Search Results (3,581)

This study compares the spatial and temporal dynamics of environmental noise in two Colombian municipalities with contrasting urban typologies: Soledad (Atlántico, >600,000 inhabitants; traffic and airport dominated) and Montelíbano (Córdoba, ~86,647 inhabitants; ferronickel mining and heavy transport dominated). A two-tier methodology integrated field monitoring under Resolution 627 of 2006 at 80 points (Soledad) and 30 points (Montelíbano), with calibrated SoundPLAN 6.0 dispersion models implementing ISO 9613-2 propagation. The central finding is that urban typology produces fundamentally different acoustic fingerprints: Soledad exhibits a strong day–night gradient (working-day mean LAeq diurnal = 73.2 dB(A), nocturnal = 68.1 dB(A); mean ΔLAeq = −5.1 dB(A)), while Montelíbano displays a near-flat profile (diurnal = 67.1 dB(A), nocturnal = 67.0 dB(A); ΔLAeq = −0.1 dB(A)), reflecting continuous mining-industrial operations. Non-compliance rates reach 83.8% (Soledad day), 96.2% (Soledad night), 60.0% (Montelíbano day) and 100% (Montelíbano night). Model validation meets international ISO 9613-2 benchmarks for Montelíbano (75% of residuals within ±5 dB(A); mean residuals −2.72/−2.92 dB(A) diurnal/nocturnal); Soledad shows higher scatter (mean residuals +5.78/+1.43 dB(A)), consistent with the greater acoustic heterogeneity of a large metropolitan environment. These results demonstrate that typology-differentiated noise management policies are needed for effective implementation of Colombia’s Anti-Noise Law (Law 2450 of 2025). Full article

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km²; equivalent to ≥10,000,000 m²) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km²; equivalent to 500,000–10,000,000 m²) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km²; equivalent to 10,000–500,000 m²) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database. Full article

Rapid coastal urbanization poses severe threats to biodiversity through habitat fragmentation, making continuous monitoring of urban ecosystems essential. While birds serve as sensitive bio-indicators, the long-term spatiotemporal dynamics of their habitats and temporal shifts in environmental drivers remain poorly understood in high-density megacities. This study addresses this gap by developing a trend-explainable machine learning framework to evaluate avian habitat suitability across the western coast of Shenzhen from 2010 to 2020. We applied a standardized filtering protocol to citizen science data and integrated occupancy modeling with a Random Forest algorithm to simulate habitat distributions at 30 m resolution. Spatiotemporal habitat alterations were quantified using Mann–Kendall trend analysis, while SHAP was utilized to diagnose the changing importance and non-linear thresholds of ecological drivers over the decade. Our findings reveal pronounced spatial heterogeneity among six avian guilds. Habitat quality for terrestrial birds, raptors, and songbirds degraded severely in northern industrial regions, whereas targeted ecological restoration facilitated recovery in southern and western urban cores. The analysis further demonstrates dynamic temporal shifts in environmental responses. The restrictive impact of anthropogenic stressors including population density and nighttime light weakened for terrestrial and canopy-dwelling guilds but intensified for waterfowl. Concurrently, natural elements such as vegetation coverage and proximity to water bodies became increasingly important. Based on these spatiotemporal patterns, we delineated five ecological zones to guide targeted conservation interventions. This research provides an analytical framework linking predictive modeling with mechanistic insights, supporting evidence-based biodiversity conservation and sustainable urban planning in rapidly developing coastal landscapes. Full article

