Search Results (6,097)

Population growth and unplanned land use significantly contribute to transforming natural hazards into disasters. Earthquake-induced losses of life and property are often linked to inadequate planning decisions. The city center of Şanlıurfa provides a recent example, where the 6 February 2023 earthquake resulted in 340 fatalities and substantial material damage. Variations in urban planning over different periods have caused disaster risk to fluctuate even across short distances. This study examines Şanlıurfa’s urban development in terms of earthquake vulnerability. Using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP), the earthquake risk map reveals elevated risk in areas near fault lines and regions with high groundwater levels. Approximately 7% of the area is classified as very low risk, 54% as low risk, 37% as moderate risk, and 2% as high risk. Limited consideration of disaster-focused planning has led to both planned and unplanned developments in hazardous zones. Consequently, construction should prioritize low-risk areas, with necessary precautions applied in high-risk zones when unavoidable. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Despite extensive research on sustainable urban mobility, non-designated crossings remain underexplored, particularly in low- and middle-income countries where they are highly prevalent. This study applies agent-based simulation to analyze pedestrian crossing behavior in commercial streets. We adopted a mixed-methods approach, combining video recordings, field observations, and structured questionnaires to capture physical conditions and user perceptions in a case in Cairo. The collected data were spatially analyzed using a Geographic Information System (GIS) to identify key spatial and behavioral variables influencing crossing decisions. These variables were then incorporated into an Agent-Based Model developed using the GAMA platform to simulate pedestrian–vehicle interactions. The simulation assessed pedestrian flow, non-designated crossing rates, average vehicle speed, and traffic volume. Results indicate strong relationships between pedestrian flow and non-designated crossings, and moderate associations between increased pedestrian activity and reduced vehicle speeds, while traffic volume shows weak correlations with pedestrian-related indicators. The model reveals distinct patterns of pedestrian crossing behavior, shaped by street configuration and traffic dynamics, and highlights critical risk points in commercial streets. Based on these findings, the study proposes seven actionable strategies to enhance pedestrian safety while supporting a more sustainable urban mobility. Full article

Understanding the spatiotemporal formation mechanisms of built cultural heritage is essential to interpreting regional cultural landscapes and informing differentiated conservation strategies. Using Fujian Province, China, as a representative mountain–sea transitional region, this study constructs a province-scale, multi-category, and dynamically oriented analytical framework to investigate the temporal evolution, spatial structure, and driving mechanisms of immovable cultural relics. Based on a georeferenced dataset of 940 immovable cultural relics, textual historical records were standardized into continuous temporal variables and integrated with GIS-based kernel density estimation, spatial autocorrelation analysis, distance-to-coast modeling, and category co-occurrence analysis. The results reveal a pronounced temporal concentration in the Ming–Qing and modern periods, with a primary formation peak during the Qing Dynasty and a secondary peak in the early 20th century driven by modern heritage. Spatially, relics exhibit significant positive spatial autocorrelation (Global Moran’s I = 0.375, p < 0.001) and form a structured dual-core pattern, consisting of a persistent coastal heritage belt and a distinct inland modern core centered in western Fujian. More than 75% of relics are located within 110 km of the coastline, confirming strong maritime orientation, while regression analysis reveals that this inland shift is primarily driven by the Modern Era rather than representing a continuous long-term trend. Category-level correlation analysis further demonstrates a clear spatial decoupling between traditional heritage and modern sites, indicating fundamentally different locational logics. Synthesizing these findings, this study proposes a dual-core driven model under a mountain–sea geographical framework, in which a path-dependent, economically reinforced coastal core coexists with a historically contingent, politically driven inland core. The results advance quantitative understanding of how multiple cultural logics, operating across different temporal scales, jointly shape complex regional heritage systems and provide a transferable framework for heritage analysis and spatially differentiated conservation planning. Full article

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems. Full article

Landscape visual impact assessment is a key component of environmental impact studies, as it enables the identification and management of negative effects on the territory. Traditional methods are often subjective, rely on expert judgement, and consider limited criteria. To address these limitations, this study proposes a quantitative index based on the integration of grey clustering and Shannon entropy complemented with Geographic Information System (GIS). This approach allows classification under uncertainty and the objective weighting of indicators related to physiographic, biotic, and anthropic factors of visual quality, fragility, and accessibility. The methodology was applied to an open-pit mine in Peru. Results show that terrain modifications, presence of artificial elements, and the alteration of water bodies significantly affect visual quality, while the absence of restoration measures, observer exposure, and vegetation type increase fragility and reduce landscape resilience. The proposed method provides a robust, transparent, and reproducible framework that overcomes subjectivity in traditional approaches, supporting more reliable environmental planning and management. Full article

