Search Results (127)

Species-wise, this study focused on three local endemic plant species of Crete (Greece) protected by the Greek Presidential Decree 67/1981, namely Calamintha cretica, Micromeria hispida, and Teucrium cuneifolium (Lamiaceae). The species-specific bioclimatic conditions (temperature, precipitation) that characterize their natural habitats were determined to identify the requirements for successful natural seed germination, using distribution dot maps and open-source geodatabases in GIS (Geographic Information Systems). To facilitate their ex situ conservation, their seed germination was investigated at four different constant temperatures (10, 15, 20, and 25 °C) in growth chambers with a photoperiod of 12 h light/12 h dark. The results indicated that their seed germination requirements showed a range-specific temperature dependence, reflecting their adaptation to local ecological conditions. Specifically, the seeds of C. cretica showed the highest germination percentages at high temperatures (96.67% at 15 °C, 90.00% at 20 °C, and 93.33% at 25 °C). The highest germination percentages (58.33–63.33%) of M. hispida seeds were observed only at a temperature range of 10–20 °C and increasing the incubation temperature to 25 °C resulted in a significant decrease in the percentages of germinated seeds (9.17%). In T. cuneifolium seeds, the highest germination was observed at 15 °C. After their successful pilot pot cultivation, the present study paves the way for future conservation efforts through ex situ seed storage of different population samples and may accelerate their sustainable utilization in different economic sectors as potential novel crops sourced from the local biodiversity pool. Full article

The availability of reliable digital models of low-voltage (LV) networks is a prerequisite for operation, planning, hosting-capacity, and asset-management studies. In practice, however, utility geodatabases often contain geometric discontinuities, implicit relationships among assets, and incomplete connectivity, which hinders their direct use as topological models for electrical studies. This work proposes a reproducible framework for topological-quality and fitness-for-use screening, applied to a real LV network derived from a utility geographic information system (GIS). The methodology converts geospatial layers into a graph representation, audits load-to-pole and endpoint-to-known-node distances, evaluates structural sensitivity to the endpoint connection tolerance, computes a load-level screening confidence score, characterizes transformer-level coverage under an explicit demand scenario, and evaluates structural sensitivity under synthetic perturbations of the line geometry. The case study comprises 38 urban blocks, 155 poles, 13 distribution transformers, 429 loads, 777 overhead LV segments, and 23 underground LV segments. The mean load-to-pole distance is 10.91 m and the mean endpoint-to-known-node distance is 4.39 m. The fixed 30 m load-to-pole assignment rule retains 416 loads (96.97%) throughout the tolerance sweep, while the number of connected components decreases from 245 to 82 and the number of virtual nodes from 817 to 439 as the endpoint tolerance increases from 0.5 to 5.0 m. The confidence score classifies 32.40% of loads as High, 59.44% as Medium, 5.13% as Low, and 3.03% as Review/Unassigned. Under the stated residential screening assumptions, four transformers exceed the adopted 80% loading threshold. Synthetic perturbations leave the load-to-pole control metrics unchanged by construction, because only line-segment geometry is perturbed, but the number of connected components varies from 78 to 236, with a maximum relative change of 187.80% with respect to the 5 m base graph. The contribution is therefore a transparent GIS quality-screening procedure, not an exact reconstruction or electrical validation of the physical LV topology. Full article

Prioritising properties for early review in dense urban renewal areas is a spatial multi-criteria decision problem in which physical condition, accessibility, environmental quality, and hazard exposure must be weighed before formal appraisal. This study develops a reproducible GIS-based multi-criteria decision analysis workflow for pre-transformation valuation support in Fatih Neighborhood, Esenler District, Istanbul, covering 54 independent property units. A 311-indicator geodatabase was screened for local discrimination, leaving 284 active sub-criteria under 10 retained main criteria. Expert pairwise judgments were converted into weights by six procedures compared under a common log-ratio objective, and the resulting TOPSIS rankings were tested for redundancy, benchmark divergence, judgment uncertainty, and structural robustness. The retained variables carry substantial overlapping spatial information, so the matrix does not provide hundreds of independent evidence signals, and equal within-group allocation concentrates influence in single-variable criteria. The leading priority tier nevertheless remains stable under calibrated weight variation and realistic judgment noise, whereas the wider rank order is sensitive to grouping, weighting, normalisation, and scoring choices. The workflow therefore separates robust priority signals from model-dependent rankings without interpreting the scores as market-value estimates, so that the intensity of field verification and appraisal can follow the reported stability of each unit. Full article

