Facilitating the social integration of the migrant population is a pivotal task in the comprehensive advancement of new urbanization. Nevertheless, this significant task is currently confronted with formidable challenges arising from the prevailing trend of unstable employment in the labor market. This paper introduces the concept of precarity, which has been the subject of extensive and in-depth discussion in the international academic community, constructs a comprehensive indicator system for assessing employment precarity, and reveals the intricate impact mechanism of employment precarity on the social integration of China's migrant population. Based on the China Migrants Dynamic Survey in 2017, the following primary conclusions were obtained through rigorous exploratory factor analysis and parallel multiple mediation effect models. Firstly, the overwhelming majority of the migrant population is in a state of varying degrees of unstable employment. When evaluating the comprehensive index of employment precarity for the migrant population from four key dimensions, employment status, contract nature, occupational industry type, and unit ownership, the value of 0.466 indicates an overall moderate level of precarity. The migrant population that fails to attain stable employment often finds itself in a disadvantaged position. These individuals typically seldom sign formal contracts, which means they lack the legal protection and stability that come with such contracts. They are concentrated in the secondary labor market, where low-threshold occupations and non-monopolistic industries prevail. Despite their arduous work, they can only earn meager wages, which severely restricts their standard of living and development opportunities. Secondly, the average level of social integration of the migrant population is 0.363. The integration levels of the economic, psychological, and behavioral dimensions are 0.102, 0.614, and 0.732, respectively. The low-level integration in the economic dimension is the primary bottleneck in the social integration process of the migrant population. Thirdly, the employment precarity of the migrant population has a significant direct negative impact on its level of social integration. It constrains social integration by influencing a series of material, social, and subjective psychological conditions. These include income stability, housing stability, social security level, local social network, local social participation, housing willingness, and settlement willingness. The indirect limiting effect accounts for 77.49% of the total limiting effect. Among the hypotheses proposed in this paper, the effect of the employment precarity–residence intention–social integration path is the most significant, and the effect of the employment precarity–income stability–social integration path is the weakest. However, the employment precarity–family migration–social integration path has not been verified and requires further in-depth exploration and elucidation. This paper enhances the indicator system for measuring employment instability, comprehensively demonstrating its characteristics such as high job-change frequency, lack of security, and disadvantaged position in the labor market. Simultaneously, it specifically reveals the impact path of employment instability on social integration. Finally, this paper introduces the concept of employment instability into domestic human geography research, offering a solid research foundation for understanding and addressing the challenges posed by instability to the social integration of the migrant population. At the practical level, in the context of employment instability becoming a trend of economic development, it is imperative to promote the social integration of the migrant population through policy and system linkages in employment, housing, social security, household registration, and other areas.

University students, a highly active demographic group in society, face substantial mental health challenges due to academic, employment, and social pressures. Existing studies suggest that the built environment is an important intervention for mental health. However, limited attention has been paid to the mechanisms through which the built environment influences depression via behavioral mediators among university students, and the spatial heterogeneity of these effects between urban and suburban areas remains underexplored. Based on data from the 2018 Chinese College Student Epidemiological Survey, this study employed a grouped path analysis model to examine the mediating effects of individual behaviors (active travel, exercise, unhealthy diet, and social interactions) on the relationship between the campus neighborhood-built environment and depression among university students in urban and suburban areas. The results indicate that campus neighborhood-built environment factors, including land-use mix, intersection density, normalized difference vegetation index (NDVI), proximity to parks, and the density of sports facilities, food delivery outlets, and fast-food outlets, indirectly affect depression by influencing individual behavior. In urban areas, an unhealthy diet mediates the relationship between campus neighborhood-built environment and depression, whereas active travel, unhealthy diet, and social interactions mediate this relationship in suburban areas. Specifically, in urban areas, intersection density, proximity to parks, and the density of sports facilities indirectly appear to influence depression by affecting an unhealthy diet. In suburban areas, the NDVI and the density of sports facilities and food delivery outlets indirectly affect depression by influencing active travel. NDVI and the density of food delivery and fast-food outlets indirectly affect depression by influencing an unhealthy diet. Land-use mix, NDVI, and the density of food delivery outlets indirectly affect depression by influencing social interactions. Additionally, campus neighborhood-built environmental factors directly affect depression among university students. Land-use mix, intersection density, and the density of food delivery outlets have considerable direct effects on depression in urban areas, while intersection density, proximity to parks, and the density of sports facilities and fast-food outlets show substantial direct effects in suburban areas. This study elucidated the mechanisms through which campus neighborhood-built environments influence depression among university students via behavioral mediators, providing empirical support for addressing inequalities in living and learning environments and mental health between urban and suburban university students. Based on this, the present study proposes two strategies—environment–behavior interventions and direct environmental interventions—to optimize the neighborhood-built environment on urban and suburban campuses and promote mental health of college students.

