Spatiotemporal Characteristics Research Articles

Spatiotemporal Characteristics and Influencing Factors of Talent Inflow in Northeast China from the Perspective of Urban Amenity

Spatiotemporal Characteristics and Influencing Factors of Talent Inflow in Northeast China from the Perspective of Urban Amenity

Spatiotemporal Evolution Characteristics and Influence Factor Analysis of the Production–Living–Ecological Space in Laiwu, China, from 2001 to 2018

Spatiotemporal Evolution Characteristics and Influence Factor Analysis of the Production–Living–Ecological Space in Laiwu, China, from 2001 to 2018

Gait Recognition Under Different Clothing Conditions Via Deterministic Learning

In real-world scenarios, the clothing difference constitutes one of the most common covariate factors that can affect the performance of gait recognition systems. This paper proposes a gait recognition method which is invariant to maximum number of clothing conditions. First, four kinds of timevarying silhouette features are selected to capture the spatiotemporal characteristics of gait motion. Second, frame-to-frame gait dynamics underlying different individuals ʼ gait features is effectively modeled by radial basis function ( RBF ) neural networks through deterministic learning. Gait patterns are represented as the gait dynamics underlying time-varying gait features. This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions. In order to eliminate the effect of clothing differences, the training patterns under different clothing conditions further constitute a uniform training dataset, containing all kinds of gait dynamics under different clothing conditions. A rapid recognition scheme is presented on published gait databases. Extensive experiments demonstrate the efficacy of the proposed method.

How to assess a university campus planning — a case study based on students’ spatio-temporal behavior

ABSTRACT The planning of university campus directly impacts students’ quality of study and living, and there is a considerable amount of research on campus planning assessment, most of which uses post-occupancy evaluation methods. This paper tries to propose a new comprehensive assessment method for campus planning, which collects spatio-temporal behavioral data of students in their daily life through GPS software, then assesses the actual effect of campus planning comprehensively by analyzing the spatio-temporal behavioral characteristics of students. This paper also verifies the reliability of the student behavior characteristics analysis through questionnaire and explores the possible biases. The results indicate that, in addition to significant advantages in reliability, accuracy, and comprehensive judgment, the proposed method can visually reflect specific issues existing within tangible campus spaces and their impact on student behavior. Therefore, it can provide practical and reliable evidence for the practice of campus planning renewal. This method used in this paper is also applicable to other fixed group living spaces within a certain scale, like communities and neighborhoods.

Measurement and Spatio-Temporal Characteristics of High-Quality Development Efficiency in Metropolitan Areas: A Case Study of the Changchun Metropolitan Area

The concept of high-quality development (HQD) is characterized by its emphasis on efficiency, equity, and environmental sustainability. In the context of China’s new urbanization development, the metropolitan area plays a crucial role in facilitating and sustaining HQD. This study focuses on the Changchun Metropolitan Area (CCMA) as a case study to measure the efficiency of high-quality development (HQDE) at the county level using the super-efficiency SBM model and spatial autocorrelation model. Additionally, we examine the spatio-temporal distribution characteristics of HQDE in terms of economy, innovation, coordination, greenness, openness, and sharing (EICGOS). The main findings are as follows: (1) The HQDE of the CCMA ranges from 0.7 to 0.8 with an initial rapid increase followed by a gradual decline; however, there are notable variations among different counties. (2) Regarding spatial structure within the metropolitan area, highest efficiency is observed in the half-hour living circle followed by the 2-h accessibility circle while lowest efficiency is found in the 1-h commuting circle. Over time, there is a declining trend in efficiency within core leading areas. (3) In terms of dimensions, CCMA demonstrates the highest level of economic development efficiency (EDE), whereas green development efficiency (GDE) exhibits lower levels compared to other dimensions. Furthermore, development efficiencies across all dimensions show a decline over time. (4) Spatially distributed patterns reveal significant agglomeration areas for HQDE within the CCMA region. High-high agglomeration areas are predominantly concentrated in the central region of Changchun and southern region of Liaoyuan while low-low agglomeration areas primarily exist in northwest Songyuan and specific counties within Changchun. To attain HQD of the CCMA, it is advisable to bolster the economic scale of the central city, mitigate developmental disparities between counties and cities, and expedite green transformations in old industrial cities. These findings offer a valuable point of reference for optimizing resource allocation at the metropolitan level and devising strategies to foster regional HQD.

