveness of typhoon damage identification. Compared to the use of Weibo texts only and the single-label classification method, typhoon attribute data provide information on the geographic context of the typhoon at the time of the texts' release, and the multi-label classification method allows the texts to belong to more than one damage category. This study shows that there are differences in the proportion of damage caused by different typhoons, which are related to the intensity and track of the typhoon, as well as the development level of the affected areas. In addition, before the typhoon makes landfall, precautions lead to transportation and public-related damage. After the typhoon makes landfall, the typhoon damage shows single and double-peak characteristics, and the different characteristics reflect the changing trends and features of typhoon damage. This study provides a scientific basis for typhoon damage identification and disaster relief in Guangdong Province.

China has numerous islands and reefs with complex terrain that are heavily impacted by tropical cyclone disasters. High-resolution tropical cyclone wind-field simulations are beneficial for representing the spatial variations in wind speeds. It is important to conduct high-resolution simulations on relatively small islands and reef areas. To explore the differences in tropical cyclone wind field simulations at various spatial resolutions in the island and reef areas of China, this study compared the modeled wind fields of historical tropical cyclones in China's island and reef areas, which have complex terrains, including plains, peaks, valleys, and cliffs, at three spatial resolutions of 1 km, 90 m, and 30 m. The wind fields were modeled using land cover and elevation data of the three spatial resolutions as inputs and validated against observed winds at eight stations. Comparisons were made regarding the differences in wind speeds of tropical cyclones with a 100-year return period at three spatial resolutions. The results showed that: (1) the 30 m resolution achieves the best accuracy, with a root mean square error of 4.28 m/s, lower than those of 90 m and 1 km by 0.08 m/s and 1.04 m/s, respectively. (2) Different spatial resolution simulations showed that wind speed errors were related to terrain types. For example, on Zhujiajian Island, located in Zhoushan City, the 30 m resolution captured the spatial heterogeneity of winds better than the other resolutions, especially for mountainous, valley, and cliff terrains. Comparisons between the simulated wind speeds at 90 m and 1 km resolutions versus those at 30 m resolution indicate that the differences in the simulation percentages are as follows: 10.06% and 12.90% for peak terrain, 19.91% and 10.44% for valley terrain, and 18.57% and 19.01% for cliff terrain, respectively. Additionally, the 30 m simulation was more sensitive to transitions between windward and leeward slope terrains. (3) For the 100-year return-period wind speeds, the 30 m resolution produced the highest values and largest spatial variations. On Zhujiajian Island, the maximum wind speeds at 1 km, 90 m, and 30 m resolutions were 71.13, 73.18, and 79.97 m/s, respectively, and standard deviations of 3.88, 3.72, and 7.18 m/s. This study demonstrates the importance of using high-resolution data to simulate tropical cyclone winds in complex terrain. However, this study had some limitations. First, the terrain correction factors need to be optimized further. The assessment method provided by the building codes tended to overestimate the impact of the terrain correction factors. In the future, more accurate terrain correction factors could be obtained using computational fluid dynamics and wind tunnel tests. Second, because of the limited types of land cover data used in the calculations, the subdivision of certain land types when calculating the surface roughness is not sufficiently detailed. Additionally, different years of land cover data were not incorporated, making it challenging to reflect the variations in surface roughness. Remote sensing can be used in the future to determine the high-resolution spatial distributions of surface roughness.

China is frequently affected by tropical cyclones, which can lead to severe economic losses. Rapid disaster loss assessment is crucial for effective emergency response. A variety of factors affect tropical cyclone disaster losses, which can be roughly categorized into hazard, exposure, and vulnerability. In the past, traditional statistical methods were used as the main tools for disaster loss assessment. To explore the potential of machine learning models, we explored five algorithms: the Light Gradient Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Back-Propagation Neural Network (BP). The maximum gust wind and rainfall of tropical cyclones were selected to represent hazards, fixed capital stock data were used for the valuation of exposure, and the GDP of each county was collected to reflect capacity or vulnerability. In addition, river network density data were used as a simple proxy to demonstrate the contribution of flood-induced tropical cyclone rainfall. The relationship between these input variables and disaster loss at the county level was developed based on the data of 81 tropical cyclone events from 2009 to 2020 in Fujian Province. The performance of these models was compared using accuracy, precision, recall, and F1 scores. The accuracies of the LightGBM, RF, XGBoost, SVM, and BP models were 0.7946, 0.7726, 0.7628, 0.2518, and 0.2681, respectively. The main findings are as follows: (1) The performance of the ensemble learning algorithms (RF, XGBoost, and LightGBM) was higher than that of the individual classifiers (BP and SVM). The LightGBM model exhibited the best performance, with accuracy, precision, recall, and F1 scores >79%. (2) Maximum hourly rainfall and maximum wind gust are two of the most important loss-inducing factors, and fixed capital stock is a better proxy for disaster exposure than GDP. (3) The modeled losses are consistent with the actual losses under different but typical tropical cyclone events, indicating that the models can be applied to future tropical cyclone events impacting Fujian Province. However, this study had some limitations. First, some natural hazards, such as floods, storm surges, and waves, were not fully considered, which introduced uncertainty into the model results. Second, the emergency response capacity and actual actions taken among counties may have varied dramatically and were neglected due to data unavailability. In the future, hazard and vulnerability variables should be obtained to extend the model inputs. In addition, whether the model parameters trained with data from Fujian Province can be applied to other provinces remains unaddressed. In the future, to develop an operational model for the whole of coastal China, county-level data of all typhoon-prone areas in China with long-term time series are needed.

