The "tidal phenomenon" of tourist flows in rural destinations—characterized by peak-season overcrowding and off-season stagnation—poses a critical barrier to sustainable development. Conventional hard strategies, such as capacity expansion and price regulation, are often inefficient and may undermine local authenticity. The soundscape, as a largely overlooked form of sensory capital, offers potential to flexibly guide tourists’ spatio-temporal behavior; however, its underlying mechanisms remain unclear. This study integrates sensory capital, cultural acoustics, and spatio-temporal fix theories to construct a "value-meaning-practice" framework, aiming to elucidate the core pathways and differentiated strategies for soundscape-based mitigation. A multi-case qualitative design encompassed six rural destinations in China: Yuantang Terraces and Libo Zhangjiang (ecological), Yongding Tulou and Dangjia Village (cultural-heritage), and Moganshan and Zhanqi Village (suburban). Data were collected through participant observation (7-15 days per site), in-depth interviews (80 respondents; approximately 120,000 words), and textual analyses (327 User-Generated Content (UGC) items and official documents). The analysis followed a three-stage coding process using NVivo 12 until theoretical saturation was achieved. The results revealed a three-stage progressive pathway—sensory capital activation, cultural acoustics empowerment, and spatio-temporal fixation—embedded within a self-reinforcing feedback loop. At the sensory capital activation level, natural soundscapes generate use value through stress reduction and attention restoration, extending tourists’ average dwell time at Yuantang Terraces by 20 min. Humanistic soundscapes generate symbolic value through nostalgia and participatory engagement. Elderly tourists in Dangjia Village planned winter return visits triggered by traditional peddlers’ calls, while younger tourists developed a sense of belonging through interactive drum performances. Soundscapes also carry exchange value as information carriers: the "rice paddy frog calls" short video in Zhanqi Village directly prompted off-season bookings, while off-season mountain singing in Yongding Tulou encouraged off-peak recommendations. At the cultural acoustics empowerment level, local communities encode productive and everyday soundscapes (e.g., rammed earth chanting symbolizing solidarity and morning pestle sounds reflecting a grain-cherishing philosophy). Tourists decode these sounds through generational, urban-rural, and cultural distance lenses, thereby fostering deep place attachment. Drum sounds initially perceived as "noisy" were reinterpreted as “powerfully beautiful” after tourists learned about their historical military and harvest functions. This internalization translates into three behavioral outcomes: off-season intention (e.g., Moganshan winter bamboo-listening participants expressed a willingness to revisit during the off-season), consumption desire (e.g., purchasing soundscape-themed teaware as memory vessels), and enhanced sharing motivation (e.g., UGC related to weaving sounds achieved significantly higher interaction rates than conventional landscape photographs). At the spatio-temporal fixation level, spatial diversion during peak seasons achieves a geographical fix: zoning of Yuantang Terraces into quiet listening and interactive areas reduced core-area crowding by an estimated 28%, while drum sounds in Dangjia Village acted as an "acoustic lighthouse," guiding tourists toward peripheral alleys. Temporal staggering during the off-season achieves a temporal fix: Libo Zhangjiang’s "summer night firefly stream soundscape" narrative increased off-season visitation by 35%, while Moganshan’s soundscape membership system (off-season points redeemable for peak-season privileges) cultivated stable off-season visitation habits. This process forms a self-reinforcing loop—"deep experience → identity reinforcement → active off-peak behavior"—providing endogenous momentum for long-term visitor balance. The mechanism exhibits differentiated emphases across rural types: ecological villages prioritize use value and restorative experiences; cultural heritage villages emphasize symbolic value and cultural authenticity; and suburban villages prioritize exchange value and flexible scene construction. The essence of soundscape mitigation lies in a systematic socio-cultural process of "sensory activation → cultural identification → spatio-temporal restructuring," with its feedback loop ensuring sustainability. The theoretical contributions are twofold: first, the study introduces an auditory dimension into sensory capital theory by operationalizing a three-dimensional value system and demonstrating how soundscapes guide spatio-temporal decisions through physiological, emotional, and cognitive pathways, thereby challenging the visual-centric bias in tourism geography; second, it establishes a cultural interpretative pathway for spatio-temporal fixation theory by demonstrating that soundscape-based cultural narratives constitute a non-economic fix, thereby extending the theory’s application from capital logic to sensory-cultural governance.