Digital terrain models (DTMs) are essential elevation datasets that represent the morphology of the Earth’s surface and play a critical role in applications, such as urban planning, civil engineering, infrastructure design, and environmental assessment. However, the excessive cost remains the major challenge in obtaining accurate terrain models. Recent advancements in low-cost inertial navigation and motion-sensing technologies offer significant potential to enhance the cost-effectiveness of surveying projects. This study investigates the vertical accuracy and operational usability of a handheld inertial measurement unit (IMU) device (Moasure 2) for DTM generation in urban environments through the comparison with traditional total station and digital levels procedures. It also assesses the device compliance with The American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards. For this purpose, a comprehensive field survey was conducted in a small urban area characterized by varied terrain morphology. The vertical accuracy of the Moasure 2 was acceptable for many urban mapping applications based on a rigorous analysis of checkpoint data and error patterns, which were quantitatively assessed relative to reference surfaces. Profile-based validation showed that the elevation differences between similar terrain types were mainly within ±25 cm, with minimal bias and symmetric error distributions. The findings indicate that Moasure 2 can be a viable alternative tool for fast DTM generation in low-cost urban projects. It offers significant advantages in terms of portability, ease of use, and reduced fieldwork time compared to conventional methodologies. Furthermore, this study addresses the critical gap in the validation of the new IMU-based surveying technology and provides evidence for choosing appropriate equipment for urban terrain modeling. Full article

The implementation of safe and reliable Autonomous Driving Stacks in complex urban environments remains a formidable engineering challenge. While classical modular pipelines provide necessary component-level interpretability, they are inherently rigid, often struggling to adapt to novel environments and failing to provide robust scene interpretation in highly interactive scenarios. In this paper, we present a modular End-to-End ROS-based autonomous driving architecture that upgrades a classical modular baseline by injecting learning-based models into its individual processing layers, integrating GaussianCaR and CLIP for dense semantic BEV perception, expanding the Hierarchical Petri Net state space for safe multi-agent reasoning, refining the planning layer with continuous curve optimization, and replacing the previous reactive controller with an Adaptive Nonlinear Model Predictive Control strategy for superior trajectory tracking. Validated in the CARLA simulator across challenging traffic scenarios and adverse environmental conditions, the proposed architecture raises the Driving Score from 53.81% to 66.46% over the previous baseline, driven by a substantial increase in the Infraction Penalty from 0.59 to 0.79, reflecting a fundamental shift towards safer and more conservative driving behavior at the cost of a moderate reduction in route completion. Against pure End-to-End approaches, our architecture achieves the highest Driving Score at 73.9% and the strongest Infraction Penalty at 0.913, demonstrating that modular interpretability and competitive End-to-End performance are not mutually exclusive. Code will be made publicly available online. Full article

Urbanization is a major driver of land-use change and ecological shifts, especially in semi-arid regions with high environmental sensitivity. This study examined urban land growth and its ecological impacts in Astana, Kazakhstan, from 2000 to 2020 and forecasted trends for 2030. Landsat imagery was classified using a Support Vector Machine (SVM) approach, and ecological conditions were assessed through spectral indices, including Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), a Tasseled Cap Wetness index (Wet), and a Normalized Difference Bare-Soil and Built-up Index (NDBSI). The Future Land Use Simulation (CA–Markov) model simulated land use under Business-as-Usual (BAU) and Ecological Priority (EP) scenarios. The results showed a significant increase in built-up land, mainly at the expense of cropland and grassland, with increased landscape fragmentation and rising LST, indicating intensifying urban heat. Ecological indices showed spatially varied responses, with localized greening in protected areas and overall environmental pressure in expanding zones. Scenario simulations suggest that policy interventions under the EP scenario can mitigate cropland loss, limit fragmentation, and enhance ecological connectivity compared with BAU. Overall, the findings show that integrating remote sensing, machine learning, and scenario modeling offers an effective framework for assessing urban–ecological dynamics and supports evidence-based planning for sustainable urban development in semi-arid cities. Full article

