Typhoons are among the most significant natural disasters affecting the eastern and southern coastal regions of China, inflicting substantial annual damage on both coastal and inland areas. Since the initiation of the reform and opening-up policy, the socioeconomic development of the coastal regions of China has been swift, leading to increased exposure to typhoons. In the context of global climate change, typhoons are expected to increase in frequency and intensity in China. Therefore, researching on the spatiotemporal pattern characteristics of typhoons impacting China is of critical importance for understanding the impact patterns and risk changes of typhoon disasters, as well as for formulating policies on disaster response, prevention, and mitigation. This study aims to provide valuable insights into the formulation of such policies. Based on these objectives, this study utilized a comprehensive dataset, including county-level socioeconomic and disaster statistics, historical typhoon wind and rainfall data, and high-precision topographic data. Using county-level administrative regions as spatial units, this study employed various methods, such as time-series statistical analysis, gravity model, geographical detector, spatial correlation analysis, and geographically weighted regression, to analyze the spatiotemporal distribution patterns and influencing factors of typhoon disaster conditions in China from 1978 to 2020. The findings of this study are as follows: (1) The number of deaths and missing persons, quantity of damaged housing, death, and missing rate per million people, and proportion of direct economic loss to GDP caused by typhoon disasters have all shown a declining trend, indicating significant achievements in disaster prevention and mitigation efforts. (2) The center of gravity of typhoon disaster-related losses has shifted southward, corresponding with the economic development of coastal regions, demonstrating a reduced disaster impact in coastal areas and an increased impact in inland areas. (3) Wind and rain induced by typhoons are the primary driving factors of disaster conditions, and topographical factors are also drivers of casualties and crop loss. (4) The two major regions, Zhejiang-Northern Fujian and Western Guangdong-Eastern Guangxi, exhibit significant characteristics of disaster condition agglomeration, closely related to typhoon activity patterns and levels of economic development. (5) There is a negative correlation between the gross local product and disaster conditions in some areas, reflecting the role of socioeconomic development in enhancing the capacity for disaster prevention and mitigation.

Suicide is a serious negative social phenomenon. In this study, we used Python technology to obtain suicide death data from a network and applied mathematical statistical and geographic spatial analyses to study the spatial-temporal characteristics of suicide deaths and the relationship between suicide rate and economic development in China from 2000 to 2018. Following conclusions were drawn from the results. (1) The number of suicide deaths in China is on the rise. Within a year, the high-incidence period of suicide deaths is from May to June, whereas the low-incidence period is from February to March. Within a month, the 1^st, 10^th, and 20^th days have the highest incidences of suicide deaths. Within a day, 77.2% of the suicide deaths occur from 06:00 to 19:00, and 09:00 and 15:00 were the peak times in which suicide deaths take place. (2) A total of 90.98% of the suicide deaths occur in southeast China. The suicide rate is higher in the southeast than in the northwest, higher in the south than in the north, and decreases gradually from east to west. At county level, a relatively high suicide rate is seen in regions spanning from Great Khingan Mountains to Yunnan Guizhou Plateau, from Qinling-Dabashan Mountains to Dabie Mountains, and from the coast of northern Jiangsu to Hainan Island. (3) Most areas in China present a low-grade suicide rate. However, low-grade areas appeared to change to high-grade areas during the period 2000–2018. The hotspots of suicide deaths spread from east to west, except for the Beijing–Tianjin–Tangshan area, Yangtze River Delta, and Pearl River Delta, which have always been suicide hotspots. (4) The spatial and temporal characteristics of suicide deaths in China are closely related to economic development, and on a city scale, the suicide rate has a significant positive correlation with the per capita GDP and urbanization rate. The impact of economic factors on suicide rate is greater on the southeast coast than on the northwest inland. An important conclusion from this study is that the gap between the rich and poor is a key factor, leading to psychological imbalance and suicidal behavior in the poor; therefore, only the new development path based on common prosperity is the road for people to reach happiness and health. In addition, in this study, we prove that network suicide data, obtained using the web-crawler technology (Python), not only have the same consistency and credibility as sampling statistics but also have a better spatiotemporal resolution, with a temporal resolution of one hour and spatial resolution of a county. Therefore, by analyzing this spatiotemporal dataset, we can scientifically extract the time differences in suicide deaths at quarterly, monthly, daily, and hourly scales and the spatial differences in suicide deaths at regional, provincial, and county scales. In the future, network suicide data may become an important data source for suicide research, and the use of the Internet to monitor suicidal behavior may become an important method of suicide intervention.

In the context of climate change, the escalating frequency of extreme weather phenomena has exacerbated the severity of compound floods in the southeastern coastal regions of China. Rising sea levels significantly contribute to the inundation of low-lying coastal urban areas. The quantitative assessment of compound flood risk offers scientific support for disaster prevention and reduction in coastal cities and for coastal management initiatives. Using Haikou City as a case study, the daily precipitation and maximum storm surge tide data from 66 typhoons that affected Haikou between 1960 and 2017 were utilized to construct compound flood combination scenarios. Based on the quantitative method of D-Flow FM (Delft3D-FLOW Flexible Mesh) numerical simulation, the potential risks of extreme rainfall and storm surge compound flood disasters under sea level rise scenarios were thoroughly investigated by integrating various scenarios. The findings revealed the following: 1) Storm surge was the primary factor contributing to compound flooding during typhoons, with the estuary of the Nandu River and the northern coast being the most affected. 2) In the scenario of maximum rainfall and storm surge combination, the inundation area of Haikou is about 148 km², which is approximately 15 times larger than the minimum rainfall and storm surge combination scenario. Moreover, in more than half of the inundated areas, the water depth exceeds 1 meter. 3) Under extreme rainfall and storm surge compound scenarios, the areas encompassing Haidian Island, Xinbu Island, and Jiangdong New Area were significantly affected by sea level rise. By 2100, the total flooding area is projected to reach about 203 km² under the RCP8.5 scenario. Sea level rise significantly amplifies urban flood risks, implying that coastal cities are poised to encounter heightened threats and manage future challenges. Through comprehensive comparisons of multiple rainfall and storm surge compound flooding scenarios under sea level rise, the temporal and spatial characteristics of the compound flooding risk were systematically evaluated. The results provide an important scientific basis for sustainable regional development, effective management, and prevention.

Social and economic losses from typhoons are increasing owing to climate change. It is of practical significance to correctly understand new characteristics and trends in typhoon activity. Based on the best track dataset of tropical cyclones from the China Meteorological Administration, the temporal and spatial variation characteristics and evolution law of northward-moving typhoons from 1949 to 2022 were analyzed using the linear trend, Mann-Kendall test, and wavelet analysis method, and the impacts of the El Niño-Southern Oscillation (ENSO) on typhoon activities were also discussed. The results showed that: (1) 275 northward-moving typhoons occurred during the past 74 years, with an average of 3.7 per year. The interannual fluctuation in typhoon frequency was large, and the upward trend was not significant. The proportion of northward-moving typhoons to the total number of generated typhoons in the Northwest Pacific was between 2% and 30%, showing a significant upward trend. (2) Northward-moving typhoons were mainly generated from July to September, accounting for approximately 88.4% of the total typhoons. The highest number of typhoons entering the defined area was 114 in August. The life-cycle intensity of northward-moving typhoons is dominated by high-intensity grades, such as super typhoons and typhoons. Among them, super-typhoons accounted for 30.5% of the total number of northward-moving typhoons, and the intensity of typhoons and above grades exceeded 70% of the total amount. In recent years, the probability of high-intensity northward-moving typhoons has increased. (3) A total of 159 northward-moving typhoons landed in China over 74 years. Most of the turning-track typhoons made landfall in Taiwan, Fujian, and Zhejiang, whereas the landing locations of landed disappearing-track typhoons made landfall more northerly. Most unlanded turning-track typhoons turned eastward near 30°N and 125–130°E, showing a significant upward trend. The generating positions of the northward-moving typhoons were mainly concentrated in the ranges of 10—20°N and 130—150°E, with a density of 4.65/10,000 km². The central generation position of the landed northward-moving typhoons was 4.2° more westward than that of the unlanded typhoons. The latitude of the central generating position of the disappearing typhoons was 2.1° northward compared to that of the turning typhoons. (4) The Niño3.4 index had significant negative and positive correlations with the frequency and life-cycle intensity of northward-moving typhoons, respectively, and it also had an obvious effect on their generating positions. There were 4.5 northward-moving typhoons in the La Niña year, which was 1.67 times the El Niño year. However, the intensity of northward-moving typhoons generated during El Niño years was significantly higher than that generated during La Niña years, and the intensity of northward-moving typhoons increased with the Niño3.4 index. The central generating position of northward-moving typhoons during La Niña years was 5.8° northward and 12.4° westward compared to that during El Niño years, which was closer to China. This study provides a basis and reference for strengthening the risk management of typhoons and improving the efficiency of disaster prevention and reduction.