Islands are sensitive zones of sea-land interaction and typical ecologically fragile areas that are highly vulnerable to natural disasters, especially marine aquaculture, which is sensitive and at high risk to typhoon disasters; additionally, they are home to aquaculture households with high economic vulnerability to typhoons and poor adaptive capacity. This study focused on Liuheng Town of Zhoushan and the Dongtou District of Wenzhou, which were severely affected by Super Typhoon Lekima, and Gouqi Town of Zhoushan, which was severely affected by Typhoon In-Fa and Super Typhoon Chanthu, as case areas. Based on data acquired from 344 questionnaire surveys of aquaculture households and interview data from various related bodies, this study used factor analysis of mixed data and hierarchical clustering on principal components to identify the types of vulnerability of island aquaculture households to typhoon disasters and reveal the characteristics of each vulnerability type, as well as to identify the discriminative indicators of household vulnerability types, for analyzing the impact of typhoon disasters and other stressors on the vulnerability of island aquaculture households to typhoons. The results showed that the aquaculture industry and aquaculture households in the island areas showed high economic vulnerability, with most shrimp, crab, and shellfish mixed farming, algae, and mussel farming households suffering serious losses from typhoons. Second, differences in exposure, sensitivity, and adaptive capacity led to three different types and characteristics of vulnerability in aquaculture households. The degree of household exposure varied across aquaculture species, with mussels having the highest, algae the next highest, and shrimp, crab, and shellfish the lowest. Island aquaculture households showed outstanding sensitivity, as reflected in their high dependence on aquaculture, significant household human capital problems, relatively limited support from social networks, and frequent exposure to typhoon disasters. The adaptive capacity of households varied across aquaculture species, with mussel households having superior adaptive capacity, and shrimp, crab, and shellfish households and algal aquaculture households having relatively poor adaptive capacity. Third, the common influencing factors of aquaculture households' vulnerability to typhoon disasters are labor shortages, frequent typhoon disasters, and inadequate infrastructure. The differences among the significant discriminant indicators, such as the degree of exposure, aquaculture species, average annual household income, age and education level of the household head, social support, number and type of adaptation strategies adopted, and cost–benefit situation, are key to the formation of different vulnerability types. Finally, multiple stressors from the climate, ecosystem, economy and markets, society, institutions, and policies mutually interact to exert cumulative effects that increase the vulnerability of fishery ecosystems and the socioeconomic vulnerability of households in island regions. This study provides important empirical evidence for governments, aquaculture households, and other relevant stakeholders in island regions to reduce their vulnerability and increase their adaptive capacity.

Islands are sensitive zones of sea-land interaction and typical ecologically fragile areas that are highly vulnerable to natural disasters, especially marine aquaculture, which is sensitive and at high risk to typhoon disasters; additionally, they are home to aquaculture households with high economic vulnerability to typhoons and poor adaptive capacity. This study focused on Liuheng Town of Zhoushan and the Dongtou District of Wenzhou, which were severely affected by Super Typhoon Lekima, and Gouqi Town of Zhoushan, which was severely affected by Typhoon In-Fa and Super Typhoon Chanthu, as case areas. Based on data acquired from 344 questionnaire surveys of aquaculture households and interview data from various related bodies, this study used factor analysis of mixed data and hierarchical clustering on principal components to identify the types of vulnerability of island aquaculture households to typhoon disasters and reveal the characteristics of each vulnerability type, as well as to identify the discriminative indicators of household vulnerability types, for analyzing the impact of typhoon disasters and other stressors on the vulnerability of island aquaculture households to typhoons. The results showed that the aquaculture industry and aquaculture households in the island areas showed high economic vulnerability, with most shrimp, crab, and shellfish mixed farming, algae, and mussel farming households suffering serious losses from typhoons. Second, differences in exposure, sensitivity, and adaptive capacity led to three different types and characteristics of vulnerability in aquaculture households. The degree of household exposure varied across aquaculture species, with mussels having the highest, algae the next highest, and shrimp, crab, and shellfish the lowest. Island aquaculture households showed outstanding sensitivity, as reflected in their high dependence on aquaculture, significant household human capital problems, relatively limited support from social networks, and frequent exposure to typhoon disasters. The adaptive capacity of households varied across aquaculture species, with mussel households having superior adaptive capacity, and shrimp, crab, and shellfish households and algal aquaculture households having relatively poor adaptive capacity. Third, the common influencing factors of aquaculture households' vulnerability to typhoon disasters are labor shortages, frequent typhoon disasters, and inadequate infrastructure. The differences among the significant discriminant indicators, such as the degree of exposure, aquaculture species, average annual household income, age and education level of the household head, social support, number and type of adaptation strategies adopted, and cost–benefit situation, are key to the formation of different vulnerability types. Finally, multiple stressors from the climate, ecosystem, economy and markets, society, institutions, and policies mutually interact to exert cumulative effects that increase the vulnerability of fishery ecosystems and the socioeconomic vulnerability of households in island regions. This study provides important empirical evidence for governments, aquaculture households, and other relevant stakeholders in island regions to reduce their vulnerability and increase their adaptive capacity.

