Search Results (3,457)

Coworking spaces have become an increasingly important component of contemporary urban economies, yet their spatial organisation remains poorly understood in rapidly developing Gulf cities. This study examines the spatial clustering and urban correlates of coworking space locations in Jeddah, Saudi Arabia. A dataset of 53 coworking spaces was integrated with points of interest, built-up land, and road-network data within a GIS framework. Spatial pattern was evaluated using Monte Carlo nearest-neighbour analysis, kernel density estimation, and Ripley’s L-function. A 1 km grid was subsequently used to model coworking space presence through binary logistic generalised linear regression, with a 2 km grid used for sensitivity analysis. Coworking spaces exhibited pronounced spatial clustering: the observed mean nearest-neighbour distance was 1023 m compared with 2667 m under complete spatial randomness (nearest-neighbour ratio = 0.384, Monte Carlo p = 0.001). The observed centred Ripley’s L-function exceeded the upper pointwise 95% CSR simulation envelope throughout the evaluated 0.25–10 km range, indicating multiscale spatial concentration. In the final 1 km model, business density (OR = 2.13, p < 0.001), built-up proportion (OR = 1.042 per percentage point, p < 0.001), and road density (OR = 1.075, p = 0.008) were positively associated with coworking space presence. Business density and built-up proportion remained significant at the 2 km scale, whereas the road-density association weakened. These findings suggest that coworking spaces in Jeddah are primarily embedded within business-intensive, highly urbanised, and accessible parts of the metropolitan area while also demonstrating sensitivity to spatial analytical scale. Full article

Soil erosion is a major environmental problem driven by land use change and unsustainable agricultural practices, particularly in Rwanda, where half of agricultural land is moderately to severely degraded. This study assessed soil loss in the Akanyaru Sub-catchment using the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and validated with field measurements from 876 rainfall events recorded on 12 runoff plots representing four land-use types. The model incorporated rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C) for 2024 and 2025, and a constant conservation practice (P) factor. R values ranged from 1100 to 2400 MJ mm ha⁻¹ h⁻¹ yr⁻¹, K from 0.20 to 0.35 t h MJ⁻¹ mm⁻¹, LS from 0 to 256, and C from 0.01 to 0.99. Estimated annual soil loss ranged from 0 to 650 t ha⁻¹ yr⁻¹. From 2024 to 2025, mean and median soil erosion rates decreased by 5.21 and 18.7 t ha⁻¹ yr⁻¹, respectively, and total annual soil loss declined by 812,654 t (11.45%). Model validation showed strong agreement with field observations (R² = 0.97, NSE = 0.95, nRMSE = 26.32%, PBIAS = −12.47%), demonstrating the reliability of the RUSLE–GIS approach for soil erosion assessment and conservation planning. Full article

Geographic information systems have traditionally focused on the representation of visual and geometric aspects of geographic phenomena, while auditory information has largely been ignored or reduced to simple numerical attributes. Although numerous spatio-temporal data models have been proposed to describe dynamic geographic processes, few of them provide explicit semantic constructs for representing sound and its evolution over space and time. This paper extends the Modeling of Application Data with Spatio-Temporal Features (MADS) conceptual model by introducing an explicitly defined acoustic semantic dimension. The extension separates source-intrinsic acoustic emission (soundfeature), source–receiver propagation and exposure (AFFECTS and associated EXPOSUREZONE instances), and receiver-centered contextual perception (PERCEPTIONASSESSMENT). These complementary constructs enable acoustic emission, propagation, temporal dynamics, spatial exposure, and perceptual assessment to be represented within a unified spatio-temporal semantic framework. The applicability of the proposed approach is illustrated through a proof-of-concept scenario and representative database queries, demonstrating how the proposed acoustic constructs can be mapped, stored, and queried using standard GIS database infrastructure. Full article

In the field of architectural heritage, industrial built heritage plays an important role. Traditional research methods such as literature studies and field surveys have gradually evolved into digital-twin and machine-learning-based approaches. However, the study of industrial heritage remains a challenge. This study employs the PRISMA framework to conduct a systematic review of the existing literature, aiming to elucidate the impact of GIS on industrial heritage sites and its potential limitations. This study conducts a systematic review of GIS-based research on industrial heritage, focusing on studies published between 2000 and 2025. Among the 2166 records initially retrieved from Scopus and the 1771 records initially retrieved from Web of Science, 77 papers were ultimately retained after screening and deduplication. A total of 32 studies were selected based on thematic screening criteria, all of which focused on analysing the spatial characteristics of industrial heritage sites using GIS technology. Among these, 12 core studies were further selected for in-depth qualitative and methodological analysis. These documents form the foundation for exploring research hotspots, methodological approaches, and technological evolution. This review contributes a five-dimensional analytical framework that systematically classifies GIS-based industrial heritage research and identifies eight structural research gaps. The findings indicate that GIS applications remain predominantly focused on spatial mapping and distribution analysis, while their integration with ecological assessment, social and participatory approaches, archaeology, and multi-source technologies remains limited. This review provides a roadmap for integrating advanced spatial analysis tools into future industrial heritage research and planning. Full article