This work examines the implementation and evaluation of shortest-path algorithm in a Geographic Information System environment integrating QGIS with a PostgreSQL/PostGIS spatial database. Spatial edge and junction layers stored in the PostgreSQL/PostGIS define a directed weighted network in which edge costs are derived from spatial distance and modified through a gradient-based cost function. The routing algorithm is implemented in Python using the PyQGIS library and a binary heap priority queue. Network data are loaded from the geodatabase and processed in memory to compute the optimal route between selected nodes. The resulting path is reconstructed as a dissolved polyline feature stored in the geodatabase and visualized within the GIS environment. The study also includes a theoretical comparison of several classical shortest-path algorithms—Dijkstra, $A^{*}$, Bellman–Ford, and Floyd–Warshall—with respect to their applicability to sparse spatial graphs typical of transportation networks. The analysis confirms the suitability of Dijkstra’s algorithm for such networks and demonstrates that routing outcomes depend directly on the definition of the edge cost function. The proposed workflow relies exclusively on open-source GISs and database technologies. Full article

Digital twins are increasingly adopted in geographic research as dynamic representations of environmental systems; however, their application to regulated river shorelines remains limited, particularly where bathymetric change, hydrological variability, and shoreline-state dynamics must be integrated within a single GIS-based framework. This study develops and validates a GIS-based spatiotemporal digital twin-oriented framework for the dam-affected shoreline downstream of the Włocławek Dam, Poland. The framework integrates four bathymetric surveys acquired in 2008–2011, water-level records, airborne laser scanning data, and three-dimensional hydrotechnical infrastructure within a unified geodatabase designed for dynamic shoreline-state reconstruction, multi-epoch analysis, and environmental monitoring. A key methodological element is the treatment of water level as a dynamic reference surface, enabling the automated delineation of inundation and exposure zones for observed and scenario-based hydrological conditions. The reconstructed bathymetric surfaces were organized as a multidimensional raster dataset with time as an explicit analytical dimension, supporting repeatable change detection, cross-sectional interpretation, and temporal trend analysis. To extend the framework beyond purely retrospective analysis, a near-real-time hydrological updating component was implemented through ingestion of operational water-level observations from the IMGW API into the geodatabase. Validation of the trend-based prediction for 2011 yielded R² = 0.967, RMSE = 0.44 m, MAE = 0.28 m, and bias = −0.06 m. The proposed framework provides a transferable geospatial basis for spatiotemporal modelling and monitoring of regulated river shoreline dynamics under changing hydrological conditions. Full article

Conceptual Site Models (CSMs) are essential tools for characterizing contaminated sites, integrating hydrobiogeochemical information to support remediation planning. Historical datasets are often underutilized, while additional investigations can be costly, limiting our understanding of contaminant dynamics. This study aims to develop a sustainable and cost-effective framework for constructing an enhanced CSM of the Mantua Lakes through the integration of historical (2008) and recent (2024–2025) sediment and water quality datasets, resulting in more than 2000 data points. Objectives included the reconstruction of a 3D geological model (55 boreholes), the estimation of contaminant masses in sediments, and the evaluation of temporal trends in contaminant distribution and natural attenuation. Sediment cores (collected at 25 cm intervals) and surface water samples were analyzed for arsenic, cadmium, chromium, mercury, and heavy petroleum hydrocarbons. A harmonized set of 39 georeferenced points enabled a multi-temporal comparison. Voronoi polygons and volumetric calculations were used to estimate contaminant mass within sediment layers. Bathymetric and stratigraphic data, consisting of 458 depth points and 64 isobaths, were integrated into a 3D geodatabase and extended into a 4D framework to capture temporal evolution. Sediments exhibited overall reductions in contaminants, particularly cadmium and hydrocarbons, while arsenic and chromium showed localized variations. Water column concentrations mirrored sediment trends, indicating significant bioattenuation. Integrating historical and recent data strengthens CSMs, provides quantitative mass estimates, and offers a comprehensive framework for understanding contaminant dynamics, natural attenuation processes, and sustainable site management. Full article

