Search Results (74)

As public concern for maritime safety grows, there is a pressing need to delve deeper into the root causes of maritime accidents and develop effective preventive strategies. Spatial-temporal analysis stands out as a powerful approach to pinpointing accident hot spots. While previous research has shed light on the spatial aspects of these incidents, a comprehensive understanding of their temporal dimensions remains elusive. This paper bridges this gap by leveraging the Space-Time Cube tool in conjunction with traditional Kernel Density analysis to chart the spatial-temporal dynamics of maritime accident hot spots. Focusing on the East China Sea, a region notorious for its high incidence of maritime accidents and home to numerous world-class ports, we present a case study that offers fresh insights. Data spanning from 1994 to 2020, sourced from the Lloyd’s List Intelligence (LLI) database, reveal the evolving landscape of maritime accidents in the area. Notably, since 2005, the Yangtze River Delta Region in China has emerged as a persistent hot spot for accidents, underscoring its significance in maritime safety discourse. Furthermore, our analysis from the 2010s detects a new hot spot expanding towards the southwest of Kaohsiung Port, China, signaling a burgeoning area of concern for maritime safety. While the Fujian coast of China has seen its share of accidents, it is not qualified as a hot spot zone. The Space-Time Cube proves to be an indispensable tool in unraveling the progression of maritime accidents, and our findings indicate that maritime accidents in certain areas may not be merely random occurrences but exhibit intricate patterns. Full article

Introduction: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States (U.S.). Mississippi has the highest rate of CRC incidence in the U.S. and has large populations of black and white individuals, allowing for studies of racial disparities. Methods: We conducted an ecological study using the county as the unit of analysis. CRC incidence data at the county level for black and white populations in Mississippi, covering the years 2003 to 2020, were retrieved from the Mississippi Cancer Registry. Age-adjusted incidence rate differences and their corresponding 95% confidence intervals (CIs) were then calculated for these groups. Getis–Ord Gi* hot and cold spot analysis of CRC incidence rate racial disparities was performed using ArcGIS Pro. We used global ordinary least square regression and geographically weighted regression (MGWR version 2.2) to identify factors associated with racial differences in CRC incidence rates. Results: Age-adjusted CRC incidence rate in the black population (median = 58.12/100,000 population) and in the white population (median = 46.44/100,000 population) varied by geographical area. Statistically significant racial differences in CRC incidence rates were identified in 28 counties, all of which showed higher incidence rates among the black population compared to the white population. No hot spots were detected, indicating that there were no spatial clusters of areas with pronounced racial disparities. As a post hoc analysis, after considering multicollinearity and a directed acyclic graph, a parsimonious multiple regression model showed an association (β = 0.93, 95% CI: 0.25, 1.62) indicating that a 1% increase in food insecurity was associated with a 0.93/100,000 differential increase in the black–white CRC incidence rate. Geographically weighted regression did not reveal any local patterns in this association. Conclusions: Black–white racial disparities in CRC incidence were found in 28 counties in Mississippi. The county-level percentage of food insecurity emerged as a possible predictor of the observed black–white racial disparities in CRC incidence rates. Individual-level studies are needed to clarify whether food insecurity is a driver of these disparities or a marker of systemic disadvantage in these counties. Full article

Vegetation cover in urban and peri-urban areas is threatened by urban sprawl, through habitat fragmentation, loss of green space, biodiversity reduction, and the urban heat island effect intensifying. The intrusion of cities into natural landscapes reduces vital ecosystem services provided by vegetation. Hence, sustainable and integrated urban planning practices are required. Our study aims to investigate the dynamics of the urban and peri-urban fabric by exploring the relationship between the green fabric distribution and recent trends in urban expansion, focusing specifically on the peri-urban areas of Iași Municipality, Romania. We designed a mixed-method approach combining a multivariate analysis of four critical indicators (vegetation cover, built-up space, land surface temperature, and population density), emerging hot-spots, and space-time cubes in a GIS environment to achieve our research aims. Our results demonstrate that uncontrolled urban expansion has manifested in diverse patterns, impacting territories next to road transport networks and with construction-suitable topography. Concurrently, the development of green spaces prevails in forests and unexpected locations such as brownfields, railway corridors, and old industrial zones, through the growth of urban greenery. This approach provides a comprehensive understanding of how urban sprawl impacts the environment and how different land types are prone to this transformation, creating a path towards sustainability, resilience, and equitable development. Full article