The dynamic risk assessment of typhoon disasters is an important decision-making basis for disaster response in the event of a major typhoon. Its goal is to dynamically predict the expected loss and disaster risk level caused by a typhoon so as to provide a basis for disaster risk early warning and emergency response. The traditional risk assessment model mainly fits the vulnerability curves of the hazard-affected bodies using historical disaster losses, and then establishes a disaster risk assessment model by coupling the risk of disaster factors, exposure, and vulnerability. However, the vulnerability curves generated by this method have problems of regional applicability, particularly in small-scale regions with small sample sizes available for fitting, leading to insufficient generalizability of the model. In addition, such models are complex and require phased hazard and vulnerability of the hazard-affected bodies research. Moreover, when employing the 3-element coupling process, it is difficult to consider other risk factors in the disaster system, such as hazard-formative environment and disaster prevention and mitigation capability. With the development of information technology, the availability of disaster risk factor data has been significantly improved, affording conditions for the fusion and application of disaster risk multi-source data. In recent years, many data-driven machine-learning models have been used to establish disaster risk assessment models. These models have the advantage that they can use large sample to improve the adaptability of the model, whereby the modeling process can consider more risk factors, the concepts of hazard and vulnerability are diluted, and the steps of model building are simplified. The integrated learning algorithm can not only improve the prediction accuracy, but more importantly, can be used to effectively evaluate the contribution value of the index to the final evaluation result. At present, China has established a six-level disaster reporting system at the national, provincial, municipal, county, township, and village levels, forming a long-term, high-precision database of disaster event cases since 2009, providing rich disaster loss information for the data fusion of risk elements. This study was based on 108 typhoon cases affecting five provinces in southeast China during 2009-2022. Nearly 4 000 county-level typhoon disaster loss samples were used to establish a dynamic typhoon risk assessment sample database that integrates 30 types of multi-source risk factor indicators. Six typhoon disaster risk assessment models were developed using the random forest algorithm to evaluate the affected population, emergency relocation population, crop-affected areas, collapsed and severely damaged houses, direct economic losses, and comprehensive risk level. Through the verification of actual disaster situations and model results, the overall accuracy of the disaster risk assessment results was found to be greater than 80%, indicating that the model has good generalizability and can be used for actual disaster assessment work. The experimental comparison shows that increasing the training sample size by 1-2 orders of magnitude can improve the accuracy of the model assessment by 3%-14%, indicating that the accumulation of disaster risk big data is of great significance in the study of disaster risk assessment. This study is expected to constitute a scientific reference for the quantitative analysis of the multiple impact factors of typhoon disaster damage and explore technical ideas for the application of disaster big data in risk management.

China has numerous islands and reefs with complex terrain that are heavily impacted by tropical cyclone disasters. High-resolution tropical cyclone wind-field simulations are beneficial for representing the spatial variations in wind speeds. It is important to conduct high-resolution simulations on relatively small islands and reef areas. To explore the differences in tropical cyclone wind field simulations at various spatial resolutions in the island and reef areas of China, this study compared the modeled wind fields of historical tropical cyclones in China's island and reef areas, which have complex terrains, including plains, peaks, valleys, and cliffs, at three spatial resolutions of 1 km, 90 m, and 30 m. The wind fields were modeled using land cover and elevation data of the three spatial resolutions as inputs and validated against observed winds at eight stations. Comparisons were made regarding the differences in wind speeds of tropical cyclones with a 100-year return period at three spatial resolutions. The results showed that: (1) the 30 m resolution achieves the best accuracy, with a root mean square error of 4.28 m/s, lower than those of 90 m and 1 km by 0.08 m/s and 1.04 m/s, respectively. (2) Different spatial resolution simulations showed that wind speed errors were related to terrain types. For example, on Zhujiajian Island, located in Zhoushan City, the 30 m resolution captured the spatial heterogeneity of winds better than the other resolutions, especially for mountainous, valley, and cliff terrains. Comparisons between the simulated wind speeds at 90 m and 1 km resolutions versus those at 30 m resolution indicate that the differences in the simulation percentages are as follows: 10.06% and 12.90% for peak terrain, 19.91% and 10.44% for valley terrain, and 18.57% and 19.01% for cliff terrain, respectively. Additionally, the 30 m simulation was more sensitive to transitions between windward and leeward slope terrains. (3) For the 100-year return-period wind speeds, the 30 m resolution produced the highest values and largest spatial variations. On Zhujiajian Island, the maximum wind speeds at 1 km, 90 m, and 30 m resolutions were 71.13, 73.18, and 79.97 m/s, respectively, and standard deviations of 3.88, 3.72, and 7.18 m/s. This study demonstrates the importance of using high-resolution data to simulate tropical cyclone winds in complex terrain. However, this study had some limitations. First, the terrain correction factors need to be optimized further. The assessment method provided by the building codes tended to overestimate the impact of the terrain correction factors. In the future, more accurate terrain correction factors could be obtained using computational fluid dynamics and wind tunnel tests. Second, because of the limited types of land cover data used in the calculations, the subdivision of certain land types when calculating the surface roughness is not sufficiently detailed. Additionally, different years of land cover data were not incorporated, making it challenging to reflect the variations in surface roughness. Remote sensing can be used in the future to determine the high-resolution spatial distributions of surface roughness.

Myanmar is a key neighbor for China and an important link in advancing the "Belt and Road" initiative, contributing to both domestic and international economic flows. Despite the border closures and restrictions imposed during the COVID-19 pandemic, the New China-Myanmar Indian Ocean Corridor has seen substantial progress. However, this development has not garnered the attention it deserves, as both national and Yunnan provincial governments continue to prioritize the China-Myanmar Economic Corridor (CMEC). This oversight results from an incomplete understanding of the changes in Myanmar's geopolitical landscape since 2000. Using a framework for national geopolitical landscape analysis, this study examines Myanmar's basic national conditions, principal relationships, and inherent contradictions, revealing how Myanmar's geopolitical landscape has evolved due to the interplay of internal and external factors, cross-field interactions, and strategic games played by multiple geopolitical actors. Specifically, the study discusses the period from 2000 to 2010, characterized by external pressure and internal stability, and the years from 2011 to 2021, marked by external conflict and internal turmoil. The evolving geopolitical landscape in Myanmar has created favorable conditions for building the New China-Myanmar Indian Ocean Corridor. From a geopolitical perspective, this paper explores the reasons behind the necessity of this new corridor and suggests a re-evaluation of China's spatial planning for major infrastructure projects in Myanmar given the country's shifting geopolitical context. The corridor's feasibility—whether measured by distance, time, costs, spatial distribution of domestic ethnic armed conflicts, or Myanmar's post-pandemic economic trends—suggests it is highly workable. In the short term, the new corridor can complement the China-Myanmar Economic Corridor, progressing concurrently; in the long term, it could gradually replace it as the main route for China-Myanmar trade. This study not only enhances understanding of the New China-Myanmar Indian Ocean Corridor but also provides a scientific rationale for its vigorous promotion.