Shore-based ponds are small water bodies widely distributed in coastal areas and play important roles in aquaculture, agricultural irrigation, and ecological regulation. Accurate and efficient water-depth monitoring is key to the scientific management of ponds and directly affects decision-making regarding water quality control, breeding density optimization, sediment monitoring, and ecological protection. To address the challenges posed by complex optical attenuation characteristics and significant sediment-reflection interference in high-turbidity aquaculture ponds, traditional water-depth inversion methods face bottlenecks, such as the insufficient generalization ability of spectral feature models. A water-depth inversion method combining UAV multispectral remote sensing and different color-space features is proposed. The UAV was equipped with a multispectral sensor to obtain high-resolution images of the pond area, and field water-depth data were collected simultaneously. Through the comprehensive application of HSV, Lab, YUV, and other color-space conversion technologies, the system extracts the spectral reflectance and color-feature parameters. Five machine learning algorithms, namely random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), were used to construct the water-depth inversion model. The model performance was systematically compared and evaluated using cross-validation and various accuracy evaluation indicators (R ², RMSE, and MAE). This research focuses on analyzing the influence mechanism of different color-space conversions on the accuracy of the model inversion. Finally, through an analysis of feature importance and model optimization, a water-depth prediction model with optimal inversion performance was selected. The experimental results showed that: (1) the water-depth inversion model based on the optimization of three color-space features significantly improved the accuracy of the water-depth inversion. Among them, the HSV color-space features were the most obvious. Combined with this feature, the accuracy of the SVR model improved the most; R ² increased from 0.36 to 0.78, RMSE and MAE decreased by 0.04 m and 0.02 m, respectively. (2) Among the five models, the accuracy of the water-depth inversion by the RF, XGBoost, and GBDT models, which are also integrated learning models, was significantly better than that of the SVR and MLPR models. After optimizing the model across different color spaces, the accuracy of the GBDT model was the highest (R ² = 0.85, RMSE and MAE are 0.05 m and 0.04 m). This research confirms that the fusion of multispectral data and color-space optimization technology can effectively improve the water-depth inversion ability in shallow-water areas and provide an efficient and low-cost technical solution for the dynamic monitoring of small and micro water bodies. This has important practical value for the fine management of aquaculture and water ecological protection.

With rapid urbanization, urban safety has become a critical issue in city governance. Among the various approaches, Crime Prevention Through Environmental Design (CPTED) has gained increasing attention owing to its effectiveness in reducing crime risk through physical environment interventions. This study systematically reviewed the development trajectory of CPTED practices in South Korea over the past four decades, dividing them into three distinct stages: initial conceptual introduction and early experimentation, formulation of guidelines and pilot implementation, and legislative revision and full-scale institutionalization. Each stage was analyzed in detail in terms of policy background, implementation strategies, and governance arrangements. Based on this review, this study constructed a framework illustrating the operational mechanism of CPTED in South Korea, highlighting the collaboration among national agencies (particularly the National Police Agency), local governments, academic institutions, and communities. The model emphasizes the importance of a standardized design process and case-based feedback to form a dynamic and iterative practice system. To demonstrate the practical application of this framework, this paper presents a detailed case study of the Yeomni-dong neighborhood in Seoul, one of the earliest CPTED pilot projects in South Korea. Through onsite assessments, risk mapping, participatory planning, and post-implementation evaluations, the project significantly improved community safety, reduced specific types of crime (such as robbery), and enhanced residents' sense of belonging and security. This case underscores the adaptability and effectiveness of CPTED principles in densely built East Asian urban contexts.The study also provides a concise overview of how CPTED-related principles have been applied in China across different administrative sectors, including the Ministry of Public Security's "Skynet Project," the Ministry of Housing and Urban-Rural Development's "Complete Residential Community Guidelines," and grassroots governance initiatives such as Zhejiang's "Neighborhood Watch" and Beijing's "Fengqiao Experience." Despite these efforts, challenges remain in China's current CPTED practices, including the lack of a unified standard system, fragmented departmental responsibilities, and insufficient integration of digital and physical safety strategies.Drawing on South Korea's institutional and practical experience, this study proposes four strategies for constructing a localized CPTED framework in China: (1) improving the legal and regulatory system by incorporating CPTED principles into national and local standards; (2) fostering cross-sector collaboration among planning, security, and governance bodies; (3) embedding CPTED assessments into urban health-check mechanisms to identify spatial safety risks; and (4) integrating CPTED principles into the entire process of urban renewal projects to promote sustainable and resilient urban environments. This study aims to provide theoretical insights and practical guidance for improving urban crime prevention strategies in China, contributing to the development of a more integrated, adaptive, and locally tailored CPTED system. It also offers valuable lessons for the convergence of spatial planning and public safety governance in rapidly urbanizing contexts.

The Leizhou Kiln is one of the three major kiln sites in the Lingnan region, which are widely distributed across the Leizhou Peninsula. Over 100 kiln sites hosting 200 individual kilns have been discovered on the Leizhou Peninsula, constituting the largest cluster of civilian kilns along the coastline of China. However, chronological studies of the Leizhou Kiln are limited, with dating primarily relying on archaeological typology, and robust absolute chronological markers are lacking. The initiation and termination ages of the key historical kilns are unclear: the evolutionary trajectory of the kiln industry is thus unclear. Key stratigraphic samples were systematically collected based on the stratigraphic features of the Leizhou Kiln. Optically stimulated luminescence (OSL) dating was used to tentatively determine the initial firing age of the Xinbu Kiln and terminal firing age of the Jiuyang Kiln, yielding preliminary chronological data. Our findings show: 1) Weathered debris from the fired bricks at the bottom of the Xinbu Kiln cultural layer produced strong luminescence with concentrated equivalent dose distributions, indicating the high reliability of the dating results. The ages obtained from the samples at the bottom of two independent cultural profiles were approximately 1.3±0.1 ka, different from the depositional ages of the topmost underlying primary sand layers (about 94.4 and 63.4 ka). This finding indicates that the kiln may have been in use since the mid-to-late Tang Dynasty. 2) The OSL dating of two independent samples from the thin bottom sandy layer within the overlying colluvium of the Jiuyang Kiln cultural layer yielded ages approaching 0.2 ka. This indicated that the kiln may have been abandoned during the late Qing Dynasty, a finding corroborated by local oral historical records documenting the cessation of kiln operations during that period. 3) This was a pioneering application of OSL dating for determining the time when the Xinbu Kiln was initiated and the Jiuyang Kiln was abandoned. This study refines the traditional view that Xinbu Kiln predominantly dated to the Song Dynasty and the Jiuyang Kiln primarily operated during the Song-Yuan period, revealing that the Leizhou Kiln provided long-term contributions to regional ceramic production in China. 4) Applying the single-aliquot regenerative-dose protocol (SAR) method effectively overcomes the limitations of traditional typology in dating complex stratigraphy and kiln sites. This study provides a foundation for systematically integrating regional kiln chronology data and establishing an integrated chronological framework for the Leizhou Kiln. This temporal framework provides a crucial basis for describing the developmental trajectory of the Leizhou Kiln and its pivotal role in the Maritime Silk Road.

