Search Results (3,383)

Wind power development is accelerating globally, leading to an imminent large-scale retirement of wind turbines and increasing the need for recycling infrastructure. This study proposes an integrated framework for recycling center site selection by combining Geographic Information System (GIS), AHP–CRITIC weighting, and Super-Efficiency Data Envelopment Analysis (DEA). A hierarchical GIS indicator system is constructed by incorporating environmental, locational, and social compatibility factors, including elevation, slope, land use, transportation accessibility, proximity to wind farms, and population-related constraints. GIS performs Euclidean distance, kernel density, and weighted overlay analyses to identify suitable areas, while indicator weights are determined through a hybrid subjective–objective approach. A Super-Efficiency DEA model is then applied, using labor and land costs as inputs and annual decommissioning quantities as output, to evaluate and rank candidate sites, with higher-ranked sites regarded as reliable locations. A case study in Xilingol, Inner Mongolia, verifies the method’s effectiveness. The proposed framework supports scientific planning for wind turbine recycling and promotes sustainable wind energy development. Full article

In distributed Geographic Information Systems (GIS), preserving topological validity without sacrificing real-time interactivity under high-frequency concurrent editing of spatial polygons remains a persistent challenge. Recent distance-based heuristic methods suffer from scale-dependent bottlenecks and unreliable topology preservation, while more robust application-layer caching mechanisms still incur severe queuing latency under intense concurrency. To overcome these limitations, we propose Geo-CRDT, a geometry-aware distributed data structure that integrates spatial constraints directly into its underlying architecture. By dynamically isolating concurrent spatial entanglements into a strictly bounded local scope $\mathcal{S}$, the system deterministically resolves complex 2D conflicts via scalar projection, repairing the local topology in $O\left(\right|\mathcal{S}\left|\right)$ time. Rigorous simulations and a 15-participant real-world case study validate that Geo-CRDT sustains low-latency responsiveness and structural reliability under extreme concurrency, offering a robust foundation for large-scale crowdsourced spatial collaboration. Full article

In arid and semi-arid regions, groundwater recharge is a key process controlling the sustainability of subsurface water resources. This study aims to assess and map the groundwater recharge potential of the Oued Cherrat watershed (Morocco) using an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). Six controlling factors were considered: lithology, lineament density, drainage network density, slope, land use/land cover derived from the Normalized Difference Vegetation Index (NDVI), and rainfall. The relative weights of these factors were determined through pairwise comparisons using the Saaty fundamental scale, and the consistency of the judgments was verified (CR < 0.1). The reclassified thematic layers were integrated into a GIS-based weighted overlay model to generate the groundwater recharge potential map. Five recharge classes were identified, ranging from very low to very high. The results show that areas with moderate recharge potential are the most widespread (approximately 37% of the watershed), while high and very high potential zones account for about 25% of the total area. These zones are mainly associated with permeable lithologies, high densities of structural discontinuities, gentle slopes, and low drainage density. In contrast, low to very low recharge potential areas are related to low-permeability formations, steep slopes, and dense drainage networks. The resulting recharge potential map provides a useful decision-support tool for sustainable groundwater management and for identifying priority areas for aquifer protection and artificial recharge planning in the Oued Cherrat watershed. Full article

The integration of building information modeling (BIM) and geographic information systems (GISs) is an important area of research aimed at improving interoperability between these domains. These domains often use different concepts for semantics such that non-interoperable vocabularies; schemas; metamodels for semantics; and, in general, non-interoperable IT architectures are used to publish semantic concepts. This study investigates the use of BIM data dictionaries for semantic classification of vector-based geospatial data in GISs, aiming to enable the use of common dictionaries and concepts to describe objects in both domains. The study addresses a particular problem: the fact that the domains use different metaconcepts to describe conceptual information and have different classification methods. The research focuses on identifying significant standards, comparing their metamodels to find similarities and explore the practical use of BIM data dictionaries for the semantic enrichment of GIS features. As a proof of concept, three approaches for the classification of features are developed and validated through implementation in the QGIS software. The results demonstrate that BIM data dictionaries can be used to semantically enrich geospatial data in GISs, with the buildingSMART Data Dictionary (bSDD) serving as a practical example. The conclusions drawn from the study are that although there are limitations and challenges, the integration of BIM data dictionaries into GISs is possible and beneficial for improving interoperability, particularly when cross-domain concepts are employed. Full article