Against the backdrop of rapid global climate change, the frequency and severity of storm surges in coastal areas are increasing, particularly in tidal river segments that are affected by storm surges and upstream river flooding. Although existing storm surge models have introduced a variety of different boundary settings, the boundary conditions provided are limited and cannot meet the current generalization needs of complex hydraulic engineering projects in China. This study considered the Feiyun River Basin as the research subject and coupled the upstream hydrodynamic model IFMS with the oceanic storm surge model ADCIRC. By utilizing the strengths of both models, a flood evolution model for the estuarine tidal river segment was established, enabling the spatiotemporal simulation of tidal levels in the Feiyun River tidal segment. The model not only effectively considers the impact of storm surge propagation at the estuary on flood evolution in the tidal river segment, but also the effect of upstream river flooding on the area. The study first validated the model with Typhoon Meranti in 2016, where the simulation results showed a high degree of agreement with the observed data series and errors were within acceptable limits. Flood processes at the Ruian, Mayu, Bishan Liqiao, and Dongtou tidal stations during Typhoons Doksuri and Khanun were simulated. The results show that the peak flood errors at all four stations were below 0.30 m, with Nash coefficients >0.80, indicating the model's capability to accurately reflect tidal level fluctuations and effectively contribute to disaster prevention and mitigation efforts in estuarine tidal segments. Finally, the study analyzed the impact of the driving forces of the upstream and downstream boundaries on tidal level predictions at three stations (Ruian, Mayu, and Bishan Liqiao). It was concluded that, compared to Mayu and Bishan Liqiao stations, the influence of the upstream boundary on Ruian can essentially be ignored, suggesting that the error from the upstream boundary under the influence of Typhoon Khanun is negligible for predicting errors at Ruian. The degree of the impact of the downstream boundary fluctuations on the three stations, from largest to smallest, was Ruian, Bishan Liqiao, and Mayu. Compared to the changes in the upstream boundary, the downstream boundary had a greater overall impact on all three stations. Additionally, when the downstream boundary changed by the same magnitude, the variation in low tide levels showed a decreasing trend from downstream to upstream, whereas the variation in high tide levels, although following the same trend, did not show a significant difference between the three. In summary, compared to the upstream boundary, the downstream boundary had a greater impact on tidal-level predictions at the three stations. The result shows that the lower boundary has a greater impact on the tidal level forecasts at three stations compared to the upper boundary. The study not only provides a new method for tidal river flood simulation in coastal urbanized areas but also offers directions for improving model simulation accuracy through analysis.

The dynamic risk assessment of typhoon disasters is an important decision-making basis for disaster response in the event of a major typhoon. Its goal is to dynamically predict the expected loss and disaster risk level caused by a typhoon so as to provide a basis for disaster risk early warning and emergency response. The traditional risk assessment model mainly fits the vulnerability curves of the hazard-affected bodies using historical disaster losses, and then establishes a disaster risk assessment model by coupling the risk of disaster factors, exposure, and vulnerability. However, the vulnerability curves generated by this method have problems of regional applicability, particularly in small-scale regions with small sample sizes available for fitting, leading to insufficient generalizability of the model. In addition, such models are complex and require phased hazard and vulnerability of the hazard-affected bodies research. Moreover, when employing the 3-element coupling process, it is difficult to consider other risk factors in the disaster system, such as hazard-formative environment and disaster prevention and mitigation capability. With the development of information technology, the availability of disaster risk factor data has been significantly improved, affording conditions for the fusion and application of disaster risk multi-source data. In recent years, many data-driven machine-learning models have been used to establish disaster risk assessment models. These models have the advantage that they can use large sample to improve the adaptability of the model, whereby the modeling process can consider more risk factors, the concepts of hazard and vulnerability are diluted, and the steps of model building are simplified. The integrated learning algorithm can not only improve the prediction accuracy, but more importantly, can be used to effectively evaluate the contribution value of the index to the final evaluation result. At present, China has established a six-level disaster reporting system at the national, provincial, municipal, county, township, and village levels, forming a long-term, high-precision database of disaster event cases since 2009, providing rich disaster loss information for the data fusion of risk elements. This study was based on 108 typhoon cases affecting five provinces in southeast China during 2009-2022. Nearly 4,000 county-level typhoon disaster loss samples were used to establish a dynamic typhoon risk assessment sample database that integrates 30 types of multi-source risk factor indicators. Six typhoon disaster risk assessment models were developed using the random forest algorithm to evaluate the affected population, emergency relocation population, crop-affected areas, collapsed and severely damaged houses, direct economic losses, and comprehensive risk level. Through the verification of actual disaster situations and model results, the overall accuracy of the disaster risk assessment results was found to be greater than 80%, indicating that the model has good generalizability and can be used for actual disaster assessment work. The experimental comparison shows that increasing the training sample size by 1-2 orders of magnitude can improve the accuracy of the model assessment by 3%-14%, indicating that the accumulation of disaster risk big data is of great significance in the study of disaster risk assessment. This study is expected to constitute a scientific reference for the quantitative analysis of the multiple impact factors of typhoon disaster damage and explore technical ideas for the application of disaster big data in risk management.

Catastrophe insurance is an important financial tool to mitigate the risk of catastrophes. After the 2008 Wenchuan Earthquake, China accelerated its exploration of a catastrophe insurance system. As one of the most natural disaster-prone provinces in China, Guangdong experiences frequent rainstorms and typhoons. Severe natural disasters have not only led to significant losses to economic development and people's lives, but have placed considerable financial pressure on governments at all levels. To promote the transformation of government functions and use of catastrophe insurance as a modern financial tool to cope with major natural disasters, Guangdong has conducted pilot work since 2016 to explore and experiment with different aspects of catastrophe index insurance. This includes the design and application of insurance systems and products. The pilot work achieved remarkable results and formed the Guangdong catastrophe index insurance paradigm. However, few studies have examined the development and application of catastrophe index insurance programs in Guangdong Province. This paper describes the research and design process, data, and key methods of typhoon catastrophe index insurance in Guangdong, in accordance with the specific catastrophe index insurance practices. Furthermore, the application of the current catastrophe index insurance program from 2016 to 2023 is reviewed. Additionally, the advantages, characteristics, and shortcomings of the program are systematically analyzed, and potential directions for improvement in the future are discussed. Several notable conclusions were drawn from this study. First, the typhoon catastrophe index insurance, which is based on the circular catastrophe box and uses typhoon intensity levels as a stratification criterion for the payout structure, offers a straightforward methodology, easy recalculations, readily accessible data, and transparent results. Second, this form of insurance facilitates rapid claim settlements, incurs low operational costs, and effectively mitigates moral hazard. Third, the existing typhoon catastrophe index insurance program may encounter high basis risk and underestimate the severity of typhoon hazards, particularly in the context of climate change and the situation wherein a single typhoon impacts multiple municipalities. Finally, improvements to the current typhoon catastrophe index insurance program in Guangdong could be achieved by more deeply and comprehensively analyzing the spatial and temporal patterns of typhoon events, incorporating additional parameters with clear physical meanings, and refining the probability distributions of typhoon disaster events. The insights outlined in this paper may potentially enhance understanding among scholars and practitioners of typhoon catastrophe index insurance programs and provide guidance for extending catastrophe insurance in other typhoon-prone areas.