The development of megacities corresponds to the macro-structure of social operations, while the lives of urban residents correspond to micro-level social practices. The interaction between these two dimensions requires the support of multi-scalar spaces to ensure the effective functioning of urban society and fulfill people's aspirations for a better life. Convenience stores have emerged as pivotal infrastructural nodes that meet daily consumption needs. The current research defines them as standardized non-place spaces. However, we found that convenience stores are often perceived as warm and therapeutic spaces, prompting us to explore how convenience stores serve as social intermediaries that connect the spatial structure of megacities with daily life. This study focuses on two core questions: How do people develop a sense of place in megacities through their interactions with convenience stores? What does the emergence of placeness in standardized non-place spaces reveal about the dynamics between people and the city? Grounded in Lefebvrian social space theory and humanistic geographical place theory, this article first establishes the conceptual relationship between spatiality and placeness through the lens of the "Proximity" concept, and then constructs an analytical framework based on material, social, and affective dimensions for qualitative research. The findings reveal that: 1) Convenience stores function as "plug-in spaces" that optimize the metropolitan ecosystem. By providing material convenience and standardized services, they transform physical spaces into social spaces, thereby offering crucial social support to urban residents. This demonstrates the distinctive social value inherent in small-scale commercial spaces in urban contexts. Building on their unique standardized service protocols and spatial power dynamics, convenience stores provide consumers with customer-centric spatial experiences. 2) While deeply embedded in the daily lives of megacity residents, convenience store spaces exhibit a dialectical combination of placeness and non-place characteristics. These stores extend across metropolitan areas through an extensive network of chain outlets. This process of placemaking serves as a means through which urban residents construct their individual life subjectivity. The emergence of convenience store placeness reflects people's proactive efforts to construct emotional anchors amid the hypermobile pace of metropolitan life. The emotional attachment to standardized spaces reflects a contemporary urban reality: the hypermobile nature of life in megacities weakens, or even eliminates, organic interpersonal connections, creating a pressing need for everyday consumption spaces that meet practical needs while also providing a sense of belonging. Based on this analysis, the study posits that understanding and enhancing the functions of such plug-in spaces in two key areas can contribute to the organic optimization of urban life: 1) Beginning with the self-practice of urban individuals, it is essential to preserve human agency in daily routines and encourage the establishment of stable interpersonal connections in public life. 2) From the perspective of urban spatial planning, commercial community spaces should be embedded in daily life, closely aligned with people's needs, and serve as nodes for constructing social bonds. Urban planning should be fundamentally oriented toward shared social development, forming a multidimensional, multi-scalar, and human-centered spatial system.

Under the strategic context of building a unified national market and promoting regional coordinated development, investigating the structural evolution and driving mechanisms of capital flow networks is crucial for optimizing resource allocation. Utilizing cross-regional investment data from Chinese listed companies (2008–2023), this study constructed a provincial capital flow network and employed blockmodel analysis, motif detection, and temporal exponential random graph models (TERGM) to identify structural dynamics and driving factors. Key findings are as follows: (1) From 2008 to 2018, network density and centralization increased, whereas the average path length decreased, signaling enhanced equilibrium and efficiency in capital allocation. After 2019, external shocks triggered fluctuations in network efficiency, revealing systemic vulnerability. Capital flows exhibited "dispersed outflow and concentrated inflow," with eastern coastal regions dominating capital exports and central-western areas gradually strengthening absorption capacity, reflecting a dynamic shift toward regional equilibrium. (2) The network is differentiated into four functional modules: primary beneficiary (e.g., Beijing and Shanghai), broker (e.g., Anhui and Sichuan), bidirectional spillover (e.g., Hebei and Shaanxi), and primary spillover (e.g., Jilin and Qinghai). The spatial distribution displayed a "club effect," where geographically adjacent provinces assumed similar roles. The core eastern regions intensified capital absorption through industrial upgrading, whereas the resource-rich central-western provinces achieved bidirectional capital interactions via policy incentives. Underdeveloped regions faced persistent net capital outflows owing to developmental constraints. (3) Capital transmission exhibited coexisting closed reciprocity and chain diffusion. Declining closed motifs indicated deepening market segmentation from local protectionism, whereas rising chain motifs indicated intensified capital agglomeration in economically active regions, exacerbating core-periphery disparities. This duality reflects inefficient resource allocation and entrenched regional imbalances. (4) The network demonstrated strong temporal path dependence and limited innovation capacity. Key drivers included product market vitality, government efficacy, and labor quality, which attracted significant capital inflows. Conversely, excessive government intervention and high local debt suppressed capital attractiveness and outflow capacity. Notably, capital flows thrived between regions with large economic disparities, while geographical proximity and cultural similarity enhanced flow intensity by reducing transaction costs and information asymmetry. Theoretically, this study advances the geographical understanding of capital spatiality by integrating blockmodeling and motif analysis to decode the functional modular architecture and micro-level transmission mechanisms. Methodologically, this study pioneers the application of TERGM to decipher the temporal evolution mechanisms of networks, thereby addressing the limitations of static analytical frameworks. Practically, the findings provide actionable insights for optimizing inter-provincial capital allocation, mitigating market fragmentation through targeted governance, and fostering cross-regional coordination to balance efficiency and equity in China's unified market construction.