This study proposes a scalable, AI-driven, and Geographic Information System (GIS)-integrated framework for intelligent route-level classification in large-scale airport transportation networks to support airport operations, logistics planning, and network-level decision-making. The framework addresses the need for practical artificial intelligence applications that combine spatial network analysis with supervised machine learning to improve route assessment and resource allocation in complex air transport systems. A structured dataset was developed using operational and traffic-related attributes, including route distance, aircraft capacity, weekly frequency, annual passenger volume, demand variability, and route performance indicators, with additional normalized features to improve data representation. A Gradient Boosting ensemble classifier was trained to categorize routes into high-, medium-, and low-priority classes. The model achieved strong predictive performance, with a testing area under the ROC curve of 0.961, accuracy of 0.922, F1-score of 0.915, precision of 0.918, and a recall of 0.922. Feature importance analysis identified demand variability and route-density indicators as the main drivers of classification, enhancing interpretability and practical trust. The proposed framework demonstrates the real-world potential of AI for scalable, explainable, and efficient decision support in airport logistics and transportation network management. Full article

Neglected tropical diseases (NTDs) such as soil-transmitted helminthiasis, lymphatic filariasis, schistosomiasis, leprosy, rabies, and food-borne trematodiasis are endemic in the Philippines. Despite global and national elimination efforts, these six NTDs remain a persistent burden to the poor, those living in Geographically Isolated and Disadvantaged Areas (GIDAs), and other vulnerable groups. This narrative review synthesized data from Field Health Services Information System (FHSIS) reports of the Philippine Department of Health (DOH) from 2020 to 2024, the available literature from electronic databases, and DOH and WHO reports focusing on the challenges, barriers, and gaps in NTD control and elimination in the country. Core challenges include complex epidemiological landscapes, lapses in disease surveillance, infrastructure, and fragmented health care systems. Gaps include access to diagnostics, insufficient funding and human resource training, and scarcity of local studies focusing on endemic NTDs. With these challenges and gaps, this review highlights the need for a real-time feedback loop system in surveillance strategy, community-based interventions, full integration of NTDs in primary health care, and collaboration between government, NGOs and private entities. Addressing these challenges and gaps is key to shifting from control to elimination. Full article

Spaceborne LiDAR systems, such as ICESat-2, provide critical data for global land cover and topography; however, their performance in rugged, vegetated landscapes requires rigorous local validation. This study evaluates the vertical accuracy of ICESat-2 ATL08 terrain height metrics in the complex Turkish Western Black Sea region, utilizing a reference dataset of high-precision terrestrial GNSS measurements. Following strict IQR-based outlier detection and photon density filtering, 1637 spatially matched segments were analyzed. The h_te_best_fit terrain height metric showed the best agreement with the terrestrial GNSS reference data, yielding an RMSE of 3.37 m and a mean bias of −0.42 m, indicating a slight underestimation of the terrain surface. The univariate analysis revealed a strong positive correlation between terrain slope and vertical error, indicating that slope is the prominent degradation factor contributing to pulse broadening. Additionally, dense forest cover was found to limit ground photon retrieval, leading to increased error margins, whereas nighttime acquisitions offered slightly improved precision. These findings suggest that while ATL08 is a valuable topographic source, slope-dependent corrections are essential for applications in mountainous environments. Full article