Background: Knowing the spatial variation and predictors of women having sole autonomy over their healthcare decisions is crucial to design site-specific interventions. This study examined how women’s sole autonomy over their healthcare choices varies geographically and what factors influence this autonomy among Bangladeshi women of childbearing age. Methods: Data were obtained from the Bangladesh Demographic and Health Survey (BDHS) 2017–18. The final analysis included data from a total of 18,890 (weighted) women. Spatial distribution, hot spot analysis, ordinary Kriging interpolation, and multilevel multinomial regression analysis were employed. Results: The study found that approximately one in ten women (9.62%) exercised complete autonomy in making decisions about their healthcare. Spatial analysis revealed a significant clustering pattern in this autonomy (Moran’s I = 0.234, p < 0.001). Notably, three divisions—Barisal, Chittagong, and Sylhet—emerged as hot spots where women were more likely to have sole autonomy over their healthcare choices. In contrast, the cold spots (poor level of sole healthcare autonomy by women) were mainly identified in Mymensingh and Rangpur divisions. Women in the age group of 25–49 years, who were highly educated, Muslim, urban residents, and had not given birth recently were more likely to have sole autonomy in making healthcare decisions for themselves. Conversely, women whose husbands were highly educated and employed, as well as those who were pregnant, were less likely to have sole autonomy over their healthcare choices. Conclusions: Since the spatial distribution was clustered, public health interventions should be planned to target the cold spot areas of women’s sole healthcare autonomy. In addition, significant predictors contributing to women’s sole healthcare autonomy must be emphasized while developing interventions to improve women’s empowerment toward healthcare decision-making. Full article

The development of digital twins for power transformers has become increasingly important to predict possible operating modes and reduce the likelihood of faults. The accuracy of these predictions relies heavily on the numerical models used, which must be both simple and computationally efficient. This work focuses on creating a simplified numerical model for a template oil-immersed power transformer (100 MVA, 230/69 KV). The study investigates how the number of elements and the strategies used to set up the mesh in the domain of interest influence the results, aiming to identify the key parameters that affect the outcomes. Furthermore, a significant effect of resolving thermal boundary layers on the accurate identification of hot spots is demonstrated. Two approaches to resolving thermal boundary layers are explored in this work. This study presents a comprehensive analysis of three numerical models for conjugate heat transfer simulations, each with distinct features and computational domain compositions. The results show that the addition of extra calculation domains leads to the emergence of new vortex structures, affecting the velocity profile at the channel inlet and altering the location of hot spots. This study provides valuable insights into the configuration and composition of calculated domains in numerical models of oil-immersed power transformers, essential for the accurate prediction of hot spot temperatures and ensuring reliable operation. Full article

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts. Full article

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies. Full article

The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments. Full article

Plasmonic intragap nanostructures (PINs) have garnered intensive attention in Raman-related analysis due to their exceptional ability to enhance light–matter interactions. Although diverse synthetic strategies have been employed to create these nanostructures, the emphasis has largely been on PINs with simple configurations, which often fall short in achieving effective near-field focusing. Three-dimensional (3D) complex PINs, distinguished by their intricate networks of internal gaps and voids, are emerging as superior structures for effective light trapping. These structures facilitate the generation of hot spots and hot zones that are essential for enhanced near-field focusing. Nevertheless, the synthesis techniques for these complex structures and their specific impacts on near-field focusing are not well-documented. This review discusses the recent advancements in the synthesis of 3D complex PINs and their applications in surface-enhanced Raman scattering (SERS). We begin by describing the foundational methods for fabricating simple PINs, followed by a discussion on the rational design strategies aimed at developing 3D complex PINs with superior near-field focusing capabilities. We also evaluate the SERS performance of various 3D complex PINs, emphasizing their advanced sensing capabilities. Lastly, we explore the future perspective of 3D complex PINs in SERS applications. Full article

Understanding the dynamics of water bodies is crucial for managing water resources and protecting ecosystems, especially in regions prone to climatic extremes. The Tumen River Basin, a transboundary area in Northeast Asia, has seen significant water body changes influenced by natural and anthropogenic factors. Using Landsat 8 and Sentinel-1 data on Google Earth Engine, we systematically analyzed the spatiotemporal variations and drivers of water body changes in this basin from 2015 to 2023. The water body extraction process demonstrated high accuracy, with overall precision rates of 95.75% for Landsat 8 and 98.25% for Sentinel-1. Despite observed annual fluctuations, the overall water area exhibited an increasing trend, notably peaking in 2016 due to an extraordinary flood event. Emerging Hot Spot Analysis revealed upstream areas as declining cold spots and downstream regions as increasing hot spots, with artificial water bodies showing a growth trend. Utilizing Random Forest Regression, key factors such as precipitation, potential evaporation, population density, bare land, and wetlands were identified, accounting for approximately 81.9–85.3% of the observed variations in the water body area. During the anomalous flood period from June to September 2016, the Geographically Weighted Regression (GWR) model underscored the predominant influence of precipitation, potential evaporation, and population density at the sub-basin scale. These findings provide critical insights for strategic water resource management and environmental conservation in the Tumen River Basin. Full article