Recently, the integration of Intangible Cultural Heritage (ICH) and tourism has generated new consumption hotspots that facilitate the transformation of cultural resources into cultural capital, producing notable economic and social benefits. Drawing on critical heritage studies, this research applies cultural capital theory and scale theory to examine Yingge Dance, a national-level ICH item originating from Chaoshan region (comprising 3 prefecture-level cities: Shantou, Jieyang and Chaozhou), China. Using participant observation and in-depth interviews, we explored the multi-scalar formation and scale transformation of cultural capital related to Yingge Dance. This study yielded three main findings. First, Yingge Dance embodies both cultural publicity and capital productivity. Its preservation and transmission depend on embodied practice, objectified transformation, and institutionalized support, reflecting a fusion of tradition and modernity as well as a symbiotic relationship between economy and culture. Second, at the individual scale, performers continually refine their bodily knowledge and skills in intergenerational practices, extending training and performance into broader community contexts. This process transforms Yingge Dance into cultural capital that fosters group cohesion and enhances community governance. At the regional scale, cultural tourism consumption and market participation accelerate the production of objectified cultural capital. Supported by local government initiatives, Yingge Dance becomes a form of urban cultural capital that stimulates tourism consumption and contributes to urban branding. At the national scale, China incorporates Yingge Dance into narratives of physical, moral, and aesthetic education, embedding it within cultural governance frameworks through top-down policy empowerment. This elevates Yingge Dance to national cultural capital that reflects cultural diversity and strengthens international competitiveness. Third, Yingge Dance has achieved the reproduction of the functions, values, and meanings by scaling up from group-level cultural capital to urban cultural capital and subsequently to national cultural capital. However, challenges emerge during this scaling-up process, including cognitive conflicts among stakeholders, blurred genre boundaries, uneven development, and diminishing authenticity. To address these issues, China promotes a scaling-down approach through policies and discursive frameworks to maintain the vitality of ICH transmission. This scaling down is reflected in the stewardship and empowerment of ICH bearers, incorporation of national ICH strategies into urban development agendas, and strengthening of local governments' narrative and discursive power. This study offers a critical perspective on understanding the practices and interactions of multiple actors in ICH inheritance. It also provides practical recommendations for cultural tourism development and the extraction of ICH value through the lens of cultural capital and scale theories.

The digital economy network can enhance the stability and risk resistance of industrial chains, thereby playing a crucial and strategic role in addressing regional development imbalances, improving resource allocation, and promoting high-quality, integrated economic growth across urban clusters. This study used a comprehensive dataset of digital economy development indicators for 41 cities in the Yangtze River Delta from 2012 to 2022. It systematically integrated measurements of the digital financial inclusion index and Internet development levels to capture and analyze the dynamic evolution of digital economy growth in the region. A modified gravity model was employed to construct a digital economy correlation matrix to quantify intercity economic linkages. Furthermore, complex network analysis combined with computer simulation techniques was used to examine the temporal evolution of network resilience and its influencing factors. The results revealed that: 1) During the study period, the network density of the Yangtze River Delta's digital economy increased by 0.245, and the regional development pattern shifted from a "single-core agglomeration" to a more diversified "multi-center linkage." Simultaneously, the frequency and intensity of interactions among cities increased significantly, while core cities strengthened their monopoly, allocation, and redistribution capacities over critical digital resources, reflecting a more optimized and coordinated network structure. 2) Structural barriers within the digital economy network gradually diminished, while cross-industry and cross-regional collaborations intensified significantly, giving rise to an open collaborative network structure. Among the subgroups, Sector I primarily functioned as an intermediary, whereas Sector IV served as the primary output core, indicating the transformation of the network's organizational form from a relatively closed internal cycle to a more open and interconnected system. 3) Over the past decade, the network structure flattened and exhibited an overall disassortative mixing pattern. The facilitation of digital economy factor flows had improved, and the network structure had evolved from a "core-periphery" configuration to a three-tier "core-sub-core-periphery" pattern. Shanghai, Hangzhou, and Nanjing emerged as core cities, whereas Hefei and Wuxi developed into secondary hubs. 4) Under random disruptions or attacks, the network collapsed when approximately 50% of its nodes were attacked. In random-attack scenarios, basic operations remained possible even when up to 85% of nodes were attacked. However, when critical nodes were deliberately targeted, the overall network performance declined rapidly, exposing inherent vulnerabilities. 5) Significant differences in urban economic development, human capital agglomeration, and technological innovation drove the interregional flow of digital factors. The complementary development of information, talent, capital, and technology enhanced network connectivity and resilience. Geographical proximity was also a critical factor that influenced network resilience. These findings provide valuable theoretical and empirical support for enhancing the resilience of the Yangtze River Delta digital economy network, reinforcing the spillover effects of core cities, and promoting efficient cross-regional resource sharing and coordinated development.

