Search Results (921)

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

Existing studies on urban public space vitality predominantly focus on single temporal scales or macro-urban levels, lacking a systematic understanding of day–night and weekday–weekend differentiation patterns at the meso-scale. This study examines 149 public spaces in the Yuexiu District, Guangzhou, employing Baidu heatmap data and the geographically weighted random forest (GWRF) model to analyze built environment impacts across four temporal scenarios. The SHAP interaction analysis is incorporated to quantitatively evaluate factor interdependencies and their temporal variations. Findings reveal significant spatiotemporal heterogeneity. Building density shows greater night-time importance while residential density exhibits enhanced daytime importance, particularly on weekend. Weekday–weekend comparison demonstrates contrasting spatial reorganization patterns, with weekday showing divergence and weekend showing convergence in factor importance distributions. The factor interaction analysis highlights stable synergistic relationships between density and diversity, alongside temporal transitions in density–residential density interactions from competitive to synergistic during night-time. Low-vitality public spaces are concentrated in peripheral areas with high building density but insufficient commercial facilities and functional mix. These findings deepen our understanding of the spatiotemporal mechanisms underlying public space vitality generation and the interaction effects among built environment factors, thereby providing an empirical foundation for the formulation of temporally adaptive planning strategies. Full article

Soil erosion poses a significant environmental challenge in semi-arid and Mediterranean regions, jeopardizing the sustainability of land and water resources. This study explores the spatio-temporal dynamics of water erosion within the Tafna watershed in Algeria, which encompasses an area of approximately 7200 km². Utilizing the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS), the assessment relies on bias-corrected simulated rainfall data that offers consistent spatial coverage over the past four decades (1983–2023). Additionally, the rainfall asymmetry coefficient (Cs) was calculated to evaluate the impact of temporal rainfall variability on soil loss. The results indicate significant spatial and temporal variability; average erosion rates vary from less than 6 t/ha/year in stable areas to 23–27 t/ha/year in steep, sparsely vegetated regions. Overall, soil erosion has increased by approximately 16% during the study period, driven by heightened rainfall aggressiveness and an intensification of erosive potential. Correlation analysis underscores the intricate relationships between rainfall, topography, and erosive dynamics, highlighting the exacerbating effect of irregular rainfall patterns (Cs). These findings underscore the Tafna watershed’s high vulnerability to both natural and human-induced pressures, reinforcing the necessity for differentiated land management and targeted soil and water conservation strategies. The methodology developed in this study provides a transferable approach for assessing water erosion in other semi-arid and Mediterranean watersheds facing similar data limitations and hydro-climatic variability. Full article

This retrospective study describes the national histopathology caseload of feline tumours submitted to a Portuguese diagnostic laboratory over a five-year period. A total of 1904 histopathology-confirmed neoplasms were analysed by biological behaviour, anatomical location, and demographic/geographical variables. Malignant tumours predominated (77.4%), whereas 22.6% were benign. Tumours most commonly involved the mammary gland (44.8%) and cutaneous/soft tissues (42.4%), together accounting for 87.2% of cases; all other sites were individually uncommon (≤5.6%). The most frequent malignant tumour types were mammary carcinoma (38.3%), fibrosarcoma (8.0%), squamous cell carcinoma (6.4%), and mast cell tumour (4.8%). Cats with malignant tumours were older than those with benign lesions (p < 0.001), and females comprised most submissions (69.3%), largely driven by mammary neoplasia. Multiple, histologically distinct tumours were identified in 8.3% of cats and were more frequent in older females (p = 0.001). Domestic Shorthairs comprised the vast majority of cases, and no significant associations were detected between breed (including pure breeds) or geographical location and tumour occurrence or biological behaviour (p > 0.05). These findings highlight a sustained predominance of malignant disease in Portuguese cats, concentrated in mammary and cutaneous/soft-tissue sites, supporting a low threshold for biopsy in older cats and systematic mammary screening in females, and continued registry-based surveillance to monitor temporal changes in tumour patterns. Full article

Background/Objectives: Recent public and scientific discussions have raised concerns about a possible link between prenatal paracetamol exposure and autism spectrum disorder (ASD). However, pharmacovigilance-based evidence remains scarce, and the role of reporting bias has not been systematically assessed. This study aimed to characterize ASD-related adverse event reports involving paracetamol in the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) and to evaluate potential disproportionality signals, considering demographic, temporal, and geographic patterns. Methods: FAERS data from January 2010 to September 2025 were screened for reports listing paracetamol as the suspect drug and ASD-related Preferred Terms. After excluding duplicates and concomitant drugs, 1776 unique cases were analyzed. Patient demographics, reporter type, and country of origin were summarized descriptively. Disproportionality was calculated using four algorithms: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Information Component (IC), and Empirical Bayes Geometric Mean (EBGM). Results: Among 172,129 paracetamol-associated reports, 1776 (1.03%) included ASD-related terms. All were classified as serious and mostly submitted by consumers (98.6%). Gender was available in 47.7% of cases, showing male predominance (68.8%). Most reports referred to fetal exposure during pregnancy. Nearly all originated from the United States (98.4%). A marked rise was observed after 2022, with 562 reports in 2023 and 1051 in 2025. Disproportionality analyses revealed consistently elevated signals (ROR = 69.8, PRR = 69.2, IC025 = 5.60, EB05 = 48.3). Conclusions: The strong disproportionality signal likely reflects increased public attention and reporting dynamics rather than a causal association. Further integration of pharmacovigilance and epidemiologic data is warranted to clarify the clinical significance of these findings. Full article

