Search Results (1,556)

The imbalance of urban public transport networks often leads to traffic congestion. Traditional planning prioritizes system optimization and single-mode travel, neglecting interactions between different modes. From an economic perspective and based on passenger travel behavior, this paper constructs a reasonable path set for multimodal networks. Using information entropy, it establishes multidimensional indicators including site path diversity entropy, destination regional entropy vectors, and weighted comprehensive entropy. Regional aggregation and coefficient of variation analyze internal balance, while scatter plots and the Gini coefficient measure global resource allocation equity. ArcGIS Pro 3.4.3 is employed for spatial analysis and visualization. An empirical study of Beijing’s six central districts reveals significant spatial heterogeneity in path distribution across functional zones: working areas exhibit concentric patterns, commercial areas form corridor agglomerations, residential areas have the highest entropy values, and transport hubs are relatively balanced. Cluster analysis based on entropy vectors effectively identifies commuter, residential, and hub station types. Some hubs show an ideal “high richness, low imbalance” state, while areas like Beijing Railway Station exhibit “low richness, high imbalance.” The Gini coefficient of 0.1864 indicates relatively balanced public transport resources overall. The “route-region-demand” collaborative analysis framework constructed in this study achieves a paradigm shift from static network structure to dynamic human-oriented evaluation, providing methodological support for equity assessment, network optimization, and resource allocation in multimodal public transport networks, and can contribute to the equitable and balanced sustainable development of public transport. Full article

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City. Full article

Due to climate change, the frequency and intensity of extreme rainfall events have increased in South Korea, resulting in recurrent urban flooding that exceeds the design capacity of conventional drainage systems. In the Dangjin Traditional Market area, comparable rainfall conditions in 2024 and 2025 caused repeated flooding, suggesting that structural improvements implemented without quantitative verification do not necessarily guarantee effective flood prevention. This study aims to support sustainable urban flood management by assessing the pre-implementation effectiveness of structural flood mitigation measures using a spatially explicit simulation approach. An ArcGIS-based rainfall–inundation simulation was conducted by integrating a 1 m LiDAR-derived digital elevation model, land cover data classified using a pixel-based Support Vector Machine, detailed building and channel datasets, and observed hourly rainfall from the July 2025 extreme event. Scenarios with and without the application of levee heightening and drainage capacity expansion were compared under identical rainfall conditions. The results indicate that the application of structural measures leads to a clear reduction in inundation extent and water depth. The proposed framework provides a practical simulation-based decision-support tool for verifying flood mitigation measures in advance and for promoting sustainable flood risk management in urban areas prone to recurrent flooding. Full article

This interdisciplinary study integrates criminological theory with geospatial methods to analyse large, multi-format datasets using geostatistical techniques. The aim is to predict where Child Criminal Exploitation (CCE) is likely to cluster, based on the spatial convergence of contextual risk factors. Drawing on insights from General Strain Theory (GST) and prior research on CCE, this study integrated seven open-source datasets capturing educational attainment, age demographics, violent crime, deprivation, and paramilitary-related violence. These variables were operationalised to construct a proxy measure for strain. Spatial analysis was conducted using ArcGIS Pro, including the Data Interoperability extension, to enable efficient integration and interrogation of multi-format geospatial data. Geospatial analysis demonstrated that contextual risk factors for CCE are spatially clustered. Using four search parameters, a small subset of wards with elevated risk were identified. This resulted in a reduction in ward locations by 85–99%, land area under investigation from 14.45% to 0.84%, and affected population from 17.91% to 1.41%, enabling more targeted and efficient resource allocation. As understanding of the contextual factors contributing to CCE improves, this methodological approach offers scalable and data-driven means of identifying high-risk areas. By integrating geospatial analysis with criminological theory, the model supports more effective safeguarding strategies and prioritisation of limited public resources. This study is limited by the absence of multi-agency datasets, which were beyond its scope. Future research aims to incorporate cross-sector data to validate and refine the model through ground-truthing, enhancing its predictive accuracy and practical applicability. Full article

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R² (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments. Full article

As a major ecological safeguard in northwestern China and an important corridor for the Belt and Road Initiative, the Hexi Corridor holds strategic significance for improving landscape structure and enhancing regional ecological security. Focusing on the Hexi Corridor, this study develops a landscape ecological risk (LER) index based on land use (LU) data from 2000, 2010, and 2020. The study employs ArcGIS spatial analysis and XGBoost-SHAP, an interpretable machine learning method, to analyze the spatiotemporal evolution of LU and LERs, as well as their driving factors. Furthermore, the Markov-FLUS model is utilized to simulate and predict LU and LER spatial patterns under multiple scenarios for 2030. The results show that: (1) The dominant land type in the Hexi Corridor is unused land, accounting for 67.33%. During the research period, the extents of unused land, grassland, and forestland showed a steady decline, while built-up land and cropland increased. (2) LERs are categorized into five types, with high risk being the most prevalent, accounting for 52.02%. Between 2000 and 2020, the total area of higher and high risks decreased by 4312 km², indicating an overall decrease in LER across the region. (3) LER is primarily influenced by annual rainfall, population density, distance to main roads, and distance to rivers. (4) Marked variations in LU patterns and LER are observed across different development scenarios projected for 2030. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article