The development and utilization of unconventional water resources has become a strategy to alleviate the agricultural water crisis in many countries and regions. To understand the research progress, hot spots, and future trends in the field of unconventional water agricultural irrigation (UWAI), this paper systematically analyzes 6738 publications based on the core database of Web of Science 1990–2023 using the scientific bibliometric analysis software CiteSpace, VOSviewer, and Scimago Graphica. The results showed that the research on UWAI is always rapidly developing. Soil science, crop science, and bioengineering are the main disciplines involved. Most research on WUAI has occurred in China and the United States. Countries with higher levels of development tend to have more influence. Collaboration among authors is fragmented, and collaboration between authors and states needs to be strengthened. Through keyword analysis, the research hotspots are summarized as follows: (1) The effects of traditional and emerging pollutants brought by unconventional water irrigation on soil physicochemical properties, crop growth, and groundwater quality; (2) the health threats caused by pollutants entering the food chain and groundwater; (3) unconventional water utilization technologies, including rainwater harvesting agriculture, precision agriculture, and urban agriculture. Future research hotspots will focus on the mechanisms of pollutant solute transport and transformation in the water–soil–crop system under non-conventional water irrigation conditions and crop physiological responses. We suggest that the research on traditional and emerging pollutants in unconventional water should be strengthened in the future, and the risk control system of unconventional water irrigation should be improved. International cooperation should be strengthened, especially with poor countries in arid regions, to promote the formation of unified international standards and guidelines for non-conventional water irrigation in agriculture. Full article

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan’s new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran’s index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. Full article

Air pollution has been standing as one of the most pressing global challenges. The changing patterns of air pollutants at different spatial and temporal scales have been substantially studied all over the world, which, however, were intricately disturbed by COVID-19 and subsequent containment measures. Understanding fine-scale changing patterns of air pollutants at different stages over the epidemic’s course is necessary for better identifying region-specific drivers of air pollution and preparing for environmental decision making during future epidemics. Taking China as an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations of the six major air pollutants (i.e., PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO) in China and revealed distinct spatiotemporal patterns for each pollutant over the epidemic’s course. The 5-year period of 2019–2023 was selected to observe changes in the concentrations of air pollutants from the pre-COVID-19 era to the lifting of all containment measures. The performance of our model, assessed by cross-validation R², demonstrated high accuracy with values of 0.92 for PM_2.5, 0.95 for PM₁₀, 0.95 for O₃, 0.90 for NO₂, 0.79 for SO₂, and 0.82 for CO. Notably, there was an improvement in the concentrations of particulate matter, particularly for PM_2.5, although PM₁₀ exhibited a rebound in northern regions. The concentrations of SO₂ and CO consistently declined across the country over the epidemic’s course (p < 0.001 and p < 0.05, respectively), while O₃ concentrations in southern regions experienced a notable increase. Concentrations of air pollutants in the Beijing–Tianjin–Hebei region were effectively controlled and mitigated. The findings of this study provide critical insights into changing trends of air quality during public health emergencies, help guide the development of targeted interventions, and inform policy making aimed at reducing disease burdens associated with air pollution. Full article

Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors. Full article

Gun and street group violence remains a serious problem in cities across the United States and the focused deterrence strategy has been a widely applied law enforcement intervention to reduce it. Although two meta-analytical studies concluded that the intervention had a significant effect on violence, questions remain about how violence changes across space and time during and after the intervention. This study applies novel geospatial analyses to assess spatiotemporal changes in gun violence before, during, and after the implementation of Philadelphia Focused Deterrence. Emerging hot spot analysis employing Space-Time cubes of ten annual time bins (2009–2018) at the Thiessen polygon level was used to detect and categorize patterns. The analyses revealed a non-significant decreasing trend across the ten-year period. Furthermore, there were ninety-three statistically significant hot spots categorized into four hot spot patterns: fourteen new hot spots; twenty-three consecutive; one persistent; and fifty-three sporadic. There was no evidence showing statistically significant hot spots for the “diminishing” pattern. Knowledge of these patterns that emerge across micro-locations can be used by law enforcement practitioners to complement data-driven problem solving and fine tune these strategies and other place-based programming. Policymakers can use findings to prioritize resources when developing complementary prevention and intervention efforts by tailoring those efforts to the different emergent patterns. Full article