In high-density urban environments, school routes serve not only as essential traffic corridors but also as critical spatial arenas for children’s socialization and urban perception. Existing urban studies largely rely on traditional Google Street View or car-centric perspectives, which inevitably lead to a significant “scale deviation” that fails to capture children’s authentic pedestrian experiences. To address this gap, in this study, a novel micro-environmental assessment framework integrating self-photographed pedestrian views (SPPV) with a method of empathy-based stories was developed. Using advanced semantic segmentation to extract microscale environmental features and integrating random forest with elastic net regression, a large-scale assessment and analysis of driving mechanism underlying child-friendly school routes in Guangzhou were conducted. The analysis yielded significant empirical findings. First, the child-friendliness of school routes in Guangzhou exhibited pronounced spatial differentiation driven by distinct community spatial characteristics, ranking from highest to lowest as follows: newly built communities, old communities, and urban villages. Each community typology exhibits distinct structural shortcomings: newly built communities establish high safety baselines but suffer from rigid car-centric layouts and underutilized spaces lacking educational stimulation; old communities maintain basic comfort but fail to translate historical heritage into interactive assets because of excessive commercial clutter; urban villages function as safety sinks, characterized by extreme high-density “handshake buildings” and severe vehicle-pedestrian conflicts. Second, analysis of micro-element driving mechanisms indicated that high-quality sidewalks and diverse natural elements were the primary positive drivers for enhancing child friendliness. Conversely, micro-level nuisances, such as pervasive e-bike encroachment, chaotic parking, and excessive spatial enclosure, create significant visual pressure and negatively affect children’s perceived safety and comfort. This study offers crucial theoretical and practical contributions. Theoretically, this study addresses the structural bias inherent in adult-centric and vehicle-oriented urban sensing, providing a low-cost, high-precision methodological framework for capturing children’s authentic perspectives. Practically, the findings highlight a pronounced mismatch between adult-led macroplanning and children’s developmental needs. The study concludes that reliance solely on macro-level road network optimization is insufficient. Urban policy and planning must transition from traditional engineering-oriented approaches to human-centric governance. Policymakers are encouraged to adopt a pedestrian-scale perspective by implementing differentiated spatial improvement strategies and enhancing affordance-based design of micro-scale street furniture. Ultimately, this paradigm shift is essential for transforming utilitarian transit corridors into multifunctional public spaces that comprehensively support children’s needs for safe mobility, natural exploration, and dynamic social interaction.

With the rapid advancement of the internet and digital communication technologies, particularly the widespread adoption of mobile internet and short-video platforms, e-commerce has undergone a new wave of transformation, giving rise to live-streaming e-commerce. As an emerging business model that integrates platform-based live streaming with online retailing, live-streaming e-commerce has, through its distinctive interactivity and immediacy, profoundly reshaped consumer purchasing behavior in China and has become a major driver of the urban digital economy. Against this backdrop, the spatial distribution of live-streaming e-commerce has become an increasingly important topic in geography, particularly in economic geography. However, existing research largely relies on single theoretical perspectives and seldom integrates the analytical logic of path dependence and path creation. In particular, a systematic theoretical explanation and empirical evidence remain lacking regarding how live-streaming e-commerce in cities anchored in the real economy develops distinctive locational patterns through interaction with pre-existing industrial structures. To address this gap, this study draws on the economic-geographical perspectives of path dependence and path creation and considers Suzhou, where the real economy and live-streaming e-commerce are closely intertwined, as a case study. Based on the spatial coordinate data on anchors engaged in live-streaming e-commerce, this study investigates the locational patterns and differentiated determinants of different types of live-streaming e-commerce on the urban scale. The results show that, first, live-streaming e-commerce in Suzhou exhibits a spatial pattern characterized by a high concentration in the urban core and multiple clusters in suburban areas. It also reveals a distinctive dual orientation towards both production bases and industrial service functions, indicating that the development of live-streaming e-commerce in Suzhou is a composite outcome of strong path dependence rooted in traditional manufacturing and path creation enabled by digital technologies and policy intervention. Second, significant heterogeneity exists in the locational patterns of different types of live-streaming e-commerce: production-based live streaming exhibits a“clusters plus dispersed points”pattern; professional live streaming exhibits a“pole plus multiple zones”pattern; brick-and-mortar live streaming an“axis plus dispersion”pattern; stall-based live streaming exhibits a“core plus nodes”pattern; and residential live streaming exhibits a“multiple zones plus dispersion”pattern. Third, the determinants showed both hierarchical and type-specific differentiation. Overall, manufacturing base, institutional policy, industrial foundation, and logistics conditions are the principal factors shaping the locational distribution of live-streaming e-commerce. By type, production-based live streaming is mainly influenced by manufacturing base and logistics; professional live streaming by institutional policy and producer services; brick-and-mortar live streaming by transport and logistics; stall-based live streaming by wholesale markets and logistics; and residential live streaming by producer services, transport, and the cultural environment. This study shows that the digital economy does not disrupted the locational logic of traditional industries; rather, it evolves through deep interaction with and restructuring of local industries, institutions, markets, and socio-spatial environments. Therefore, the locational distribution of live-streaming e-commerce results from the joint operation of path dependence and path creation. These findings enrich the theoretical implications of economic geography in the digital era, and offer a new perspective for understanding the synergistic relationship between inherited local development trajectories and emerging innovation paths in urban digital economic development.