Sustainable urban transportation is fundamentally linked to public health outcomes, specifically the mitigation of fatal traffic risks under environmental stress. While stressors like adverse weather affect entire cities, traditional road safety models often assume uniform risk, thereby masking the spatial inequalities inherent in the urban fabric. This study addresses this gap by investigating the geographically heterogeneous impact of environmental stressors—including rainfall, surface moisture, and lighting conditions—on the conditional probability of fatal crash outcomes in Melbourne, Australia. Analyzing 43,075 severe crashes through a multi-stage geospatial framework (Getis-Ord Gi* and Geographically Weighted Logistic Regression), this research diagnoses how varying urban development patterns mediate the lethality of these stressors. The findings unmask a critical “threshold-crossing” pattern for wet surfaces, where risk transitions from protective to hazardous based on local infrastructure form and street geometry. Significant spatial inequalities are identified: high-density inner-urban cores and adjacent coastal corridors exhibit a heightened sensitivity to visibility failures and moisture, whereas newer industrial peripheries show stronger protective “risk compensation” effects. These results reveal a systemic mismatch between historical urban form and contemporary climate-driven public health risks. By identifying localized “lethality thresholds”, this study provides a robust evidence base for integrated planning and equitable resource allocation. It enables urban planners to move beyond generalized safety warnings toward targeted structural interventions, ensuring that sustainable transportation networks prioritize safety equity for all citizens regardless of their location within the urban environment. Full article

Against the backdrop of rapid urbanization and climate warming, the urban heat island (UHI) effect has severely affected ecological security and public health. Existing studies have often focused on single-city analyses or large-sample averages, with insufficient attention to the nonlinear driving mechanisms of UHI under different hydrothermal contexts. This study selects Wuhan and Xi’an as representative cities, constructing an explainable machine learning framework to interpret and compare UHI intensity across feature importance, nonlinear responses, factor interactions, and spatial differentiation. The results show that, in Wuhan, the top five factors contribute 62.4%, reflecting a composite dominance of ecology, spatial morphology, location, and human activities. In Xi’an, the top five factors contribute 72.0%, indicating a more concentrated dominant structure. Nonlinear responses reveal that key factors like NDVI have distinct effect thresholds and mechanisms in the two cities. Spatially, Wuhan displays a continuous gradient pattern characterized by center-promoting and peripheral-suppressing effects, whereas Xi’an presents a block-like mosaic structure composed of multiple juxtaposed districts. These differences suggest that UHI mitigation should move beyond a uniform control model and instead adopt climate-sensitive strategies that account for the dominant factor combinations, response thresholds, and spatial organization of each city. The proposed framework and findings provide scientific support for understanding UHI mechanisms under different hydrothermal contexts and offer targeted implications for thermal environment regulation and spatial planning in cities with similar climatic and environmental characteristics. Full article

The rapid growth in vessel size associated with global maritime trade is placing increasing pressure on port infrastructure worldwide. In Brazil, many existing ports face structural limitations due to insufficient navigational depth and limited opportunities for spatial expansion, often constrained by urban encroachment. In this context, identifying suitable coastal locations for deep-draft port development has become a key strategic challenge for long-term planning. This study develops a GIS-based spatial suitability model to identify segments of the Brazilian coastline with favourable conditions for deep-draft port infrastructure capable of accommodating large vessels, including post-Panamax ships. The approach considers physical constraints, environmental restrictions and basic logistical connectivity within a multi-criteria spatial framework implemented through map algebra. The model is conceived as a strategic screening tool to support early-stage decision-making rather than a detailed feasibility assessment. The results identify nine coastal locations with the highest suitability scores, indicating that highly favourable conditions for deep-draft port development are spatially limited. Notably, one of these candidate locations partially overlaps with an existing port-related cluster, suggesting consistency between the model outputs and real-world port development patterns. In contrast, large portions of the southeastern coastline (particularly in São Paulo and Paraná) exhibit lower suitability due to a combination of urban pressure, environmental constraints and limited depth conditions. Overall, the findings reveal a spatial mismatch between Brazil’s main economic core and the coastal areas with more favourable natural conditions for new port infrastructure. The proposed framework contributes a transparent and transferable spatial decision-support tool that can assist policymakers in identifying priority areas for future port development and in balancing investments between the expansion of existing ports and the development of new locations. Full article

