Search Results (7,656)

Urban employment restructuring generates job–housing mismatches whose planning consequences depend on spatial scale and temporal trajectory, yet existing data sources rarely combine fine spatial resolution with long temporal coverage. This study applies multiscale geographically weighted regression (MGWR) to reconstruct annual employment surfaces at 1 km² resolution for Tianjin, China, from 2010 to 2023, using a single anchor year of mobile signaling data. Spatial relationships estimated from 2020 records were transferred across time using annual updates of Points of Interest (POI) density, nighttime light intensity, and population density, with aggregate consistency enforced against official employment totals. Validation confirmed the spatial ordering of employment concentration in the calibration year (Spearman ρ = 0.509, RMSE = 2856 jobs/km²), temporal stability in an independent year (ρ = 0.522, RMSE = 2640 jobs/km² for 2023), and consistency with district statistics (R² = 0.718). Directional robustness to anchor-year selection was further supported by reverse-transfer validation (ρ = 0.652) and by a 2023-anchored reconstruction that replicated the principal directional findings. Three findings emerged that remain invisible at conventional administrative resolution: employment decentralization was spatially bounded at the intermediate ring by an economic activity intensity gradient; the dual-core structure remained morphologically stable while the two poles diverged functionally due to structurally different employment bases; and employment agglomeration change preceded spatial job–housing co-location adjustment by approximately three years. This exploratory lag is consistent with a theoretical assumption of sequential adjustment—a city-specific exploratory signal whose planning implications warrant further investigation in other restructuring cities. Full article

Urban Heat Island (UHI) effects are increasingly affecting Mediterranean coastal cities, where climate change, urbanization, and seasonal tourism intensify thermal stress and environmental vulnerability. In this context, climate-oriented planning and green infrastructure are recognized as key strategies for urban adaptation. This study investigates the spatiotemporal evolution of Land Surface Temperature (LST) and vegetation cover in the coastal municipality of Lignano Sabbiadoro (northeastern Italy) through the analysis of Landsat imagery acquired between 1984 and 2023. Summer LST and Normalized Difference Vegetation Index (NDVI) maps were derived from June–August observations and used to assess long-term thermal dynamics, vegetation patterns, and Urban Heat Island development. Meteorological data indicate a significant increase in mean annual air temperature, with a warming trend of approximately 0.57 °C per decade between 1984 and 2023. Correspondingly, Landsat-derived LST maps reveal a marked intensification of summer surface temperatures, with mean summer LST increasing from 32.16 °C in 1984–1993 to a peak of 35.91 °C in 2004–2013, followed by a slight decrease to 35.77 °C during 2014–2023. During the same period, the proportion of municipal surfaces characterized by temperatures above 35 °C increased from 10.7% to more than 60%, while cooler areas (<30 °C) declined from 17.7% to 2.3%. The comparison between LST and NDVI patterns revealed a persistent inverse relationship between vegetation cover and surface temperature, with coastal pinewoods, green spaces, and water bodies consistently exhibiting lower thermal values than densely urbanized sectors. A key methodological contribution of the study is the operational integration of satellite-derived thermal remote sensing into the Green Plan of Lignano Sabbiadoro. LST mapping was used to identify priority areas for climate adaptation measures, including ecological corridors, wooded landscape connections, urban green corridors, and depaving interventions. The results demonstrate how multi-temporal thermal analysis can support evidence-based planning by linking climate assessment with the spatial prioritization and design of green infrastructure strategies. The proposed workflow provides a transferable framework for integrating remote sensing into climate-informed planning processes in Mediterranean coastal cities and other urban contexts increasingly exposed to heat-related risks. Full article

