Search Results (64)

The Xizang–Sichuan region is rich in tourism resources, yet its complex geography and lagging transportation infrastructure have resulted in pronounced spatial disparities in tourism development. From a sustainability perspective, such disparities can lead to a ‘rich-get-richer’ cycle: over-tourism and ecological stress in high-accessibility cores, versus underdevelopment and resource idling in low-accessibility peripheries. To systematically examine the spatial structure of tourism in this region, this study uses counties as basic units and integrates the Analytic Hierarchy Process (AHP) with an improved gravity model for tourism network potential accessibility (TNPA) to assess regional tourism development levels and the network-based accessibility among counties. A comprehensive evaluation framework was established across three dimensions: tourism resource endowment, service capacity, and socio-economic support. Travel times between county centers were obtained from the Amap API, and a TNPA index was computed to reflect each county’s potential for tourism interaction with the rest of the region. Results indicate that resource endowment dominates the evaluation, with scenic quality as the critical factor. TNPA exhibits a pronounced core–periphery differentiation, with Chengdu and surrounding areas forming a high-value network core, while most counties in Xizang show extremely low network potential. Equity analysis reveals a significant imbalance in the distribution of network accessibility between Sichuan and Xizang. Under the baseline setting, the population-weighted Gini coefficient, coefficient of variation, and Theil index reach 0.677, 1.724, and 0.884, respectively, indicating that tourism network potential remains highly concentrated in a limited number of counties. The population-weighted mean TNPA of Sichuan is also far higher than that of Xizang, revealing a distinct interregional accessibility gap. The development–TNPA mismatch analysis further identifies counties where tourism development foundations are relatively strong but network integration remains weak. Robustness checks indicate that the high-development–low-TNPA pattern is not simply an artefact of the median-based classification, with the most evident cases mainly concentrated in Aba and Garze in western Sichuan and a few counties in Xizang. The study highlights the asymmetric relationships among resource endowment, network accessibility, and equity, providing a scientific basis for optimizing cross-regional tourism cooperation and transportation corridors in the Xizang–Sichuan region. Full article

To address the key challenges of distributed generation and loads, insufficient edge computing capacity, significant data privacy risks among multiple participants, and immature market mechanisms in county-level distribution networks, this paper presents a transaction-driven two-tier distributed collaborative optimization approach for interconnected integrated energy systems. We develop a market-oriented architecture that combines upper-layer price coordination with lower-layer autonomous optimization. The overall system is decoupled using just two types of non-sensitive data—local electricity prices and regional net power—while preserving the operational independence and data privacy of all stakeholders. We further devise a Two-Stage Distributed Transactional Optimization (TSDTO) mechanism. This mechanism reformulates the intraday multi-variable collaborative optimization into a single-variable electricity price search problem, substantially reducing algorithm iterations and communication overhead. Simulations are conducted on three typical interconnected integrated energy systems in a county in northern China. The results demonstrate that the proposed method maintains main transformer power within safe limits, effectively lowers daily operating costs, and boosts the renewable energy accommodation rate. Compared with the conventional subgradient method, our algorithm offers higher computational efficiency, along with improved convergence and real-time performance. The proposed approach is capable of achieving a relatively satisfactory balance among privacy protection, low computational complexity, on-site renewable energy utilization, and rapid real-time operation. This paper provides theoretical references and guidance for the low-carbon, cost-effective, coordinated and sustainable operation of modern county-level power systems and integrated energy systems. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not adequately control for regional crossing-density exposure and frequently ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), a crossing-count-normalized cumulative incident intensity metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), suggesting that incident intensity is spatially clustered rather than random. Local indicators identify statistically significant high and low intensity county clusters. Distributional analysis shows that AIPX within high-intensity clusters was best represented by lognormal and Johnson SU distributions. Machine learning models achieved strong classification performance (AUC ≈ 0.85). Explainability methods consistently identified temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for broader geographic, operational, exposure, and network-structure differences rather than direct causal drivers. The findings indicate a pattern consistent with regional and network-level exposure regimes concentrated along freight-intensive corridors. The study provides a transparent analytical workflow that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments. Full article