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems. Full article

Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1) Urban vitality exhibits a multi-center, clustered structure, gradually expanding from gentle to steeper slopes over time, with activity patterns shifting from an afternoon peak to an all-day distribution. (2) Significant differences in regional vitality resilience were observed: the core vitality areas exhibited stable ST-AOI spatial patterns, flexible temporal rhythms, and strong adaptability; the emerging vitality areas recovered quickly with low losses, while low-vitality areas showed slow recovery and insufficient resilience. (3) The density of commercial service facilities and the level of housing prices were continuously enhancing factors for vitality improvement, whereas the density of subway stations and the degree of functional mix played key roles in supporting resilience during the COVID-19 pandemic. (4) The synergistic effect between transportation systems and commercial facilities is crucial for forming high-vitality zones in mountainous cities. In contrast, reliance on a single factor tends to lead to vitality spillover. This study provides a crucial foundation for promoting sustainable urban development in Guiyang and other mountainous regions. Full article

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a temporal exponential random graph model. The findings reveal three primary insights: First, the overall network exhibits “high connectivity and strong clustering” traits. Enhanced efficiency in intercity resource allocation fosters cross-regional factor flows, resulting in multi-tiered connectivity corridors. Industrial linkages and policy interventions drive the development of a polycentric and clustered configuration. Second, the individual city network exhibits a core–periphery dynamic structure. A diamond-shaped framework dominated by hub cities in the national strategic regions directs factor flows. Development of strategic corridors enables peripheral cities to evolve into secondary hubs by leveraging structural hole advantages, reflecting the continuous interplay between network structure and geo-economic factors. Third, driving factors involve nonlinear interactions within a multi-layered system. Path dependence in topology, gradient potential from nodal attributes, spatial counterbalance between geographic decay laws and multidimensional proximity, and adaptive self-organization are collectively associated with the transition of the urban network toward a multi-tiered synergistic pattern. By revealing the dynamic interplay between network topology and multidimensional driving factors, this study deepens and advances the theoretical connotations of the “Space of Flows” theory, providing an empirical foundation for optimizing regional governance strategies and promoting high-quality coordinated development of Chinese cities. Full article

Understanding regional disparities in Chinese modernization is essential for achieving coordinated and sustainable development. This study develops a multi-dimensional evaluation framework, integrating grey relational analysis, entropy weighting, and TOPSIS to assess provincial modernization across China from 2018 to 2023. The framework operationalizes Chinese-style modernization through five dimensions: population quality, economic strength, social development, ecological sustainability, innovation and governance, capturing both material and institutional aspects of development. Using K-Means clustering, kernel density estimation, and convergence analysis, the study examines spatial and temporal patterns of modernization. Results reveal pronounced regional heterogeneity: eastern provinces lead in overall modernization but display internal volatility, central provinces exhibit gradual convergence, and western provinces face widening disparities. Intra-regional analysis highlights uneven development even within geographic clusters, reflecting differential access to resources, governance capacity, and innovation infrastructure. These findings are interpreted through modernization theory, linking observed patterns to governance models, regional development trajectories, and policy coordination. The proposed framework offers a rigorous, data-driven tool for monitoring modernization progress, diagnosing regional bottlenecks, and informing targeted policy interventions. This study demonstrates the methodological value of integrating grey system theory with multi-criteria decision-making and clustering analysis, providing both theoretical insights and practical guidance for advancing balanced and sustainable Chinese-style modernization. Full article

The advance and delay of the rainy season is among the most frequently cited effects of climate change in the central Ecuadorian Andes. However, its assessment is not feasible using the indicators recommended by the standardized indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), designed to detect changes in intensity, frequency, or duration of intense events. This study aims to analyze such advances and delays through harmonic analysis in Tungurahua, a predominantly agricultural province in the Tropical Central Andes, where in situ data are scarce. Daily in situ data from five meteorological stations were used, including precipitation, maximum, and minimum temperature records spanning 39 to 68 years. The study involved an analysis of the region’s climatology, climate change indices, and harmonic analysis using Cross-Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) to identify seasonal patterns and their variability (advance or delay) by comparing historical and recent time series, and Krigging for regionalization. The year 2000 was used as a study point for comparing past and present trends. Results show a generalized increase in both minimum and maximum temperatures. In the case of extreme rainfall events, no significant changes were detected. Harmonic analysis was found to be fruitful despite of the missing data. Furthermore, the observed advances and delays in seasonality were not statistically significant and appeared to be more closely related to the geographic location of the stations than to temporal shifts. Full article