Remote sensing change captioning aims to describe disaster-related changes from bi-temporal imagery, yet existing methods typically produce image-level captions without explicit regional evidence, limiting interpretability and weakening the link between generated language and actual changed areas. We present ChangePixel, a single-backbone framework that upgrades remote sensing change captioning into pixel-level, evidence-grounded change narration without introducing new manual grounding labels. ChangePixel incorporates three lightweight modules: a Bi-Temporal Change-Aware Transfer Adapter (BCTA) that converts shared pre- and post-event visual features into change-aware grounding representations, a Change Region Grounding Planner (CRGP) that localizes a compact set of informative changed regions before narration begins, and a Weak Evidence Alignment Bridge (WAB) that converts released change captions into phrase-to-region weak supervision. Through this design, the model jointly produces a global change caption and region-level evidence in the form of pixel masks paired with corresponding local change phrases. Experiments on the Remote Sensing Change Caption (RSCC) dataset and LEVIR-CC demonstrate that ChangePixel provides caption quality (ROUGE 19.52/ST5-SCS 76.91 on RSCC; CIDEr-D 56.82 on LEVIR-CC under zero-shot transfer) that is competitive with general-purpose vision–language models (VLMs) while adding pixel-level spatial evidence to change narration; additionally, evidence localization is quantified on LEVIR-MCI through semantic change-mask metrics, reaching Change mIoU 33.8 (15.3 points higher than a non-learned pixel-difference floor of 18.5), whereas phrase-to-region alignment is assessed qualitatively pending a dedicated grounding benchmark. The proposed framework offers a practical path from coarse image-level captioning to evidence-grounded disaster understanding. Full article

The spatial organisation of plasma membrane proteins such as G protein-coupled receptors (GPCRs) plays a critical role in regulating cell signalling, function, and ultimately cell fate. Resolving this organisation requires techniques capable of probing dynamics at the single-molecule level with high spatial and temporal resolution. In this study, we employ the complementary fluorescence fluctuation spectroscopy approaches, Fluorescence Correlation Spectroscopy (FCS), Photon Counting Histogram Analysis (PCH), Raster Image Correlation Spectroscopy (RICS) and Number and Brightness Analysis (N&B), in conjunction with Fluorescence Recovery After Photobleaching (FRAP), to investigate the membrane organisation of the C-X-C chemokine receptor 4 (CXCR4), a GPCR known to undergo ligand-induced reorganisation. At the nanoscale, FCS highlighted opposing effects on diffusion after agonist (CXCL12) and inverse agonist (IT1t) treatment, whilst RICS also showed ligand-mediated changes in particle number. Both single-point and image-based brightness analyses (PCH and N&B) showed increased brightness after CXCL12 treatment, consistent with the pre-internalisation clustering of CXCR4. At the microscale, FRAP showed an increase in immobile CXCR4, not visible to FFS approaches, following CXCL12 stimulation. This integrated approach, performed on a single commercial confocal microscope, provides valuable insight into the reorganisation of CXCR4 in the plasma membrane over a range of temporal and spatial scales, which are not detectable using standard imaging. Full article

Over the past three decades, the United States has experienced a notable increase in weather-related disasters, including hurricanes, floods, tornadoes, wildfires, and severe storms, posing growing challenges to healthcare preparedness and public health systems. This study analyzes Federal Emergency Management Agency (FEMA) disaster declarations from 1989 to 2019 to characterize temporal and geographic trends in weather-related events. Data after 2019 were excluded to avoid confounding effects associated with the COVID-19 pandemic, which disrupted disaster declarations, resource allocation, and healthcare system demands. Using descriptive statistics, generalized linear mixed models, and spatial clustering techniques, we identified substantial increases and nonlinear patterns in disaster declarations, with variation across hazard types and regions. These trends reflect evolving hazard exposure, regional differences, and policy-driven declaration practices. Although this study does not directly measure health outcomes or social vulnerability, the observed patterns have important implications for healthcare system capacity, workforce preparedness, and populations known to be disproportionately affected by disasters. The findings highlight the need for climate-informed training, data-driven preparedness planning, and integration of disaster trend analysis into nursing education and public health practice. Strengthening the ability of healthcare systems to anticipate and respond to evolving disaster patterns is critical for advancing resilience and promoting equitable health outcomes in the context of climate change. Full article