The position of urban road networks along the spectrum from geometric order to organic growth reflects the long-term tension between institutional planning and adaptive accommodation to local conditions, yet systematic quantification of this continuum in large historical urban datasets remains absent. This study digitizes road network data for 256 Qing Dynasty county cities using GIS methods and proposes a Geometric–Organic Index (GOI), a composite measure formed by equal-weight averaging of four sub-indicators: cross-junction ratio (CrossR), orientation regularity (OrientR), block regularity (BlockR), and connectivity ratio (ConnR). The results show that the GOI follows a continuous unimodal distribution (mean 0.346, median 0.341, n = 220, representing cities for which all four sub-indicators including block regularity could be computed), confirming that the geometric–organic dimension constitutes a continuum rather than a binary classification. Across the 182 cities for which both GOI values and external city wall regularity scores are simultaneously available, wall regularity correlates significantly with road network GOI (Pearson r = 0.320, p < 0.001). Mediation analysis reveals that topographic relief influences road network structure indirectly through wall regularity (Sobel z = −2.15, p = 0.032; Bootstrap 90% CI excludes zero), establishing city walls as morphological templates through which environmental constraints are transmitted to the urban interior. Primal-graph-based syntax-style validation on 173 cities (the 182-city set restricted to n_nodes ≥ 10 and available road files) further shows that GOI correlates significantly with intelligibility (r = 0.403, p < 0.001) and synergy (r = 0.463, p < 0.001), while mean local integration correlates negatively with GOI (r = −0.349), revealing a structural trade-off between global order and local efficiency. The equal-weight GOI scheme proves robust across 624 weighting combinations (Kendall $\tau$ = 0.928 for near-equal-weight combinations), and a global spatial autocorrelation test (Moran’s I = 0.137, p = 0.001) indicates weak spatial clustering without undermining the principal conclusions. This study provides the first large-sample empirical test of the morphological transmission hypothesis linking a city’s outer boundary to its interior road network, and offers a transferable quantitative framework for urban morphological typology. 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

This paper examines the relationship between urban green spaces and sport participation among Italian adults, using microdata from the Italian National Institute of Statistics (ISTAT) for the period 2014–2022. The analysis focuses on whether living within walking distance of equipped green areas is associated with individual physical activity behavior, as urban green infrastructure may represent a cornerstone of social and environmental sustainability and act as crucial determinants of active lifestyles and urban community well-being. Two binary indicators are constructed to identify individuals who practice sport either occasionally or regularly. Probit and logit regression models are used to estimate the association between access to public green spaces and sport participation, controlling for individual socio-demographic, economic and health characteristics, as well as geographical and time effects. The results reveal a statistically significant positive relationship between proximity to green spaces and physical activity. Average marginal effects indicate that living near a park increases the probability of practicing sport by 40.2 percentage points among occasional participants and by 27.3 percentage points among regular participants. These findings provide empirical evidence that accessible public green infrastructure plays a crucial role as an environmental determinant of healthier lifestyles. From a policy perspective, the results support the role of local governments in promoting physical activity through investments in accessible and well-designed urban green infrastructure, highlighting the prominent role of dedicated parks in promoting public health sustainability and inclusive urban planning frameworks. Full article

Urban outdoor exercise spaces are important public infrastructures for physical activity, but their users may be exposed to concurrent air pollution and unfavorable thermal environmental conditions. This study developed an activity-weighted framework to assess the compound ozone–heat exposure risk in urban outdoor exercise spaces. Taking the central districts of Beijing as the study area, we integrated the mobile phone signaling-derived visitation frequency, 1 km ground-level O₃ estimates, the 30 m Landsat-derived land surface temperature (LST), the land cover composition, road network indicators, and three-dimensional building morphology variables. An activity-weighted compound ozone–heat exposure risk index (COHER) was constructed by combining the normalized daily visitation frequency, monthly mean O₃, and area of interest (AOI)-level mean LST. The results showed that the visitation frequency, O₃, and LST exhibited mismatched spatial patterns, highlighting the need for compound exposure assessment. COHER values ranged from 0.0000 to 0.1918 and were strongly right-skewed, with 49 outdoor exercise spaces identified as the top 10% high-risk sites. These high-risk spaces had a substantially higher visitation frequency and mean LST than the remaining spaces, whereas O₃ differences were small and not statistically significant. Exploratory XGBoost–SHAP analysis suggested that the built-up intensity, building height variability, and potential airflow obstruction were relatively important environmental correlates of COHER. The proposed framework provides a relative place-based screening tool for identifying priority outdoor exercise spaces for exposure-sensitive planning and risk mitigation. Full article