This study proposed a theoretical framework of "spatial justice perception-psychological transformation-behavior formation." Using multisource data, including semi-structured interviews, participatory observations, and secondary materials, the research applied a constructing grounded theory approach involving a four-level coding analysis: initial coding, focused coding, axial coding, and theoretical coding. By deconstructing individuals’ perceptions of spatial injustice, this study reconstructed the dimensions of spatial justice and examined the differences in perceptions and behavioral mechanisms between tourists and local residents. The key findings are as follows: (1) Both tourists and residents derive their spatial justice perceptions from issues of spatial injustice, which are reconstructed in four core dimensions: distributive justice, procedural justice, recognition justice, and restorative justice. For tourists, the psychological transformation mechanism operates as follows: perceptions of distributive, recognition, and restorative injustice trigger disappointment in service value, alienation in the cultural experience, and a sense of lack of ecological education, leading to imbalanced spatial value perception. Perceptions of distributive and recognition injustice shape cognitive biases such as deviation from group preferences and a lack of cultural respect, resulting in conflicts with spatial discipline. Perceptions of distributive and restorative injustice weaken behavioral confidence and decision-making autonomy, causing diminished spatial agency. For residents, perceptions of distributive, recognition, and restorative injustice induce survival anxiety, cultural anxiety, increased economic burden, reinforced doubts about fairness, and a heightened sense of deprivation, leading to perceived spatial survival pressure. Perceptions of procedural injustice foster intergroup opposition, intensify value conflicts, and erode group trust, resulting in a torn spatial identity. Perceptions of procedural and restorative injustice jointly reduce decision autonomy and undermine developmental confidence, leading to reduced spatial empowerment. (2) Structural differences exist between tourists and residents in spatial justice perception and behavior, rooted in the profound tension between two spatial attributes—"paradise" vs "homeland"—and their respective subject positions. Tourists who occupy transient, consumption-oriented positions prioritize experiential rights. They often expressed dissatisfaction through negative word-of-mouth, passive participation, on-site compensation, or rule-breaking adventures. Their negative behaviors generate short-term public opinion and operational pressure, whereas their long-term effects can undermine destination attractiveness. Residents situated in fixed livelihood-dependent positions emphasize their rights to survival, development, and culture. They tend to adopt resistance measures, such as boycotts, collective action, external appeals, covert resistance, and strategic gaming. Their negative actions easily provoke governance conflicts and social risks, potentially undermining the legitimacy of governance in the long term. This study contributes theoretically by deconstructing differences in spatial justice perceptions between tourists and residents of Potatso National Park, and deepens the connotation of spatial justice theory in ecological conservation contexts, complementing existing research focused on urban development. Second, it innovatively proposes "restorative justice" as a key dimension in ecological conservation, defining it as "dismantling structural roots of injustice through systematic intervention to halt the reproduction of spatial conflicts and achieve sustainable reconciliation of eco-social relations." Finally, moving beyond macro-structural perspectives, this study constructed a micro-level analytical framework, offering new theoretical tools to analyze the psychological formation of spatial justice and advance national park governance. Practically, this study systematically identifies real-world issues in Potatso National Park’s conservation and management, analyzes the underlying mechanisms, and proposes targeted, multi-dimensional governance strategies tailored to different stakeholders. This study provides concrete pathways for enhancing park governance effectiveness.