Typhoon gales can lead to accidents such as the breakage and collapse of transmission line towers, affecting the operational safety of power systems. Therefore, the risk assessment of transmission line towers during typhoon disasters is important. Taking all transmission towers in Zhejiang Province as an example, a typhoon disaster vulnerability assessment model for transmission line towers based on "excess loss" for both continuous and discrete variables was proposed based on tower attributes, geographical information, and typhoon disaster data. Utilizing the reanalysis data of typhoon parameters and wind fields from the past 68 years, a typhoon gale hazard assessment model was established based on the extreme value theory, and the statistical parameters of wind speed intensity under typical scenarios were analyzed. Furthermore, based on the regional disaster system theory and through a coupling analysis between typhoon gales and tower vulnerability, a risk assessment model for typhoon transmission line towers was developed. The results indicate the following: (1) the hazard of typhoon gales decreases from southeast to northwest, with differentiated distributions due to the local terrain and other factors. As the return period increased, a nonlinear increasing trend was observed. Taking the maximum wind speeds with a return period of 20 years and 100 years as examples, the wind speed intensities across Zhejiang Province range from 23.5-50.9 m/s and 32.6-68.9 m/s, respectively. Therefore, different wind resistance strategies should be adopted based on specific prevention requirements. Notably, the typhoon parameter wind field model used in this study had certain errors compared to the actual measured wind speeds. Therefore, in practical applications, particularly in complex terrain areas, it is necessary to combine local observational data for model calibration and application. (2) The comprehensive vulnerability of towers under the influence of typhoons generally exhibits a distribution pattern that is high in the south and low in the north, which is closely related to the terrain. Regions with high vulnerability (>1) were mainly located in central and southern Zhejiang and the coastal areas. Moderate vulnerability (0.5-1) is distributed in the Jinqu Basin and the offshore areas from Taizhou to Ningbo. The northeastern plain of Zhejiang had a relatively low tower vulnerability (<0.5). (3) The risk of transmission line towers generally exhibits a pattern of being high in the south and low in the north, with higher risks along the coast and lower risks in inland areas. There are significant local differences. In southeastern Wenzhou, Taizhou, and southern Lishui, the risk level of the towers was the highest. The southern part of Ningbo, Zhoushan, western Quzhou, and eastern Jinhua had the second highest risk. Additionally, some areas in Shaoxing, Huzhou, and Hangzhou have towers with higher risks that need to be addressed, which is consistent with the actual investigation findings. These results provide the necessary technical support for disaster risk assessments. Risk management plans should be adopted based on regional differences.

Islands are sensitive zones of sea-land interaction and typical ecologically fragile areas that are highly vulnerable to natural disasters, especially marine aquaculture, which is sensitive and at high risk to typhoon disasters; additionally, they are home to aquaculture households with high economic vulnerability to typhoons and poor adaptive capacity. This study focused on Liuheng Town of Zhoushan and the Dongtou District of Wenzhou, which were severely affected by Super Typhoon Lekima, and Gouqi Town of Zhoushan, which was severely affected by Typhoon In-Fa and Super Typhoon Chanthu, as case areas. Based on data acquired from 344 questionnaire surveys of aquaculture households and interview data from various related bodies, this study used factor analysis of mixed data and hierarchical clustering on principal components to identify the types of vulnerability of island aquaculture households to typhoon disasters and reveal the characteristics of each vulnerability type, as well as to identify the discriminative indicators of household vulnerability types, for analyzing the impact of typhoon disasters and other stressors on the vulnerability of island aquaculture households to typhoons. The results showed that the aquaculture industry and aquaculture households in the island areas showed high economic vulnerability, with most shrimp, crab, and shellfish mixed farming, algae, and mussel farming households suffering serious losses from typhoons. Second, differences in exposure, sensitivity, and adaptive capacity led to three different types and characteristics of vulnerability in aquaculture households. The degree of household exposure varied across aquaculture species, with mussels having the highest, algae the next highest, and shrimp, crab, and shellfish the lowest. Island aquaculture households showed outstanding sensitivity, as reflected in their high dependence on aquaculture, significant household human capital problems, relatively limited support from social networks, and frequent exposure to typhoon disasters. The adaptive capacity of households varied across aquaculture species, with mussel households having superior adaptive capacity, and shrimp, crab, and shellfish households and algal aquaculture households having relatively poor adaptive capacity. Third, the common influencing factors of aquaculture households' vulnerability to typhoon disasters are labor shortages, frequent typhoon disasters, and inadequate infrastructure. The differences among the significant discriminant indicators, such as the degree of exposure, aquaculture species, average annual household income, age and education level of the household head, social support, number and type of adaptation strategies adopted, and cost–benefit situation, are key to the formation of different vulnerability types. Finally, multiple stressors from the climate, ecosystem, economy and markets, society, institutions, and policies mutually interact to exert cumulative effects that increase the vulnerability of fishery ecosystems and the socioeconomic vulnerability of households in island regions. This study provides important empirical evidence for governments, aquaculture households, and other relevant stakeholders in island regions to reduce their vulnerability and increase their adaptive capacity.

Rainfall and sea surface temperature grid data, as well as rainfall data from coastal stations in China, were used to obtain the spatiotemporal response characteristics of summer rainfall along the Chinese coast to ENSO and analyzed interdecadal changes in summer rainfall. The results show that: (1) Summer rainfall along the coast of China was significantly affected by ENSO and can be divided into three regions, with Lianyungang and Yunao as the boundaries. The Niño3.4 index was negatively correlated with summer rainfall along the Bohai and Yellow Sea coasts, positively correlated with that of the East China Sea coast, and not significantly correlated with that of the South China Sea coast. (2) On an interdecadal timescale, the relationship between summer rainfall along the coast of China and the Niño3.4 index was unstable. The negative correlation between summer rainfall along the Bohai and Yellow Sea coasts and the Niño3.4 index was significant before and after 1980 and 2010, respectively. The positive correlation along the East China Sea coast became insignificant after the 1980s, whereas the correlation along the South China Sea coast remained insignificant. (3) On the interdecadal timescale, the summer Niño3.4 index, winter Arctic Oscillation (AO) index in the previous year, and spring Antarctic Oscillation (AAO) index in current year were significantly negatively correlated with summer interdecadal rainfall along the Bohai and Yellow Sea coasts and positively correlated with summer interdecadal rainfall along the East China Sea coast. Summer interdecadal rainfall along the coast of the South China Sea was significantly negatively correlated with the spring Arctic Sea ice index in the current year. Regarding the Niño3.4 index, the high sea surface temperature in the Central and Eastern Pacific triggered a negative Pacific-Japan-type interconnection wave train in the 500 hPa geopotential height field, resulting in a decrease in interdecadal rainfall along the Bohai and Yellow Sea coast and an increase in interdecadal rainfall along the East China Sea coast. When the winter AO in the last year and spring AAO in the current year were in a positive phase, the abnormal anticyclone in the southern part of Baikal Lake at 850 hPa wind field guided the airflow in the mid to high latitudes southward, causing a weakening of the East Asian summer monsoon and a decrease in interdecadal summer rainfall along the Bohai and Yellow Sea. In addition, the strong, westward position of the subtropical high pressure in the northwest Pacific increases the upward movement, increasing interdecadal summer rainfall along the East China Sea coast. The interdecadal variation of spring Arctic Sea ice stimulates the opposite atmospheric circulation pattern that induced interdecadal variation of summer rainfall along the South China Sea coast in the 850 hPa wind and 500 hPa geopotential height fields.