Lokoja, the capital of Kogi State, Nigeria, situated at the confluence of the Niger and Benue Rivers, has experienced rapid urban expansion alongside heightened environmental risks, including flooding and ecosystem degradation. Using multi-temporal Landsat imagery (2000, 2010, 2020, 2024), Random Forest classification, and landscape metrics, this study analyses spatio-temporal patterns of urban growth and fragmentation in this underrepresented mid-sized African city. Urban land cover expanded from 6668 ha in 2000 to 15,985 ha in 2024 (net ~140% growth), following a non-linear trajectory of rapid expansion (2000–2010), partial consolidation (2010–2020), and renewed growth with intensified fragmentation (2020–2024). This growth caused severe ecological impacts: dense forest declined by 99.7% (from 373 ha to 1 ha), woodland by 73.9%, and core natural land cover by 23% to 13.8% of the landscape, below critical ecological thresholds. Edge density rose by 121%, exacerbating urban heat, runoff, and biodiversity loss, while apparent gains in grassland largely reflect secondary succession rather than recovery. This study recommends enforcing development restrictions below 10 m in elevation, with 100 m riparian buffers; restoring 500 ha of native corridors; mandating 20% urban tree canopy cover; and establishing community-based green space monitoring. The findings provide empirical insights into sustainability challenges facing mid-sized African cities and offer transferable strategies for ecologically sensitive urban planning. Full article

Urban heat stress and deteriorating air quality are environmental risks in semi-arid cities, positioning urban forests as vital nature-based solutions for climate adaptation. Despite growing recognition of urban greening imperatives, South Africa’s (SA) Northern Cape Province remains characterized by sparse vegetation Land Use/Land Cover (LULC) and built environment expansion. The study’s research problem focuses on how vegetation LULC dynamics influence urban forests’ potential in mitigating heat stress and atmospheric pollution in arid urban systems. The study adopts a multi-scale analytical approach, conducting the LULC and NDVI analysis through a multi-temporal Landsat satellite imagery analysis quantifying LULC change from 2004 to 2024. Grounded in the Integrated Spatial Justice-Ecosystem Services (ISJES) Framework, the analysis reveals significant decline in dense vegetation LULC from 9021.77 km² (2.4%) to 1262.10 km² (0.3%), while barren land expanded from 73,417.01 km² (19.7%) to 222,866.82 km² (59.8%) intensifying urban thermal exposure. Built-up areas expanded from 91.06 km² to 357.072 km², further constraining ecological buffers across the province’s urban nodes and undermining urban climate resilience. The Global Moran’s I statistic for the NDVI change surface (I = 0.7843, Z = 443.87, p < 0.0001) confirms spatial clustering of degradation hotspots of NDVI decline affecting 66.5% of the study area. Furthermore, Geographically Weighted Regression (GWR) results confirm that vegetation loss is being driven by the combined and spatially differentiated effects of mining proximity, urban expansion, livestock pressure, declining rainfall, and rising temperatures. In terms of thermal regulation findings, the Getis-Ord Gi hot spot analysis identifies significant NDVI decline covering 23.5% of the study area at the 99% confidence level, expanding to 33.5% and 39.5% at the 95% and 90% confidence levels, respectively; hence, there is a need for urban forest corridors, climate-sensitive spatial planning frameworks, and targeted greening interventions in heat-vulnerable arid geographies. This study provides the first comprehensive, multi-decadal quantification of vegetation loss across SA’s largest province. Full article