To test whether the collective construction land market and the state-owned construction land market in Nanhai District of Foshan City, the leading demonstration area of rural collective operational construction land, are integrated at different stages, this study collected a total of 3127 urban and rural construction land transaction data samples (2010-2022) from seven townships (streets) in Nanhai District of Foshan City, and used the three-factor split-plot design to carry out statistical modeling and difference significance test to explore the impact of different land ownership, land use and spatial location on the market price of urban and rural construction land; Two-factor split-plot design is adopted simultaneously to analyze the independent effect and interactive influence of land use and location factors on the price of state-owned/collective construction land. The study found that (1) in the bottom-up local system exploration stage (2010-2015), there was no significant difference in the market price of collective and state-owned construction land (P>0.05), indicating that, from the perspective of land price to measure the urban and rural construction land market, the urban and rural unified construction land market in Nanhai District has basically formed an integration in the bottom-up local system exploration stage. (2) Location (Township Street) factors have a significant impact on the price of collective and state-owned commercial land and collective industrial land, but have no significant impact on the price of state-owned industrial land. There is heterogeneity in how land prices across different ownership types and uses respond to location factors. (3) In the top-down central system reform stage (2016-2022), the prices of collective land for commercial use and state-owned land for commercial use converge, while the prices of collective industrial land and state-owned industrial land differ. This indicates that after the top-down central pilot reform policy allows collective construction land to enter the market, the prices of collective land for commercial use and state-owned land for commercial use tend to converge. In contrast, the prices of collective industrial land and state-owned land for industrial use differ through market mechanism adjustments, and the price formation mechanism converges. The synergistic effects of land ownership, use, and location on the prices of urban and rural construction land reflect the joint influence of property rights, use, and location on market prices. The essence of unification lies in the coordinated effects of market-oriented system reforms, land supply, and demand on the price formation mechanism. The research shows that introducing a split-plot design in the study of the land market economy, for statistical modeling and difference-significance tests, can effectively reveal the differential mechanism of urban and rural construction land price formation. This method is highly feasible and effective for deconstructing the multidimensional driving factors of land price differences and provides a new paradigm for the empirical study of land market economics.

In the context of ecological civilization construction and high-quality development, exploring the mechanisms of local environmental governance transformation under globalization is of substantial academic significance for understanding regional sustainable development. Existing studies on environmental governance in the Pearl River Delta (PRD) have mainly examined the driving forces from a single perspective, such as economic development, industrial structure, and globalization. However, relatively limited attention has been paid to identifying the hierarchical roles and evolutionary dynamics of multiple factors from a multi-scalar interaction perspective, particularly the intermediary roles of institutional spillovers from Hong Kong and Macao in regional governance. To address this gap, this study develops an analytical framework of "globalization input-local response-environmental governance" from the perspective of the geography of sustainability transitions. Using the GeoDetector method, this study systematically examined the spatiotemporal evolution of environmental regulation intensity and its driving mechanisms across nine cities in the PRD from 1990 to 2018. The main findings are as follows. First, environmental regulation intensity in the PRD has increased continuously over time, exhibiting distinct stage-based transitions and forming a spatial gradient centered on highly open cities such as Shenzhen and Guangzhou. Second, the driving forces of local environmental governance evolved from an endogenous, development-driven stage dominated by economic growth and urbanization to a multi-scalar governance mode characterized by interactions between globalization inputs and local institutional responses. Lastly, institutional opening channels play a critical bridging role in governance transition. In particular, the number of first-class ports demonstrates the strongest explanatory power, indicating that global environmental rules and governance concepts are primarily embedded in local governance systems through institutional channels of opening. Under the influence of Hong Kong’s and Macao’s institutional spillovers, these processes further promote local institutional innovation and improve governance capacity. This study suggests that, in highly open regions, environmental governance transformation is driven jointly by global rule diffusion, local institutional absorption, and cross-jurisdictional institutional interactions. By revealing the evolutionary mechanisms of environmental governance from a multi-scalar interaction perspective, this study extends the explanatory power of the geography of sustainability transitions in the context of cross-border institutional interactions and regional institutional diversity. It provides new theoretical insights and empirical evidence for understanding environmental governance transformation in open regions.