Traditional distribution network planning evaluation commonly relies on a unified indicator system, which is insufficient to reflect the heterogeneous characteristics of emerging distribution networks across different regions and development stages. To overcome this limitation, this paper proposes a data-driven method for constructing planning evaluation indicators for emerging distribution networks. First, based on an existing comprehensive indicator system, key factors of county-level distribution networks are identified to classify typical planning scenarios, and a preliminary scenario-oriented indicator system is established with expert knowledge. Second, data-driven techniques are employed for indicator selection. The maximum relevance and minimum redundancy (mRMR) method and the Random Forest (RF) algorithm are introduced to evaluate indicator relevance and importance, respectively, and a game-theoretic combination method with coefficient-of-variation (CV) correction is used for comprehensive screening. Finally, a county-level case study is conducted to validate the proposed method. The results show that the proposed method can adjust the planning evaluation indicator system according to changes in distribution network characteristics under different scenarios and performs well in the studied cases. This method provides a practical framework for constructing adaptive indicator systems for distribution network planning evaluation. Full article

The northeastern margin of the Qinghai–Tibet Plateau is an ecologically fragile region that faces severe habitat fragmentation, which directly threatens regional biodiversity conservation and ecological security. To address this challenge, this study constructed a hierarchical “source-corridor-node” ecological network for the Gannan Tibetan Autonomous Prefecture by integrating Morphological Spatial Pattern Analysis (MSPA), the Minimum Cumulative Resistance (MCR) model, landscape connectivity assessment, and gravity modeling. The key results are as follows: (1) The Gannan Yellow River Water Source Replenishment Area contains 11 core ecological source regions, which are predominantly located in the southeastern regions of Diebu County and Zhouqu County, covering a total area of 4237.81 km²; (2) Ecological resistance analysis identifies high-resistance zones concentrated in anthropogenically active river valleys and urban belts (e.g., Hezuo urban area, Awanzang Town, and the G213 corridor). Low-resistance zones are predominantly situated in protected ecological enclaves (e.g., Zhagana Geopark and Gahai Wetland Reserve); (3) A total of 55 ecological corridors were identified, with a total length of 4355.77 km. Among these, 26 were classified as key ecological corridors, primarily distributed in Diebu and Zhouqu counties in the eastern part of Gannan Prefecture. These areas feature relatively concentrated ecological sources, and the key corridors play a critical role in connecting isolated ecological patches and maintaining regional ecological connectivity. (4) Across the entire territory of Gannan Prefecture, a total of 81 first-level ecological nodes and 53 second-level ecological nodes were delineated. As the core hub of the regional ecological network in Gannan Prefecture, Diebu County encompasses 60 First-level and 41 Second-level ecological nodes, respectively. The hierarchical “source-corridor-node” ecological network constructed in this study effectively enhances the overall landscape connectivity of the area. This progressive analytical framework—integrating source identification, corridor extraction, and node diagnosis—provides a scientific basis for biodiversity conservation, territorial ecological restoration, and sustainable development in high-altitude ecologically fragile zones. Full article

The conservation of traditional villages has shifted from isolated site-by-site protection to regional collaboration, and exploring pathways for their sustainable development has become a key focus of research. Existing research still falls short in areas such as the integration of heritage value into decision-making mechanisms and the establishment of systematic conservation frameworks, leading to prominent issues of isolated conservation and homogeneous development. Taking traditional villages in Yanchuan County, China, as a case study, this research aims to establish a clustered conservation system and achieve a transition towards networked collaborative governance. The study utilised field surveys and literature review to establish a database and systematically catalogue heritage resources; it combined the Analytic Hierarchy Process (AHP) and the Delphi method to construct a value evaluation system and identify distinctive features; and it integrated cluster theory with GIS spatial analysis to construct a clustered conservation framework across three dimensions: classification and grading, symbiotic models, and the overall spatial pattern. The results indicate that: (1) the spatial distribution of villages in Yanchuan County is uneven, and the villages themselves exhibit significant homogeneity in their characteristics; (2) core characteristics include Loess culture, cave dwellings and revolutionary heritage sites, with comprehensive scores ranging from 0.4437 to 0.9116; these are classified into three protection levels, identifying five categories of villages of value. (3) Five major cluster zones were delineated based on resource and spatial characteristics. By integrating river basins and transport corridors, a comprehensive protection framework of ‘one belt, two wings, two centers and five zones’ was established, alongside three types of cluster symbiosis models, thereby achieving regional resource integration and enhancing collaborative efficiency. The cluster-based protection system proposed in this study can effectively address the challenges facing the conservation and development of traditional villages, providing a feasible solution for regional collaborative protection, and holds practical significance for cultural heritage management and sustainable development. Full article