Urban river corridors play an important role in reducing urban heat and supporting vegetation, yet their environmental influence is still poorly understood in rapidly growing African cities. This study examined the spatial and temporal relationship between vegetation cover (NDVI), land surface temperature (LST), and built-up intensity (NDBI) along the Kebena River corridor in Addis Ababa, Ethiopia, using Landsat 8 and 9 imagery from 2015, 2018, 2022, and 2026. A buffer-based approach (0–250 m, 250–500 m, and 500–1000 m) was applied to evaluate how these indicators change with distance from the river. The results reveal a clear environmental gradient. The 0–250 m zone consistently showed higher vegetation cover, lower surface temperature, and lower built-up intensity compared with the outer zones. Pearson correlation analysis indicated a significant negative relationship between NDVI and LST (r = −0.49 to −0.64) and a strong negative relationship between NDVI and NDBI (r = −0.83 to −0.90), while NDBI and LST were positively correlated (r = 0.51 to 0.67). Regression analysis further showed that a 0.1 increase in NDVI reduced LST by approximately 2.7–3.9 °C. The river corridor continued to provide measurable cooling benefits, notwithstanding a slight reduction in vegetation cover over the study period. The lower built-up intensity near the river in 2026 is consistent with the timing and recorded activities of the recent Kebena River rehabilitation programme. These results indicate that conservation and restoration of urban river corridors can improve ecological resilience, mitigate the effects of urban heat and support the sustainability of green infrastructure in rapidly urbanising cities. This study offers evidence to guide conservation planning and long-term management of urban riparian ecosystems in Addis Ababa and other cities facing similar environmental pressures. Full article

Urbanization profoundly influences social, economic, and environmental systems, imposing a comprehensive understanding of spatial and temporal land use and land cover (LULC) transformations. This study aims to quantify the LULC changes from 2014 to 2023 in Penang Island, Malaysia, using SPOT 6 and SPOT 7 satellite imagery with a 1.5 m spatial resolution. After preprocessing and transforming data, five LULC classes—namely built-up, forest, water bodies, agriculture and horticulture, and barren land—were classified. The Support Vector Machine (SVM) classifier achieved accuracies of 90.8% in 2014, 91% in 2019, and 94.2% in 2023, with kappa coefficients of 0.85, 0.84, and 0.9, respectively. Analysis at the district level revealed that built-up area decreased by 4.53 km², forest expanded by 10.32 km², water bodies grew by 0.26 km², agriculture and horticulture increased by 8.47 km², and barren land declined by 11.84 km². Interestingly, the decline in built-up areas presents a paradox to the conventional narrative of urban growth, which typically anticipates an increase in developed land over time. This counterintuitive trend invites further inquiry into factors that may have driven such a reversal in urbanization patterns. Nevertheless, the findings align with SDG 11 and the NPP, which advocate for sustainable and resilient urban development. Full article

Understanding the spatio-temporal structure of ground deformation in caldera systems remains a major challenge because multiple processes may coexist and interact across different spatial and temporal scales. The Campi Flegrei caldera provides an exceptional natural laboratory to investigate these dynamics through long-term geodetic observations. Here, we integrate three decades of multi-mission Differential Interferometric Synthetic Aperture Radar (DInSAR) data to retrieve the spatio-temporal evolution of the ground displacement field. We analyse the resulting dataset using a kinematic decomposition in which the deformation field is expressed as the sum of (i) a time-invariant spatial pattern modulated by a time-dependent amplitude and (ii) a spatially variable constant-velocity component. Our results show that the observed deformation is largely governed by a dominant spatial pattern that remains remarkably stable through time, while its temporal amplitude reproduces both subsidence and uplift phases. In addition, a secondary constant-velocity component, with amplitudes of only a few mm yr⁻¹, is required to account for residual spatial variability, particularly in the south-eastern sector of the caldera. A transition in deformation behaviour around 2012–2013 is associated with a south-westward expansion of the dominant deformation pattern. The inferred dominant spatial patterns are consistent with a persistent deformation source located at a depth of approximately 3–4 km, whereas the secondary spatially variable constant-velocity field is compatible with a deeper expansion process. These findings indicate that long-term caldera deformation can be effectively represented within a low-dimensional kinematic framework. This approach provides a robust basis for the interpretation of geodetic time series and for the identification of changes in deformation regime in restless caldera systems. Full article

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine the evolution of reported bicycle–vehicle injury accidents in the Lisbon Metropolitan Area (LMA). The framework combines Geographic Information Systems (GIS)-based spatial statistics with Emerging Hotspot Analysis (EHA) to identify and track changes in accident clustering over time, across pre-, during-, and post-COVID-19 containment periods. This study contributes by applying Emerging Hotspot Analysis to bicycle accident data, an approach still largely unexplored, and by proposing a sequential and integrated framework that links traditional spatial analysis methods with dynamic hotspot detection and machine learning techniques, enabling a shift from static pattern identification to enhanced interpretation of evolving accident occurrence patterns and hotspot dynamics. Results reveal evidence of spatial consolidation and changing hotspot distributions over time, with emerging hotspots increasingly located in suburban transition zones and at the edges of existing cycling infrastructure. These patterns may reflect changes in mobility demand and infrastructure provision, although the absence of exposure data prevents a direct assessment of this relationship. Complementary analysis using forest-based machine learning models identifies key factors associated with hotspot formation and accident severity, including crash type, temporal patterns (e.g., day of the week), and environmental conditions such as slope and lighting. These findings highlight the value of combining spatio-temporal analysis with predictive modelling to support data-driven urban planning and targeted safety interventions. Lisbon provides a relevant case study for cities undergoing similar transitions toward sustainable transport systems. Full article