China is currently experiencing an aging population. Moreover, the overall health status of older adults in China remains a concern, with mental health issues becoming increasingly prominent. Early research on older adults’ mental health focused mainly on environmental determinants, particularly the objective characteristics of the built environment. Recently, the impact of the built environment subjective evaluation (BESE) on mental health has gradually attracted scholarly attention. Most studies have neglected the influence of built environment utilization (BEU) or actual activities occurring in these spaces on mental health outcomes. To address this gap, this study uses data from older adults in 20 communities in Guangzhou, China, and applies structural equation modeling to examine the relationships between BESE, BEU, and mental health among urban older adults, with a specific focus on the mediating roles of social capital, place attachment, and community participation. The results indicate that (1) older adults’ BESE levels show a significant positive correlation with their mental health status, with those with a more positive BEP exhibiting a tendency for better mental health. BEU also demonstrated a significant positive association with mental health among older adults. The BEP significantly affected mental health across all income levels. However, the BEU significantly affected mental health only in the low-income group. (2) Structural equation modeling results showed that both BESE and BEU have significant positive correlations with older adults’ mental health. Notably, compared with the objective features of built environments, older adults’ actual activities in community environments have a more significant impact on their mental health. This indicates that improving older adults' satisfaction with and frequency of using community environments effectively enhanced their mental health. (3) Mediation analysis revealed that place attachment is essential in mediating role between BESE and mental health. Specifically, older adults with higher BESE and BEU scores, potentially because of the intrinsic protective mechanisms of place attachment, were more likely to actively participate in community activities, thereby reporting better psychological states. We found no evidence that strengthening social capital alone can mediate the association between BESE and mental health. (4) The impact of BESE and BEU on the mental health of older adults varied according to age group, education level, and income level. These findings provide practical guidance for developing aging-friendly communities in China. This study revealed significant associations between BESEs/BEUs and older adults' mental health, with utilization behaviors showing more prominent effects. Thus, social capital, place attachment, and community participation play significant mediating roles. This study highlights the need to address internal heterogeneity among older adult populations. Particularly for low-income older adults, planners, designers, and policymakers should prioritize the provision of physical exercise facilities and ensure the safety and comfort of public open spaces.

This study aims to address limitations in existing healthy city research, such as the failure to comprehensively measure environmental features (including spatial quality) and the reliance on assumptions of linear relations. It intends to reveal nonlinear and spatially heterogeneous relations between built environmental attributes and running activities. The study area covers the main urban area of Fuzhou. Multisource data, including building profile data, road network data, points of interest, and street-view images, were used. These data were analyzed using spatial statistics and deep learning algorithms, such as semantic segmentation, object detection, and image regression, to develop a comprehensive framework for evaluating urban built environment attributes. This framework incorporates two- and three-dimensional environmental elements as well as spatial quality characteristics. We then employed the Extreme Gradient Boosting algorithm and SHAP explainers to summarize the types of nonlinear relations between built environment features and residents’ running activities. Combined with K-means clustering analysis, we classified street types according to local and spatial heterogeneity in the built environment’s impact on running activity. The results indicate that (1) there are limited differences in the influence of two-dimensional and three-dimensional physical environmental features, environmental subjective perception, and physical disorder of the built environment on running activity. This finding suggests that analyses confined to conventional physical environmental features alone are inadequate for examining environmental effects on health behavior. Notably, factors, such as building density, POI density, POI mixture, greenery visibility, sidewalk visibility, and safety perception, ranked highly in terms of influence intensity. (2) Six types of nonlinear relations emerged between built environment factors and running density. These include: (I-1) a positive relation with an increasing marginal effect (including sky openness and blue visibility); (I-2) positive relation with a decreasing marginal effect (including POI density and street furniture); (I-3) positive relation with a marginal effect that first increases and then decreases (including POI mixture and safety perception); (II-1) negative relation with a decreasing marginal effect (including garbage and distance to parks and green spaces); (II-2) negative relation with a marginal effect that first increases and then decreases (including building density and aesthetic perception); and (III) U-shaped relation with an initially negative effect followed by a positive effect (including street aspect ratio and greenery visibility). Different types of nonlinear relations require different environmental optimization strategies. (3) There is also spatial heterogeneity in the influence of the built environment on running activities. Based on this spatial heterogeneity, street segments in the study area were classified into five types: low-frequency running streets driven by motorization-oriented design (17%), high-frequency running streets driven by lush greenery and human scale (10%), medium-frequency running streets driven by safety quality (12%), low-frequency running streets induced by functional diversity and physical disorder (7%), and low-frequency running streets with no significant influencing factors (51%). The dominant factors influencing or inhibiting running differed substantially across street types. Strategies targeting specific regions should be implemented based on the spatial heterogeneity of the environmental factors.

