The school–residence spatial relationship is a key factor in understanding urban spatial structure and travel-to-school behavior of students. Analyzing the change law and the spatial characteristics of travel-to-school distance can provide a basis for improved accessibility of urban educational facilities and enable enrolment of students from the neighborhood. Based on one complete month of mobile phone signaling data for May 2018, the changes in student density with the travel-to-school distance was analyzed using MATLAB and Mann–Kendall Trend Test, and the pattern and the spatial structure of travel-to-school were explored. The results revealed that: (1) With increase in travel-to-school distance, the student density showed a decrease in truncated power law distribution, and it is concentrated within the travel-to-school distance of 5.0 km; (2) According to the sudden change points of the student density growth rate, the threshold distance for travel to kindergartens is 1.30 km, and for primary schools and secondary schools is 1.50 km. The school–residence spatial structure is divided according to the threshold of travel-to-school distance and the scope of attendance; (3) The dominant flow of travel-to-school is generally from urban peripheral and marginal areas to the urban core area, and partly from marginal areas to peripheral areas; (4) The pattern of travel-to-school is polycentric, and the study centers are mainly located in the urban central district north of the Hun River. The urban core area has the strongest attraction of students, while the marginal area has the weakest.

In the face of increasing disturbance factors, resilience has become an important criterion for measuring the sustainable development of cities. Quantitatively describing the development process of urban resilience and identifying key areas and important dimensions of urban resilience are of scientific significance for understanding the evolutionary law of urban resilience, guiding regional risk prevention, and building an environment for urban resilience development. For this study, taking Nanchang City as a case study and dividing the natural water network groups, the resilience index system was constructed from scale, density, morphology, and function by drawing on the theory of landscape ecology on the basis of considering the internal relationship between urban development attributes and disturbance factors. On this basis, the study focuses on the evolution process and development differences of resilience in various dimensions from the water network groups and quantitatively describes the coordinated development status and adaptive phase characteristics of urban resilience. This study not only enriches the research scale and perspective of urban resilience but also provides specific spatial guidance for formulating resilient urban planning and promoting sustainable urban development.

The rapid development of virtual social networks has transformed word-of-mouth (WOM) communication, breaking traditional spatial and temporal constraints and profoundly influencing consumer decision-making and corporate image-building. Internet word-of-mouth (IWOM) in tourist attractions is a mediated context formed through the interaction between tourist groups and the environmental settings of tourist destinations, significantly influenced by the interplay of media and physical spatial environments of the attractions. This study constructs a multidisciplinary framework to analyze the mechanisms driving IWOM, employing machine learning to measure the impact of environmental factors. Additionally, it explores the characteristics of IWOM from macro and micro perspectives. The key findings are as follows: (a) Tourists exhibit high satisfaction and positive experiential perceptions toward the scenery of attractions, with tourist attractions, management services, and transportation facilities being the core elements of concern. The spatial distribution of IWOM in tourist attractions is dependent on the destination, displaying a pattern of “large agglomeration with small dispersion” within regions. (b) The distribution of IWOM in natural ecology, historical and cultural, and modern amusement tourist attractions exhibits a multi-center pattern, while industrially integrated tourist attractions demonstrate a single-center distribution. Scenery, history, projects, and porcelain are the core high-frequency terms in the IWOM of various typical tourist attractions. (c) Scenery has emerged as a decisive factor in the IWOM of tourist attractions, particularly significant in industrially integrated attractions. Government attention and internet popularity are identified as the primary media environmental factors influencing IWOM. The impact of various factors on IWOM shows significant spatial heterogeneity. Systematically identifying the characteristics and mechanisms influencing IWOM in tourist attractions can provide effective theoretical support for the management and sustainable development of tourist destinations.

Regional clustering has become a distinctive feature of the homestay industry in terms of spatial distribution in many countries. However, identifying and quantifying the advantageous locations suitable for the agglomeration of the homestay industry at the micro-scale is still in the exploratory stage. Therefore, with the support of multi-source data and the spatial entropy weight superposition method, drawing on concepts from physics such as gravitational and magnetic fields, and based on relevant location theories, the environmental field strength and its quantitative model are proposed to evaluate micro-locations suitable for homestay development. Finally, a case study was conducted in Wuyuan, China. The results show that the homestay industry is mainly distributed along traffic lines and rivers, which is greatly affected by tourism resources and rural settlements, and the degree of agglomeration is gradually increasing; The environmental field strength is closely related to the accessibility of infrastructure and the development of tourism resources, and the overall structure tends to be networked, polycentric and “core-periphery”; there is a positive linear correlation between the distribution of homestay industry and environmental field strength. This study provides a basis for optimising decision-making related to the sustainable planning and site-selection of tourism destinations and the homestay industry.

As economic globalization and industrial restructuring advance, inter-firm interactions have become increasingly close, and the structural characteristics of urban investment networks have undergone significant changes. From an enterprise investment context, this study integrates complex network theory and resilience theory to construct a comprehensive evaluation framework for urban investment network resilience and to conduct corresponding measurements. Drawing on the investment perspectives of the listed companies, this study integrates complex network theory and resilience theory and adopts a three-stage research approach of “network construction–topological analysis–attack simulation” to construct a comprehensive evaluation framework for urban investment network resilience and to conduct corresponding measurements. First, an urban investment linkage network is constructed based on cross-regional investment data of the listed companies; second, network analysis methods are employed to measure static resilience characteristics; and finally, dynamic resilience evolution is evaluated through deliberate attack simulations. The results indicate that, during 2010–2022, the urban network structure in the Yangtze River Delta (YRD) region became increasingly complex, while its risk resistance capacity improved. The scope of inter-city linkages broadened across all cities, yet the pace of development exhibits pronounced spatial heterogeneity—the investment radiation range of core cities expanded from 17 to 39 cities, whereas peripheral cities witnessed relatively sluggish growth in their number of connected partners. The overall urban network exhibited a rising trend in comprehensive resilience, with the composite resilience index increasing from 0.556 to 0.706, indicating a steadily strengthened capacity to withstand and adapt to external shocks. Furthermore, the article further examines policy implications and optimization strategies for the industrial investment network of the Yangtze River Delta urban agglomeration from a resilience perspective.

Understanding the trade-offs and synergies among ecosystem services is fundamental to regional ecological management, yet systematic assessments that integrate multidimensional coordination dynamics with nonlinear driving mechanisms remain limited. Taking the Poyang Lake Basin as the study area, this research evaluates six key ecosystem services, including food production, water yield, carbon storage, soil conservation, habitat quality and net primary productivity, from 2000 to 2020 using the InVEST model, the RUSLE equation, and the CASA model. In contrast to previous studies that focus on pairwise service comparisons or static assessments, this study introduces a mechanical equilibrium model within a principal component analysis framework to measure the coordination degree of ecosystem service provision across multiple dimensions, and further employs the XGBoost-SHAP machine learning approach to disentangle the nonlinear effects and threshold characteristics of natural and anthropogenic drivers. The results reveal that the basin maintains a basic coordination state overall, yet exhibits a pronounced core-periphery spatial pattern alongside a three-phase adaptive trajectory of coordination, diffusion, and restabilization over time. Rainfall variability emerges as the predominant driver, exhibiting a U-shaped nonlinear relationship with two critical thresholds, while vegetation dynamics, economic growth, and land use change display distinct marginal effects and interactive influences with topographic conditions. The integration of mechanical equilibrium modeling with interpretable machine learning enables a coherent analytical chain from coordination state identification to causal attribution. These findings provide both theoretical contributions to ecosystem service interaction research and practical implications for differentiated watershed management and ecological conservation strategies.