Developing a deep understanding of the evolutionary driving mechanisms of university–industry collaborative innovation networks among Chinese cities is of great significance for advancing sustainable urban development. Based on university–industry collaborative patent data from 275 prefecture-level and above cities in China during the period 2004–2020, this study constructs an intercity university–industry collaborative innovation network and employs the temporal exponential random graph model to analyze its evolutionary driving mechanisms. The results indicate that the network structure has become increasingly complex over time and exhibits pronounced small-world characteristics in the later stages. Network formation is distinctly non-random and is jointly shaped by endogenous structural effects and exogenous factors. Diffusion, connectivity, and closure effects are all significant, while intercity collaborative ties are influenced by multidimensional proximity, including economic, geographic, and organizational proximity. Moreover, the network structure demonstrates strong temporal stability. In the context of high-intensity collaboration, cities place greater emphasis on economic and organizational proximity, and cities with higher levels of economic development and prior experience in high-intensity collaboration are more likely to establish collaborative ties. Furthermore, eastern cities tend to collaborate with partners at similar levels of economic development, whereas cities in central and western regions display a more pronounced core–periphery pattern. Overall, from the perspective of intercity university–industry collaborative innovation networks, this study provides new empirical evidence and insights for promoting coordinated regional innovation capacity and sustainable urban development. Full article

Investigating plant responses to climate change is critical for understanding phylogeography and devising conservation strategies. This study focuses on the Lycoris aurea (L’Hér.) Herb. complex in East Asia, a system characterized by high cytotype diversity (2n = 12–16), to test whether ecological niche differentiation drives its spatio-temporal evolution. We integrated dynamic niche modeling to reconstruct distribution dynamics from the Last Interglacial (LIG) to the future (2100). Results indicate that mainland China populations have expanded northward since the LIG, establishing their current patterns, while island populations (Taiwan, Ryukyu) remained relatively stable due to geographic constraints. Under future warming scenarios, the complex is projected to further expand northward. We identified key migration corridors, with high inter-cytotype connectivity in the Sichuan-Hubei region and intra-cytotype migration in the Yunnan Plateau and Nanling region. Although the two dominant cytotypes currently exhibit niche equivalency, they show distinct climatic sensitivities—Cytotype II is driven by precipitation and Cytotype IV by temperature—and are projected to diverge spatially in the future. These findings elucidate the evolutionary history of L. aurea and provide a reference for the conservation and utilization of Lycoris germplasm. Full article

Horse flies (Tabanidae) negatively affect livestock by reducing productivity, compromising animal welfare, and serving as mechanical vectors of pathogens. However, the spatial processes shaping their community organization in southern Brazil’s Coastal Plain of Rio Grande do Sul (CPRS) remain poorly understood. To address this, we conducted standardized Malaise-trap surveys and combined them with historical–contemporary comparisons to examine distance–decay patterns in community composition. We evaluated both abundance-based (Bray–Curtis) and presence–absence (Jaccard) dissimilarities using candidate models. Across sites, Tabanus triangulum emerged as the dominant species. Dissimilarity in community structure increased monotonically with geographic distance, with no evidence of abrupt thresholds. The square-root model provided the best fit for abundance-based data, whereas a linear model best described presence–absence patterns, reflecting dispersal limitation and environmental filtering across a heterogeneous coastal landscape. Sites within riparian forests and conservation units displayed higher diversity, emphasizing the ecological role of protected habitats and the importance of maintaining connected corridors. Collectively, these findings establish a process-based framework for surveillance and landscape management strategies to mitigate vector, host contact. Future directions include integrating remote sensing and host distribution, applying predictive validation across temporal scales. Full article

Tularemia is a well-known zoonotic disease around the world, with particularly high rates in certain geographic areas of the U.S. Though the disease is regularly reported, it is classified as a rare condition owing to the relatively low number of cases detected annually. Interestingly, however, the number of cases in the U.S. has shown a positive upward trend through time. The aim of this study was to summarize, interpret, compare, and contextualize temporal trends in tularemia epidemiology at the national scale within the U.S. utilizing long-term data sets encompassing the 23-year span from 2000 to 2022. We used two secondary data sets: (1) case data reports from the National Notifiable Disease Surveillance System (NNDSS) of the Centers for Disease Control and Prevention (CDC) and (2) the National Inpatient Sample (NIS) of hospitalization discharge records. In addition to investigating patterns, we were interested in the utility of using hospital discharge records as a means of indirect epidemiological surveillance of this rare disease. Both data sets highlight the high variability in annual cases through time but underscore the highest risk of disease among patients classified as White and male, as well as the extraordinarily high rates among American Indian/Alaska Native populations, particularly those with pulmonary tularemia disease. Descriptive epidemiological summaries and statistical comparisons are provided across the time series for sex, age, ethnoracial identity, and geography; hospitalization characteristics are also described. Our desire to use case rates from hospitalization records as a surrogate for CDC case incidence rates did not provide the desired precision, though hospital discharge records do provide valuable and useful information necessary to estimate general high-risk groups for tularemia through time. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article