Knowledge of the Oestroidea and Muscoidea fauna of Greece and Cyprus remains fragmentary, with substantial parts of the two countries having never been systematically surveyed. The present study verifies the presence of Scathophaga stercoraria (Linnaeus, 1758) in Cyprus and records 16 new species from Greece, belonging to five calyptrate families: [Anthomyia illocata Walker, 1856 (Muscoidea: Anthomyiidae); Scathophaga lutaria (Fabricius, 1794) (Muscoidea: Anthomyiidae); Fannia pallitibia (Rondani, 1866); Fannia pusio (Wiedemann, 1830) (Muscoidea: Fanniidae); and Coenosia sp. nov. 1, Coenosia sp. nov. 2, Lispe flavicincta Loew, 1847, Lispe nuba Wiedemann, 1830, Lispe orientalis Wiedemann, 1824, Lispe cf. sericipalpis (Stein, 1904), Potamia littoralis Robineau–Desvoidy, 1830 (Muscoidea: Muscidae); Apodacra radchenkoi Verves and Khrokalo, 2015, Craticulina tabaniformis (Fabricius, 1805), Miltogramma rutilans Meigen, 1824, Nyctia lugubris (Macquart, 1843) (Oestroidea: Sarcophagidae), and Linnaemya lithosiophaga (Rondani, 1859) (Oestroidea: Tachinidae)]. These records are based on the examination of 152 dry-pinned specimens from 58 localities, collected between 1978 and 2026 across Greece and Cyprus using a combination of passive (animal-baited traps, UV-bright pan traps) and active (hand collecting, net sweeping) sampling methods, together with insect material from the entomological collections of the National Museum of Natural History Goulandris and the Melissotheque of the Aegean. In addition, the first checklists of the family Fanniidae and the subfamily Scathophaginae for Greece and Cyprus are presented. Collectively, the findings presented expand the documented diversity of Greek and Cypriot Calyptratae and refine the current understanding of their biogeographic patterns, providing an updated framework for taxonomic, ecological, forensic, and other applied entomological research within the two countries. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article

The external relations of small towns are an important dimension in the regional urban system. However, the “metropolitan bias” in existing studies results in a lack of empirical verification of their characteristics, hindering effective regional policymaking. Applying Central Flow Theory (CFT), mobile phone data, and a multiscale geographically weighted regression (MGWR) model, this study investigates the spatially non-stationary associations between built environment factors and the “city-ness” and “town-ness” of small towns in the Yangtze River Delta. The results show: (1) Enterprise density in metropolitan shadow areas is positively associated with cross-city jobs–housing separation; in peripheral areas, both enterprise density and housing prices exhibit a strong correlation with intra-municipal jobs–housing separation. (2) Middle schools consistently correlate with localized intra-municipal flows, suggesting a plausible spatial anchoring role; around metropolises, medical and commercial facilities link to recreational flows and commuting town-ness, while in distal small towns, medical facilities coincide with intratown jobs–housing balance, and commercial facilities correlate with localized consumption and cross-town employment mobility. (3) Higher road network density corresponds to a shrinking commuting radius near metropolises and intra-municipal intertown interconnection in distal towns, rather than mere external relation channels. This study empirically supports CFT at the small-town scale, explores plausible mechanisms, and informs differentiated planning strategies. Full article

Groundwater is a vital freshwater resource that supports domestic, agricultural, and industrial activities in many regions worldwide. Accurate groundwater potential mapping (GPM) is essential for sustainable water resource management; however, traditional empirical and statistical approaches often struggle to capture the complex, nonlinear relationships among hydrogeological variables. In recent years, machine learning (ML) has emerged as a powerful data-driven approach for improving GPM accuracy and efficiency. This review synthesizes findings from 83 peer-reviewed studies published between 2015 and 2025, focusing on widely used ML algorithms such as Random Forest, Support Vector Machines, Artificial Neural Networks, and hybrid models. The review evaluates key methodological aspects, including input parameter selection, data partitioning, integration with GIS and remote sensing, and model justification techniques. It also discusses common challenges such as data limitations, regional variability, and model interpretability. The results indicate that ML-based approaches can significantly enhance groundwater prediction when supported by appropriate data and validation strategies. Future research directions include explainable artificial intelligence, uncertainty quantification, multi-source data integration, and improved model transferability. This review provides a comprehensive reference for advancing reliable and sustainable groundwater potential mapping. Full article

This paper presents an integrated and scalable methodology for assessing the feasibility of Renewable Energy Communities (RECs) in rural and agricultural settings, particularly in areas with limited technical and consumption data. By incorporating Geographic Information System (GIS) data, photovoltaic potential estimation, and energy consumption profiling, the study provides a decision-support framework suitable for various municipalities. A case study conducted in Caorso, a municipality in northern Italy, showcases the framework’s capability to model energy exchanges and estimate self-sufficiency levels for a predominantly rural area. The results highlight seasonal variations in energy production and consumption, identifying opportunities for improvement through energy storage and enhanced energy-sharing strategies. Overall, the proposed approach supports municipalities in the pre-feasibility assessment of RECs by enabling the evaluation of local renewable potential and minimum rooftop utilization thresholds under limited data availability. Full article