Given less than five years remaining until the target year for the first phase of China’s dual carbon goals, this paper studies carbon allowance allocation with an in-depth study of Jiangsu Province due to its significant role in driving the Yangtze River Delta’s pioneering achievement of the dual carbon goals. This study considered 2017 (the intermediate target year) as the base year and incorporated socio-economic data such as population, GDP, and the urbanization rate. Then, methods including the entropy weight method, gravity model and social network analysis were applied to classify Jiangsu’s 95 counties. From a regional coordination perspective, carbon governance clusters were constructed with the Shapley value, based on which spatial heterogeneity patterns were analyzed, and a carbon quota allocation was proposed. The findings reveal that: (1) The dominant factors influencing cross-scale carbon reduction capacity at the county level are natural carbon sink capacity (indicator weight: 0.180) and urbanization rate (indicator weight: 0.145). (2) The correlation between carbon reduction factors among different districts and counties exhibits an uneven spatial pattern. And the spatial configuration exhibits a multi-tiered, network-like distribution. (3) Through conducting spatial analysis and spatial grouping, Jiangsu could be divided into 14 county-level carbon governance alliances, with the number of member counties ranging from 4 to 10 within each alliance. (4) The allocation of carbon quotas in Jiangsu exhibits a distinct descending gradient from the southern to the northern regions, which is coupled with the regional economic geography. This is exemplified by the highest quota in Jiangyin (496.46 Mt) in the south and the lowest in Lianyun (34.90 Mt) in the north. It is concluded that two carbon emission reduction pathways should be established as a priority: (a) Tongshan-Gulou (Xuzhou)-Yunlong-Quanshan-Jiawang and (b) Tianning-Jiangyin-Zhangjiagang-Changshu-Taicang-Kunshan. Full article

Invasive alien plants (IAPs) pose a severe and escalating threat to biodiversity and ecosystem services in China. However, a systematic nationwide assessment that identifies invasion hotspots, quantifies their overlap with protected area networks, and pinpoints critical conservation gaps is still lacking. This hinders the development of spatially targeted management strategies. To address this, we developed an integrated analytical framework coupling the Maximum Entropy (MaxEnt) model with the InVEST habitat quality model. Using a high-resolution, county-level distribution database of 293 IAPs, we mapped potential species richness and habitat degradation across China. The geo-detector model was further employed to identify the primary environmental factors and their interactions. Spatial overlay analysis was conducted to delineate core invasion habitats (areas of high invasion suitability and high degradation) and assess their coverage within China’s national nature reserves. Nighttime light intensity (DMSP, 34.39%), annual precipitation (Bio12, 14.16%), and mean diurnal range (Bio2, 11.82%) were the factors with the highest contribution in the model, highlighting the statistical interaction between anthropogenic pressure and climatic conditions. The core invasion habitat spanned 20.10 × 10⁴ km², predominantly (66.04%) concentrated in high-intensity human disturbance zones. Notably, only 11.18% of this core habitat falls within existing national nature reserves, revealing a vast conservation gap of 17.85 × 10⁴ km². Our results indicate a profound spatial mismatch between invasion hotspots and the current protected area network in China. We prioritize southeastern coastal urban agglomerations-characterized by high anthropogenic pressure (DMSP), high precipitation (Bio12), and low diurnal temperature range (Bio2)-for immediate monitoring and intervention. This integrated assessment provides a national-scale, spatially explicit prediction of invasion risk for 293 plant species in China, and offers an evidence-based decision-support tool for optimizing invasive species management and biodiversity conservation. Full article