Geographic Information Systems (GIS)-based spatial analyses have become an important tool for prehistoric research globally. Sri Lanka holds a distinctive prehistoric record in South Asia, supported by extensive investigations. In contrast, the systematic applications of GIS analyses for prehistoric studies on the island are comparatively limited. This study examines the spatial distribution of prehistoric sites in the Kegalle district, where recent documentation suggests it differs from previous estimates. The study identified 16 new prehistoric sites, bringing the total to 22, including six already documented, representing the first GIS-based systematic expansion of the prehistoric site inventory in this district in six decades. Three analyses, Kernel Density Estimation (KDE), Least-Cost Path (LCP), supported by 3D terrain modelling and corridor analysis, were applied to examine site distribution and modelled movement potential. KDE results provided a preliminary spatial visualization showing higher site density around the Ma Oya basin and the Seethawaka Ganga, a tributary of the Kelani River basin; given the small sample size (n = 22), these patterns should be treated as survey coverage indications rather than confirmed settlement distributions. LCP indicated valley-oriented modelled movement potential, intermediate elevation site distribution and key topographic convergence points across the landscape. Corridor analysis identified low-gradient valley routes as probable topographic movement zones along the modeled least-cost paths. The integrated results suggest a preliminary pattern of valley-oriented site distribution and topographically favorable movement terrain concentrated around the Ma Oya and Kelani River basins, treated as exploratory spatial indications pending validation through future systematic survey and radiocarbon dating. This study presents one of the first systematic applications of an integrated GIS-based analytical framework for prehistoric spatial analysis in Sri Lanka, suggesting how such approaches can generate testable hypotheses and provide actionable guidance for future archaeological fieldwork in regions where comprehensive chronological data remain limited. Full article

With the growing adoption of electric vehicles, the proper siting of electric vehicle charging stations (EVCSs) has become a critical issue in urban transportation planning. This study addresses the EVCS siting problem in Kuwait through a spatial decision-making approach. A total of 25 spatial criteria covering transportation, land use, environmental conditions, and energy infrastructure were evaluated. Criterion weights were calculated from expert judgments using the Fuzzy SIWEC and SWARA methods. The results showed a high level of consistency between the two weighting methods, with a Spearman rank correlation coefficient of ρ = 0.9090 and a Pearson correlation coefficient of r = 0.8376. The final weights indicated that tourism, culture, and entertainment areas (C2.3, 0.05046), parking areas (C1.3, 0.04910), road accessibility (C1.4, 0.04813), and retail and dining areas (C2.6 to C2.7, 0.04708 to 0.04757) were the most influential factors in EVCS planning. All criteria were spatially represented in a geographic information systems environment, normalized to the [0–1] range according to their benefit and cost directions, and integrated through weighted overlay analysis to produce a continuous EVCS suitability map. Based on this suitability surface, 133 candidate EVCS alternatives were assigned to areas with relatively high suitability values and active urban land-use characteristics. The extracted raster suitability values of these candidate alternatives ranged approximately between 0.640 and 0.860, indicating that the assigned points were concentrated in spatially favorable areas rather than being randomly distributed. The ranking results obtained from TOPSIS and VIKOR showed that the top six alternatives were identical in both methods, and alternative A123 ranked first with a VIKOR value of 0.007548 and a TOPSIS value of 0.884213. Sensitivity analysis showed that changes in criterion weights affected suitability values and transition zones, while the overall spatial pattern of highly suitable areas remained stable. The findings suggest that the proposed GIS-MCDM framework provides a practical preliminary decision-support basis for spatial screening and investment prioritization in EVCS planning, particularly in hot-climate cities. Full article

Interventions implemented in geographic space (geo-interventions) have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To solve this, we produced a framework for transforming spatial data to model potential actions for generating geo-interventions. Finally, we conducted a case study of reducing traffic collisions in Toronto, Canada, to demonstrate the framework, which produced a machine learning model that discovered geo-interventions modifying red light camera, transit shelter, and wayfinding infrastructure predicted to reduce collisions by 5.7%. We highlight the importance of the framework for bridging research and practice through unified understanding, actionable outputs, human guidance, and iterative refinement. With recent advances in big data and artificial intelligence, we envision an acceleration in the discovery of geo-interventions and emergence of interdisciplinary work towards predicting accurate and precise future real-world outcomes at scale. Full article