The rapid growth of social media has introduced new concepts and technical approaches for disaster management. This paper reviews the characteristics of social media data and its application potential in disaster management research, providing a new research perspective for the field of disaster management. Taking the impact of Typhoon Doksuri in Fujian Province in 2023 as a case study, this research employs Latent Dirichlet Allocation (LDA) topic modeling to analyze the practical application effectiveness of social media data at different stages of disaster management from three perspectives: the spatiotemporal distribution of posts, trend analysis of different types of entities, and evolution of topic content. These findings indicate that the synchronous relationship between the popularity of related topics on Weibo and the impact of a disaster event confirms the effective application of social media data in disaster management. By monitoring the dynamics of information dissemination on social media, we can determine the occurrence status and impact scope of disasters in real time. During disasters, different user types have different foci. Individual users tend to focus more on the restoration of living facilities and the supply of relief materials, whereas organizational users concentrate on disseminating information about disasters and emergency response measures. The information provided by different types of users can provide a more comprehensive and diversified perspective on disaster perceptions for disaster management. Analysis of the evolution of topic content can reflect the evolution of emergency response dynamics and public attention needs in different cities at different stages of disaster management, thereby developing more practical emergency response strategies. Through the mining and analysis of social media data, this study recognizes the entire process of disaster occurrence from the perspective of social media data, thereby enriching the relevant theoretical and empirical research. Future research could be conducted from perspectives such as utilizing other multisource data, integrating machine learning and deep learning technologies to enhance the accuracy of topic information extraction, and exploring the application of social media data to post-disaster emergency rescue and infrastructure support.

Catastrophe insurance is an important financial tool to mitigate the risk of catastrophes. After the 2008 Wenchuan Earthquake, China accelerated its exploration of a catastrophe insurance system. As one of the most natural disaster-prone provinces in China, Guangdong experiences frequent rainstorms and typhoons. Severe natural disasters have not only led to significant losses to economic development and people's lives, but have placed considerable financial pressure on governments at all levels. To promote the transformation of government functions and use of catastrophe insurance as a modern financial tool to cope with major natural disasters, Guangdong has conducted pilot work since 2016 to explore and experiment with different aspects of catastrophe index insurance. This includes the design and application of insurance systems and products. The pilot work achieved remarkable results and formed the Guangdong catastrophe index insurance paradigm. However, few studies have examined the development and application of catastrophe index insurance programs in Guangdong Province. This paper describes the research and design process, data, and key methods of typhoon catastrophe index insurance in Guangdong, in accordance with the specific catastrophe index insurance practices. Furthermore, the application of the current catastrophe index insurance program from 2016 to 2023 is reviewed. Additionally, the advantages, characteristics, and shortcomings of the program are systematically analyzed, and potential directions for improvement in the future are discussed. Several notable conclusions were drawn from this study. First, the typhoon catastrophe index insurance, which is based on the circular catastrophe box and uses typhoon intensity levels as a stratification criterion for the payout structure, offers a straightforward methodology, easy recalculations, readily accessible data, and transparent results. Second, this form of insurance facilitates rapid claim settlements, incurs low operational costs, and effectively mitigates moral hazard. Third, the existing typhoon catastrophe index insurance program may encounter high basis risk and underestimate the severity of typhoon hazards, particularly in the context of climate change and the situation wherein a single typhoon impacts multiple municipalities. Finally, improvements to the current typhoon catastrophe index insurance program in Guangdong could be achieved by more deeply and comprehensively analyzing the spatial and temporal patterns of typhoon events, incorporating additional parameters with clear physical meanings, and refining the probability distributions of typhoon disaster events. The insights outlined in this paper may potentially enhance understanding among scholars and practitioners of typhoon catastrophe index insurance programs and provide guidance for extending catastrophe insurance in other typhoon-prone areas.

A brief review of the development of ethnogeography over the past century and geography-oriented theories of minzu over the past 30 years shows that ethnogeography, which is a prominent subdiscipline of geography in the first half of the 20th century, is being eroded to an "important but not conspicuous" field in China. Geography-oriented theories of minzu highlight the significance of "geography" to the development, integration and identification of minzu, whereas the understanding of "geography" is complicated and diverse, and a direction for establishing ethnogeographical theory has not been proposed. Meanwhile, narrow ethnogeography as an independent research field or subdiscipline is devoid of a systematic theoretical system. Considering both modern and contemporary geographical perspectives as well as geography-oriented theories of minzu, three accessible approaches that can strengthen the integrity of ethnogeography and promote the integration of geographical disciplines are proposed. First, the theoretical system of broad ethnogeography could be integrated through a "state-region-place" framework. Second, the multidimensional interpretations of minzu could be bridged with disciplinary traditions of natural science, social science, and humanities to form a threefold "nature-society-humanities" interpretation system. Finally, geographical theories of minzu that emphasizes connection, mobility, and transformation could be developed from the perspective of geography as a discipline rather than a subject. The integrity of ethnogeography will be demonstrated through bridging the gaps among theories, interpretations, and knowledge, and the influence of geography will be extended. More importantly, minzu and nations are understood, not interpreted, geographically.