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

Spatiotemporal Distribution Characteristics and Influencing Factors of Dissolved Potentially Toxic Elements along Guangdong Coastal Water, South China

In this study, five potentially toxic elements (PTEs) (Hg, Pb, As, Zn, and Cu) and five physicochemical factors (pH, dissolved oxygen, salinity, chlorophyll a, and chemical oxygen demand) relating to surface seawater were measured along the coast of Guangdong Province (GD), China, during three seasons in 2019. Comprehensive analyses were conducted to explore the distribution characteristics, pollution degrees, and influencing factors of PTE. The results showed that the concentrations of PTE varied spatially and seasonally due to these elements’ complex sources and behaviors. Pb was the most abundant toxic element, reaching considerable contamination levels. Overall, the average pollution degrees of Cu, As, and Zn were higher around the east coast of GD, while Hg and Pb levels were higher in the west coast region of GD. The correlation analysis showed that the variation of different physicochemical parameters had different degrees of influence on PTE transport and transformation. This study can help environmental managers gain deeper insight into the influence of complex factors on PTE and improve the efficiency of pollution control in this significant subtropical coastal area.

Spatiotemporal structure of foraging and path integration errors by fiddler crabs, Leptuca pugilator

Path integration is the navigational process by which animals construct a memory of a previous location by continuously measuring and summing their movements to form a single home vector pointing to the starting location. It is intrinsically error prone, subject to random errors and, potentially, to systematic errors in either measurement or the summing algorithm. Both types of errors lead to an incorrect vector memory and thus to an error in homing. Because the errors are incurred when animals move, they are theoretically predictable from the movements. We analyzed the behavior of fiddler crabs (Leptuca pugilator) as they performed foraging excursions followed by homing with varying degrees of error. From video recordings we measured body orientations and locations and computed these spatiotemporal path characteristics: duration, distance, turns, bearing and arc sector. These were analyzed for their effect on, separately, the magnitude, and the direction, of crabs’ homing error. The magnitude of the homing error was predicted by arc sector, Δbearing and path length, and several interactions. The direction of the homing error was predicted by interactions including arc sector x Δbearing, arc sector x turns, and Δbearing x turns. Covariance among these factors results in a path that traces a large arc while maintaining body orientation toward the burrow direction and leads to an error with the same clockwise/counterclockwise sign as the arc and the body turns. These results place L. pugilator’s path integration mechanism among others with known systematic errors.

Spatiotemporal Characteristics and Driving Factors of Ecosystem Regulation Services Value at the Plot Scale

The value of ecosystem regulation services (ERSV) is a crucial aspect of gross ecosystem product (GEP). Understanding and mastering the spatiotemporal evolution characteristics and driving factors of ERSV is essential for the efficient management of regional ecosystems. This study proposes an ERSV accounting model at the plot scale and uses the barycentric analysis method, the optimal parameters-based geographical detector model (OPGD), and the constraint line extraction method to analyze the spatiotemporal evolution characteristics, main driving factors, and constraint rules of ERSV in Yunyang District, Hubei Province in 2016, 2018, 2020, and 2021. The results show that (1) In the temporal dimension, the overall ERSV of the district increased by CNY 1.664 billion from 2016 to 2021, with an increase rate of 3.68%. The contribution values of climate regulation function and water retention function to ERSV was significant. (2) In the spatial dimension, the ERSV was high in the north and south and low in the middle, with high-value areas mainly located in woodland and wetland areas. The center of gravity of the ERSV increase shifted to the southwest by 12,455.42 m, while the center of gravity of the reduction shifted to the southwest by 3582.79 m from 2016 to 2021. (3) The interaction of any two driving factors had greater explanatory power for the spatial differentiation of ERSV than that of a single driving factor, and all of them showed nonlinear or double factor enhancement characteristics. The human active index (HAI) and construction land proportion (CLP) were the leading anthropogenic factors, while the land surface temperature (LST) and NDVI were the leading natural factors. (4) The ERSV could maintain a high and stable value output when the HAI was less than 0.3, CLP was less than 15%, LST was between 18 and 22 °C, and NDVI was greater than 0.5. These results can guide the practices of ecology, production, and life in Yunyang District and contribute to the high quality and sustainable development of the regional ecology and economy.