The spatio-temporal compression effect of information and communication technology (ICT) accelerates the dynamic coupling between residents' consumption behaviors and urban space. Existing research often focuses on a single perspective and rarely accounts for the interaction between different types of space and behavior. In this study, we integrated the human-land framework with multi-source data to systematically analyze the spatial distribution patterns and correlation of five categories of consumption facilities and behaviors in downtown Nanjing. The results show that: 1) the consumption facility space represented by POI data presents a "one-master-many-times" agglomeration mode, and it has the unbalanced characteristics of more in the south of the Yangtze River and less in the north of the Yangtze River, more in the center and less in the periphery, and all five consumption facility types highly concentrated in the Xinjiekou-Confucius Temple-Hunan Road node area, displaying clear local heterogeneity. 2) The consumption behavior space represented by check-in data presents a polycentric structure with lower aggregation intensity than facility space. The vitality of the old city and the new city is evidently divided into two levels. The five types of activity spaces are all centered on Xinjiekou, but there are some differences in their agglomeration degree and spatial distribution characteristics. 3) The pattern of consumption facilities and behaviors shows "overall co-location, local dislocation", showing the spatial dependence of strong center and weak periphery; On the correlation level, the grade matching of the old city of Nanjing is good, and the peripheral new city has the problem of high planned energy level but insufficient agglomeration power, and the anti-magnetic effect has not yet formed. 4) The five types of related patterns all follow the pattern of "high center-low periphery", and the consumption centers show different levels and functions, with the highest degree of catering concentration and multi-center spatial structure; the shopping category is "the old city is strong-the new city is weak", and the high-high nodes gather in the commercial mature and cultural tourism intensive areas; leisure shows the multi-core development characteristics of the ancient capital; life is well developed in mature communities, but it is easy to have a "functional vacuum" at the junction of the district; the descending law of the level of the center-periphery of accommodation is obvious, which is highly related to transportation and service resources; facilities and their corresponding activities are similar and cross-complementary, confirming a trend toward integrated consumption scenes. This study provides a comprehensive analysis of the heterogeneous characteristics of five types of consumption facilities and behaviors, addressing gaps in earlier research that emphasized only shopping and catering, and offers practical insights for supporting Nanjing's efforts to develop an international consumption center and promote balanced development between the old and new urban areas.

As a crucial visual aid for security information representation, thematic maps not only help public security management departments understand regional security conditions but also guide police deployment and patrol planning. However, when faced with large volumes of police data and diverse requirements. Pan-maps, which serve as augmentations of traditional standard maps, encompass generalized mapping expressions derived from the fusion of standard maps and map-like representations. They offer a comprehensive display of the diverse and intricate facets of security information. Therefore, from the perspective of multilevel security prevention and control, this study uses sub-bureaus, police stations, and police offices as examples, integrates the theory of pan-map visualization dimensions and knowledge graph methods, and proposes a thematic risk map recommendation method tailored to multilevel security needs, providing effective mapping guidance for security personnel. First, this study synthesizes a knowledge system for thematic risk map recommendations by delineating the interrelationships among the expression requirements, visualization dimensions, security data, and characteristics of different tiers of public security entities. Subsequently, an ontology layer centered on six entities— prevention entities, data characteristics, visualization dimensions, expression requirements, visualization forms, and security data— was designed. A knowledge graph for thematic risk maps was then created through knowledge extraction, and knowledge was converted into feature vectors by leveraging knowledge embedding. A cosine similarity calculation model was then applied to assess and combine the similarity across three dimensions: user map representations, data characteristics, and visualization preferences, as well as their corresponding counterparts in the knowledge system, namely, expression requirements, data features, and visualization dimensions. Overall similarity was computed, and personalized recommendations were generated based on similarity rankings. The results showed that the proposed recommendation mechanism had a high accuracy rate for recommending multilevel prevention subjects. The corresponding results of the risk map knowledge system ranked within the top two of the recommendation list, with the police station level reaching 100% and the sub-bureau and police station levels both being greater than or equal to 90%. Compared to sub-bureaus and police stations, the police offices placed greater emphasis on the refined expression of thematic maps and less demand for statistical expression. Therefore, there were fewer suitable map types and a higher recommendation accuracy. Overall, the methods proposed in this study can match and recommend appropriate thematic map content and visualization forms. The overall recommendation accuracy for different levels of prevention entities is more than 90%, which demonstrates the effectiveness and reliability of this study. In summary, this study significantly enhances the efficiency and precision of social security risk prevention and control, offering a valuable reference for the advancement of public security informatization.