Owing to historical reasons, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) features a unique "one country, two systems" institutional framework. Facilitating the integration and connectivity of transportation among Hong Kong, Macao, and the Mainland is crucial for the high-quality development of the GBA. Previous studies about borders have primarily focused on national (supranational) or administrative boundaries within a country's territory. However, studies on the unique institutional differences in the GBA are insufficient. Additionally, most studies do not perform dynamic border effects measurements using big traffic flow data. This study utilizes toll-collection data from highways in the GBA for 2021 and 2023, as well as cross-border traffic data, to construct a traffic-flow network for the GBA. Complex network analysis and border-effect measurement methods are employed to investigate the spatial structure of the GBA traffic-flow network and its dynamic changes in border effects. The results indicate that, in terms of the overall spatial structure of traffic flow in the GBA, the network exhibits a unique "dual-core edge" structure, with the Guangzhou-Foshan, and Shenzhen-Dongguan-Huizhou regions serving as dual cores. In contrast, the overall coverage and connectivity strength of the passenger-flow network are higher than those of the freight-flow network. Regarding the dynamic changes in the spatial structure of traffic flow from Hong Kong and Macao, the coverage and density of the traffic-flow network in 2023 are significantly higher than those in 2021. Traffic flows from Hong Kong and Macao have begun to extend beyond the border toward the northern regions, thus accelerating the integration of transportation within the GBA and forming a spatial pattern of "cross-strait connectivity and all-area interconnection." However, because of their peripheral positions in the traffic network and the presence of border effects, the importance of Hong Kong and Macao in the GBA traffic-flow network remains relatively weak. Based on the dynamic measurement results of border effects, the obstruction coefficients between Hong Kong and the Mainland, as well as between Macao and the Mainland, are significantly higher than those between various counties within the Mainland. The obstruction coefficients for passenger vehicles are generally lower than those for freight vehicles. Following the outbreak of the pandemic, the obstruction coefficients of the GBA traffic-flow network have increased dynamically, thus indicating a reduction in obstructive border effects. This study expands the quantitative research framework of border effects in traffic-flow networks, thus promoting integrated transportation development in the GBA and facilitating its integration development goals.

Under the influence of information technology and high-speed transportation networks, which compress space and time, the region's population has achieved large-scale fluidity. Examining the temporal heterogeneity of intercity travel networks and its influencing mechanism can help optimize regional spatial organization and provide a scientific basis for regional integrated development. Based on Baidu migration data from January to April 2023, this study uses a PPML(Poisson Pseudo Maximum Likelihood) gravity model and interaction term testing to compare the scale, pattern, and influencing factors of intercity travel networks during weekdays, weekends, and holidays in the Yangtze River Delta region. The results indicate the following: 1) The intercity travel network in the Yangtze River Delta region exhibits temporal heterogeneity characteristics. During weekdays, intercity travel primarily consists of cross-city commuting and business trips, with the lowest daily average scale. This forms a V-shaped intercity travel structure covering Shanghai, southern Jiangsu, Northern Zhejiang, and Southern Anhui. The positive effects of destination city population size and economic status on intercity travel are enhanced. On weekends, intercity travel is dominated by business trips and leisure activities, and residents tend to take shorter trips, which means that intercity distances pose greater hindrances to intercity travel. During holidays, intercity travel is primarily for leisure and entertainment and for visiting friends and relatives, with the highest daily intensity. The promotional effect of destination city population size on intercity travel is weakened, and intercity travel is less hindered by intercity distances. Compared to the effects of geographical distance, economic status, and population size on the scale of intercity travel during weekdays, travel duration, or geographical distance, tends to pose a greater hindrance on weekends and a lesser hindrance during holidays. The promotional effect of economic status is intensified on weekends but diminishes during holidays. Meanwhile, the promotional effect of population size weakens both on weekends and during holidays.2) Push–pull factors encompass the level of urban development and the incentives that trigger individual travel. In terms of urban development level, indicators such as population size, economic status, and industrial structure reflect the comprehensive strength and development status of a city, influencing its ability to serve as both a starting and destination point for intercity travel. From the perspective of various individual travel incentives, residents pay more attention to various urban resources such as income levels, public service quality, and tourism resources to meet their personal needs for production and living. The primary types of population movements vary across different time periods, shifting between cross-city commuting, business travel, and leisure and entertainment. As a result, the dominant factors among push-pull elements also change, leading to significant variations in the effectiveness of each factor. Intermediate obstacles are the key factors limiting intercity travel. On the one hand, while the level of integration in the Yangtze River Delta region continues to improve, and transportation facilities are gradually improving, geographical distance remains a crucial intermediate obstacle. On the other hand, administrative and cultural differences between different provinces increase residents' adaptation costs, forming "invisible barriers" that hinder cross-province population interactions. The hindrance posed by intermediate obstacles to intercity travel also varies across different travel periods. The effects of push-pull factors exhibit temporal heterogeneity. The small-world characteristics of the intercity travel network during weekdays are more evident, and the central city has a more prominent structural core status. On weekends, the geographical proximity of the intercity travel network improves, with close "center-hinterland" connections and enhanced inter-provincial boundary effects. During holidays, the overall intensity of the intercity travel network increases, with the most significant increase in medium- and long-distance cross-provincial travel. The provincial boundary effect and spatial proximity effect decrease, weakening the structure of the intercity travel network.

Public transportation is a vital means to alleviate urban congestion. Despite substantial investments in public transit infrastructure in China, the development of urban public transportation has been unsatisfactory, with many city residents still favoring car travel. The extensive use of personal vehicles occupies limited road resources, thus exacerbating traffic congestion and environmental pollution. The built environment extensively influences residents' travel choices. Existing studies often describe the characteristics of the built environment from the perspective of the origin, destination, and public transit stops, lacking attention to the out-of-vehicle segments before and after using public transit; moreover, they mainly focus on the built environment faced by transit riders, without fully considering the alternative transit chains for car travelers. Accordingly, this study adopts a trip chain perspective. Combining resident travel surveys and streetscape data from the main urban area of Wuhan, and simulating travel paths using Baidu Maps, this study uses a random forest model to comprehensively analyze the impact of the built environment at the origin, destination, and out-of-vehicle segments on the choice between public transit and private vehicle. The results indicate the following: (1) The performance of the random forest model is superior to that of the traditional Logistic model, and it can reveal the nonlinear relationship between the built environment and travel behavior. At the same time, considering the out-of-vehicle environment also better understands the competitive environment between public transit and private vehicle, thereby improving the model's predictive ability, (2) the built environment is the main factor influencing the preference for public transit, and the out-of-vehicle environment's influence on travelers is no less than that of the built environment at the origin and destination. The preference for transit and built environment factors exhibit a nonlinear relationship, with some factors having different impacts at the origin and destination. Specifically, the population density, intersection density, and transit stop density at the origin and destination have very similar effects on the preference for transit, while the land use mix and job density differ. The proportion of roads and fences in the out-of-vehicle environment show a clear threshold effect, while the proportion of sidewalks and visible green index exhibit a saturation effect. (3) The mechanisms by which the built environment influences the choice of public transit and private vehicle can be summarized into three categories: elastic adjustment, limited support, and direct drive. These findings reveal the effective range of built environment factors in enhancing the attractiveness of transit, providing more rational and precise targeting for policy-making. This study addresses the issue of insufficient detail in the built environment in current research, incorporating the out-of-vehicle environment and alternative modes of travel into the analysis framework of transit preference, providing more intervenable built environment factors to enhance the attractiveness of transit, and offering insights for integrating nonlinear impact relationships into urban planning practice.

