Search Results (432)

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

Traditional villages in ecologically fragile and multi-ethnic frontier regions are increasingly threatened by rapid urbanization and socio-economic transformation. Northwestern Yunnan, located in the longitudinal valleys of the Hengduan Mountains, represents a key cultural landscape of plateau agropastoral civilization and ethnic interaction, yet its spatial organization and clustering mechanisms remain insufficiently understood. This study develops a four-dimensional analytical framework integrating four dimensions—spatial morphology (village distribution patterns and density), geomorphological conditions (elevation, slope, and terrain features), cultural attributes (ethnic composition and historical-cultural corridors), and architectural typologies (dominant residential structure types) to examine 246 officially recognized traditional villages. Using GIS-based spatial statistics, kernel density estimation (KDE), spatial autocorrelation, and a hierarchical overlay model, the study identifies the spatial structure (distribution patterns and density gradients), environmental adaptability (relationships with elevation, slope, and hydrological conditions), and multidimensional clustering characteristics (integrated clustering intensity across four analytical dimensions) of settlements. The results reveal a highly uneven and a statistically significant clustered spatial pattern ($R$ = 0.606, Moran’s $I$ = 0.251, p < 0.05) characterized by a “two corridors–six clusters–multiple nodes” structure. Settlement distribution demonstrates strong coupling with mid-elevation plateau basins, river valley systems, and trade-cultural corridors shaped by the Ancient Tea Horse Road. Multidimensional integration further classifies villages into three typologies—comprehensive, specialized, and general clusters—reflecting different levels of coordination among spatial, environmental, cultural, and architectural dimensions. These findings reveal the spatial regularities and multidimensional clustering characteristics of officially recognized traditional villages in Northwestern Yunnan, and suggest that environmental setting, historical corridors, and cultural-architectural features jointly shape the current recognized heritage landscape. The proposed framework provides a context-sensitive basis for differentiated heritage conservation and rural management in mountainous multi-ethnic regions. Full article

This study quantifies the spatiotemporal evolution of non-communicable disease (NCD) mortality risk associated with temperature extremes in the Metropolitan Area of the Valley of Mexico (MAVM) from 2000 to 2019. Using a Bayesian risk assessment framework, we analyzed 747,131 deaths to evaluate the impact of extreme temperature indices (Tn90p, Tn10p, TNn, Tx90p, Tx10p, TXx, DTR) across demographic and geographic dimensions. Results reveal a significant intensification of mortality risk, particularly for circulatory and metabolic diseases after 2005 and 2014. Risk expansion analysis identified 16 cases of robust relative risk (RR) intensification, predominantly among elderly populations. Females and males aged 65+ with metabolic diseases exhibited the highest thermal vulnerability. Our analysis further indicates a systematic shift in mortality risk toward higher nocturnal temperatures and reduced diurnal variability, suggesting a transition from cold-related stress to persistent nighttime heat exposure. Spatial Bayesian modeling shows a progressive homogenization of environmental risk across the metropolitan area, with high-risk thermal profiles expanding from the urban core toward peripheral municipalities, reducing the extent of previously lower-risk zones. Notably, the number of municipalities in the highest risk category for females aged 65+ with metabolic diseases increased by 550%, while for males of the same age, the expansion reached 163%. These findings indicate that vulnerability in megacities is a dynamic process driven by nocturnal warming and thermal instability. They highlight the urgent need to integrate climate-sensitive planning strategies—such as the identification and preservation of climatic refuge zones—into urban development policies, alongside continuous monitoring of temperature-related health risks. Full article