Accurately quantifying carbon dioxide (CO₂) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO₂ emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO₂ emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO₂ emissions in this region. Correlation analysis reveals a significant positive relationship (p < 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO₂ emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout. Full article

Background: Vestibular schwannoma (VS) stereotactic radiosurgery (SRS) requires high target conformality and rapid dose falloff to spare adjacent organs at risk (OARs), particularly the brainstem. HyperArc (HA) is an automated noncoplanar volumetric-modulated arc therapy (VMAT) approach designed to standardize and streamline cranial SRS planning and delivery. We compared CyberKnife (CK) with HA for single-fraction VS SRS and evaluated the impact of multileaf collimator (MLC) leaf width. Methods: Fifteen VS cases previously treated with single-fraction CK SRS (12.5 Gy) were retrospectively replanned using HA. HA plans used four preconfigured noncoplanar partial arcs and were created with either a standard 5.0 mm MLC (HA-SMLC) or a 2.5 mm high-definition MLC (HA-HDMLC). HA plans were normalized to match the prescription dose target coverage of the corresponding CK plan for each of the patients. Endpoints included planning target volume (PTV) dosimetric statistics (Dmean, Dmin, Dmax, D98%), Paddick conformity index (PCI), Paddick gradient index (GI), ICRU Report 83 homogeneity index (HI), brain V12Gy, and brainstem Dmax. Because plans were generated for the same patients, paired comparisons were performed using two-sided Wilcoxon signed-rank tests (p < 0.05). Results: Both HA techniques achieved a higher near-minimum target dose than CK, with significantly higher PTV D98% (CK 12.35 ± 0.52 Gy; HA-SMLC 12.54 ± 0.28 Gy; HA-HDMLC 12.57 ± 0.35 Gy; p < 0.05). HA reduced target hotspots, with lower PTV Dmax than CK (CK 15.25 ± 0.32 Gy; HA-SMLC 14.70 ± 0.39 Gy; HA-HDMLC 14.73 ± 0.32 Gy; p < 0.05), and improved homogeneity and dose falloff as reflected by HI and GI (p < 0.05). CK achieved the highest conformity by PCI (p < 0.05), while HA-HDMLC improved PCI compared with HA-SMLC (p < 0.05). Brain V12Gy and brainstem Dmax were low and did not differ significantly among techniques. Conclusions: HA provides dosimetric performance comparable to CK for single-fraction VS SRS, with improved near-minimum PTV dose, reduced hotspots, and steeper dose gradients. Although CK showed the highest PCI overall, conformity improved with HA when a high-definition MLC was used. Overall, these findings support HA, particularly HA-HDMLC, as an efficient and clinically practical option for VS SRS treatment planning. Full article

Urban logistics systems face growing delivery demand and complex traffic and operational constraints, which make unmanned delivery carriers, including unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), a promising solution. Existing studies typically focus on a single delivery carrier type and rely on idealized assumptions, overlooking heterogeneous cooperation under multiple stations, multiple time windows, and real-world transport conditions. To address these gaps, we propose the Multi-Station UAV–UGV Cooperative Delivery Scheduling Problem with Temporally Discontinuous Service Availability (MSUUCDSP) to minimize the total travel and waiting time of UAVs and UGVs. To solve the problem, we propose a mixed-integer linear programming (MILP) model with a novel mathematical approach and a Hybrid Large Neighborhood Search (HLNS) algorithm. Additionally, we adopt a Hidden Markov Model (HMM)-based map-matching method and big data techniques to capture realistic operational characteristics. Computational experiments are conducted on various realistic instances under four diverse scenarios. Results show that UAV–UGV cooperation significantly improves efficiency, reducing total time cost by 17.12% compared with single-mode delivery, and they reveal substantial discrepancies between idealized assumptions and realistic scenarios. We further develop an ArcGIS-based simulation to support practical implementation. The findings provide valuable insights for decision-making and engineering applications for logistics operators. Full article

Soil organic carbon (SOC) stocks in cacao agroecosystems are characterized by accumulating large amounts. They depend on the balance between organic matter inputs (plant residues, roots) and losses (decomposition, erosion), being closely related to climatic conditions, soil nature, vegetation type, topography, and land management practices. The objective of this study was to quantify SOC stocks (0–30 cm) and assess key soil fertility indicators across 107 georeferenced sampling locations in cacao production systems of Guamal (Meta, Colombian Llanos Piedmont). Soil pH varies between extremely acidic and moderately acidic (3.8–6.0; mean 4.57), while available P (Bray II) and exchangeable bases showed low concentrations. Organic carbon concentration averaged 1.18% and bulk density averaged 1.17 g cm⁻³. SOC stocks averaged 41.10 Mg C ha⁻¹, ranging from 7.49 to 81.55 Mg C ha⁻¹, evidencing marked spatial contrasts in carbon storage. Spearman correlations highlighted coupled soil chemical controls, including positive associations of pH with Ca²⁺ and P availability and strong negative associations of pH and P with exchangeable Al³⁺, consistent with acidity-driven fertility constraints. Principal component analysis (PCA) further identified a dominant fertility gradient structured by pH, P availability, and Ca²⁺, and a second axis related to organic carbon and cation retention. Spatial modeling using inverse distance weighting (IDW) in ArcGIS supported the visualization of SOC stock variability across the study area. Overall, the results indicate that SOC stocks in these predominantly sandy soils are strongly influenced by acidity-related constraints and heterogeneous nutrient status, underscoring the need for site-specific management to jointly enhance soil fertility and climate-mitigation potential in cacao systems. Therefore, it would be advisable in the future to address the study of differential variations in soil C storage related to chemical fertilizer application rates, especially in the long term. Full article