As the offshore wind industry expands, improving operation and maintenance (O & M) efficiency while reducing the levelized cost of electricity (LCOE) has become increasingly important. This study develops an intelligent inspection route optimization framework for 21 offshore wind turbines located in the Changhua offshore wind farm of Taiwan. The framework integrates Geographic Information System (GIS) spatial information, dynamic sea-state conditions, labor costs based on Taiwan’s Labor Standards Act, and vessel fuel consumption into a comprehensive cost evaluation model. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to two practical scenarios: full-field routine inspections and targeted maintenance missions. Experimental results show that PSO achieved the shortest route for full-field inspections, reducing the travel distance to 20.125 km compared with 23.976 km obtained by GA. In contrast, for targeted maintenance involving eight turbines, GA generated a shorter route of 5.719 km, outperforming PSO’s 6.456 km. For the scenarios investigated in this study, PSO showed superior performance in the 21-turbine inspection task, whereas GA achieved better results in the 8-turbine maintenance task. The proposed framework provides an effective decision-support tool for offshore wind farm O & M planning, improving maintenance efficiency while reducing operational costs. Full article

This paper focuses on the analysis of long-term land surface temperature (LST) dynamics and land-use changes in the city of Košice, Slovakia, during the period 1985–2025. The analysis is based on multi-temporal Landsat satellite imagery processed within a geographic information system (GIS) environment. Non-parametric statistical methods, including the Mann–Kendall trend test and the Theil–Sen slope estimator, were applied at the pixel level to identify the direction, magnitude, and statistical significance of long-term trends. Land-use changes were evaluated using CORINE Land Cover data together with the NDVI and NDBI spectral indices. The results revealed a statistically significant increase in land surface temperature across almost the entire urban area, with the mean LST increasing by 5.83 °C between 1985 and 2025. The analysis also confirmed a strong positive correlation between built-up areas and LST values, whereas vegetation cover exhibited a significant cooling effect represented by a strong negative correlation with surface temperature. Spatial analysis identified pronounced warming hotspots concentrated mainly in industrial and newly urbanized areas, while vegetation-stabilized zones showed lower warming intensity or localized cooling trends. The findings highlight the dominant influence of urbanization processes on the city’s thermal regime and emphasize the importance of urban vegetation as a key adaptation element for mitigating the surface urban heat island effect. The study also illustrates the added value of integrating remote sensing data, GIS tools, and pixel-based trend analysis in the assessment of long-term changes in the urban thermal environment of medium-sized Central European cities. The results provide a spatial basis for climate adaptation planning and future assessments of urban thermal comfort and environmental quality. Full article

Under the “dual carbon” goals, Taiyuan, a prefecture-level administrative unit and energy-intensive region in Shanxi Province, China, has experienced changes in soil carbon storage and soil carbon flux under rapid urbanization and industrialization. To clarify the spatial patterns of soil carbon storage and flux, 26 field sampling sites, including 78 soil samples, were analyzed using laboratory measurements and an optimized tunable diode laser absorption spectroscopy–geographic information system (TDLAS–GIS) integrated monitoring approach. This study investigated the spatial patterns of soil carbon storage and flux and discussed their potentially associated factors, providing an exploratory workflow for regional carbon monitoring. The results showed clear spatial heterogeneity, with an average soil organic carbon (SOC) content of 10.86 g/kg. High-SOC areas were mainly located in the southern and southwestern plains, while lower SOC levels occurred in urban expansion zones and highly disturbed surfaces. The western mountainous areas were important ecological barriers but were not the highest measured SOC zones. At the site level, arable land and forestland showed higher mean SOC values than grassland, with average SOC contents of 12.47, 12.07, and 8.27 g/kg, respectively, although these land-use-related differences were not statistically significant. Soil carbon flux was relatively higher in some mountainous regions and industrial–ecological transition areas but lower in several urban expansion areas. The results suggest that urbanization and industrial activity may be associated with changes in SOC and soil-atmosphere CO₂ exchange. This study describes the spatial variation characteristics of soil carbon storage and flux, establishes a reproducible TDLAS–GIS workflow for regional carbon monitoring, and provides exploratory support for ecological sustainability, sustainable land management, and the “dual carbon” strategy in northern resource-based cities. Full article

Cultural heritage assets in seismic metropolitan regions are increasingly exposed to interacting natural hazards, yet disaster risk assessments for historic buildings often remain limited to single-hazard interpretations. This study addresses this gap by developing a Geographic Information Systems (GIS)-based multi-hazard risk assessment for Ferhatpaşa Mosque, a sixteenth-century Ottoman heritage asset located in Çatalca, Istanbul. Eight spatial parameters were evaluated at the neighborhood scale: slope, elevation, aspect, precipitation, distance to fault lines, distance to hydrological features, land use, and soil capability. The model was developed through Weighted Overlay analysis and interdisciplinary expert-based weighting. Distance to fault lines and precipitation received the highest weights, each accounting for 17.22% of the model, followed by distance to hydrological features and soil capability, each weighted at 13.89%. The final risk map classified 71.99% of the study area as medium risk, 28% as low risk, and 0.02% as high risk. Ferhatpaşa Mosque was located within the medium-risk zone, approximately 29,600 m from active fault lines, 250 m from the nearest dry streambed, 800 m from the nearest stream, and 320 m from the nearest high-risk zone. These findings demonstrate that the mosque’s risk profile is shaped not by seismic proximity alone, but by the cumulative interaction of topography, precipitation, hydrology, soil conditions, and land-use characteristics. The proposed model provides a spatial decision-support framework for integrating cultural heritage conservation into sustainable disaster risk reduction and local risk mitigation planning. Full article