Historical underground workings in the Maleia–Livezeni sector of the Petroșani Coal Basin were reconstructed from four archival mining plans and a coordinate-referenced Drawing Exchange Format (DXF) dataset. Twelve extraction-sector polygons, principal galleries, exploitation limits, dated mining stages, and 567 internally checked mining-elevation annotations associated with Coal Seam No. 3 were integrated in a geographic information system (GIS) using the Romanian Stereo 70 coordinate reference system (EPSG:3844). Net elevation differences between 2007 and 2026 at seventeen surface benchmarks were used as response variables. Nearest-feature relationships and mining-exposure indicators for 50, 100, and 150 m neighbourhoods were evaluated using exploratory Spearman rank correlations. Among the originally selected 50, 100, and 150 m neighbourhoods, cumulative gallery length within 50 m showed the strongest planimetric association with |Δ_Z| (ρ = 0.734, p = 0.0008, q = 0.010), while extraction-sector coverage and gallery length remained positively associated at broader scales. R19 showed the greatest immediate extraction-sector exposure, whereas R14, which recorded the largest elevation loss, was characterised by a broader concentration of surrounding workings. R09 recorded the smallest elevation change and was approximately 470 m from the nearest reconstructed extraction sector. Cumulative mining configuration was more informative than nearest-sector distance alone for interpreting spatially variable net surface-elevation change in this legacy mining setting and highlighted the limitations of archival GIS reconstruction. Full article

Electric vehicles (EVs) are attracting choice and adoption as a travel mode in global transportation systems, driven by technological innovation, economic considerations, and advancing sustainable urban development. The planning, development, design, and location of EV charging stations are challenging tasks for underdeveloped countries such as Pakistan. To propose the site location for an EV charging station, this study comprises Multi-Criteria Decision Analysis and Geographic Information Systems. The study adopts a mixed-methods design, combining qualitative expert input with quantitative spatial analysis to investigate technical, social, and infrastructural criteria. The study considers indicators, including site suitability, population density, interaction between land use and transport, road network, transportation interactions, existing electricity grid infrastructure, and existing fueling and charging stations. This research presents pairwise expert comparisons, indicating that grid capacity (0.28) and demand (0.30) are important factors to consider. A composite suitability Index (CSI) through GIS-weighted overlay was used to classify the area into low, medium, and high suitability zones. According to the CSI, 26% of the area falls in highly suitable areas for EV infrastructure in Islamabad, and 41% falls in medium-to-high suitable areas. The remaining 33% area lies in low-suitability peripheral zones near the hilly region of Islamabad city, which is not considered suitable for EV infrastructure. A total of 67% of the city’s area is suitable for EV infrastructure. Full article

Biotope mapping provides an essential spatial framework for biodiversity conservation, ecosystem management, and sustainable landscape planning. However, a standardized and transferable GIS-based methodological framework for biotope identification and mapping is still lacking in Türkiye, limiting the systematic integration of biodiversity considerations into spatial planning processes. This study develops an integrated GIS-based biotope mapping framework for Çınarcık District, Yalova Province, Türkiye, integrating Digital Elevation Model (DEM), CORINE Land Cover 2018, Forest Management Plans, stand characteristics, vegetation, floristic, and hydrological data through spatial analyses. Additionally, 30 national and 15 international studies were systematically reviewed to identify the common indicators, data sources, and methodological components used in biotope mapping and to establish the proposed GIS-based framework. The proposed approach identified four main biotope groups (forest, aquatic, agricultural, and urban). Based on ecological similarity and growing environment characteristics, 12 sub-biotope types were identified within the forest biotopes. Forest biotopes were the dominant ecological units, mainly characterized by broadleaved communities dominated by Fagus orientalis, Castanea sativa, Tilia tomentosa, and Quercus petraea. A total of 72 forest stand types were identified, with Fagus orientalis-dominated forests in plateau environments representing the largest sub-biotope type (36.40%). The proposed framework provides a repeatable and transferable, inventory-based methodology that can serve as a preliminary decision-support tool for biodiversity assessment, conservation planning, and sustainable landscape management in forested landscapes with similar ecological characteristics, pending future field-based validation. Full article

Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems. Full article

Vegetation dynamics are highly sensitive to climate variability and constitute a key indicator of ecosystem condition, particularly in arid and semi-arid environments where field observations are often limited. This study investigated the spatiotemporal dynamics of vegetation and their relationships with climatic factors in the Quaraqum Watershed during 2001–2022 using multi-source remote sensing datasets. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery, Land Surface Temperature (LST) was obtained from MODIS products, and precipitation data were obtained from the CHIRPS dataset. Spatial and temporal patterns and trends were examined using Geographic Information System (GIS) techniques, while the relationships between NDVI and climatic variables were quantified using Pearson correlation and multiple linear regression analyses. The results revealed a pronounced west-to-east decrease in precipitation accompanied by increasing temperature across the watershed. Annual precipitation reached maximum values of 389.20 mm in 2019 and 370.79 mm in 2009, whereas the lowest values, 172.50 mm and 171.50 mm, were recorded in 2008 and 2021, respectively. Spring was the wettest season, whereas summer was the driest. Vegetation cover was highest in spring and exhibited an increasing long-term trend, while autumn and winter showed the lowest vegetation cover and declining trends. Correlation analysis revealed a strong positive relationship between NDVI and precipitation and a weaker negative relationship between NDVI and LST. These results suggest that precipitation availability was more strongly associated with vegetation variability than thermal conditions. Multiple regression analysis further indicated that precipitation was the primary climatic variable explaining annual NDVI variability, while the inclusion of LST did not substantially increase the explained variance of the model. Overall, these findings highlight the dominant role of moisture availability in shaping vegetation dynamics in the Quaraqum Watershed, with thermal conditions representing an additional environmental factor affecting vegetation responses. Full article

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

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article

Ecotourism, as one of the most important forms of sustainable tourism, plays a significant role in the conservation of natural resources, the economic development of local communities, and the achievement of sustainable development goals. However, the sustainable development of this sector requires the accurate identification of suitable areas and the simultaneous assessment of the ecological capacities and limitations arising from anthropogenic activities, an issue that has received less attention at the national scale, particularly in countries with high environmental diversity such as Iran. Therefore, the present study was conducted with the aim of assessing the potential for ecotourism development in Iran based on Geographic Information System (GIS) and Spatial Multi-Criteria Decision Analysis (SMCDA). In line with the scope of the sub-factors evaluated, the assessed construct is referred to throughout this study as Environmental–Anthropogenic Ecotourism Suitability (EAES), reflecting an environmental-quality and anthropogenic-pressure perspective rather than a comprehensive assessment of ecotourism sustainability. The main innovation of this study lies in the simultaneous integration of natural and anthropogenic factors at the national scale and the sensitivity assessment of the results through the design of different development scenarios. In this study, a set of natural sub-factors including protected areas, vegetation cover, slope, precipitation, land use, and natural attraction density, as well as anthropogenic sub-factors including settlements, roads, accommodations, mines, industrial parks, dams, power transmission lines, dust, and the Global Human Modification (GHM) index were used. The weighting of the sub-factors was carried out using the Best–Worst Method (BWM), and spatial suitability maps were subsequently generated through the Weighted Linear Combination (WLC) method in the GIS environment. To evaluate uncertainty and examine the effect of the relative importance of sub-factors, three scenarios including natural factor dominance, anthropogenic factor dominance, and a balanced scenario were designed and analyzed. The results showed that protected areas (0.19), vegetation cover (0.17), and natural attraction density (0.16) were the most important natural sub-factors, while the GHM index (0.16), roads (0.15), and settlements (0.14) were the most important anthropogenic sub-factors affecting ecotourism development. The spatial pattern of the results indicated the concentration of areas with good potential in the Alborz and Zagros mountain ranges, the Hyrcanian forests, and parts of the protected areas of Iran. In the balanced scenario, approximately 24.8% of Iran’s area was classified within the suitable and highly suitable classes, while 46.3% was classified within the moderately suitable class. Furthermore, comparison of the scenarios showed that the use of one-dimensional approaches may lead to unrealistic estimates of ecotourism capacity. Overall, the results of this study indicate that the sustainable development of ecotourism in Iran requires the adoption of an integrated approach in which the conservation of natural assets and the management of anthropogenic interventions are simultaneously considered. The proposed framework can serve as a decision-support tool for spatial planning, investment prioritization, and sustainable ecotourism development policymaking in Iran and other similar regions. Full article