The advancement of modern industrialization and urbanization has intensified survival pressure and existential crisis among urban residents, causing alienation and estrangement, alongside convenience. These conditions generate motivations to temporarily leave cities and pursue belonging. Rural tourism has emerged as an attractive option to counter urban alienation, yet existing research largely treats rural tourism as a whole or emphasizes daytime settings, focusing on positive impacts while neglecting day-night differences. Despite growing popularity, rural nighttime tourism remains underdeveloped, with many tourists departing after daytime activities. Understanding experiential patterns is therefore essential before advancing rural nighttime tourism products. This study examines the nighttime dimension of rural tourism, analyzing urban tourists’ experiencing as the primary consumer groups. Data were collected from online texts (travelogues and reviews) and semi-structured, in-depth interviews and then analyzed using grounded theory coding Findings reveal that rural nights constitute a liminal spatiotemporal process, marked by dual transformations: spatial (urban→rural) and temporal (day→night). Specifically: 1) At nightfall, rural landscapes shift as natural features gain prominence while cultural landscapes and conveniences diminish, producing ambiguous spaces distinct from urban cycles and rural daylight. Tourists, in turn experience heightened multisensory perception, anonymization, and subconscious exposure, becoming sensitized "liminal beings". 2) Within this liminal space-time, cognitive tension arises through simultaneous familiarity and unfamiliarity, forcing trade-offs between adventure/risk and safety/boredom. Emotional tension combines nostalgia and fear, expressed through nostalgiacore (nostalgia-oriented), dreamcore (ambiguous), and weirdcore (fear-oriented) sensations. 3) These dual tensions generate both positive and negative outcomes, deconstructing the initial "escape-from-city, belong-to-countryside" motivation. The objects and intensity of escape/belong motivations dynamically transform, creating a paradoxical desire to both escape from and belong to rural settings. Outcomes include temporary belonging through utilitarian or flow experiences, spiritual belonging through identity reinforcement and poetic creation, and escape driven by negative encounters and reliance on urban modernity. The paradoxical orientation of rural night experiences represents a distinctive form of liminal experience. Ambiguity in rural nightscapes serves as the source of meaning, while tourists’ sensitized states provide the cognitive pathway. Perception of tension is the core process of the paradoxical orientation, while the deconstruction of escape and belonging is the final experiential result. By developing a theoretical model, this study highlights the fluidity of liminal space-time, reveals paradoxical orientations, and deepens the understanding of the multidimensional nature of rural tourism experiences.

Labor geography has become a focal point in geographical research because of its unique analytical lens on the spatial dynamics of labor and its interaction with economic geographical patterns. However, existing studies lack a systematic review of research hotspots in this field, both domestically and internationally, and fail to conduct in-depth comparisons of their differences or explore their underlying causes. To address this gap, this study employs CiteSpace to map research hotspots and the evolutionary trajectory of labor geography by analyzing high-quality Chinese and international literature since 2000, while comparing the similarities and differences between domestic and international research in this field. The results show that research in China has primarily examined the spatial distribution and migration trends of the labor force at the national, regional, and urban scales. It also focuses on mechanisms by which labor influences regional development during industrial upgrading, how industrial segmentation shapes the labor market, and disparities in employment characteristics. Owing to the distinctive urban-rural dual structure in China, issues related to the rural labor force have become research hotspots. Foreign labor geography also focuses on the spatial distribution of the labor force and emphasizes the key concept of "labor mobility," that is, behaviors of adaptation, resistance, and reshaping exhibited by the labor force at different spatial scales during economic activities. Foreign research also examines local governance of the labor market and considers immigrant and refugee labor in the process of globalization as important research objects. Additionally, issues of employment inequality affecting female and ethnic minority laborers have received close attention. Although Chinese and foreign research exhibit convergence in certain thematic hotspots, differences in academic environments, market mechanisms, and data foundations have led to divergence between Chinese and international scholarship. Research in China demonstrates characteristics typified by a practical emphasis with relatively limited theoretical grounding, a policy-driven thematic orientation, and a predominant reliance on quantitative methods. By contrast, foreign research is characterized by a critical stance in theoretical foundations, diversified thematic development, and a methodological approach integrating qualitative and quantitative techniques. Since 2000, Chinese and international labor geography research has shown clear evolution across four key dimensions: research participants, thematic foci, analytical scales, and methodologies. Concurrently, intellectual exchange and mutual learning between these research communities have intensified. Against the backdrop of globalization entering a phase of adjustment, future research in labor geography needs to balance Chinese and international perspectives, strengthen theoretical dialogue, explore the applicability of Western theories to specific domestic issues in China, deepen understanding of interactions between global production networks and local labor markets, and investigate the impact of social networks and technological transformations on labor spatial patterns. This study provides not only an informed frame of reference for comprehending the far-reaching influence of labor on development and its spatial dimensions but also a significant basis for fostering a more globally dialogic research paradigm in labor geography.