Elucidating the trade-offs and synergistic relationships between different ecosystem services is essential to optimize the benefits of ecosystem services and ensure their proper management for human well-being and ecosystem health. However, previous studies have focused only on quantitative analysis based on statistical relationships to explore ecosystem service trade-offs and synergistic relationships as a whole; additionally, some of them lack scientific expression of spatial and temporal differences within regions. Therefore, here, we explored the trade-offs and synergies among ecosystem services in the Taihang Mountains region and conducted ecological service zoning based on the findings to support ecological conservation and high-quality development in the Taihang Mountains and North China Plain. We employed yield spatialization, the InVEST model, and ArcGIS kernel density analysis to assess the interactions among ecosystem services: provisioning (food supply), regulating (water yield and carbon density), supporting (soil retention and habitat quality), and cultural services (leisure and recreation) in the study area. Linear Pearson correlation coefficients and non-linear bagplots were utilized to analyze the interrelationships among these services. Based on the bagplot results, the geographic patterns of ecosystem service trade-offs/synergies and the distribution of dominant services were identified. The results revealed considerable trade-offs between food supply and both regulating and supporting services, with most of the latter exhibiting synergistic relationships with one another. In contrast, leisure and recreation services showed a neutral relationship with other services. Among ecosystem services, carbon density services demonstrated the highest synergistic effects, whereas food supply services exhibited the most conflicts. The various ecosystem trade-off/synergy zones and dominant service distributions generated through bagplot mappings may optimize management methods for multiple ecosystem services. Overall, these findings provide significant insights for improving ecological service zoning and natural resource management. 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

Non-communicable diseases (NCDs) are the leading causes of mortality in Serbia, with cardiovascular diseases (CVDs) accounting for a substantial share of premature mortality. In alignment with Sustainable Development Goal (SDG) Target 3.4, which aims to reduce premature mortality from NCD by one-third by 2030 relative to 2015, this study forecasts changes in CVD mortality counts at the municipal level in Serbia. Time-series data for the period 2005–2022 were analyzed within a spatio-temporal forecasting framework implemented in the Space Time Pattern Mining toolbox in ArcGIS Pro (Version 3.1). Three established forecasting models (Curve Fit Forecast, Exponential Smoothing, and Forest-based) were applied, and the most accurate model for each municipality was selected using location-specific municipality-level validation. The results reveal pronounced spatial variation: approximately half of the municipalities (51.2%) are forecasted to experience a decline in CVD mortality counts by 2030, while others are expected to show increases or no statistically significant change. Forecasted differences range from a 15.1% decrease to a 13.9% increase across municipalities, indicating heterogeneous spatial trajectories and suggesting that achieving SDG Target 3.4 may remain challenging without targeted interventions across municipalities where mortality reductions are not forecasted. Although the study does not introduce new forecasting methods, it provides a novel spatially disaggregated application of multi-model forecasting to support municipality-level monitoring of SDG 3.4. The results underscore the need for geographically differentiated public health policies and demonstrate the value of spatial forecasting approaches for supporting equitable and targeted health planning. Full article

Throughout the long history of human development, a large number of activity relics have been left on the earth, among which settlements are important carriers for studying human construction activities. In the era without modern active environmental control technology, humans used their experience to create the miracle of harmonious coexistence between humans and nature. Especially in the construction of settlements under complex environmental stress, it is the crystallization of human wisdom. For China, the settlements of ethnic minorities, due to their unique culture and harsh living environment, are undoubtedly key objects for studying the wisdom of human settlement construction. Therefore, this study takes the Tujia rural settlements in the mountainous environment of northeastern Sichuan as the research object and uses the spatial analysis function of ArcGIS to construct a complete “culture-space” environmental adaptation wisdom research system. The research results show that there is a close relationship between the cultural wisdom and spatial construction wisdom of the Tujia people in northeastern Sichuan. Cultural wisdom plays a key role in guiding settlements to adapt to terrain, water resources, and climate, etc., thus presenting a highly coordinated mechanism between the overall distribution of Tujia rural settlements in northeastern Sichuan and the construction of settlement space and the environment. The “culture-space” environmental adaptation research framework proposed in this study can provide a reference for the study of rural settlement space worldwide, and the clear settlement environmental adaptation strategies in the research can provide guidance for the construction of modern mountainous town spaces. Full article