River basins are not merely geographical spaces but also cultural-historical ecosystems, where the spatial patterns of Intangible Cultural Heritage (ICH) profoundly reflect the long-term interaction between human and environment, as well as contemporary transformations. While international research on ICH has evolved from conceptual clarification to interdisciplinary theory-building, and spatial quantitative methods have been widely applied to cultural heritage analysis, the spatial patterns, multi-scale structures, and “natural-human” driving mechanisms of ICH in continental arid river basins—particularly in the Tarim River Basin (TRB, China’s largest inland river and a key corridor of the Silk Road)—remain underexplored. To address this gap, this study takes 313 ICH items in the TRB as the research object. It uses ArcGIS 10.8.1 to visualize their spatial distribution and employs an integrated methodology—including global Moran’s I, kernel density estimation (KDE), DBSCAN spatial clustering, and geographical detector (Geodetector)—to systematically reveal their spatial characteristics and influencing factors. The findings indicate that: (1) The distribution of ICH exhibits a multi-scale feature of “global randomness with local clustering”: spatial autocorrelation is not significant at the county level, while at the micro-geographical scale, a dendritic structure characterized by “one axis, three cores, denser in the north and sparser in the south” emerges, which is highly coupled with the river network. DBSCAN clustering further identifies a “mainstem axis–tributary node” cluster system and a relatively high proportion of peripheral “noise” heritage points. (2) Agglomeration patterns vary significantly across different ICH categories, with traditional craftsmanship showing high clustering, while traditional sports, entertainment, and acrobatics display highly fragmented distributions. (3) The study reveals and validates a ternary “Water–Tourism–Urbanization” driving framework that predominantly shapes the spatial heterogeneity of ICH: water resources constitute a fundamental ecological threshold, whereas tourism development and urbanization have emerged as more explanatory social driving forces, with widespread nonlinear enhancement interactions between natural and human factors. This research moves beyond the traditional view of river basins as static cultural “containers,” providing empirical evidence for their dynamic nature as “cultural-ecological co-evolutionary systems.” The proposed ternary framework not only offers a new perspective for understanding the spatial resilience of ICH in arid regions and the potential risks of “spectacularization” and “spatial polarization” amid rapid changes, but also provides a scientific basis for spatial governance, culture-tourism integration, and the formulation of conservation strategies for ICH at the basin scale. Full article

Farmland shelterbelts are crucial elements in safeguarding agricultural ecological security and sustainable development, with their precise extraction being vital for regional ecological monitoring and precision agriculture management. However, constrained by their narrow linear distribution, complex farmland backgrounds, and spectral confusion issues, traditional remote sensing methods encounter significant challenges in terms of accuracy and generalization capability. In this study, six representative deep learning semantic segmentation models—U-Net, Attention U-Net (AttU_Net), ResU-Net, U²-Net, SwinUNet, and TransUNet—were systematically evaluated for farmland shelterbelt extraction using high-resolution Gaofen-6 imagery. Model performance was assessed through four-fold cross-validation and independent test set validation. The results indicate that convolutional neural network (CNN)-based models show overall better performance than Transformer-based architectures; on the independent test set, the best-performing CNN model (U-Net) achieved a Dice Similarity Coefficient (DSC) of 91.45%, while the lowest DSC (88.86%) was obtained by the Transformer-based TransUNet model. Among the evaluated models, U-Net demonstrated a favorable balance between accuracy, stability, and computational efficiency. The trained U-Net was applied to large-scale farmland shelterbelt mapping in the study area (Alar City, Xinjiang), achieving a belt-level visual accuracy of 95.58% based on 385 manually interpreted samples. Qualitative demonstrations in Aksu City and Shaya County illustrated model transferability. This study provides empirical guidance for model selection in high-resolution agricultural remote sensing and offers a feasible technical solution for large-scale and precise farmland shelterbelt extraction. Full article

Groundwater and water supply systems are increasingly vulnerable to contamination, yet most assessments consider either hydrogeological or infrastructure risks. This study introduces the Total Integrated Network (TIN) approach, a framework designed to evaluate vulnerability comprehensively from source to tap. Field investigations were conducted in Varaždin County, Croatia, focusing on the Belski Dol spring, Briška reservoir, and PS Filipići. Hydrochemical analyses, stable isotope of water (δ¹⁸O, δ²H), tritium, noble gases, and radon concentrations were monitored and combined with system-level assessments. Results show that the Belski Dol spring exhibits high stability and low vulnerability, with a TIN index of approximately 25%, supported by long groundwater residence times and consistent water quality. PS Filipići displayed moderate vulnerability (35%), while the Briška reservoir showed the highest index (53%), linked to elevated radon and nitrate concentrations and infrastructure-related risks. These findings indicate that natural hydrogeological protection alone cannot ensure safe drinking water. The TIN approach highlights the importance of integrating aquifer conditions with distribution system performance to identify critical control points and prioritize interventions. This integrated methodology offers a more realistic basis for water safety management, supporting proactive measures to safeguard supply resilience and public health. Full article