Spliceosomal snRNAs, the Essential Players in pre-mRNA Processing in Eukaryotic Nucleus: From Biogenesis to Functions and Spatiotemporal Characteristics.

Spliceosomal small nuclear RNAs (snRNAs) are a fundamental class of non-coding small RNAs abundant in the nucleoplasm of eukaryotic cells, playing a crucial role in splicing precursor messenger RNAs (pre-mRNAs). They are transcribed by DNA-dependent RNA polymerase II (Pol II) or III (Pol III), and undergo subsequent processing and 3' end cleavage to become mature snRNAs. Numerous protein factors are involved in the transcription initiation, elongation, termination, splicing, cellular localization, and terminal modification processes of snRNAs. The transcription and processing of snRNAs are regulated spatiotemporally by various mechanisms, and the homeostatic balance of snRNAs within cells is of great significance for the growth and development of organisms. snRNAs assemble with specific accessory proteins to form small nuclear ribonucleoprotein particles (snRNPs) that are the basal components of spliceosomes responsible for pre-mRNA maturation. This article provides an overview of the biological functions, biosynthesis, terminal structure, and tissue-specific regulation of snRNAs.

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

ObjectiveIn this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.MethodsSpatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male–female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.ResultsThe proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.ConclusionThe proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

The spatiotemporal characteristics and driving mechanisms of subsurface marine heatwaves in the Xisha Region

Under the background of global climate change, Subsurface Marine Heatwaves (SSMHWs) have become a ‘hot-spot’ research due to their significant impacts on marine ecosystems. Temperature data from the ECCO2 for the years 1992 to 2021 is used to research the spatiotemporal characteristics of SSMHWs in the upper 500 m over the Xisha region (110°E to 113°E, 15°N to 18°N). This study indicates that SSMHWs of high intensity occur in the Xisha area, with the vertical maximum intensity at approximately 100 m. These events exhibit significant seasonal variations, with the highest intensity occurring in May. The intensity of SSMHW in the analysis region varies interannually. SSMHWs have intensified at a rate of 1.53°C·Days/Year and tend to shift to deeper water over the past three decades. A block-based method for SSMHW identification is proposed, in order to take the vertical extent of the event in consideration. By analyzing the eight most intense events based on spatial cumulative intensity, it suggested that warm mesoscale eddies may play an important role on the spatial distribution of the SSMHWs. Statistical analysis shows that the intensity and coverage of the warm mesoscale eddy in the Xisha zone may influence the intensity and evolution of the SSMHWs. Additionally, only 18.27% of SSMHW events occur with apparent surface marine heatwave signals. These findings are vital for uncovering SSMHW dynamics in the Xisha area, and are important for future monitoring, early warnings and marine conservation.

Slowing the expansion of impervious surfaces: the key to promoting high-quality and sustainable regional development.

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4-5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City.

Spatio-Temporal distribution characteristics and driving factors of traditional villages in the Yellow River Basin.

Currently, research on traditional villages mainly focuses on the current development status and evolutionary trends in specific regions, with relatively limited studies from a macroscopic and holistic perspective on the spatiotemporal evolution of traditional villages. Therefore, this study selects traditional villages in the Yellow River Basin (YRB) as the research object. By analyzing the spatiotemporal distribution characteristics and driving factors of traditional villages (TVs) in the basin, it aims to further promote high-quality development in the YRB and protect traditional cultural resources. Based on data from 892 village points of the first to sixth batches of TVs in the YRB, ArcGIS 10.8 spatial analysis techniques were employed to analyze the overall spatial pattern of TVs in the YRB. The results indicate: (1) In the basin, TVs are more numerous in the east than the west and more in the south than the north, forming clusters and contiguous distributions, with dense areas primarily in the upstream regions dominated by Qinghai Province and the midstream areas along the Shanxi-Shaanxi coast. (2) The number and scale of TVs in the basin generally exhibit an increasing trend, with imbalanced provincial distribution. More recent years show a more balanced distribution of villages and proportions, with a higher number of villages in the mountainous and plateau regions of the basin. (3) The layout center of TVs within the basin evolves with each batch, showing a migration pattern from north to south, back to north, and finally east to west. (4) The interaction of natural and social factors plays a synergistic role in driving the spatiotemporal distribution pattern of TVs. Among these, natural geographical factors are the primary factors. TVs are more commonly found in regions with low altitude sunny slopes, mild climate, abundant precipitation, proximity to ancient roads and rivers, gentle slopes, and soil predominantly comprising loess, brown earth, and alluvial soils. The cultural environment is a secondary factor, with TVs often located in areas with larger populations, developed economies, and rich cultural heritage.