Prolonged drought in Chile has imposed increasing pressures on water-dependent tourism activities, although its effects have been assessed only fragmentarily and rarely linked to tourism-relevant indicators. This study provides an integrated hydro-climatic and economic assessment of drought impacts on two tourism categories especially sensitive to water availability: snow and mountain tourism, and tourism related to water bodies and watercourses. For this purpose, time series of snow cover, streamflow, precipitation, and water quality were analyzed for 2000–2024, complemented by sectoral statistics and indirect indicators of economic impact. Trend analyses used linear regression, the Mann–Kendall test, Sen’s slope, Pettitt change-point detection, and Spearman correlations between hydroclimatic variables and tourism proxies. Results show a significant decline in snow cover across most of northern and central Chile, with strong signals in basins critical for winter tourism and a common temporal break in 2009. Widespread streamflow reductions were also detected in rivers from central, southern, and Patagonian Chile, although with differing magnitude and timing. In contrast, water-quality changes were limited and spatially heterogeneous. In the ski sector, reduced snow accumulation was associated with shorter ski seasons, fewer skier-days, and lower direct employment. For rafting, declining streamflow was associated with reduced hydrological suitability, indicating less favorable potential operating conditions. Overall, drought affects tourism significantly but unevenly, depending on geography, hydrological regime, activity type, and data availability. The proposed integrated assessment helps identify differentiated drought-impact pathways and supports more climate-resilient tourism management. Full article

Binary building change detection in high-resolution optical remote sensing images requires accurate temporal comparison and precise boundary localization. Existing methods often apply cross-temporal interaction uniformly to all spatial tokens at a selected feature level. When true changes are sparse, appearance differences at unchanged locations may still produce nonzero interaction responses. To address this issue, we propose SwinEADFormer, a difference-conditioned and discrepancy-aware cross-temporal interaction framework. A shared Swin-Tiny encoder extracts hierarchical bitemporal features. An image-pair-specific router generated from S3 difference cues and S4 semantic context modulates cross-temporal attention outputs before residual addition. The model explicitly computes the discrepancy between the two temporal interaction directions and fuses it with learned and direct S3 difference evidence. This design conditions interaction on estimated change evidence while preserving direct temporal differences. A multi-scale decoder produces the final change map. Experiments on LEVIR-CD, WHU-CD, and SYSU-CD show competitive region-level performance against representative baselines. On LEVIR-CD, SwinEADFormer achieves the highest mean B-recall and B-F1. Five-seed ablations show that $\mathrm{\Phi }$ fusion and bidirectional interaction provide the clearest improvements, while the router yields smaller mean gains. Full article

The overexploitation of groundwater resources is a significant concern due to the potential risks associated with a decline in freshwater availability. Future planning and policymaking should consider long-term groundwater availability and urban expansion patterns to understand urban growth. This study aims to investigate the impact of land cover change on groundwater depletion. Further, the land surface temperature (LST) and vegetation change using Normalized Difference Vegetation Index NDVI analysis have been performed to find the spatial spread of urbanization and its impact on surface temperature in the area. For groundwater assessment, the Gravity Recovery and Climate Experiment (GRACE) data have been used, while for land cover, NDVI, and LST assessment, Landsat data have been used. The GRACE-based groundwater storage (GWS) anomaly has been correlated with Global Precipitation Measurement (GPM) data. An annual groundwater storage decline of ~7.01 mm/year was identified. Groundwater and land-cover changes were evaluated at five-year intervals from 1990 to 2025. The urban expansion from 838 to 1470 km² coverage shows the rapid expansion and its impact on vegetation and groundwater recharge in the area. The results demonstrate a rapid increase in the urban area, which affected the vegetation and increased the surface temperature in the area. Urban expansion reduced vegetation cover and infiltration, contributing to elevated land surface temperature and groundwater depletion. This study focused on integrating the groundwater impacts due to other environmental variables, i.e., temperature increase and vegetation decrease. The temporal increase in urban expansion decreases the infiltration rate, which impacts the groundwater storage and depletion, as shown by the linear trend. These findings underscore the urgent need for effective groundwater management and vegetation management policies and integrated urban planning strategies to ensure the long-term sustainability of freshwater resources. Full article