The Gulf of Mexico, a geologically complex environment, supports mesophotic coral ecosystems, with reefs such as the Pinnacle Trend, Flower Garden Banks National Marine Sanctuary, the Florida Middle Ground reef system, and Pulley Ridge. Mountain Top Bank is a dome-shaped hardground feature located 60–150 m below the sea surface along the Mississippi–Alabama shelf. It appears to prolong the Pinnacle Trend towards the southeast, bridging the gap between mesophotic coral reefs east and west of the Mississippi Canyon. Shipborne high-resolution multibeam data (bathymetry, backscatter, and water-column) and an AUV photomosaic were collected over the site during several oceanographic expeditions. Data were analyzed and compiled into an ArcGIS geodatabase to produce the first benthic habitat map of Mountain Top Bank. The site is characterized by a network of outcrops and boulders interspersed within a predominately sandy environment. Different seabed features were correlated with the presence and abundance of a diverse array of biota across the phyla of Cnidaria, Porifera, Mollusca, Chordata, Echinodermata, and Rhodophyta. We found the benthic assemblage to be similar to those found at the Pinnacle Trend, supporting the hypothesis that Mountain Top Bank is part of the same reef system and acts as a topographic bridge between ecosystems on the east and west of the Mississippi Canyon. Full article

Accurate and reproducible delineation of stream bankfull geometry remains a persistent challenge in environmental planning. To address this gap, we developed the Cross-Sections and Dimensions Tool, a semi-automated, slope-based method for extracting stream cross-sections and estimating bankfull width, elevation and depth using high-resolution elevation data. The tool applies a configurable slope threshold to identify bank edges, generates perpendicular cross-sections from a stream centreline, and stores all outputs in a structured geodatabase to ensure transparency and reproducibility. Validation against manually delineated bankfull polygons across 191 km of stream length in Greater Melbourne, Australia, demonstrated strong spatial agreement, with an average F1 score (a measure of prediction-observation overlap) of 74% and a mean absolute error of 0.64 m in bankfull elevation. The tool was most reliable in larger streams (Strahler order 5 and above) with low to moderate vegetation canopy cover (<80%). We also investigated the practical visibility limits of small or indistinct channels typically encountered by human mappers and verified that the tool did not produce unrealistic channel delineations. This approach advances geomorphic feature extraction by grounding bankfull delineation in deterministic geometry rather than hydrological recurrence or data-driven modelling. In practice, it enables scalable, transparent, and repeatable analysis of stream morphology for ecological assessment, infrastructure planning, and waterway management. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Humans typically describe spatial location using names, hierarchies, and relative positions (e.g., east of, inside), yet mainstream GIS represents places primarily through geometric coordinates, rendering qualitative spatial queries computationally challenging. We introduce the Qualitative Place Model (QPM), a GIS-native framework that transforms standard boundary datasets and place layers into structured knowledge bases of Qualitative Place Description (QPD). QPM provides a homogeneous representation whereby administrative units and physical places are treated uniformly as Place entities. The model materializes a compact set of local relations, hierarchical containment, directional neighbourhood, and optional proximity, that support rich inferences through sound logical operations (inverse relationships and per-predicate transitive closure). We implement QPM as an ArcGIS Pro toolbox that computes and persists QPDs within a geodatabase, with optional RDF export for SPARQL querying. This implementation enables natural-language-style spatial queries such as “Where is x?” or “Which places are north of x?” within standard GIS workflows. Evaluation on Wales (UK) administrative, postal, and electoral hierarchies plus a comprehensive place layer demonstrates robust performance: QPM generated 95.8% of expected basic-place statements (52,821 places) and achieved 89.7–96.5% coverage across administrative hierarchies. All QPDs proved unique under our deterministic signature. Despite compact storage requirements, directional relations expand by more than an order of magnitude (10.6× overall expansion) under logical closure, demonstrating substantial inferential power from a minimal stored representation. QPM complements geometric GIS with an explainable qualitative layer that aligns with human spatial cognition while remaining fully operational within standard GIS environments. Full article

The contemporary landscape is characterised by overlapping values and pressures, where ecosystem services and cultural spaces are used by diverse categories of users. In fragile contexts such as the Phlegraean Fields in Italy, the exponential growth of mass tourism has intensified the anthropogenic impacts, exacerbated by limited landscape awareness among local communities. Thus, walkability fosters direct exploration, while experiential transects provide a lens to read ecological, cultural, and perceptual layers of places. Together with digital storytelling, these approaches converge in a phygital approach that enriches physical experience without supplanting it. The study covered approximately 115 km of routes across five municipalities, combining road audits, an 11-item survey, participatory mapping, and ArcGIS StoryMaps. Results showed a structurally complex and functionally fragile mobility system: sidewalks are discontinuous, lighting insufficient, less than one quarter of the network is fully pedestrian, and cycling facilities are almost absent. At the same time, digital layers diversified routes and supported situated learning. By integrating geo-spatial analysis and phygital tools, the research demonstrates a replicable strategy to enhance the awareness and sustainable enjoyment of complex landscapes. The present research is part of the PNRR project Changes ‘PE5Changes_Spoke1-WP4-Historical Landscapes Traditions and Cultural Identities’. Full article