Typhoons are extreme weather phenomena that seriously affect the daily lives of residents and regular functioning of society. As one of the most typhoon-prone countries in the world, China is constantly affected by typhoons and their secondary disasters, which can cause significant casualties and economic losses. The extent of damage caused by typhoons is inversely proportional to the effectiveness of the emergency response. Therefore, accurate and comprehensive access to damage information is critical for rescue and recovery. Social media, which is characterized by low collection costs and rich content, is an important means of collecting disaster information. With the development of social media, it has become increasingly important to accurately and comprehensively identify social media texts related to typhoons. In this study, by combining typhoon attribute data and a multi-label classification method with Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) models, a typhoon damage identification method based on Weibo texts and deep learning is proposed to identify the damage caused by severe and super typhoons that made landfall in Guangdong Province from 2010 to 2019. First, texts related to typhoon damage were identified from the massive Weibo texts and further classified into five damage categories: transportation, public, electricity, forestry, and waterlogging. The typhoon damage characteristics were comparatively analyzed using spatial distribution, time curves, and quantity curves. The results showed that the accuracy of typhoon damage classification was high, with an F1 score of 0.907 for identifying typhoon damage-related texts and 0.814 for further classifying them into five damage categories. Typhoon attribute data and multi-label classification methods have improved the accuracy and comprehensiveness of typhoon damage identification. Compared to the use of Weibo texts only and the single-label classification method, typhoon attribute data provide information on the geographic context of the typhoon at the time of the texts' release, and the multi-label classification method allows the texts to belong to more than one damage category. This study shows that there are differences in the proportion of damage caused by different typhoons, which are related to the intensity and track of the typhoon, as well as the development level of the affected areas. In addition, before the typhoon makes landfall, precautions lead to transportation and public-related damage. After the typhoon makes landfall, the typhoon damage shows single and double-peak characteristics, and the different characteristics reflect the changing trends and features of typhoon damage. This study provides a scientific basis for typhoon damage identification and disaster relief in Guangdong Province.

China has numerous islands and reefs with complex terrain that are heavily impacted by tropical cyclone disasters. High-resolution tropical cyclone wind-field simulations are beneficial for representing the spatial variations in wind speeds. It is important to conduct high-resolution simulations on relatively small islands and reef areas. To explore the differences in tropical cyclone wind field simulations at various spatial resolutions in the island and reef areas of China, this study compared the modeled wind fields of historical tropical cyclones in China's island and reef areas, which have complex terrains, including plains, peaks, valleys, and cliffs, at three spatial resolutions of 1,000 m, 90 m, and 30 m. The wind fields were modeled using land cover and elevation data of the three spatial resolutions as inputs and validated against observed winds at eight stations. Comparisons were made regarding the differences in wind speeds of tropical cyclones with a 100-year return period at three spatial resolutions. The results showed that: (1) the 30 m resolution achieves the best accuracy, with a root mean square error of 4.28 m/s, lower than those of 90 m and 1 km by 0.08 m/s and 1.04 m/s, respectively. (2) Different spatial resolution simulations showed that wind speed errors were related to terrain types. For example, on Zhujiajian Island, located in Zhoushan City, the 30 m resolution captured the spatial heterogeneity of winds better than the other resolutions, especially for mountainous, valley, and cliff terrains. Comparisons between the simulated wind speeds at 90 m and 1,000 m resolutions versus those at 30 m resolution indicate that the differences in the simulation percentages are as follows: 6.57% and 7.61% for peak terrain, 21.28% and 17.35% for valley terrain, and 22.85% and 23.37% for cliff terrain, respectively. Additionally, the 30 m simulation was more sensitive to transitions between windward and leeward slope terrains. (3) For the 100-year return-period wind speeds, the 30 m resolution produced the highest values and largest spatial variations. On Zhujiajian Island, the maximum wind speeds at 1,000 m, 90 m, and 30 m resolutions were 71.13, 73.18, and 79.97 m/s, respectively, and standard deviations of 3.88, 3.72, and 7.18 m/s. This study demonstrates the importance of using high-resolution data to simulate tropical cyclone winds in complex terrain. However, this study had some limitations. First, the terrain correction factors need to be optimized further. The assessment method provided by the building codes tended to overestimate the impact of the terrain correction factors. In the future, more accurate terrain correction factors could be obtained using computational fluid dynamics and wind tunnel tests. Second, because of the limited types of land cover data used in the calculations, the subdivision of certain land types when calculating the surface roughness is not sufficiently detailed. Additionally, different years of land cover data were not incorporated, making it challenging to reflect the variations in surface roughness. Remote sensing can be used in the future to determine the high-resolution spatial distributions of surface roughness.

The rapid growth of social media has introduced new concepts and technical approaches for disaster management. This paper reviews the characteristics of social media data and its application potential in disaster management research, providing a new research perspective for the field of disaster management. Taking the impact of Typhoon Doksuri in Fujian Province in 2023 as a case study, this research employs Latent Dirichlet Allocation (LDA) topic modeling to analyze the practical application effectiveness of social media data at different stages of disaster management from three perspectives: the spatiotemporal distribution of posts, trend analysis of different types of entities, and evolution of topic content. These findings indicate that the synchronous relationship between the popularity of related topics on Weibo and the impact of a disaster event confirms the effective application of social media data in disaster management. By monitoring the dynamics of information dissemination on social media, we can determine the occurrence status and impact scope of disasters in real time. During disasters, different user types have different foci. Individual users tend to focus more on the restoration of living facilities and the supply of relief materials, whereas organizational users concentrate on disseminating information about disasters and emergency response measures. The information provided by different types of users can provide a more comprehensive and diversified perspective on disaster perceptions for disaster management. Analysis of the evolution of topic content can reflect the evolution of emergency response dynamics and public attention needs in different cities at different stages of disaster management, thereby developing more practical emergency response strategies. Through the mining and analysis of social media data, this study recognizes the entire process of disaster occurrence from the perspective of social media data, thereby enriching the relevant theoretical and empirical research. Future research could be conducted from perspectives such as utilizing other multisource data, integrating machine learning and deep learning technologies to enhance the accuracy of topic information extraction, and exploring the application of social media data to post-disaster emergency rescue and infrastructure support.

Typhoons and their associated disaster chains pose serious threats to the lives and property of coastal residents, and they remain a focal point for research and response. Previous studies on typhoon disaster chains often employed high-dimensional symmetric Copula models to establish the joint distribution of multiple hazard factors, however they failed to explore the complex nonlinear and asymmetric dependencies among them. This study aimed to depict these complex relationships more comprehensively and efficiently to provide a more accurate typhoon hazard assessment. Focusing on Zhoushan, a city comprising numerous islands in Zhejiang Province that faces multiple typhoon threats, this study employed the C-Vine Copula function to model the complex dependencies among "strong wind-rainstorm-storm surge" in the typhoon disaster chain. Utilizing observational data from 1979 to 2018, this study involves three main steps: first, fitting the marginal distribution of each hazard factor and identifying the best one from Lognormal, Gamma, GEV (Generalized Extreme Value), and Burr functions based on the K-S test; second, fitting the bivariate joint distributions of wind speed-rainfall and wind speed-storm surge using Gaussian, Clayton, Gumbel, Frank, and Joe Copula functions, and determining the best fit based on the AIC (Akaike Information Criterion); and finally, estimating the trivariate joint probability distribution and corresponding return periods for wind speed-rainfall-storm surge using the C-Vine Copula function. This revealed (1) a strong correlation between wind speed and rainfall observed within regular value ranges (non-extreme conditions), were best represented by the Frank Copula, In addition, wind speed and storm surge exhibit an upper-tail dependence, best captured by the Gumbel Copula. (2) The rainfall distribution under certain wind speed conditions revealed dual peaks, whereas the storm surge distribution maintained a uniform pattern, with the best joint distribution fitting the Gumbel Copula. (3) Considering a 100-year return period for individual variables, the bivariate return periods for wind speed-rainfall and wind speed-storm surge events were significantly reduced to 29 and 30 years, respectively, while the trivariate return period for the wind speed-rainfall-storm surge combination was further reduced to 17 years. Overall, the C-Vine Copula function effectively characterizes the complex nonlinear and asymmetric dependencies among the typhoon disaster chain "strong wind-rainstorm-storm surge", reducing high-dimensional parameter estimation complexity. This method provides new insights for constructing joint probability and return period models for multiple hazard factors and offers a scientific basis for disaster risk assessment and management strategies. Therefore, this enhances the accuracy of disaster prevention and mitigation efforts. Additionally, the application of the C-Vine Copula assists to deeply understand the mechanisms and development processes of natural disasters, providing new tools for on-site emergency response and decision-making.