Proper municipal solid waste (MSW) management is vital for mitigating environmental degradation and protecting public health. Landfill site selection remains a complex spatial decision-making challenge, balancing ecological, social, and economic parameters. This study presents a comprehensive systematic review of 175 peer-reviewed articles published between 2016 and 2026, evaluating the evolution of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) frameworks. The findings indicate that road accessibility (93.7%), surface and groundwater protection (91.4%), slope gradients (84.6%), and settlement buffer zones (78.9%) represent the most critical and universally applied siting criteria. Digital Elevation Models (81.9%) and geological maps (57.3%) serve as foundational geospatial datasets. While the Analytic Hierarchy Process (AHP) remains the dominant weighting technique (63.41%), recent trends show an increasing adoption of hybrid multi-criteria models and optimisation algorithms. Geographically, research output is led by India, Iran, and Turkey, peaking significantly in 2025. Crucially, this review exposes prominent methodological shortcomings, notably a heavy reliance on subjective expert validation (73.8%), whereas quantitative validation, sensitivity analysis, and uncertainty assessment remain critically underutilised. In contrast to earlier reviews, this review offers a thorough and critical synthesis of GIS- and MCDA-based approaches to landfill site selection by carefully evaluating methodological advancements, examining the advantages, disadvantages, and limitations of current approaches, and incorporating statistical trends with a structured methodological framework. This approach highlights important research gaps and offers evidence-based suggestions for creating more transparent, reliable, and sustainable techniques for landfill site selection by selecting, screening, and including relevant articles. To foster sustainable urban planning, future research must prioritise standardised evaluation frameworks, rigorous uncertainty quantification, and the integration of artificial intelligence and machine learning with spatial modelling. Full article

Accurate forecasting of nitrogen dioxide (${\mathrm{NO}}_2$) in high-traffic urban environments is a critical challenge for public health management and environmental policy. This study presents a hybrid physics-informed machine learning pipeline for ${\mathrm{NO}}_2$ dispersion modeling and short-term forecasting along Viale Regione Siciliana in Palermo, Italy, one of the highest traffic-density corridors in Europe, over six full years (2020–2025) of hourly data resolved at approximately 11 m. Station measurements and weather data are harmonized hourly over the OpenStreetMap road network, converted into emissions with COPERT-Italy (COmputer Program to calculate Emissions from Road Transport) factors, and dispersed with the SIRANE street-network model; the chain is also inverted to recover corridor traffic from the observed ${\mathrm{NO}}_2$. The resulting field is joined with the measured predictors in the machine learning stage, and the output is mapped in GIS Geographic Information System. The hourly scatter between observations and the SIRANE model built on 44,752 h is centered on the 1:1 line, with a Pearson correlation of $\mathit{r}=0.83$. Of two “memory-less” ensembles on an identical predictor set, XGBoost (eXtreme Gradient Boosting) returns the better $R^2$, mean absolute error, and RMSE Root Mean Square Error at all twelve recursive lead times, declining from $R^2=0.79$ at $t+1$ h to a plateau of 0.66 at $t+12$ h, compared to 0.76 to 0.59 for random forest; both reproduce the mean field almost exactly ($r\ge 0.97$), with the XGBoost residual staying between 1.0 and 1.7 $\mathsf{\mu }$g m⁻³ at every horizon and that of random forest growing to 4.3 $\mathsf{\mu }$g m⁻³ by $t+12$ h. The proposed pipeline offers a transferable methodology for urban air quality management in traffic-dominated environments. Full article

Posidonia oceanica (Delile) is a Mediterranean seagrass species whose fallen leaves accumulate along shorelines, forming compact structures known as banquettes. Despite their ecological and protective functions, these deposits remain insufficiently documented across the Mediterranean and are frequently removed before the summer season due to tourism and aesthetic considerations. This study develops and tests a Geographic Information System (GIS)-based methodology for mapping and inventorying P. oceanica beach-casts across five representative pilot areas along the Italian coastline (Western Sardinia; Liguria; Northwestern Sicily; Elba and Giglio Islands, Tuscany; Gargano coast, Apulia). The approach integrates historical Google Earth imagery with spatial datasets to characterize beach-cast occurrence and persistence and to describe their distribution in relation to selected geomorphological, environmental, and anthropogenic variables. Based on historical Google Earth imagery covering approximately three decades (~1998–present), beach-casts were identified at least once on 94% of the 393 surveyed beaches, indicating that the phenomenon is widespread across the investigated areas, with permanent banquettes representing a substantial component (63%). The spatial distribution of beach-casts was also examined in relation to shoreline dynamics, beach surface type, coastal vulnerability, and beach use, providing a descriptive characterization of their environmental and anthropogenic context, while the relative influence of the considered factors requires further investigation. The resulting standardized and reproducible framework provides a basis for future monitoring and susceptibility assessments across broader coastal sectors and can support evidence-based management of P. oceanica beach-casts. Full article