While parameters such as slope and aspect are routinely considered in solar radiation modelling, the role of terrain or surface roughness remains underexplored, with no universally accepted method for its calculation. This study compares several approaches to quantifying terrain or surface roughness in several geographical information system (GIS) environments (ArcGIS, QGIS, WhiteboxTools, and SAGA GIS) and introduces local fractal dimension, computed using a custom Python script, as an additional metric. The aim is to evaluate the influence of surface roughness on potential solar radiation modelling and to examine its relationship with other terrain parameters. The analysis is based on case studies from both a rugged alpine environment in the Tatra Mountains (Tichá and Kôprová dolina (valleys), Kriváň peak; 944–2467 m a.s.l.) and an urban environment (the city of Poprad, near the High Tatras, Slovakia). The results demonstrate that surface roughness can significantly affect potential solar radiation modelling in areas with high surface variability. The findings are applicable not only to solar radiation studies, but also to other fields of spatial modelling, where incorporating surface roughness can improve the accuracy and robustness of spatial analyses and predictions. Full article

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

Urban green spaces (UGS) are critical regulators of carbon sequestration in industrial cities; however, the configuration mechanisms underlying their carbon dynamics remain insufficiently understood. This study investigates how landscape configuration influences carbon sequestration capacity in Lanzhou and Baotou using multi-temporal datasets from 2000, 2011, and 2022. Net primary productivity (NPP) derived from the CASA model was employed to represent carbon sequestration capacity. An integrated XGBoost-SHAP framework was applied to identify dominant configuration metrics, nonlinear responses, and structural thresholds. The XGBoost model showed stable predictive performance across the three periods, with test-set R² values ranging from 0.470 to 0.510 in Lanzhou and from 0.325 to 0.379 in Baotou. The results reveal systematic and persistent differences in configuration-driven controls between the two cities. In Lanzhou, aggregation-related metrics, particularly COHESION, consistently exert the strongest influence across all three periods, indicating that spatial cohesion and connectivity function as primary stabilizing mechanisms in a mountainous, valley-constrained urban system. Carbon sequestration performance increases once sufficient structural integration is achieved, with aggregation thresholds remaining relatively stable, for example AI values of approximately 0.31–0.34 across 2000–2022, reflecting the importance of maintaining ecological continuity under semi-arid climatic stress. In contrast, Baotou is more strongly regulated by fragmentation-related metrics, especially edge density (ED) and division index (DIVISION), suggesting that its relatively open terrain and industrial spatial structure render carbon sequestration more sensitive to patch separation and edge proliferation. Here, fragmentation acts as a dominant structural constraint, limiting vegetation productivity once spatial disintegration intensifies; for example, ED thresholds shifted from approximately −0.23 in 2000 to −0.56 in 2022. Landscape–carbon relationships exhibit pronounced nonlinear and threshold-dependent behavior in both cities. Rather than responding gradually to structural modification, NPP shifts across identifiable transition points that remain broadly stable over time; for instance, Lanzhou’s AI threshold remains within 0.31–0.34, whereas Baotou’s ED threshold changes from −0.23 to −0.56 across 2000–2022, indicating that these thresholds represent intrinsic structural characteristics of the respective urban ecological systems. However, the magnitude and configuration logic of these thresholds differ between Lanzhou and Baotou, confirming the existence of city-specific nonlinear regimes. These findings demonstrate that urban carbon sequestration operates through context-dependent configuration pathways shaped by terrain, climatic constraints, and long-term spatial organization. The study advances understanding of how structural heterogeneity governs carbon dynamics in arid and semi-arid industrial cities and provides a quantitative basis for configuration-sensitive land planning. Full article

This study examines how rural Latinx students from California’s San Joaquin Valley make sense of and navigate access to four-year universities within a geographically and institutionally constrained college-going landscape. While prior research on Latinx college access has largely centered on urban contexts, this article highlights how race and rural place shape the availability and quality of information about four-year universities as experienced and interpreted by rural Latinx students. Guided by the nepantla stage of the college-conocimiento framework and using Chicana/Latina feminist pláticas as methodology, narratives from rural Latinx undergraduates who reflected on their high school advising and recruitment experiences were analyzed. Findings show that students perceived that counselors used labor expectations to position agricultural and low-wage work as presumed futures for students, even as they pursued four-year enrollment amid uneven institutional support. Students also reflected that four-year pathways were also less accessible through routinized counseling practices that standardized two-year enrollment and framed four-year options as less feasible. Students further described limited outreach from four-year institutions, which widened information gaps and placed the burden of navigating college on students and families. Implications emphasize the need for more equitable counseling practices and accessible outreach strategies that expand four-year information and support in rural regions. Full article