The provision of a high-quality ecological environment is essential for the quality of life of residents. As an important component of the urban ecological environment, the relationship between urban green spaces and public health requires further investigation. This paper provides a comprehensive review of the Chinese and international literature on how urban green spaces affect mental well-being. First, it introduces different approaches of measuring the use of and exposure to urban green spaces. The most commonly used indicators for measuring the use and exposure to urban green spaces include Surrounding Greenness, Access to Green Spaces, Green Viewing Rate and Green Space Quality and Usage Satisfaction. The main advantages of Surrounding Greenness are wide spatial coverage, long timespan, and low cost; however, the accuracy of measuring exposure is relatively low. Researchers have extensively used access to green spaces. Because the bird's-eye perspective cannot fully reflect resident perceptions of park green spaces, scholars have used the green view ratio, which has the advantages of wide coverage, low cost, easy access, and small data deviation. Greenspace quality and usage satisfaction are also important measurement indicators, and their main advantages are low operational difficulty and the ability to reflect residents' subjective evaluations more accurately. It then elucidates the "environmental stress reduction-restoration-instoration" mechanisms underlying the effect of urban green spaces on mental well-being. Specifically, urban green spaces can affect the mental health of residents by reducing the harm arising from heat and pollution, restoring capacity, and building capacity. Green spaces alleviate environmental pressure by purifying air, reducing noise, and alleviating the heat island effect, thereby promoting residents' mental well-being. People can alleviate their psychological stress and restore their ability to control attention by viewing green spaces, thereby protecting their mental health and providing a favorable and convenient venue for residents to conduct physical activities and socialize with their neighbors, which is beneficial to their mental wellbeing. Subsequently, it illustrates the moderating effect of opportunities to use urban green spaces, motivation to use urban green spaces, and ease of using urban green spaces on mental wellbeing from a "socio-ecological" perspective. Finally, it indicates that the current body of literature has several limitations and that future research agendas should be centered on research content, data, perspectives, and methods. Specifically, (1) for research content, the effect of green spaces on the mental well-being of different social and cultural groups is poorly understood. Therefore, it is necessary to strengthen the analysis of the sociocultural mechanism of the effect of urban green spaces to enrich the existing research framework. (2) Most previous studies used one method to measure the level of greenspace exposure or usage. It is advisable to use a variety of methods to measure the level of greenspace exposure or usage both subjectively and objectively. (3) From a research perspective, most previous studies have used a research paradigm based on local and static analysis, failing to solve the Uncertain Geographic Context Problem (UGCoP). Therefore, it is necessary to adopt a human-centered perspective and accurately measure the impact of green space exposure on residents' mental well-being in their residential neighborhoods, workplaces, and other activity spaces. (4) Researchers need to solve the problem of residential self-selection when investigating the effect of urban green spaces on mental well-being and explore nonlinear complex relationships using advanced methods such as machine learning.

Air cargo is an important component of transportation and plays a vital role in the efficient allocation of high-quality resources on global and regional scales. Air cargo contributes significantly to regional economic development by strengthening inter-regional cooperation and resource integration. However, air cargo geography has received relatively less attention from the research community. Existing studies have analyzed the spatial pattern of air cargo using a limited cross-sectional data from selected years, lacking an analysis of its influencing factors. Based on spatial statistics and panel data of air cargo, this study explores the evolution process and characteristics of China's air cargo pattern on a 20-years time scale and quantitatively reveals its key influencing factors. The research findings are as follows: 1) Air cargo in China has transitioned from the rapid development stage to the stable development stage in the past 20 years; 2) Air cargo volume in China is mainly concentrated in the eastern region, and in the past 20 years, China's air cargo center of gravity has been generally located at the junction of Anhui, Henan, and Hubei provinces, showing a spatial displacement trend from Henan to Anhui to Hubei; 3) The pattern of air cargo network in China remains relatively stable, forming a rhombic structure with Beijing, Shanghai, Guangzhou, and Shenzhen as the core; 4) Air cargo development in China is influenced by factors such as urban scale, industrial structure, and ground transportation development. Among them, urban economy, transportation, warehousing, postal and telecommunications industry, and technological investment have a significant positive impact on air cargo volume, whereas the wholesale and retail trade industries have a significant negative impact. For air logistics hubs, the influencing factors are consistent with those of the entire sample airport. However, for non-aviation logistics hubs, population size and research and technology services have a significant positive impact, whereas ground transportation accessibility has a significant negative impact. This study enriches the long-term time-series analysis and quantitative research content in the field of air cargo and has significance for the development of air transportation geography and the construction of a strong civil aviation industry in China.

China is frequently affected by tropical cyclones, which can lead to severe economic losses. Rapid disaster loss assessment is crucial for effective emergency response. A variety of factors affect tropical cyclone disaster losses, which can be roughly categorized into hazard, exposure, and vulnerability. In the past, traditional statistical methods were used as the main tools for disaster loss assessment. To explore the potential of machine learning models, we explored five algorithms: the Light Gradient Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Back-Propagation Neural Network (BP). The maximum gust wind and rainfall of tropical cyclones were selected to represent hazards, fixed capital stock data were used for the valuation of exposure, and the GDP of each county was collected to reflect capacity or vulnerability. In addition, river network density data were used as a simple proxy to demonstrate the contribution of flood-induced tropical cyclone rainfall. The relationship between these input variables and disaster loss at the county level was developed based on the data of 81 tropical cyclone events from 2009 to 2020 in Fujian Province. The performance of these models was compared using accuracy, precision, recall, and F1 scores. The accuracies of the LightGBM, RF, XGBoost, SVM, and BP models were 0.794 6, 0.772 6, 0.762 8, 0.251 8, and 0.268 1, respectively. The main findings are as follows: (1) The performance of the ensemble learning algorithms (RF, XGBoost, and LightGBM) was higher than that of the individual classifiers (BP and SVM). The LightGBM model exhibited the best performance, with accuracy, precision, recall, and F1 scores >79%. (2) Maximum hourly rainfall and maximum wind gust are two of the most important loss-inducing factors, and fixed capital stock is a better proxy for disaster exposure than GDP. (3) The modeled losses are consistent with the actual losses under different but typical tropical cyclone events, indicating that the models can be applied to future tropical cyclone events impacting Fujian Province. However, this study had some limitations. First, some natural hazards, such as floods, storm surges, and waves, were not fully considered, which introduced uncertainty into the model results. Second, the emergency response capacity and actual actions taken among counties may have varied dramatically and were neglected due to data unavailability. In the future, hazard and vulnerability variables should be obtained to extend the model inputs. In addition, whether the model parameters trained with data from Fujian Province can be applied to other provinces remains unaddressed. In the future, to develop an operational model for the whole of coastal China, county-level data of all typhoon-prone areas in China with long-term time series are needed.