Driven by the “double carbon” goal, photovoltaic power generation has become a key pathway for the low-carbon transformation of energy. Focusing on Guangdong Province, a major energy-consuming region, this study constructs a multi-system coordinated evaluation framework and index system that integrate “resource endowment-development demand-bearing constraints” to evaluate the suitability and emission reduction benefits of photovoltaic land. Spatial clustering analysis of power demand and development potential was innovatively introduced to address the mismatch between supply and demand in photovoltaic development and to provide a scientific basis for the optimal layout of regional photovoltaic land. The results show that (1) the potential for photovoltaic land development in Guangdong Province is substantial, and the highly suitable photovoltaic area is 13,292 km² (accounting for 7.3 % of the total land area). When the highly suitable area is fully developed (Q4 development scenario), the annual photovoltaic power generation in Guangdong Province reaches 960.5 billion kWh, which can meet the electricity demand of the entire province. (2) The spatial differentiation of photovoltaic land development potential in Guangdong Province is significant. The highly suitable and suitable areas account for 38% of the assessed area and are mainly distributed in the hills of eastern Guangdong and the Leizhou Peninsula. The low-suitability areas (generally suitable, less suitable, and unsuitable areas) account for 62% of the assessment area and are mainly distributed in the mountainous areas of northern Guangdong and the core area of the Pearl River Delta. Photovoltaic development in northern Guangdong is limited by undulating terrain, while available space in the Pearl River Delta is insufficient due to dense construction land. (3) Clustering results of suitable photovoltaic areas and electricity demand indicate that the mismatch between supply and demand of photovoltaic power generation in Guangdong Province is significant. The clustering results identify a conflict pattern of “supply less than demand” in the Pearl River Delta and “supply greater than demand” in eastern Guangdong. Huizhou City, as a high-high agglomeration area, demonstrates both local consumption capacity and radiation capacity toward the Pearl River Delta, indicating high priority for development. (4) The potential emission reduction benefits of photovoltaic power generation in Guangdong Province are significant. The development of all highly suitable areas could save 293 million tons of standard coal per year and reduce 781 million tons of CO₂ emissions, corresponding to an emission reduction value of 53.29 billion yuan. At the same time, emissions of SO₂, NOₓ, and TSP could be reduced by 6.46 million tons, 2.94 million tons, and 4.99 million tons, respectively. Vegetation under photovoltaic panels provides an additional carbon sink of 27,900 tons, contributing non-negligible emission reduction and ecological benefits. This study provides a decision-making paradigm of “potential assessment-spatial optimization-benefit quantification” for the precise allocation of provincial photovoltaic land. It proposes establishing differentiated land-use rules strictly linked to development potential levels, incorporating spatial synergy elements into the photovoltaic land planning system, and ultimately achieving a win-win outcome between energy transformation and land space optimization.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

With the rapid development of urban regionalization and networking of high-speed transport, intercity travel has increasingly played a key role in China's economic and social development and socioeconomic functional connections. However, amidst global change and uncertainty, the event disturbance-oriented theory and empirical research on intercity travel is still insufficient to improve the ability of transportation systems to cope with disturbances. Since uncertainty is prevalent in transport operations, improving intercity travel behavior resilience (ITBR) and grasping the spatiotemporal pattern of demand-side intercity travel fluctuation to restrain risk is essential for resilient transport construction. Based on related theories and analysis methods of spatial interaction and intercity travel, this study refines the definition of ITBR. A measurement model of ITBR was constructed based on long-term intercity travel data and the general properties of disturbance events. Furthermore, COVID-19 disturbance was used as a case study to reveal the adaptive pattern of intercity travel and the spatiotemporal pattern of ITBR over three years. The results show that the evaluation of ITBR based on seasonal and holiday trends reveal spatiotemporal patterns of intercity travel fluctuations influenced by disturbance events. The impact of the COVID-19 pandemic on intercity travel is as follows: peak of the national pandemic > peak of the Omicron variant > peak of the multipoint fluctuation. The intensity of intercity travel decreased linearly with an increase in distance, and intercity travel during the three stages is lost by 0.86, 1.03, 1.15 percentage points, respectively, with an increase of 50 km. The average intercity travel distances of residents in these three stages were shortened by 52.55 km, 65.31 km, and 105.16 km, respectively. The value of ITBR decreased from the multipoint fluctuation period to the national pandemic period because of the Omicron outbreak. Overall, ITBR showed a gradual increasing trend during the study period. Meanwhile, ITBR in these three stages was characterized by obvious spatial differentiation and regional agglomeration. Compared to existing research, this study further expands existing research focusing on intra-city travel behavior resilience by exploring ITBR on the regional scale.