Dynamics changes of coastal aquaculture ponds based on the Google Earth Engine in Jiangsu Province, China

Monitoring the spatiotemporal variation in coastal aquaculture zones is essential to providing a scientific basis for formulating scientifically reasonable land management policies. This study uses the Google Earth Engine (GEE) remote sensing cloud platform to extract aquaculture information based on Landsat series and Sentinel-2 images for the six years of 1984 to 2021 (1984, 1990, 2000, 2010, 2016 and 2021), so as to analyze the changes in the coastal aquaculture pond area, along with its spatiotemporal characteristics, of Jiangsu Province. The overall area of coastal aquaculture ponds in Jiangsu shows an increasing trend in the early period and a decreasing trend in the later period. Over the past 37 years, the area of coastal aquaculture ponds has increased by a total of 54,639.73 ha. This study can provide basic data for the sustainable development of coastal aquaculture in Jiangsu, and a reference for related studies in other regions.

Post-earthquake spatiotemporal evolution characteristics of typical landslide sources in the Jiuzhaigou meizoseismal area

Post-earthquake spatiotemporal evolution characteristics of typical landslide sources in the Jiuzhaigou meizoseismal area

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

α1-Adrenergic Receptors Control the Activity of Sinoatrial Node by Modulating Transmembrane Transport of Chloride Anions

Norepinephrine (NE), which is released by sympathetic nerve endings, causes an increase in the frequency of spontaneous action potentials in the pacemaker cardiomyocytes of the sinoatrial node (SAN), also known as the “pacemaker” of the heart. This results in an increase in heart rate (HR). It is known that two types of postsynaptic adrenoreceptors (ARs), α1-AR and β-AR, can mediate the effects of NE. The role of α1-AR in the sympathetic control of heart rate and SAN automaticity, as well as the membrane mechanisms mediation the effects of α1-AR on the pacemaker, have not yet been elucidated. In this study, we utilized immunofluorescence confocal microscopy to examine the distribution of α1A-AR in the SAN of rats. Additionally, we assessed the expression of α1A-AR mRNA in the SAN tissue using RT-PCR. Furthermore, we investigated the impact of α1-AR stimulation on key functional parameters of the pacemaker, including the corrected sinus node recovery time (SNRT/cSNRT) and the SAN accommodation, using the Langendorff perfused heart technique. We also used optical mapping of the electrical activity of perfused, isolated tissue preparations to study the effect of α1-AR stimulation on the spatiotemporal characteristics of SAN excitation. We tested the effects of chloride transmembrane conductance blockade on alteration of functional parameters and pattern of SAN excitation caused by α1-AR. Fluorescent signals corresponding to α1A-AR have been identified in SAN cardiomyocytes, indicating the presence of α1A-AR at protein level. The expression of α1A-AR in SAN has been also confirmed at the mRNA level. The stimulation of α1-AR affects SAN functioning Phenylephrine (PHE) utilized as α1A-AR agonist causes a decrease in SNRT/cSNRT, as well as an acceleration of SAN accommodation. These effects were rate dependent and were observed at a high frequency of pacemaker tissue stimulation. PHE induces changes in the excitation pattern of the SAN. The effects of PHE on functional parameters and SAN excitation pattern are attenuated by Ca2+-dependent chloride channel blocker NPPB but remains unaffected by the protein kinase C inhibitor BIM. Our results suggest that cardiac α1-ARs are important for maintaining function of SAN pacemaker at high heart rates and that α1-AR signalling cascades in the SAN target Ca2+-dependent chloride channels are involved in the α1-adrenergic modulation of the electrophysiological properties of the heart pacemaker.

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.