Landslide susceptibility assessment is important for disaster prevention and sustainable land-use planning in mountainous regions. However, conventional discretization methods often overlook threshold effects in conditioning factors, and many machine learning models still have limited interpretability. This study develops an integrated framework that combines ChiMerge discretization, Weight of Evidence (WOE) transformation, and tree-based ensemble learning to map landslide susceptibility in Jiuzhaigou County, Sichuan Province, China. A landslide inventory of 164 points was compiled from field investigations and hazard records, and fourteen topographic, geological, and environmental conditioning factors were derived from multi-source spatial datasets. Continuous factors were discretized using ChiMerge, a supervised chi-square-based discretization method that identifies statistically meaningful thresholds according to the distributions of landslide and non-landslide samples. WOE values were then calculated to quantify the association between each factor class and landslide occurrence. Three WOE-based ensemble models, WOE-CatBoost, WOE-LightGBM, and WOE-RF, were constructed and compared. All models showed high predictive performance (AUC > 0.90), with WOE-CatBoost performing best (AUC = 0.9432). Its high and very high susceptibility zones covered 28.59% of the study area but contained 85.96% of observed landslides. High-risk areas were mainly concentrated in steep valleys, fractured lithological zones, erosion belts, and areas affected by engineering activities, such as road construction, slope cutting, tourism infrastructure development, and settlement expansion. The proposed framework improves prediction accuracy and interpretability and provides spatial support for landslide prevention and sustainable land-use management. Full article

Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability. Full article

Water erosion is a critical degradation process that reduces fertility and agricultural sustainability, especially in semi-arid regions. The Universal Soil Loss Equation (USLE) allows for the quantification of this phenomenon using factors such as rainfall erosivity (R) and topography (length-slope, LS). In this study, both factors were estimated and analyzed in the Cañitas sub-basin, located in the semi-desert area of the state of Zacatecas, Mexico, characterized by irregular precipitation and limited data availability. The objective of this study is to estimate and analyze the R factor and LS factor to evaluate their influence on soil water erosion processes. Records from five meteorological stations (1986–2022) were used, along with the Modified Fournier Index (MFI) and Geographic Information Systems (GIS) tools, generating spatial maps of rainfall erosivity and topography. An average R factor of 81.69 MJ∙mm/ha∙h∙year was estimated, consistent with the values obtained using the MFI. The LS factor shows that the northwestern area of the study zone has the most extensive and steepest slopes (up to 20). This study analyzes the R and LS factors to identify areas vulnerable to water erosion and to understand the influence of climate and topography in a semi-arid region, which can serve as a reference for planning conservation actions and managing watersheds in semi-arid areas with high climatic variability. Full article

Existing studies have mainly focused on either intangible cultural heritage (ICH) or traditional villages separately, while limited attention has been paid to their coupled spatial relationship and influencing mechanisms at the provincial scale. To address this gap, this study investigates the spatial characteristics and influencing factors of 869 national and provincial intangible cultural heritage (ICH) items and 777 traditional villages in Yunnan Province using Geographic Information Systems (GISs) and geographic detector methods. The results indicate significant differences in their spatial distribution patterns: ICH exhibits a “multi-core clustering” structure, whereas traditional villages present a “dual-core clustering with multiple dispersed patches” pattern. The study further reveals a spatial mismatch as well as a significant positive spatial correlation between ICH and traditional villages. Natural environmental conditions and historical-cultural factors jointly shape their spatial differentiation, while socio-economic factors such as urbanization exert a stronger influence on ICH distribution, and demographic and economic conditions more strongly affect traditional villages. This study contributes to the literature by integrating cultural landscape theory with GIS-based spatial analysis to reveal the spatial interaction mechanisms between ICH and traditional villages in Yunnan Province. The findings provide theoretical support and practical implications for cultural heritage conservation, rural revitalization, and territorial spatial planning in ethnically diverse border regions. Full article