High-speed rail (HSR) stations can influence the expansion of the surrounding construction land. However, relevant studies face three main limitations: influence scope estimation lacking a theoretical foundation, less focus on whether the impacts of HSR stations on construction land expansion vary, and misjudgment of the drivers of HSR stations on construction land expansion. To address these research questions, this study first conducts a literature review to theoretically analyze the influence of HSR stations on the surrounding construction land expansion and then identifies the ideal curve for the influence distance of HSR on construction land expansion based on location theory and distance decay theory. Using the 24 stations of the Hainan Roundabout Railway (HRR) as an example, we revealed differences in the influence of various HRR stations on construction land expansion through GIS technology, buffer analysis, and nonlinear fitting to quantitatively analyze the expansion of construction land around HRR stations, identifying the impact range and direction of different HRR stations on the expansion of construction land. Building on the identification of heterogeneous impact results, the study further employed Geodetector to analyze the factors and reasons for the differentiated results of construction land expansion around different HRR stations from four dimensions: attributes of the socioeconomic environment, location conditions, HRR station attributes, and natural conditions. The results show that: (1) after the construction and operation of each HRR station, the surrounding construction land has expanded; the Hainan Eastern Ring HSR (the East Ring) has increased 1.70 km² around each station per year and the Hainan Western Ring HSR (the West Ring) has increased 1.25 km² around each station per year. (2) The changing trend of construction land expansion around 20 of 24 HRR stations conforms to the ideal curve, with the impact range of construction land expansion concentrated within 0.5 km-3.5 km, and the influence intensity of impact ranging from 0.06 km² to 6.64 km². (3) The impact directions of construction land expansion around 20 HRR stations are mainly in three types of directions: "HSR-main urban area," "HSR-town center," and "HSR-scenic spot." This is because the expansion of construction land around HRR stations is not only influenced by the spillover effects of the stations, but also by the traction effect of the main urban areas, town centers, or tourist areas where the HRR stations are located. The stations along the East Ring of Hainan mainly expanded towards the main urban areas, whereas the stations along the West Ring of Hainan mainly expanded towards town centers. (4) Differences in the scope of the influence of each HRR station on the surrounding construction land expansion were mainly related to several variables, ordered as follows: socioeconomic environment, location conditions, attributes of the HRR station, and natural conditions. The GDP density of the towns where the HRR stations were located had the highest impact intensity at 0.51, followed by population density at 0.49, whereas the average elevation had the lowest impact intensity at 0.12. This study analyzed the mechanism and ideal curve of construction land expansion around HSR stations, establishing a logical basis for studying the spillover effects of HSR stations. In addition, this study analyzes the various impacts of HSR stations on the expansion of surrounding construction land and the reasons for these differences, providing a scientific basis for the current operation and future location of HSR stations. This study also offers methodological insights into the impacts of other infrastructures on the expansion of construction land in surrounding areas.

Air cargo is an important component of transportation and plays a vital role in the efficient allocation of high-quality resources on global and regional scales. Air cargo contributes significantly to regional economic development by strengthening inter-regional cooperation and resource integration. However, air cargo geography has received relatively less attention from the research community. Existing studies have analyzed the spatial pattern of air cargo using a limited cross-sectional data from selected years, lacking an analysis of its influencing factors. Based on spatial statistics and panel data of air cargo, this study explores the evolution process and characteristics of China's air cargo pattern on a 20-year time scale and quantitatively reveals its key influencing factors. The research findings are as follows: 1) Air cargo in China has transitioned from the rapid development stage to the stable development stage in the past 20 years; 2) Air cargo volume in China is mainly concentrated in the eastern region, and in the past 20 years, China's air cargo center of gravity has been generally located at the junction of Anhui, Henan, and Hubei provinces, showing a spatial displacement trend from Henan to Anhui to Hubei; 3) The pattern of air cargo network in China remains relatively stable, forming a rhombic structure with Beijing, Shanghai, Guangzhou, and Shenzhen as the core; 4) Air cargo development in China is influenced by factors such as urban scale, industrial structure, and ground transportation development. Among them, urban economy, transportation, warehousing, postal and telecommunications industry, and technological investment have a significant positive impact on air cargo volume, whereas the wholesale and retail trade industries have a significant negative impact. For air logistics hubs, the influencing factors are consistent with those of the entire sample airport. However, for non-aviation logistics hubs, population size and research and technology services have a significant positive impact, whereas ground transportation accessibility has a significant negative impact. This study enriches the long-term time-series analysis and quantitative research content in the field of air cargo and has significance for the development of air transportation geography and the construction of a strong civil aviation industry in China.