High-resolution records and fine structure comparisons between different climate proxies are invaluable for elucidating the detailed variations and mechanisms of the East Asian Summer Monsoon (EASM) intensity. Based on eight high-precision ²³⁰Th dates and 448 δ¹⁸O data from a stalagmite (YX53) collected from Yongxing Cave in the Shennongjia Forestry District, Hubei Province, China, this study reconstructed a high-resolution EASM evolution sequence spanning 52.45 to 64.44 ka BP. This sequence completely covers Dansgaard-Oeschger (DO) events 18 to 15, and partly covers DO14. This record not only reveals the rapid onset and gradual decline pattern of DO18 during the Marine Isotope Stage (MIS) 4/3 transition but also delineates the sub-millennial-scale internal oscillations within DO17a and two sub-oscillations during DO16a that are comparable in duration and intensity. It also clearly documents two precursor events (PEs), namely PE17 and PE16. The results demonstrated that the EASM intensity exhibited a one-to-one correspondence with the Greenland ice core record throughout the DO18–15 events. However, in the YX53 δ ¹⁸O, Arabian Sea albedo, SCH02 δ ¹⁸O, and Cariaco Basin albedo records, the intensities of the PE17 and PE16 events are consistently lower than those of the subsequent DO17a and DO16a events. In contrast, the Greenland ice core record shows that the intensities of the PE17 and PE16 events are comparable to those of the DO17a and DO16a events, respectively. Furthermore, the evolution of EASM intensity during the DO events exhibited notable similarities to low-latitude climate processes. During the onset and termination phases of the DO events, changes in the EASM intensity resembled the more gradual transitions that are characteristic of low-latitude records. However, within the DO events themselves, the EASM exhibited internal structures analogous to those observed in low-latitude processes, such as the two sub-oscillations during DO16a that are comparable in duration and intensity, and the rapid-onset–slow-termination transition pattern of DO18. These consistencies indicate a significant modulating influence of low-latitude processes on EASM intensity during the MIS4/3 transition. We propose that the Western Pacific Warm Pool (WPWP) may have influenced the pace of EASM variability and contributed to centennial-scale oscillations within DO events through mechanisms such as air-sea interactions. Based on a stalagmite δ ¹⁸O record from Shennongjia, this study reconstructs EASM variability during DO events 18 to 15. Through detailed comparisons and analyses of transitional events, we reveal the modulating role of low-latitude processes on the EASM during the MIS4/3 transition. These results suggest that while high-latitude processes primarily drive the initiation and termination of millennial-scale EASM enhancement events, low-latitude processes, which are likely influenced by the WPWP, may shape the intensity and internal structure of these enhancements within DO events.

In the current era of oversaturated consumer goods, "crappy" goods—defined as low-cost, shoddy commodities with inferior materials, limited practicality, and poor durability—have shaped an urban culture that fosters overconsumption and resource wastage. Fueled by the ascendancy of social media shopping in China, such goods have rapidly gained prevalence and evolved into a prominent cultural phenomenon. Against this backdrop, video logs (vlogs), which originally emerged on social media as a medium centered on documenting daily life, have gradually been transformed into marketing tools for Multi-Channel Network agencies. The "exquisite girl" vlog series serves as a typical example, generating significant controversy and attention in online spaces. From the perspective of Spectacle 2.0, this study employed a mixed-methods approach to social media content analysis, taking the "exquisite girl" vlog series as the research subject. Through cross-platform browsing analysis of the videos and visual and textual analysis of their content and descriptive texts, this study explored how such elaborately crafted social media advertisements promoted cheap products through performances in everyday spaces, and how the promotion of these cheap products, in turn, reconstructed the social and cultural meanings of everyday spaces. The main findings are as follows: On the one hand, everyday spaces constituted the core scenes of the "exquisite girl" vlog series, shaping Spectacle 2.0-style images that functioned as advertisements for "crappy" goods. These images consisted of three key elements: the video protagonists (young urban women with relatively high incomes, strong professional capabilities, and adept at managing households), the everyday spaces they inhabited, and the commodity-displaying activities taking place within these spaces. As these images were essentially social media-based advertisements, everyday spaces, the "exquisite girls," China's short video industry, digital platforms, and cheap products collectively constructed a materialized "pseudo-environment" that appealed to women's consumption capital. This environment was characterized by the commodification of daily life, in which even mundane spaces and routines were packaged to stimulate purchasing behavior, blurring the boundaries between authentic daily experiences and commercial promotion. On the other hand, the Spectacle 2.0 constructed through the visual and textual narratives of the "exquisite girl" vlog series endowed everyday spaces with new meanings. Originally serving functional purposes, such as residence, work, leisure, transportation, and retail, these everyday spaces had been redefined under the influence of social media-driven linguistic and visual aesthetics, which carried inherent contradictions. The result was a new type of space: a feminized, photogenic, and homogenized commodity space. This reconstruction of meaning in everyday spaces reinforced the stereotypical connection between women, domestic labor, and physical display. Women in these vlogs were often portrayed engaging in activities such as decorating living spaces with cheap products, demonstrating household chores, or showcasing their appearance—activities that perpetuated traditional gender roles and framed women's worth through their ability to create an "exquisite" and visually appealing lifestyle. This study makes a critical contribution to the existing research on the mutual construction of social media and urban aesthetics under Spectacle 2.0. By expanding the analysis from the perspective of gendered everyday spaces, this study clarifies the bidirectional interactive relationship between platform atmospheres and these spaces. Drawing evidence from the online marketing discourse surrounding "crappy" goods, this study reveals the core role of such discursive systems, thereby offering a new perspective for research on digital urban consumption geography. The findings suggest that future research should focus on bidirectional interactions between digital platforms and everyday consumption spaces rather than unidirectional influences. Additionally, this study calls for greater attention from the digital creative and cultural industries—as well as from relevant research and policy discussions—to the consumer society saturated with cheap products shaped by Spectacle 2.0. Overall, this study highlights the pressing issues arising from this phenomenon, including excessive resource consumption and lack of spiritual life—problems that require collective reflection and proactive solutions to promote more sustainable and equitable patterns of consumption in the digital age.