This paper investigates the multi-objective optimization of the peak–valley difference, operating cost, and carbon emissions for urban logistics battery-swapping stations (BSSs) under photovoltaic uncertainty and stochastic demand. Unlike conventional plug-in charging, battery swapping decouples energy replenishment from the vehicle dwell time, enabling rapid service, but introducing discrete swap arrivals and power–inventory coupling challenges that continuous-load models cannot capture. A Stackelberg game-based framework models grid–BSS interactions, where the grid acts as the leader by setting time-of-use prices and BSSs respond by optimizing charging/discharging schedules. Carbon emissions are quantified using real-time carbon intensity data obtained from the Electricity Maps platform. The battery-swapping demand is modeled as a Poisson process, and a unified power–inventory coupling model captures the bidirectional dependence among PV generation, grid purchases, energy storage operations, and battery inventory dynamics, where the inventory feasibility constrains the power decisions. For multi-station coordination, an adaptive ADMM decomposes the problem into parallelizable sub-problems. Case studies of a 49-vehicle fleet across three BSSs in Qingdao, China, show that, compared with a no-optimization baseline, the proposed method reduces the peak–valley difference by approximately 21.6%, the operating cost by approximately 10.2%, and carbon emissions by approximately 15.7%. Compared with the single-objective counterparts, the multi-objective formulation further improves the peak–valley difference by approximately 26.9% and increases emission reduction by approximately 16.9%; paired t-tests on repeated runs indicate statistical significance (p < 0.05). The framework provides a scalable methodology for low-carbon BSS scheduling with explicit power–inventory coupling. Full article

Spatial mapping of PM_2.5 in complex urban and suburban terrains remains challenging for classical geostatistical interpolation. This study evaluates a Random Forest (RF) framework for high-resolution air pollution mapping and compares its performance with ordinary kriging in the Kraków region. The analysis integrates measurements from 51 low-cost air quality sensors with topographic and meteorological predictors, including elevation, temperature, relative humidity, and wind speed. Five representative hours during a relatively windless, inversion dominated day were selected to examine hourly variability in pollution patterns. Model robustness was assessed using leave-one-out (LOO) cross-validation, while interpretability was addressed through permutation-based predictor importance analysis. The RF model achieved high predictive accuracy (R² = 0.85 to 0.95) and good spatial stability with an LOO standard error below 5%. Elevation consistently emerged as the dominant predictor, confirming the key role of terrain-controlled accumulation, while temperature and humidity gained importance during evening and nighttime hours. The RF approach captured fine-scale transport features along river valleys that were not resolved by ordinary kriging, which produced smoother but less interpretable surfaces. The results demonstrate that RF mapping provides an accurate and explainable support to traditional geostatistical methods for analyzing urban air pollution dynamics in complex terrain. Full article

Mining-resource-based cities, as distinctive human–environment systems, face urgent challenges from intensified urbanization and mining, leading to land imbalance and ecosystem service degradation. To enhance resilience, it is essential to identify the evolution and drivers of ecosystem services and construct targeted ecological compensation models. This study focuses on Xinzhou, a representative mining city in China, and systematically analyzes three aspects: (1) spatiotemporal dynamics of land use and ecosystem service value (ESV) from 2000 to 2023 using Markov chains, equivalent factor method, hotspot and sensitivity analyses; (2) identification of ESV driving mechanisms through an integrated “stepwise regression + geographical detector” framework; and (3) formulation of ecological compensation models via quantification of priority indices, demand intensity coefficients, and compensation standards. Key findings indicate that land conversion was concentrated in coalfield zones and surrounding built-up areas, involving 2,518,341.75 hm² (35.76% of total area), primarily characterized by a reduction in farmland and expansion of forest, grassland, and construction land. ESV showed a striped spatial pattern, with higher values in mountainous zones and lower values in valleys and basins with frequent human activity. The northwest coalfield region experienced an initial decline followed by a recovery in ESV. Annual mean temperature emerged as the dominant driver, while DEM influence increased annually. All factor interactions exhibited synergistic effects, with natural variables exerting greater influence than socio-economic ones. Ecological compensation demand was high overall, especially in Wutai, Kelan, and Pianguan counties, with high-value compensation areas mainly distributed in the eastern and central parts of Xinzhou. Looking ahead, a compensation framework prioritizing ecological–economic optimization should be developed, guided by zoned, typological, and dynamic configurations. By analyzing ecosystem governance from the perspective of a mining-resource-based city, this study enhances global ecosystem service evaluation frameworks and offers a replicable model to advance transnational ecological cooperation and green urban transformation. Full article