High-resolution CO₂ fossil fuel emission data are critical for developing targeted mitigation policies. As a key approach for estimating spatial distributions of CO₂ emissions, top–down methods typically rely upon spatial proxies to disaggregate administrative-level emission to finer spatial scales. However, conventional linear regression models may fail to capture complex non-linear relationships between proxies and emissions. Furthermore, methods relying on nighttime light data are mostly inadequate in representing emissions for both industrial and rural zones. To address these limitations, this study developed a multiple proxy framework integrating nighttime light, points of interest (POIs), population, road networks, and impervious surface area data. Seven machine learning algorithms—Extra-Trees, Random Forest, XGBoost, CatBoost, Gradient Boosting Decision Trees, LightGBM, and Support Vector Regression—were comprehensively incorporated to estimate high-resolution CO₂ fossil fuel emissions. Comprehensive evaluation revealed that the multiple proxy Extra-Trees model significantly outperformed the single-proxy nighttime light linear regression model at the county scale, achieving R² = 0.96 (RMSE = 0.52 MtCO₂) in cross-validation and R² = 0.92 (RMSE = 0.54 MtCO₂) on the independent test set. Feature importance analysis identified brightness of nighttime light (40.70%) and heavy industrial density (21.11%) as the most critical spatial proxies. The proposed approach also showed strong spatial consistency with the Multi-resolution Emission Inventory for China, exhibiting correlation coefficients of 0.82–0.84. This study demonstrates that integrating local multiple proxy data with machine learning corrects spatial biases inherent in traditional top–down approaches, establishing a transferable framework for high-resolution emissions mapping. Full article

As low-altitude transportation becomes essential to global integrated transport systems, developing extensive and well-structured networks in urban agglomerations is crucial for fostering regional synergy and enhancing three-dimensional transport. Focusing on the Beijing–Tianjin–Hebei urban agglomeration, this study integrates multi-source data within a three-stage research framework: (1) node suitability assessment, (2) route optimization, and (3) network structure evaluation. It systematically evaluates the suitability of county-level general aviation airports and township-level vertiports. Building on the suitability analysis, a hierarchical route network is constructed using a modified gravity model augmented by spatial correction mechanisms. Finally, spatial syntax analysis, supplemented with equity and robustness assessments, is applied to evaluate network accessibility, topological efficiency, and resilience. The key findings are as follows: (1) The suitability classification identifies 43 Class A, 86 Class B, and 71 Class C general aviation airports, revealing a spatial pattern characterized by higher density in the east, lower density in the west, and a multi-nodal clustering structure. Township-level vertiports markedly increase terminal-node coverage. (2) The optimized hierarchical network includes 114 primary, 180 secondary, and 366 tertiary routes, bridging previous regional connectivity gaps. (3) High values of network integration, choice, spatial intelligibility, and equity-adjusted accessibility indicate robust performance, fairness in service distribution, and resilience under potential disruptions. This study offers a methodological paradigm for the systematic development of low-altitude transport networks and provides valuable references for evidence-based planning of urban agglomeration air mobility systems and the strategic development of regional low-altitude economies. Full article

As wastewater monitoring networks continue to expand the monitoring of various targets, it is important to ensure these networks remain both representative of their monitored populations and flexible enough to accurately predict shifts in an expanding list of targets. In this study, we analyzed the levels of SARS-CoV-2, influenza A (InfA), and influenza B (InfB) detected in untreated wastewater during the 2023–2024 respiratory season at 70 locations participating in the Ohio Wastewater Monitoring Network. Locations with the first detection that are seasonal hotspots and sites reaching peak concentration for each target were compared and analyzed for dependence on healthcare access and population characteristics, such as population size and density, county traffic, and demographic and socioeconomic factors. The trends in these three respiratory viruses were found to closely mirror trends in clinical indicators including the number of cases and positive tests with wastewater levels providing a two-week lead for SARS-CoV-2 and no lead for influenza on these clinical indicators. InfA was first detected in more affluent sewersheds that were less racially and ethnically diverse and had higher traffic counts, while none of the parameters tested had an effect on InfB first detects. The seasonal hotspots varied for all three respiratory viruses, where InfA hotspots were exclusively in the northeast, InfB was in the southeast and east border areas, and SARS-CoV-2 wastewater hotspots concentrated around central and northwestern Ohio. While wastewater monitoring networks may not offer full coverage of all populous areas, we have shown that a spatially distributed and highly diverse network is needed for early detection of various respiratory targets. Full article