This study presents the preliminary results of the design and implementation of an advanced data management infrastructure developed to enhance the study, interpretation, and preservation of historical and archaeological contexts. Conducted within the framework of the PNRR CHANGES Project, Spoke 6, the initiative promotes the integration of scientific research, digital innovation, and cultural heritage enhancement. One of the principal outcomes of the project is the development and configuration of ARPAS (“Analisi del Rischio nel Paesaggio Stratificato” or “Risk Analysis of Stratified Landscape”), a centralised Geospatial Database capable of ensuring reliable data archiving, real-time analytical processing, and collaborative information sharing among researchers and institutions engaged in cultural heritage management. The paper discusses key methodological challenges related to the heterogeneity of available documentation and the limitations of existing tools currently used for heritage research and protection in the Italian, and particularly Sicilian, context. At the same time, it highlights the potential of the proposed system in terms of data accessibility, verifiability, and query ability, as well as its ability to integrate and interrelate heterogeneous datasets within a multilayered, interdisciplinary framework for cultural landscape research. The pilot deployment focuses on a geographic area in southeastern Sicily, drawing upon documentation of the cultural landscape across four provinces—Agrigento, Catania, Ragusa, and Siracusa—and integrating archaeological, architectural, and environmental data to support risk assessment and heritage conservation strategies. Results appear to demonstrate ARPAS’s potential to improve the completeness of information, manage stratification across temporal layers, and support predictive and preventive analyses for cultural heritage at the landscape level. Full article

CerMapp is a multi-platform and system application designed to address a critical gap in veterinary public health: the lack of a standardized, national-scale geodatabase for wildlife diseases. This gap has long hindered the effective application of GIS and remote sensing in spatial epidemiology. Currently deployed at the prototype level in Aosta Valley, NW Italy, the application’s core innovation is its ability to generate a structured, analysis-ready data repository, which serves as a foundational resource for One Health initiatives. Developed by the National Reference Center for Wildlife Diseases on the ESRI ArcGIS Survey123 platform v.3.24, CerMapp enables veterinarians, foresters, and wildlife professionals to easily collect and georeference field data, including species, health status, and photographic evidence using flexible methods such as Global Navigation Satellite System or manual map entry. Data collected via CerMapp are stored in a centralized geodatabase, facilitating risk analyses and detailed geospatial studies. This data can be integrated with remote sensing information processed on cloud platforms like Google Earth Engine or within traditional GIS software, contributing to a comprehensive and novel wildlife health registry. By promoting the rational and standardized collection of essential geospatial data, CerMapp data may support predictive disease modeling, risk assessment, and habitat suitability mapping for wildlife diseases, zoonoses, and vector-borne pathogens. Its scalable, user-friendly design ensures alignment with existing national systems like the Italian Animal Disease Information System (SIMAN), making advanced geospatial analysis accessible without requiring specialized digital skills from field operators or complex IT maintenance from institutions. Full article

Urban managers and decision-makers may approach Urban Environmental Quality (UEQ) assessment with perspectives that range from highly pessimistic to highly optimistic scenarios. The objective of this study was to introduce a scenario-driven spatial decision support system framework for optimizing UEQ zoning. The proposed framework includes six steps: (1) building a geodatabase of criteria, (2) standardizing criteria using minimum and maximum methods, (3) determining criteria weights using the Analytic Hierarchy Process (AHP) method, (4) combining criteria and creating scenarios using the OWA method, (5) analyzing UEQ maps with statistical analyses, and (6) examining variability through histogram analysis of UEQ values across scenarios. The results indicate that, among environmental and infrastructural criteria, air pollution and population density had the most significant impact on UEQ zoning in Isfahan city. In the five decision-making scenarios (highly pessimistic, pessimistic, neutral, optimistic, and highly optimistic), 8% (19), 12% (15), 16% (12), 21% (8), and 25% (5) of Isfahan’s area were classified as poor, respectively. Additionally, the percentage of the population in poor classes across the scenarios was 5% (14), 10% (11), 13% (7), 17% (5), and 20% (3), respectively. The findings demonstrate that the proposed framework offers high flexibility and capability for assessing UEQ across different decision-making scenarios. Full article