Search Results (319)

This study investigates the influence of environmental comfort and landscape visibility on node extraction and tour route optimization by integrating spatial data analysis with site design. Three algorithmic models—environmental comfort analysis, dynamic tour route analysis, and multidimensional plot value evaluation—were developed using Grasshopper (GH) combined with Python 3.12.0. These models comprehensively quantified the solar radiation and wind conditions in Dazhai Village, Chengbu County, simulated visitor perspectives to calculate landscape visibility, and derived a quantitative visual perception index. Analysis of 197 sampling points revealed superior environmental comfort and scenic views at the village’s peripheries and open areas. Based on annual comfort duration percentages and dynamic tour evaluation coefficients, 13 activity nodes with comfort duration rates exceeding 25.68% were identified, enabling the extraction of scientifically optimized tour routes. The planning scope was further refined by integrating the village’s visual perception index to account for multi-factor influences. Establishing a digital model for traditional village activity node extraction, tour route optimization, and plot value evaluation effectively enhances spatial analysis’s efficiency and scientific rigor. This approach enriches the design methodology system for environmental comfort and landscape visibility in traditional villages while offering new perspectives for their conservation research. Full article

Urban roadside forests are vital components of green infrastructure that provide multiple ecosystem services, contributing to climate regulation, environmental quality, and urban resilience. This study assessed the multifunctional ecosystem services of roadside tree communities along four representative road types—Coastal Scenic, Commercial Arterial, Residential Secondary, and Industrial Park Roads—in Weihai, a coastal city in eastern China. Based on a complete tree inventory (6742 individuals from 38 species) integrated with the i-Tree Eco model, we quantified three key ecosystem services, carbon storage and annual sequestration, air-pollutant removal, and stormwater interception, and monetized their benefits. Results indicate that roadside forests stored approximately 1120 tons of carbon and sequestered 78 tons annually (≈USD 0.53 million; CNY 3.85 million), removed 1.28 tons of air pollutants per year (≈USD 9370; CNY 68,400), and intercepted 1560 m³ of stormwater (≈USD 5560; CNY 40,600). Commercial Arterial and Coastal Scenic Roads yielded the highest total ecosystem-service values, while Residential Secondary Roads achieved the greatest per-area efficiency. These findings highlight the significant contribution of urban roadside forests to sustainable and climate-resilient city development and underscore their potential role in urban forest planning and management. Full article

Forest recreation is irreplaceable for the protection and sustainable development of urban environments. Understanding the structural characteristics of forest recreation networks in urban areas thus offers valuable theoretical and practical insights. Grounded in social network theory and spatial analysis of recreational behavior, this study leverages point of interest (POI) data for forest attractions, forest land cover data, and user-generated content (UGC) trajectory data to analyze the evolution of the forest recreation network in the Chang-Zhu-Tan Green Heart (CZTGH) of China—the world’s largest metropolitan ecological green heart area. Findings reveal that the forest recreation network of CZTHGH exhibits a multi-center, clustered spatial pattern, with a weakened radiative influence from core to peripheral areas. While recreational behaviors are increasingly fragmented and localized, this has not undermined the network’s overall function; instead, it has fostered systemic adaptability through multiple, functionally complementary clusters, accompanied by a marked shift in activity preference toward ecologically oriented spaces such as arbor forests, shrublands, and scenic forests, alongside a significant decline in non-forest recreation. Furthermore, a high degree of spatial alignment is observed among recreation supply nodes, public demand, and forest resources, indicating synergistic spatial coordination between recreational use and ecological conservation. Findings support an analytical framework integrating recreation supply, recreation demand, and forest resources, providing practical references for the sustainable use of ecological spaces in similar urban areas. Full article

This study examines how the values of authenticity and integrity can be integrated into territorial and landscape planning, moving beyond a restoration-based view of heritage. It focuses on the defensive architecture system of Castellón province (Spain), which features around 150 fortifications forming a continuous landscape between coastal and inland areas. In a context of urban pressure, rural depopulation, and heritage tourism, this research explores how the management of these assets can be aligned with coherent territorial strategies. The aim is to assess the material, visual, and symbolic coherence of the system and to understand the tensions between physical conservation, cultural authenticity, and landscape transformation. The methodology combines documentary review, spatial analysis using GIS, and fieldwork, applying qualitative indicators of material authenticity, territorial integrity, and scenic value adapted from ICOMOS guidance and established scientific literature. The results reveal a clear contrast: 62% of urban castles are restored or consolidated, while 71% of rural ones remain in ruins, and 82% preserve high visual integrity. This paradox shows that heritage sustainability is less dependent on formal reconstruction (only 14% are fully restored) than on maintaining relationships between architecture, environment, and community. This study proposes an integrated territorial management approach that links conservation, use, and landscape as interdependent components of a single cultural system. Full article