Tomb robbery poses a severe threat to cultural heritage preservation, necessitating precise prevention strategies adaptable to grassroots grid-based governance. This study examines the spatial differentiation characteristics and influencing factors of tomb robbery in City B. Using 150 crime records (2011–2019) from the China Heritage Crime Information Center, it aims to identify vulnerable localities for targeted control. The "township" served as the basic analytical unit, aligning with grassroots local administrative and policing units in China. At this scale, we integrated multi-source data including demographic/economic statistics, points of interest, and transportation networks. Methodologically, spatial patterns were first identified using kernel density estimation (KDE) and Standard Deviational Ellipse (SDE) analysis. Subsequently, to build a vulnerability assessment model, we tested several machine learning classifiers (Random Forest, XGBoost, CatBoost) predicting crime occurrence (binary: 0=no crime, 1=crime occurred) based on theoretically derived environmental, guardianship, and population indicators. XGBoost demonstrated superior performance (Accuracy ≈ 75.83%, AUC ≈ 80.02%) and informed the selection of eight key factors. Critically, we improved the traditional Vulnerable Localities Index (VLI) method by employing Shapley Additive exPlanations (SHAP) analysis on the trained XGBoost model to objectively derive data-driven weights (contributions) for these factors, replacing subjective expert scoring. The results highlight distinct spatial patterns and dynamics: (1) Tomb robbery crimes display a "broad coverage, local concentration" pattern. While 41.8% of the 122 townships recorded incidents, high-frequency townships (≥2 incidents) constituted nearly 20%, concentrated in relic-rich central/eastern regions. SDE analysis confirmed a strong spatial association between the overall crime distribution and the concentration of both national and provincial Key Protected Heritage Sites (KPSs), particularly aligning with provincial KPSs. (2) A multi-scale target selection strategy emerged: Macro-level KDE hotspots are spatially adjacent to dense clusters of KPSs. However, micro-level SHAP interpretation reveals criminals tend to bypass the well-protected core areas of these KPSs, shifting instead towards selecting more vulnerable, less-monitored targets situated in surrounding fields, reflecting rational risk-reward assessment. (3) SHAP quantified key factor impacts, identifying significant inhibitors and facilitators of crime: low population density, geographical remoteness (evidenced by negative contributions from total road length and railway presence), and low economic activity (negative from per capita industrial output) are associated with higher vulnerability, aligning with reduced guardianship. Water bodies significantly inhibit crime, likely by restricting accessibility. Conversely, farmland/forest influence was indistinct. Notably, the geographical distribution of public security authorities and cultural heritage administrations showed negligible impact on location selection at the township scale. Building upon these SHAP-derived weights, the study generated a township-level graded Prevention and Control Vulnerability Map, classified into five distinct levels using the Jenks natural breaks method. This map provides actionable intelligence directly serving grid-based governance. It offers scientific support for implementing tiered responses and dynamic adjustments based on vulnerability levels, facilitating differentiated resource allocation: prioritizing enhanced monitoring in high-vulnerability zones while maintaining standard protocols elsewhere. This data-driven framework aims to enhance the overall efficiency of regional cultural heritage protection, extending crime geography applications to rural heritage crime and offering empirical insights for optimizing policing and heritage management strategies.

Virtual cities and their built environments play a significant role in video games. Understanding their interactions with physical spaces and considering appropriate planning responses to this interaction are crucial for advancing digital twin technology and urban development in the digital age. Through qualitative and quantitative analyses of representative games such as Black Myth: Wukong, Genshin Impact, and SimCity, this study identifies a cyclical four-stage interaction pathway: (1) Perception: extraction of cultural symbols, (2) Re-organization: gaming-driven reconstruction, (3) Re-understanding: virtual urban areas understanding, and (4) Feedback: real-world application. Perception and Re-organization highlight that virtual space design does not merely replicate real-world spatial characteristics, but rather reorganizes them according to specific aesthetic and functional principles, thereby operationalizing Kevin Lynch's Image of the City theory. This design process is reflected in form and function perspectives. From a form-based perspective, games such as Genshin Impact exemplify how spatial distribution heterogeneity is evident, even in virtual environments. For example, an analysis of the in-game item distribution using the ht-index (value = 5) and log-log plots indicates that although the upper tail approximates a straight line, the lower tail exhibits a rapid decline, suggesting structured spatial heterogeneity. Similarly, a comparative analysis of Florence in video games versus its real-world counterpart supports the idea that virtual spaces selectively reinterpret, rather than mirror physical environments. From a functional perspective, city-building games, such as SimCity and Emperor: Rise of the Middle Kingdom, deconstruct cities into core elements (housing, industry, service facilities, geographic surroundings, and natural resources) and integrate these components through distinct game mechanics. Re-understanding and Feedback emphasize the influence of virtual cities on urban planning education and tourism. In particular, city-building games have a substantial, yet often underestimated, impact on urban planning practices, public participation, and collaborative projects. By lowering the knowledge barriers associated with professional urban planning, virtual cities offer an accessible platform for the public to engage with urban dynamics and foster a more informed and participatory planning approach. Similarly, video games significantly shape place perception in tourism. The immersive experiences provided by virtual cities can inspire players to visit real-world counterparts of game locations, resulting in phenomena such as 'anime pilgrimages.' Notable examples include the Fate series in Japan and the recent Black Myth: Wukong in China, both of which stimulated tourism by reinforcing place attachment through interactive storytelling and visual aesthetics. Based on this interaction pathway, we propose two planning recommendations. First, fostering interdisciplinary collaboration between urban planners, architectural scholars, and game designers can facilitate the dissemination of architectural and urban cultures on digital platforms. Second, leveraging the interactive and participatory nature of video games can enhance the public's understanding of urban systems, paving the way for game-based participatory planning and promoting multi-stakeholder engagement in urban development. This study contributes to emerging theoretical frameworks such as Digital Twins by elucidating the bidirectional relationship between physical and virtual urban spaces, thereby offering new insights into the evolving role of digital environments in shaping real-world urban experiences.