In the era of the knowledge economy, talent is important for the development of any country, region, or city, and attracting foreign talent to stay and settle down has gradually become an important issue. In this study, we analyzed the spatial distribution of highly educated migrants, their willingness to settle in the place they migrated to, and the social influencing factors from the perspective of migration networks using the 2017 China Migrants Dynamic Survey. We explored the differences in the influence of inter-provincial and intra-provincial migration patterns on the willingness of migrants to settle. The main conclusions are as follows: First, the willingness of the highly educated migrants to settle down shows a distribution pattern of "high in the north and low in the south." Migrants, especially highly educated people who are willing to migrate across provinces, showed highest preference for settling down in Beijing, Shanghai, and Tianjin, whereas those willing to migrate within provinces showed preference for settling down in Tibet, Shandong, and Hubei.. Second, the friendship and kinship networks of highly educated migrants show a clear pattern of "high in the north and low in the south," and the business network shows a clear pattern of "high in the west and low in the east," and the network of township ties has a clear pattern of "high in the east and low in the west." Third, migrant networks provide material and emotional social support for the migration of highly educated individuals and significantly influence their willingness to settle. Among these networks, kinship and friendship offer the greatest support and exert the strongest pull on their migration decisions. In contrast, the influence of township ties on the willingness of highly educated migrants to settle is smaller, highlighting a clear difference from the stronger reliance of migrant workers on township ties. Fourth, highly educated intraprovincial migrants, owing to their shorter migration distances and lower migration costs, are more influenced by kinship in their willingness to settle. By contrast, highly educated inter-provincial migrants who lack kinship ties in their destination areas showed a lower willingness to settle and are more influenced by friendships and hometown connections. Future research on talent migration and policies should place greater emphasis on social factors, thereby enriching the study of talent mobility from a societal perspective. This strategy is also beneficial in practice for attracting and retaining talent, not only through economic incentives but also by leveraging social policies. This fosters the integration of talent into destination cities and contributes to the socioeconomic development of these areas, thereby propelling the implementation of a talent-driven national strategy.

Urban agglomerations are the main spatial carriers of national and regional urbanization development. The study of their spatial association networks is of great significance for optimizing the allocation of urban resources, promoting the process of regional integration, and facilitating the high-quality development of urban agglomerations. The Yangtze River Delta urban agglomeration (YRDUA) is one of the most economically active regions in China. Its spatial association network structure was the main feature of economic and social development in China's "14th Five-Year Plan." Based on the perspective of flow spaces in terms of both the city and township scales, multi-source data were used in this study and included online car-hailing origin-destination travel data, combined with complex network models and a quadratic assignment procedure, to analyze the structural characteristics and driving mechanism of the spatial association network of the YRDUA. Results show that: 1) spatial association network based on online car-hailing flow in the YRDUA has spatial dependence and hierarchical characteristics, and intensities of network association are mostly coupled with levels of economic development; 2) spatial association network in the YRDUA displays spatial spillover effects, leading to an overall pattern of high equilibrium in southern development and strong single-point development capacity in the north; 3) the structural features of spatial association network in the YRDUA differ between townships and urban scales, with some high-level townships in certain transportation networks failing to exert their driving role at the urban level; and 4) economic development status, population vitality, the level of urban construction, and administrative division ownership and geographical location differences between townships have significant impacts on the spatial association network structure in terms of the township scale. The differences in administrative divisions are most important. Online car-hailing travel data were used in this study to effectively supplement the links between township streets within and between cities. This data also revealed intercity links. Thus, the development characteristics of spatial units on different scales were reflected, and research and social management needs were satisfied on a fine scale. In addition, by introducing spatial big data and analyzing the influence mechanism from various aspects, such as socioeconomics, the driving factors of the spatial network of urban agglomerations were systematically identified at the small-scale level, which will help with more reasonable planning within the city and play a role in the development of urban agglomerations by enhancing the attractiveness of individual cities. This study expands the research perspectives on the cooperative development of urban agglomerations on different scales, providing theoretical references and practical support for the promotion of the coordinated development of urban agglomerations as a whole.

As China enters a critical transition period towards a knowledge-based economy, the optimization of the educational attainment structure and spatial distribution of the civil service, a crucial component of modernizing the national governance system and capacity, exerts a profound influence on enhancing government efficiency and fostering social progress. Based on recruitment data from the 2023 Guangdong Provincial Civil Service Examination, this study employs spatial statistical methods and Multivariate Logistic Regression analysis to examine the urban hierarchy migration model of newly graduated students admitted to civil service positions and its influencing factors. The research findings indicate: (1)The migration model of admitted newly graduated students are predominantly migrate down the urban hierarchy. For non-equivalent-level migrations, destinations are mainly fourth-, third-, and first-tier cities, whereas for equivalent-level migrations, destinations are primarily fourth-, third-, and second-tier cities. (2)Among personal attributes (including gender, university ranking, and the tier of the city where the graduation school is located), except for graduates from universities in the fourth- and fifth-tier cities, all other groups predominantly migrated down the urban hierarchy. (3)In terms of work-unit level and job requirements (including educational qualifications and work experience), the admitted candidates across all groups predominantly migrated down the urban hierarchy. (4) The results of the multiple logistic regression showed that personal attributes, work-unit level, and job requirements jointly affected the choice of migration mode for admitted candidates. Personal attributes and job requirements have a relatively significant impact; the better the personal attributes and the higher the job requirements, the more likely they are to migrate up the urban hierarchy. The effect of job conditions was not significant. (5)The results of the mechanism analysis revealed that the choice of migration model among newly graduated students is a complex and dynamic decision-making process underpinned by the interplay of multiple factors, such as government policy guidance, job characteristics, personal factors, and urban conditions. The decision-making and selection process in the selection mechanism for the migration mode of newly recruited graduates in the civil service examination is complex and dynamic, involving the interaction of multiple factors, such as government policy guidance, job characteristics, personal factors, and urban conditions, with objective limitations, personal abilities and resources, and subjective willingness. Compared to other non-establishment employment models, this employment model has a stronger possibility of migrating down the urban hierarchy, which may be caused by significant differences in the sources of job settings, competition intensity, and subjective willingness. To achieve the strategic goal of building a high-quality young civil service, the government should consider the multifaceted and complex interactions within the recruitment process, leverage its administrative functions, flexibly adjust job settings, and attract and retain outstanding newly graduated students through measures such as optimizing the urban environment and enhancing public service levels. This study has significant implications for local governments in formulating scientific and reasonable civil service recruitment policies tailored to local conditions, and guiding newly graduated university students to make informed and rational decisions when applying for civil service positions.

To enhance the scientific rigor and practical relevance of disaster resilience evaluation in mountainous rural communities, this study developed a multilevel assessment framework based on the Pressure-State-Response (PSR) model by integrating the entropy method and Analytic Hierarchy Process (AHP). The framework comprised three dimensions (pressure, state, and response), nine elements, and 32 indicators tailored to the unique environmental and socioeconomic contexts of mountainous regions. Focusing on four representative communities (Taoyuan, Caogu, Niulang, and Qunying) in the Anning River Basin of Liangshan Prefecture, Sichuan Province, China, a combination of field surveys, GIS spatial analysis, and multi-source datasets were used to empirically evaluate community resilience. The key findings revealed the following: (1) The comprehensive resilience scores ranked Taoyuan > Niulang > Qunying > Caogu. Taoyuan's top performance stemmed from its designation as a national disaster prevention demonstration community featuring robust infrastructure and frequent emergency drills, whereas Caogu's lowest resilience resulted from its high-altitude topography, aging population, and inadequate infrastructure. (2) State resilience contributed most significantly to overall resilience (51.43%), with the building quality (C9) being the pivotal driver. Pressure resilience was predominantly influenced by the proximity to active faults (C2) and population exposure to geological hazards (C6), whereas response resilience relied on disaster-monitoring equipment (C26) and early warning efficiency (C27). (3) A synergistic optimization strategy was proposed, emphasizing risk zoning and engineering controls (pressure layer), housing retrofitting and social capital cultivation (state layer), and intelligent early warning systems integrated with indigenous knowledge (response layer). The study validates the applicability of the PSR model in mountainous rural contexts, highlighting a "state resilience dominance with response capacity gaps" pattern. Notably, communities with higher state resilience demonstrate stronger recovery capabilities despite elevated hazard pressures, underscoring the importance of robust infrastructure and social cohesion. Conversely, insufficient investment in monitoring technologies and external rescue coordination hinders response effectiveness in remote villages such as Caogu. The framework provides methodological support for tailored disaster-prevention planning, particularly in ethnic regions where traditional ecological knowledge complements modern governance. However, limitations include a focus on earthquakes and geological hazards, excluding concurrent multi-hazard scenarios (e.g., wildfires and pandemics), and a static assessment that overlooks temporal resilience dynamics. Future research should incorporate longitudinal monitoring and cross-scale interactions to refine the generalizability of the model. This study advances the theoretical integration of socioecological systems into resilience assessments and offers actionable insights for sustainable rural development in hazard-prone mountainous areas.