Human trampling in urban forest parks has received increasing attention, yet its effects on microbial community assembly remain elusive. This study investigated how trampling influences soil physicochemical properties and microbial communities in Zhuyuwan Scenic Area. Neutral and null community models were used to analyze the effects of trampling on microbial assembly processes. Trampling altered both soil physicochemical properties and microbial diversity. Fungal richness differed significantly between control and light-trampling plots. Soil bulk density (SD) was strongly negatively correlated with other soil physical properties, which were positively intercorrelated. Model analyses showed that light trampling strengthened stochastic processes in bacterial community assembly, whereas heavy trampling reduced this effect. Increasing trampling intensity intensified the influence of stochastic processes on fungal community assembly. Bacterial communities were mainly shaped by heterogeneous selection, while fungal communities were primarily governed by dispersal limitation. These results enhance understanding of how trampling disturbance influences microbial community assembly and provide a theoretical basis for the ecological management and restoration of urban forest parks. Full article

The sustainable development of ecologically sensitive areas, such as geoparks, requires a comprehensive understanding of the complex interactions between tourism expansion and ecosystem services (ESs). This study investigates these relationships through a case study of the Enshi Grand Canyon—Tenglongdong Cave UNESCO (United Nations Educational, Scientific, and Cultural Organization) Global Geopark, a representative karst landscape in China. We developed an integrated analytical framework that combines multi-source data with coupled modeling approaches, including the Integrated Valuation of ES and Tradeoffs (InVEST), Geographically and Temporally Weighted Regression (GTWR), Boosted Regression Tree (BRT), and structural equation modeling (SEM). This framework overcomes the limitations of single-method analyses and enables a comprehensive diagnosis of the spatiotemporal drivers and pathways influencing ES dynamics. Using this approach, we analyzed the evolution of ESs and their driving factors from 2010 to 2020. The results reveal that natural factors remained the dominant drivers of ESs (accounting for over 73% of total variation), while tourism impacts increased substantially over time and exhibited pronounced spatial heterogeneity. Specifically, (1) the tourism-driven expansion of construction land occurred largely at the expense of cultivated land and grassland, directly reducing ESs; (2) proximity to scenic areas intensified negative ecological effects, whereas proximity to roads and hotels displayed more complex, and occasionally positive, influences; and (3) tourism primarily affected ESs indirectly through land use/cover change (LUCC). This study provides a transferable framework for analyzing tourism–ecosystem service interactions and underscores the necessity of ecological zoning and adaptive management in vulnerable karst regions, offering valuable insights for the sustainable governance of other fragile ecosystems worldwide. Full article

Urban greenway trails serve as a vital link between urban populations and the natural environment, playing a key role in enhancing quality of life and promoting physical and mental well-being. We propose an interpretable machine learning framework applied to 424 geotagged footprint images from the Bada Shanren Meihu Scenic Area in China. Our main findings are as follows: (1) The key factors influencing trail landscape preferences include the Water Visibility Index (WVI), Building Landscape Index (BVI), Freedom Index, and Greenery Visibility Index (GVI). (2) For WVI, SHAP values significantly increase around the 0.05 threshold. BVI has a critical threshold of 0.17, with a strong influence below it and a reduced effect above it. The Freedom variable shows an inverse relationship, with minimal contribution below 0.21 and a sharp increase above this threshold. GVI maintains high SHAP values at lower levels (GVI ≤ 0.66), but its predictive utility decreases at higher values. (3) Landscape preferences are significantly positively correlated with naturalness, wildness, WVI, and openness, with water landscapes being the strongest driver. In contrast, artificial factors, V_Low, and H_Purple significantly suppress preferences. This suggests that human intervention and certain color tones may reduce the attractiveness of the landscape. Full article