With the advancement of the "Belt and Road" initiative, China's investment in Africa has increased steadily in recent years, and trade volume has expanded continuously. However, due to the frequent occurrence of armed conflicts in Africa, the security of China's overseas interests in the region has become an increasingly prominent issue. Based on the Armed Conflict Location & Event Data Project (ACLED), this paper employs research methods such as kernel density analysis, hotspot analysis, and multi-distance spatial clustering analysis to explore the spatiotemporal evolution trends of armed conflict events in Africa from 1997 to 2024, as well as their risk impact on China's overseas interests. The analysis reveals several important results: 1) At present, armed conflicts in Africa are mainly concentrated in regions such as Sudan, Kenya, Somalia, Nigeria, Burkina Faso, the eastern Democratic Republic of Congo (DRC), Morocco, and Tunisia, forming a pattern of multiple points of occurrence. In terms of organizational structure, the main driving forces behind these armed conflicts are armed opposition groups, terrorist organizations, and militia groups. Underlying factors such as ethnic differences, religious divisions, conflicts between farmers and herders, and competition for resources are the fundamental causes of these conflicts. 2) From 1997 to 2024, the spatial distribution of China's direct investment stock in Africa shows a transition from a "multi-point dispersed" pattern to a "relatively concentrated" one. Southern Africa, being distant from the core areas of conflict, is the most stable region for China's investment in Africa. In contrast, the escalating conflict situations in East, West, Central, and North Africa have gradually worsened the investment environment, leading to the continuous expansion of China's investment cold spots and revealing a clear tendency to avoid conflict-affected areas.3) Before 2011, armed conflict events in Africa involving China were relatively rare. However, since 2011, the number of such incidents has increased significantly. From a spatial perspective, the clustering of armed conflicts involving China has gradually intensified, with these clusters emerging in regions that were already severely affected by armed conflicts.In hotspot investment countries such as the Democratic Republic of Congo (DRC), Kenya, Burundi, Somalia, and Uganda, armed conflicts have become a significant threat to China's overseas interests. Although China's investment scale in conflict-stricken areas such as Nigeria, Sudan, the Central African Republic, and Ethiopia is limited, the severe conflict situation in these regions still poses significant security risks. In the long term, if conflicts in Africa are not controlled effectively, China's investment confidence will be undermined, leading to a reduction in overseas expansion, delays in projects, and hindrance of the high-quality advancement of the "Belt and Road" strategy. This paper systematically analyzes the impact of armed conflicts on China's investment in Africa, revealing the change trend of investment layout from high-risk areas to more stable and secure regions, thus filling a gap in research on the relationship between investment security and conflicts. Furthermore, by delving into the risk characteristics of conflict events involving China's interests, this study not only provides an in-depth analysis of these risks but also proposes targeted policy recommendations, offering both theoretical support and practical guidance for mitigating the adverse effects of armed conflicts on China's overseas interests. These findings provide new perspectives for future research and offer practical references for China's investment decision-making and risk management in Africa.

In the context of an accelerating society, young individuals in contemporary cities face mounting psychological pressures and existential anxiety. This has led to an increasing demand for environments that offer therapeutic relief and emotional refuge. This study takes the "Time-Farm," an urban green space, as a case study to explore the construction of urban therapeutic landscapes, the perception of young individuals, and the ontological and epistemological foundations of healing. Drawing on Tim Ingold's dwelling perspective, this study examines epistemologies of human-nature relations, embodied and enmined practices, and the temporality of landscapes to propose a multidimensional analytical framework. This framework serves to interpret how landscapes can be healing for youth and offers a new explanatory approach for reconstructing subjectivity in an accelerated society. The findings suggest that therapeutic landscapes are not simply the result of static natural environments but are co-constructed through a relational epistemology that views human and non-human life as interdependent, cohabiting participants. In the Time-Farm, the therapeutic effect of space emerges from its founders' intentional rejection of anthropocentrism. Plants are not regarded as decorative objects but as co-dwelling beings with agency. Through sustained interaction and affective care, caregivers' non-representational practices fostered a shared coexistence epistemology that resonated with urban youths. As youth began to participate in the maintenance and experience of the space, their involvement signaled bodily engagement and an epistemic transformation: therapeutic feeling required the recognition of human–nature interdependence. This shift in understanding is a necessary precondition of ecological connections and emotional resonance. Moreover, the therapeutic process is deeply embedded in the convergence of embodied and enmined practices and temporal rhythms of the landscape. The young participants engaged in sensory-rich, emotionally resonant activities that activated bodily awareness and relational connections to a place. The therapeutic effect does not occur as an immediate experience but rather unfolds over time through repeated interactions, seasonal cycles, and the slow growth of living organisms. The temporality of the landscape mirrors the rhythms of individual transformations and supports sustained therapeutic effects. Theoretically, this study challenges the reductive paradigm that equates therapeutic effects solely with environmental conditions. Instead, it proposes a relational and procedural approach integrating epistemology, embodiment, and temporality. By employing Ingold's dwelling perspective, this study provides an interpretive tool rooted in environmental anthropology for understanding therapeutic landscapes. Practically, it offers insights for designing urban green spaces: therapeutic environments must move beyond the mere presence of greenery to foster relational understanding, embodied participation, and long-term interaction.As an urban green space, the Time-Farm demonstrates how therapeutic landscapes can be cultivated through cohabitative epistemology, integrated mind-body practices, and sustained care. The model of a "human-environment-life community" provides an ecologically holistic perspective on landscape-making and offers a fertile direction for theoretical innovation in future therapeutic landscape research.

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.