Urbanization in mountainous valley regions is constrained by rigid topography, generating complex correlations between spatial growth and ecological security. The coupling between urban expansion and landscape ecological risk (ERI) was evaluated for six representative valley cities in western China from 1985 to 2023. Annual land-cover data (CLCD) and fine-scale terrain models were integrated with expansion metrics, slope gradient analysis, and spatial statistics to identify growth trajectories and risk reorganization. Urban growth shifted from edge expansion to leapfrog development as valley floors became saturated. Two vertical trajectories emerged: a low-slope lock-in pattern (e.g., Lanzhou) where development remains largely on slopes < 6° and an uplift towards mid-slopes pattern (e.g., Chongqing), where expansion increasingly occurs on 6–25° terrain. ERI correspondingly showed three spatial typologies: valley contrast, heterogeneous mosaic, and high-risk background dominance. Although ERI generally declined, reflecting structural hardening with rising built-up land shares, the spatial clustering of risk remained stable. GeoDetector results indicate that terrain sets a baseline for ERI differentiation, but its explanatory power varies across cities and is often surpassed by land-cover composition. These findings support differentiated governance, requiring strict controls on slope disturbance in uplift cities and prioritizing corridor connectivity in lock-in cities. Full article

Against the backdrop of rapid urbanization, the human–land relationship in the mountainous regions of Southwest China (Sichuan, Yunnan, Guangxi, Chongqing, and Guizhou) confronts dual pressures from terrain constraints and development demands, shaping a uniquely complex evolutionary pattern. To clarify the evolutionary laws of the regional human–land system, this study focuses on the period of 2000–2020, integrating land use, socioeconomic, and topographic data to construct a comprehensive analytical framework of “Human Activity Intensity (HAI)–Land Use Dynamic Degree (LUDD)–decoupling model–geographic detector.” This framework is employed to explore the spatio-temporal evolution characteristics of the human–land pattern, the differentiation of decoupling modes, and the underlying driving mechanisms. The key findings are as follows: Human Activity Intensity (HAI) presents a stable spatial pattern of “agglomeration in low-altitude areas and dispersion in high-altitude areas,” undergoing a three-stage temporal evolution of “terrain anchoring–policy constraint–all-round expansion.” Land use dynamics are predominantly governed by terrain: low-altitude river valley plains exhibit significant changes, while high-altitude karst regions remain relatively stable, with an overall policy-responsive fluctuation of “rise–fall–rebound.” Human–land decoupling forms a continuous spectrum encompassing four modes: “collaborative optimization–extensive transition–rigid stagnation–advantageous aggregation,” with strong negative decoupling dominating low-altitude favorable areas and recessive decoupling prevailing in high-altitude mountainous areas. In terms of driving mechanisms, terrain factors serve as the rigid foundation of the human–land relationship, while the urban–rural population structure, urbanization level, and land use intensity act as core human drivers. Additionally, the interaction of factors such as “terrain–economy–transportation” plays a crucial role in the differentiation of decoupling modes. This study clarifies the evolutionary logic of “terrain laying the foundation and human factors shaping the pattern” for the human–land relationship in Southwest China’s mountainous regions, providing scientific support for the coordinated advancement of regional economic development and ecological protection, as well as a Chinese case study for global research on human–land coordination in ecologically fragile mountainous areas. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article