The construction and development of the metro have reshaped the activity spaces of residents and provided them with abundant opportunities to access various resources. However, existing literature often overlooks the impact of increased access to resources due to the metro system on housing prices. This study, based on residential transaction data from 2017 to 2019 obtained from Beike and metro line and station data from Chengdu, employed network analysis to define a 15-minute activity space for 56,999 residential samples in 2,609 neighborhoods around metro stations, serving as the basis for decomposing resource allocation. Subsequently, an XGBoost model was developed to explore the nonlinear impacts of accessibility and resource allocation at the neighborhood and cross-regional levels on housing prices. The results show that 1) the closeness centrality of the nearest metro station primarily affected housing prices. 2) Additionally, the relative importance of resource allocation on housing prices at the neighborhood and cross-regional levels was 34.60% and 19.55%, respectively, highlighting the significance of resource allocation at the cross-regional level. The development of the metro has reshaped residents' activity spaces, increasing access to various resources. The value of resources obtained through the metro has significantly affected residents' willingness to pay. Furthermore, resource allocation at the two levels reflected a different impact on housing prices. At the neighborhood level, the relative importance of factories and comprehensive hospital facilities was 8.02% and 7.47%, respectively. Meanwhile, the relative importance of parks and comprehensive hospital facilities were 4.85% and 3.86%, respectively, at the cross-regional level. 3) The relationship between accessibility, resource allocation characteristics, and housing prices is complex and nonlinear. Regarding accessibility, the travel time to the nearest metro station and housing prices exhibited a roughly U-shaped relationship, whereas the closeness centrality of the nearest metro station positively affected housing prices. In terms of resource allocation characteristics, different facilities had varying degrees of impact on housing prices at the neighborhood and cross-regional levels. Specifically, parks and primary and secondary schools at the neighborhood level showed an overall positive impact on housing prices, while factories, comprehensive hospitals, and shopping services at the neighborhood level generally suppressed housing prices. Additionally, business and financial service facilities at the neighborhood level showed an inverted U-shaped relationship with housing prices. At the cross-regional level, comprehensive hospital facilities and primary and secondary school facilities had opposite effects on housing prices compared to park facilities and shopping services. The impact of factories at the cross-regional level on housing prices was unstable. Business and financial service facilities at the cross-regional level demonstrated an inverted U-shaped relationship with housing prices, with a suppressive effect when the count of these facilities exceeded 423. The study findings provide valuable insights into sustainable metro development and rational resource allocation.

Livelihood capital is the core element of sustainable livelihood development for rural households. Based on the survey data of 615 growers in the main rubber-producing areas of Hainan and Yunnan province, this paper constructs an evaluation system of rubber growers' livelihood capital within the sustainable livelihood analysis framework and calculates key indicators, such as farmers' livelihood capital index, livelihood activity diversity index, and household income diversity index. The multiple regression model and the mediating effect model are used to explore the influence mechanism of livelihood capital on the sustainable livelihood of rural households. The following three conclusions are drawn: (1) The accumulation of livelihood capital of rural households is conducive to enriching the structure of household livelihood activities and income sources, especially the accumulation of financial capital, which is more likely to improve the diversity of household livelihood activities. Regional differences exist in the impact of livelihood capital structures on rural household livelihood outcomes. Natural capital endowment has a significant effect on the income diversification level in Hainan, but there may be a "curse" effect in Yunnan. (2) Geographical characteristics, production decisions, and farming household characteristics significantly impact sustainable livelihoods. The altitude of the village and the distance from the county seat pose certain challenges for farmers in diversifying their livelihoods. Although the law of rubber tapping production may weaken farmers' production and business activities, it can better stabilize farmers' incomes owing to its strong flexibility in labor arrangements, which may be beneficial for diversifying livelihoods; however, it does not necessarily lead to diversified incomes. While the characteristics of poor households can help rural households gain policy support, building capacity is the key to achieving a sustainable livelihood. (3) There are intermediary effects of livelihood capital on livelihood activities and household income, but the mechanisms of action are different. For the diversity of farmers' livelihoods, 52.70% of the catalytic effect of livelihood capital is achieved by improving the ability to integrate livelihood capital. Optimizing household employment strategies contributed to the promotion of livelihood capital on the income diversification index, with a mediating effect of 30.52%. The main contribution of this study is to clarify how livelihood capital can achieve the goal of diversifying household livelihood activities and income by coordinating livelihood capacity and employment strategies. Therefore, to enhance the comprehensive livelihood capacity of rural households, the coordination capacity of livelihood capital, livelihood capacity building should be enhanced, and employment information channels should be expanded.