Infrastructure and economic networks can be used to describe different dimensions of urban systems, and a close relationship exists between them. Therefore, it is important to study the dynamic coupling relationship between them to promote the formation and development of urban networks. Relevant studies have compared and analyzed the evolution characteristics of urban network structures from different dimensions; however, relatively few studies have focused on the dynamic coupling relationship between high-speed rail and remote investment networks. The formation and development of high-speed rail networks has become a key factor affecting remote investment networks, and the layout of a remote investment network also affects the development of a high-speed rail network. To this end, we used high-speed railway operation service data and related transaction data of listed companies from 2008 to 2023 to construct high-speed railway and remote investment networks reflecting intercity high-speed railway and enterprise remote investment connections, respectively. QAP correlation analysis was used to explore the overall correlation between the two networks. Using the quadrantal diagram method and the coupling coordination degree model, we discuss the spatiotemporal coupling coordination of the two networks, identify the types of cities and urban connections, and propose operational planning suggestions for cities and high-speed railways. The results indicate the following: 1) Both the scale and density of the high-speed rail network and the off-site investment network show a rapid growth trend, and the expansion of the high-speed rail network is significantly faster than that of the off-site investment network. There is a significant positive correlation between high-speed rail and the remote investment network as a whole, and the degree of correlation first increases and then decreases. 2) In terms of space, cities with the simultaneous development of high-speed rail and remote investment networks are primarily concentrated in the administrative and economic centers of the eastern region. However, cities with lagging high-speed rail networks are mainly located in non-transportation hub cities in the economically developed areas of the Pearl River Delta, Dalian, and Urumqi. Moreover, cities with advanced high-speed rail networks are mainly centered in the central and eastern regions around the main high-speed rail lines. 3) Cities with synchronous development of high-speed rail and remote investment networks are mainly located in developed cities within the five major urban agglomerations. The pairs of cities with lagging high-speed rail networks are mainly located between the core cities of different urban agglomerations. Pairs of cities with advanced high-speed rail networks are typically positioned between cities that are closer together and contain at least one city with a less developed economy. The relevant research results can provide policy support for China's high-speed rail operation planning and urban system construction.

Skilled migration is a pivotal phenomenon underpinning globalization that has attracted widespread scholarly interest; a nuanced understanding of the patterns and mechanisms of skilled migration is considered essential for fostering coordinated regional development and enhancing labor market allocation. To compare the domestic and international talent mobility research in the 21^st century and advance China's talent mobility theory, In this paper, we reviewed Chinese and English literature from fields such as geography, urban planning, demography, economics, and management since 2000 via quantitative and qualitative analysis. Initially, we conducted bibliometric and keyword co-occurrence analyses using CiteSpace to identify research hotspots and trends; subsequently, we performed Qualitative Data Analysis Miner qualitative analysis as a supplementary approach to derive in-depth insights and determine connections between literature sources. Herein, we summarize the research hotspots regarding skilled migration, domestically and internationally. We found that concerning research hotpots, domestic research emphasizes the spatial distribution of different types of talent, the impact of urban factors on skilled migration, and the interplay between talent and urban innovation. In contrast, international research emphasizes more on the mobility decisions of highly skilled migrants within a globalization context, focusing on the impact of international high-skilled migration on the destination and origin countries, the micro-level mechanisms of migration decision-making (such as family dynamics, gender roles, and early career stages), and social adaptation in destination countries (including identity, sense of belonging, and related factors). Theoretically, both domestic and international studies are based on labor migration theory, examining skilled migration mechanisms from macro-regional and micro-individual perspectives, and expanding theoretical frameworks to include amenities and the creative class. However, owing to differences in social, economic, institutional, and developmental contexts among countries, these theories are not entirely applicable in practice, particularly regarding their explanatory power in the Chinese context, which requires further examination. Furthermore, in terms of research progress, we found that international studies are increasingly highlighting the life course of migrants and the socio-cultural micro-mechanisms influencing skilled migration, emphasizing the adoption of a combination of quantitative and qualitative analytical methods. Conversely, domestic research predominantly focuses on the spatial patterns of skilled migration and the effects of macro-urban factors, relying on quantitative analyses. Future research in China should aim to examine micro-mechanisms, develop indigenous theoretical frameworks to foster theoretical innovation, and enhance the integration of qualitative and quantitative methods. Moreover, exploiting the potential of big data and emerging technologies could contribute towards overcoming limitations associated with data acquisition. On the basis of our survey of current theories and methods, we propose the following four directions for future research: (1) strengthening investigations into the micro-mechanisms of skilled migration; (2) developing indigenous theoretical frameworks to support theoretical innovation in China; (3) promoting the integration of qualitative and quantitative research methods; and (4) enhancing data acquisition and exploiting big data and advanced technologies to overcome the current limitations associated with acquiring data.

In the digital economy, developing skills among women with disabilities is crucial for improving their employment opportunities and income. This study examines the Aide Training School in Yixing City, Jiangsu Province as a case study to explore skill development pathways for women with disabilities based on the empowerment theory. The integration of government, market, and social resources, enhances their expertise in traditional crafts, strengthens market competitiveness, promotes economic independence, and supports social integration. Research Methods This study employed qualitative research methods, including participatory observation and in-depth interviews. Targeted interviews were conducted with 25 participants, including women with disabilities, institution heads, and disabled artisans at the Aide Training School in Yixing City. Data related to the Training School was also collected. Using an intersectional perspective, this study draws on Sirivaddhana's (1998) three empowerment methods for women and Qian Ning's (2020 internal and external framework to analyze skill development and reemployment processes, leading to self-empowerment and identity transformation. Results and Conclusions: 1)The Aide Training School integrates government, market, and social resources, to provide comprehensive skill training and employment support for women with disabilities. This has significantly improved their proficiency in traditional crafts, such as Yixing purple sand pottery. The local cultural significance of Yixing purple sand pottery serves as "local identity capital" for women with disabilities, enhancing their market competitiveness and economic independence. 2) Skill development and re-employment transform the social identity and roles of women with disabilities. They transition from marginalized, unemployed, or low-income groups to skilled professionals with stable incomes, improving their social standing and self-perception. Additionally, they gain social recognition through the identity of "purple sand pottery culture communicators." 3) Skill development expand social participation opportunities. Through market sales, cultural exhibitions, and social services, these women build social networks, increase their influence, gain greater decision-making power, reinforcing empowerment outcomes. This study provides novel perspectives and methodologies for research on the skill development and reemployment among women with disabilities in China. Theoretically, it applies empirical case analysis to substantiate the explanatory utility of empowerment theory in elucidating the pathways and mechanisms of skill development for women with disabilities, thereby enriching the application of empowerment theory within this demographic and compensating for the previous neglect of this group within the empowerment theory. The proposed "human-land interaction empowerment" mechanism adds localized, intersectional insights to feminist geography. Practically, this research deepens the understanding of the empowerment process for women with disabilities, particularly in enhancing self-awareness, strengthening capabilities, and achieving skill development, thus providing a practical foundation for the evolution of subsequent related theories and, to some extent, advancing the career development of individuals with disabilities and providing innovative employment strategies.