The rapid development of information technology has triggered an explosion of data, marking the era of big data. A wide range of transportation big data has been used in urban space and travel behavior studies since the beginning of this century. Mobile phone signaling data in particular have many advantages: they have prevalent spatial and temporal coverage, high tracking stability, satisfactory resolution, and low cost. The description of urban phenomena and the analysis of their forming mechanisms using mobile phone signaling data are thoroughly studied by previous research. The next course of action is to tackle specific urban problems. This study summarizes the application progress of mobile phone signaling data in job-housing relationships and travel behavior studies, discusses the application prospects of mobile phone signaling data in transportation carbon emissions research based on past applications and the existing literature on low-carbon transportation, and proposes a research framework and several future directions for studies using mobile phone big data to examine job-housing relationships, travel behavior, and transportation carbon emissions. We first provide a brief introduction to the features of mobile phone signaling data in comparison with other commonly used data types, including their type, content, and spatial-temporal resolution. We then review the existing applications in job housing and travel research. Regarding the jobs-housing relationship, prior studies employ mobile phone signaling data to detect the spatial distribution of workplaces and residences of urban dwellers, analyze jobs-housing relationship features and urban spatial structure characteristics, and examine the factors influencing jobs-housing relationships. Regarding travel behavior, studies employ mobile phone signaling data to identify stays and trips, infer trip modes, detect trip routes, and explore the universal laws of human mobility. Next, we also discuss how mobile phone signaling data can be applied to transportation carbon emissions research. Indeed, mobile phone signaling data can be used in the calculation of transportation carbon emissions and analysis of the relationships between urban spatial structure, individual travel behavior, and transportation carbon emissions, and its wide coverage and large sample size can be exploited to fill research gaps and problems that have yet to be resolved using traditional traffic datasets. Finally, we present a research framework underlining the indirect and direct effects of the jobs–housing relationship and travel behavior on transportation carbon emissions. We also propose future directions in study contents and methodological innovations by recommending long time-series longitudinal studies, large-scale comparative studies, and new population and transportation phenomena. We further recommend fusing multi-source big and small data, incorporating machine learning algorithms into traditional statistical analyses, and constructing digital twin models. Examining the jobs–housing relationship, travel behavior, and transport carbon emissions using mobile phone signaling data is essential for clarifying the interactions between urban and regional structures, travel behavior characteristics, and transport carbon emissions. It has important implications for emissions reduction and sustainable development in the context of proposing carbon peaking and carbon neutrality goals.

Research on urban spatial networks based on "flow" data has become a new paradigm in the assessment of urban spatial connections and the delineation of metropolitan influence areas in urban geography and territorial spatial planning. Research on urban connections in Chongqing focuses primarily on districts and counties within the city's administrative region, utilizing passenger or cargo flow data to study the spatial structure of the network within Chongqing. However, few studies have been conducted on cross-provincial administrative regions between Chongqing and its neighboring areas, which does not align with the actual influence of Chongqing's metropolitan area. Based on highway traffic passenger flow data, this study employs social network and GIS spatial analysis methods to study the urban network spatial structure between Chongqing and its neighboring areas from the perspective of passenger flow connections. The results indicate the following: (1) Chongqing's central urban area serves as the absolute core of the urban network, with Changshou, Jiangjin, and Bishan as important nodal cities. Fuling, Bishan, and Changshou exhibit notable accessibility within the network, while Fuling, Qianjiang, Jiangjin, and Wanzhou play prominent intermediary roles. There are no prominent regional nodes outside Chongqing's administrative area, and the growth poles for the development of the Chengdu–Chongqing Economic Circle need further cultivation. (2) The passenger flow network between Chongqing and its neighboring areas exhibits a three-tiered axial connection, with the overall network displaying a distinct radial characteristic. The urban clusters in northeastern Chongqing form a distinct band-shaped axis along the Yangtze River with Chongqing. The urban clusters in southeast Chongqing and their neighboring areas exhibit radial axes, with relatively weak connections to the central urban area. Some areas in Guang'an and Dazhou have overcome provincial administrative boundaries, and the network hierarchy is distributed according to "4(level 1)+15(level 2)+18(level 3)." (3) The cohesive subgroups between Chongqing and its neighboring areas demonstrate a high degree of geographical proximity, forming a "core–periphery" structure. This reflects the significant influence that factors such as spatial distance and road extensions exercise on the road passenger transport network. Conducting research on the spatial structure of cross-administrative urban networks from the perspective of highway passenger flow holds significant theoretical and practical value for enriching research on the spatial structure of cross-administrative metropolitan areas and promoting the linkage between Chongqing's metropolitan area and its surrounding regions.

Implementing preferential policies for bus transfers is an important measure for promoting the development of public transportation. Although public transportation extends the travel time of passengers, the preferential policies reduce the travel costs to a certain extent. On the basis of IC card data of public transport, an income method model was constructed to evaluate the cost of passenger flow transfer time after the implementation of the preferential transfer policy in Xiamen and compare it with the reduced fees due to the policy regulations. To better assess the overall benefit of bus transfer travel, the transfer passenger flow was divided into four categories: transfer zero cost passenger flow, transfer additional cost passenger flow, transfer extra time cost passenger flow, and transfer extra time+cost passenger flow. The spatial distribution characteristics of various types of passenger flow are analyzed from five aspects: station, line, traffic area, density of travel starting and ending points, commuting and non-commuting of travel. With regard to stations, a large number of different types of passenger flowed into the area centered on Yueyang Community. As regards route, No. 24 mainly gathered a large number of different types of passenger flows. As regards transportation areas, numerous different types of passenger flows gathered in the transportation communities around the subway and the island's Bus Rapid Transit (BRT) lines. In terms of OD point density, each station of Rail Line 1 and the BRT stations had large numbers of passengers at the starting or ending points. In terms of commuting and non-commuting behaviors, the activity range of various passenger flows during commuting was smaller, the span was shorter, and the cross-island passenger flow was relatively small. In contrast, the passenger flow during non-commuting behavior showed a more evident cross-island trend, and the span was generally longer. This study devised a new passenger flow classification method to evaluate the effectiveness of a preferential policy for bus transfers. Further, it affords a reference for public transport operators to better comprehend the needs and behaviors of passengers and accordingly formulate more effective policies and measures.