Wind influences human activities both directly and indirectly. Directly, it affects, among others, transportation and wind energy systems through its direction and intensity, while extreme wind events can cause severe damage to infrastructure and buildings and even casualties. Indirectly, the movement of air masses is strictly associated with all meteorological phenomena, highlighting the crucial role of wind in shaping weather conditions. In the context of climate change, anomalies in global and regional circulation patterns modify the characteristics of surface winds. Consequently, investigating long-term wind variability and trends is essential for assessing climate change impacts on the environment and society. The climatology of near-surface (10 m) winds over Greece for the period 1991–2020 is examined using a high-resolution regional reanalysis dataset, focusing on the mean wind speed, mean daily maximum wind gust, and the frequency of strong-wind days. The results reveal substantial spatial and temporal variability, with the most pronounced upward trends of these parameters observed over the Aegean Sea and northeastern Greece. Statistically significant trends are detected mainly during winter and summer. In particular, January and August exhibit the strongest positive trends, locally exceeding 0.05 m s⁻¹ per year for mean wind speed and 0.1 m s⁻¹ per year for mean daily maximum wind gust. Moreover, the frequency of strong-wind days increases in several regions with local trends exceeding 0.2 days per year. These findings highlight the value of high-resolution regional reanalyses for characterizing near-surface wind variability and trends over areas of complex terrain. Full article

Net primary productivity (NPP) is a core diagnostic variable of terrestrial carbon cycling, yet consistent annual NPP simulation remains challenging in alpine heterogeneous regions where topography, hydrothermal gradients, vegetation structure, and nutrient constraints interact. Remote-sensing products such as MODIS provide valuable observational constraints, whereas light-use-efficiency models such as CASA retain process transparency and scenario transfer capability. This study develops a CASA-based, MODIS-constrained framework for annual NPP simulation over the Gannan Plateau. The framework preserves a locally parameterized CASA baseline and adds a geographically weighted regression (GWR) residual-alignment layer trained on CASA–MODIS residuals during 2005–2013. The fitted correction relationship was then applied to the 2014–2020 temporal transfer period and evaluated in a 2030s SSP scenario transfer experiment. MODIS was treated as the correction target rather than ground truth, and GLASS was adopted as an independent product-level benchmark. During 2014–2020, the GWR-corrected product showed improved pooled pixel-level agreement with the MODIS-constrained target relative to parameter-localized CASA, with R² increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m⁻² yr⁻¹. Residual Moran’s I also decreased, indicating weaker residual spatial organization after correction. Product-level comparison with GLASS showed a moderate, directionally consistent improvement relative to uncorrected CASA, although this comparison was not interpreted as ground-truth validation. The 2030s scenario transfer experiment indicated that the correction layer changed the spatial expression of NPP divergence among SSP pathways. Overall, the proposed framework provides a process-model-preserving and observation-constrained approach for improving agreement between annual NPP estimates and the MODIS-constrained target in alpine heterogeneous regions, while its applicability remains subject to product uncertainty, spatial dependence, and future nonstationarity. Full article

The new generation is growing up in a context where sustainability and climate change (CC) are considered the “issues of our time”. In line with this, education plays a crucial role in raising awareness of CC among young people. However, one of the main challenges is showing CC’s effects, as they are often perceived as temporally and spatially distant, and many individuals struggle to visualise the real impact that CC’s consequences will have on the future world. The present study describes the co-design of a 360° video showing the effects of CC in familiar environments, conducted with a class of pre-teens. Co-design is a method that enables end-users, who are not trained in design, to work alongside professionals in creative processes. Moreover, participating in collaborative workshops reduces the distance between users and the topic addressed, enhancing their engagement and awareness of it. The results show that participants were satisfied with the developed content and enjoyed taking part in the co-design process. These findings suggest the potential of participatory workshops for developing 360° videos with students and provide a foundation for future investigations about their educational impact. Full article