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments. Full article

The rapid growth of electric vehicle (EV) charging demand poses significant challenges to distribution networks (DNs), particularly during public holidays when concentrated peaks occur near scenic areas and urban transport hubs. These sudden surges can strain transformer capacity and compromise supply reliability. Fixed soft open points (SOPs) are costly and underutilized, limiting their effectiveness in DNs with multiple transformers and asynchronous peak loads. To address this, from the perspective of power supply companies, this study proposes a mobile soft open point (MSOP)-based approach to enhance the hosting capacity of EV charging stations. The method pre-installs a limited number of fast-access interfaces (FAIs) at candidate transformers and integrates a semi-rolling horizon optimization framework to gradually expand interface availability while scheduling MSOPs daily. An automatic peak period identification algorithm ensures optimization focuses on critical load periods. Case studies on a multi-feeder distribution system coupled with a realistic traffic network demonstrate that the proposed method effectively balances heterogeneous peak loads, matches limited interfaces with MSOPs, and enhances system-level hosting capacity. Compared with fixed SOP deployment, the strategy improves hosting capacity during peak periods while reducing construction costs. The results indicate that MSOPs provide a practical, flexible, and economically efficient solution for power supply companies to manage concentrated holiday charging surges in DNs. Full article

Cultural ecosystem services (CES) represent fundamental expressions of human-environment interactions. A comprehensive assessment of CES supply and demand offers a robust scientific foundation for optimizing the transformation of ecosystem service values to improve human well-being. This study integrates multi-source datasets and employs Maximum Entropy (MaxEnt) modeling with the ArcGIS platform to analyze the spatial distribution of CES supply and demand in Hunan Province, a typical mountain tourism regions in China. Furthermore, geographical detector methods were used to identify and quantify the driving factors influencing these spatial patterns. The findings reveal that: (1) Both CES supply and demand demonstrate pronounced spatial heterogeneity. High-demand areas are predominantly concentrated around prominent scenic locations, forming a “multi-core, clustered” pattern, whereas high-supply areas are primarily located in urban centers, water systems, and mountainous regions, exhibiting a gradient decline along transportation corridors and river networks. (2) According to the CES supply-demand pattern, Hunan Province can be classified into demand, coordination, and enhancement zones. Coordination zones dominate (45–70%), followed by demand zones (20–30%), while enhancement zones account for the smallest proportion (5–20%). (3) Urbanization intensity and land use emerged as the primary drivers of CES supply-demand alignment, followed by vegetation cover, distance to water bodies, and population density. (4) The explanatory power of two-factor interactions across all eight CES categories surpasses that of any individual factor, highlighting the critical role of synergistic multi-factorial influences in shaping the spatial pattern of CES. This study provides a systematic analysis of the categories and driving factors underlying the spatial alignment between CES supply and demand in Hunan Province. The findings offer a scientific foundation for the preservation of ecological and cultural values and the optimization of spatial patterns in mountain tourist areas, while also serving as a valuable reference for the large-scale quantitative assessment of cultural ecosystem services. Full article

Avalanche susceptibility mapping is vital for disaster prevention and infrastructure safety in cold mountain regions under climate change. Traditional machine learning (ML) approaches have demonstrated strong predictive capacity, yet their limited interpretability and difficulty in identifying threshold effects hinder their broader application in geohazard risk management. To overcome these limitations, this study develops an explainable ML framework that integrates remote sensing data, topographic and climatic variables, and SHapley Additive exPlanations for the Kanas Scenic Area transportation corridor in the Chinese Altay Mountains. The framework evaluates five classifiers: Random Forest, XGBoost, LightGBM, Soft Voting, and Stacking, and using sixteen conditioning factors that capture topography, climate, vegetation, and anthropogenic influences. Results show that LightGBM achieved the best performance, with an AUC of 0.9428, accuracy of 0.8681, F1-score of 0.8750, and Cohen’s kappa of 0.7366. To ensure transparency for risk decisions, SHAP analyses identify Terrain Ruggedness Index, wind speed, slope, aspect and NDVI as dominant drivers. The dependence plots reveal actionable thresholds and interactions, including a TRI plateau near 5–7, a slope peak between 30° and 40°, a wind effect that saturates above about 2.5 m s⁻¹, and a near-river high-risk belt within 0–2 km. The five-class map aligns with independent field observations, with more than three quarters of events falling in moderate to very high zones. By integrating explainable ML with remote sensing, this study advances avalanche risk assessment in cold region transportation corridors and strengthens the robustness of regional susceptibility mapping. Full article