Urban agglomerations are the main spatial carriers of national and regional urbanization development. The study of their spatial association networks is of great significance for optimizing the allocation of urban resources, promoting the process of regional integration, and facilitating the high-quality development of urban agglomerations. The Yangtze River Delta urban agglomeration (YRDUA) is one of the most economically active regions in China. Its spatial association network structure was the main feature of economic and social development in China's "14th Five-Year Plan." Based on the perspective of flow spaces in terms of both the city and township scales, multi-source data were used in this study and included online car-hailing origin-destination travel data, combined with complex network models and a quadratic assignment procedure, to analyze the structural characteristics and driving mechanism of the spatial association network of the YRDUA. Results show that: 1) spatial association network based on online car-hailing flow in the YRDUA has spatial dependence and hierarchical characteristics, and intensities of network association are mostly coupled with levels of economic development; 2) spatial association network in the YRDUA displays spatial spillover effects, leading to an overall pattern of high equilibrium in southern development and strong single-point development capacity in the north; 3) the structural features of spatial association network in the YRDUA differ between townships and urban scales, with some high-level townships in certain transportation networks failing to exert their driving role at the urban level; and 4) economic development status, population vitality, the level of urban construction, and administrative division ownership and geographical location differences between townships have significant impacts on the spatial association network structure in terms of the township scale. The differences in administrative divisions are most important. Online car-hailing travel data were used in this study to effectively supplement the links between township streets within and between cities. This data also revealed intercity links. Thus, the development characteristics of spatial units on different scales were reflected, and research and social management needs were satisfied on a fine scale. In addition, by introducing spatial big data and analyzing the influence mechanism from various aspects, such as socioeconomics, the driving factors of the spatial network of urban agglomerations were systematically identified at the small-scale level, which will help with more reasonable planning within the city and play a role in the development of urban agglomerations by enhancing the attractiveness of individual cities. This study expands the research perspectives on the cooperative development of urban agglomerations on different scales, providing theoretical references and practical support for the promotion of the coordinated development of urban agglomerations as a whole.

The Fourteenth Five-Year Plan of China has proposed the acceleration of the construction of a strong transport nation. The Outline for the Construction of a Powerful Transportation State emphasizes that it is necessary to focus on the general objective of the construction of a powerful transportation state and create a "123 traveling and transportation circle in the country" and reach the new standard of 1-hour commuting metropolitan area, 2-hour connecting urban agglomeration, and 3-hour coverage of the major cities in the country, which determines the importance of the accessibility of urban agglomeration in the strategy. The high-speed railway network in the twin-city economic circle of the Chengdu-Chongqing region enhances inter-city accessibility and has a spillover effect on socioeconomic development. Starting with HSR network accessibility, in this study, the evolution of the accessibility pattern after the opening of the HSR in 2015, 2020, and 2025 was analyzed using the shortest inter-city travel time, weighted average travel time, and daily accessibility index. Based on the gravity model used to measure the economic reinforcement effect generated by HSR network accessibility, the spatial Durbin model was used to explore the spillover effect of HSR network accessibility on the tertiary industry's economic development from 2015 to 2020. The study results demonstrate that the HSR network improves the level of urban accessibility, narrows the gap of accessibility level between cities, weakens the regional accessibility circle structure, and exhibits significant corridor effect. The HSR network generates a significant spatiotemporal convergence effect. The direction of spatiotemporal convergence of core cities is to expand uniformly to their surroundings, and the direction of expansion of edge cities is mainly to spread along newly opened HSR lines in the form of a belt. The improved accessibility of the HSR network will strengthen regional economic ties, narrow the gap between the attractiveness levels of non-core cities, and enhance the twin-core phenomenon. Increased accessibility is conducive to the economic development of the tertiary industry in neighboring cities, with an increase of 1 percentage point in the accessibility of the high-speed rail network in the neighboring region, leading to an increase of 0.3088 in the local tertiary industry. An increase in the tertiary economic level of neighboring cities will inhibit the development of the local tertiary economy, which may be because the Chengdu-Chongqing region is now more competitive than cooperatives in terms of urban relations; therefore, regional development is still in the siphoning stage. The possible contribution of this study is reflected in the use of accessibility as the core research and explanatory variable to explore the economic spillover effects of accessibility of high-speed rail networks in typical case regions. It aims to reveal the impact of high-speed rail network accessibility on the economy and industry, summarize the theory of spillover effect, and provide a theoretical reference for high-speed rail construction, regional planning, and economic layout optimization.

Chinese people have become important international buyers of second homes in many destination countries, particularly Malaysia, Thailand, and other Southeast Asian countries. In the past decade, the aging population and the quest for a better life have become pressing concerns in China and have triggered an increase in transnational second-home purchases in other countries. However, despite the significant and rapid growth of transnational second homes in China, little is known about the nuanced relationship between buying motives and life satisfaction. Current studies in the Western context offer limited theoretical and practical implications for Chinese transnational second homes because Chinese buyers exhibit different motives and have a distinct understanding of a good life. Based on the push-pull theory, this study examines Chinese transnational second-home buyers' motivation and life satisfaction and the relationship between these two constructs. Data were drawn from 340 Chinese transnational second-home buyers of R&F Princess Cove in Johor Bahru, Malaysia. Structural equation modeling (SEM), Importance-Performance Map Analysis (IPMA), and multi-cluster analysis (MGA) were used to process the data. Our empirical results show that, in comparison to Western second-home buyers, Chinese second-home buyers' tourism and residential experiences and overall life satisfaction are significantly affected by pull motivations, while push motivations exhibit less influence. Among all the dimensions of push motivations, the natural and tourism environment, cultural and life atmosphere, and service facilities are crucial motivations in order of priority. Economic factors (such as prices and cost of living) also influence but are not the most important factors. Both tourism and residential experience significantly affect Chinese second-home buyers' overall life satisfaction, with residential experience exhibiting a higher influence. Women and larger second-home groups value residential experiences more than other groups, while smaller buyers value travel experiences more. This study provides new evidence for future studies on Chinese transnational second homes and responds to the current academic discussions on second-home buyers' motives in transnational contexts. Finally, this study has practical implications for domestic second-home destination construction and marketing.