Medicinal plants are used all over the world to prevent, cure, and manage many different diseases. The aim of this study was to provide knowledge on different medicinal plants that are native to Pike County, Eastern Kentucky, USA. The study involved two stages of activity. First, it involved a survey of some county locals to identify medicinal plants used for different medical purposes. The second part dealt with searching research databases like PubMed and Google Scholar to find out if any of those plants, identified in the survey, have any published scientific studies on them. The results of the survey identified 14 locally used medicinal plants (Asimina triloba, Callicarpa americana, Chimaphila umbellate, Cichorium intybus, Eupatorium perfoliatum, Monotropa uniflora, Paulownia tomentosa, Phytolacca americana, Portulaca oleracea, Sassafras albidum, Ampelopsis glandulosa, Ulmus rubra, Verbascum thapsus, and Xanthorhiza simplicissima) belonging to different families, plant types and used for a wide variety of purposes. Most plants belonged to the Ericaceae and Asteraceae families, were mostly herb type, while the most common plant part was berries, leaves and roots. The survey also showed that the local population use these plants for a variety of purposes, such as a food additive, insect repellant, antirheumatic, antiarthritic, coffee alternative, laxative, antitussive, analgesic, or anti-infective. Sometimes these plants and plant substances are used raw, made into tea, or even made into an edible jam product. For the second part of the study, all the plants were supported by multiple published studies. The most common pharmacological activity among the plants was antimicrobial, followed by anticancer, antioxidant and anti-inflammatory activities. Eastern Kentucky is well known for its scenic Appalachian Mountains, but the area holds potential for innovative herbal medicine as well. More interest and research are needed to further explore the treasure of medicinal plant use knowledge resting in this area. Additionally, more phytopharmacological and phytochemical studies are needed to investigate the scientific potential of traditionally used medicinal herbs from this region. Full article

Rapid urbanization in China has intensified spatial and social disparities between urban and rural areas, posing major challenges to sustainable rural development. Traditional top-down rural construction and evaluation models often neglect villagers’ everyday practices, resulting in mismatches between spatial planning and actual use. This study develops a cross-scale, bottom-up framework for assessing rural construction through social network analysis (SNA), taking Xiongfan Village in Dawu County, Hubei Province, as a case study. At the village scale, the comparison between the “Public Space Structure Network” and the “Villagers’ Space Usage Behavior Network” reveals a significant mismatch between spatial compactness and behavioral dispersion, with high-frequency activities concentrated along the north–south axis while peripheral and east–west spaces remain underutilized. At the township scale, GPS-based analysis shows that the revitalization of Xiongfan transformed it from a peripheral node into a central hub, restructuring the network into a new pattern of “characteristic towns—traditional villages—ecological scenic areas.” These findings highlight the dual role of rural construction in both meeting residents’ daily needs and fostering regional integration. The proposed cross-scale SNA framework not only advances methodological tools for evaluating rural construction but also provides practical guidance for inclusive, resilient, and sustainable urban–rural development in line with the UN Sustainable Development Goals (SDGs). Full article

Building healthy communities is crucial for creating healthy cities and improving residents’ well-being. Old residential areas, with their substantial stock and elevated health risks, require urgent environmental upgrades. However, the relationship between community health promotion factors and resident sentiment, a crucial indicator of subjective well-being, in old residential areas remains poorly understood. By integrating big data-based community health promotion factors and Weibo data within the 15-min living circle of old residential areas in Xi’an, we developed an XGBoost model and employed SHAP analyses to interpret predictive outcomes. Results show that healthy facilities were dominant influencing factors in old residential areas. Densities of parking, supermarkets, education, package stations, and scenic spots exhibit nonlinear relationships with positive sentiment, indicating clear threshold effects and saturation effects. Two dominant patterns were observed in interactions between dominant factors and their strongest interacting factors. Four environment–sentiment patterns were identified for targeted planning interventions. It is recommended that planners and policymakers account for density phases and synergistic combinations of the dominant factors to optimize community health within old residential areas. The findings offer empirical support and planning insights for fostering healthy, sentiment-sensitive retrofit in old residential areas within the 15-min living circle. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article