Under the influence of information technology and high-speed transportation networks, which compress space and time, the region's population has achieved large-scale fluidity. Examining the temporal heterogeneity of intercity travel networks and its influencing mechanism can help optimize regional spatial organization and provide a scientific basis for regional integrated development. Based on Baidu migration data from January to April 2023, this study uses a PPML(Poisson Pseudo Maximum Likelihood) gravity model and interaction term testing to compare the scale, pattern, and influencing factors of intercity travel networks during weekdays, weekends, and holidays in the Yangtze River Delta region. The results indicate the following: 1) The intercity travel network in the Yangtze River Delta region exhibits temporal heterogeneity characteristics. During weekdays, intercity travel primarily consists of cross-city commuting and business trips, with the lowest daily average scale. This forms a V-shaped intercity travel structure covering Shanghai, southern Jiangsu, Northern Zhejiang, and Southern Anhui. The positive effects of destination city population size and economic status on intercity travel are enhanced. On weekends, intercity travel is dominated by business trips and leisure activities, and residents tend to take shorter trips, which means that intercity distances pose greater hindrances to intercity travel. During holidays, intercity travel is primarily for leisure and entertainment and for visiting friends and relatives, with the highest daily intensity. The promotional effect of destination city population size on intercity travel is weakened, and intercity travel is less hindered by intercity distances. Compared to the effects of geographical distance, economic status, and population size on the scale of intercity travel during weekdays, travel duration, or geographical distance, tends to pose a greater hindrance on weekends and a lesser hindrance during holidays. The promotional effect of economic status is intensified on weekends but diminishes during holidays. Meanwhile, the promotional effect of population size weakens both on weekends and during holidays. 2) Push-pull factors encompass the level of urban development and the incentives that trigger individual travel. In terms of urban development level, indicators such as population size, economic status, and industrial structure reflect the comprehensive strength and development status of a city, influencing its ability to serve as both a starting and destination point for intercity travel. From the perspective of various individual travel incentives, residents pay more attention to various urban resources such as income levels, public service quality, and tourism resources to meet their personal needs for production and living. The primary types of population movements vary across different time periods, shifting between cross-city commuting, business travel, and leisure and entertainment. As a result, the dominant factors among push-pull elements also change, leading to significant variations in the effectiveness of each factor. Intermediate obstacles are the key factors limiting intercity travel. On the one hand, while the level of integration in the Yangtze River Delta region continues to improve, and transportation facilities are gradually improving, geographical distance remains a crucial intermediate obstacle. On the other hand, administrative and cultural differences between different provinces increase residents' adaptation costs, forming "invisible barriers" that hinder cross-province population interactions. The hindrance posed by intermediate obstacles to intercity travel also varies across different travel periods. The effects of push-pull factors exhibit temporal heterogeneity. The small-world characteristics of the intercity travel network during weekdays are more evident, and the central city has a more prominent structural core status. On weekends, the geographical proximity of the intercity travel network improves, with close "center-hinterland" connections and enhanced inter-provincial boundary effects. During holidays, the overall intensity of the intercity travel network increases, with the most significant increase in medium- and long-distance cross-provincial travel. The provincial boundary effect and spatial proximity effect decrease, weakening the structure of the intercity travel network.

Owing to historical reasons, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) features a unique "one country, two systems" institutional framework. Facilitating the integration and connectivity of transportation among Hong Kong, Macao, and the Mainland is crucial for the high-quality development of the GBA. Previous studies about borders have primarily focused on national (supranational) or administrative boundaries within a country's territory. However, studies on the unique institutional differences in the GBA are insufficient. Additionally, most studies do not perform dynamic border effects measurements using big traffic flow data. This study utilizes toll-collection data from highways in the GBA for 2021 and 2023, as well as cross-border traffic data, to construct a traffic-flow network for the GBA. Complex network analysis and border-effect measurement methods are employed to investigate the spatial structure of the GBA traffic-flow network and its dynamic changes in border effects. The results indicate that, in terms of the overall spatial structure of traffic flow in the GBA, the network exhibits a unique "dual-core edge" structure, with the Guangzhou-Foshan, and Shenzhen-Dongguan-Huizhou regions serving as dual cores. In contrast, the overall coverage and connectivity strength of the passenger-flow network are higher than those of the freight-flow network. Regarding the dynamic changes in the spatial structure of traffic flow from Hong Kong and Macao, the coverage and density of the traffic-flow network in 2023 are significantly higher than those in 2021. Traffic flows from Hong Kong and Macao have begun to extend beyond the border toward the northern regions, thus accelerating the integration of transportation within the GBA and forming a spatial pattern of "cross-strait connectivity and all-area interconnection." However, because of their peripheral positions in the traffic network and the presence of border effects, the importance of Hong Kong and Macao in the GBA traffic-flow network remains relatively weak. Based on the dynamic measurement results of border effects, the obstruction coefficients between Hong Kong and the Mainland, as well as between Macao and the Mainland, are significantly higher than those between various counties within the Mainland. The obstruction coefficients for passenger vehicles are generally lower than those for freight vehicles. Following the outbreak of the pandemic, the obstruction coefficients of the GBA traffic-flow network have increased dynamically, thus indicating a reduction in obstructive border effects. This study expands the quantitative research framework of border effects in traffic-flow networks, thus promoting integrated transportation development in the GBA and facilitating its integration development goals.

Coastal cities are highly vulnerable to compound flooding in which multiple flood drivers interact via complex nonlinear mechanisms under climate change. Although numerous studies have focused on individual flood drivers, integrated analyses of the spatiotemporal variations and compound effects remain limited. This study applied a high-resolution MRI-AGCM3-2-S climate model and the TempestExtremes tracking algorithm to construct a 6-hourly Tropical Cyclone (TC) track dataset affecting Haikou from 1960 to 2099. Storm tides during the TCs were simulated using the D-Flow FM model, whereas upstream river discharges were modeled with CaMa-Flood, incorporating climate-model-derived runoff data. Using rainfall data from the climate model, we applied the peak-over-threshold method and extreme value analysis to systematically assess changes in storm tides, rainfall, and upstream discharge under climate change. These analyses guided the construction of compound flood scenarios for simulating extreme events. Using a compound flood simulation model, we assessed the hazards under 10-year and 50-year Return Periods (RPs) for historical (1960–2014) and future (2015–2099) periods. Results indicate that significant differences exist in the compound flood characteristics between historical and future periods. In the 90^th percentile scenario, all three flood drivers exhibited higher future thresholds, suggesting an increased risk of compound extreme flood events. The probability of concurrent heavy rainfall and high discharge events increased by 40.9%, whereas the probability of simultaneous high storm surge and high discharge events increased by 58.3%. Despite the potential reduction in extreme event intensity, the frequency of compounding events has increased significantly. Extreme value analysis revealed that extreme storm surges and upstream discharge events became more severe and extreme rainfall events showed a decreasing trend. For high RPs (e.g., 50-year events), the projected storm tides and upstream discharges significantly exceeded historical levels. Specifically, projected increases in storm surge levels (+0.24 m under 50-year RP) and upstream discharge (+1,271.13 m³/s) are offset by a 16.5% decline in 100-year accumulated rainfall for Haikou when compared to historical period. Third, compound flood simulations showed that under the 10-year RP scenario, the total inundation area slightly increased, but the flood volume and maximum depth decreased, indicating the stabilization of the flood hazard. However, under the 50-year RP scenario, both the inundation area and flood volume increased substantially, with the area experiencing flood depths greater than 3 m expanding by 56.5%. The most severe flooding occurred along the northern coastal areas and banks of the Nandu River, where the inundation extent and flood severity increased markedly. These findings provide valuable insights for flood risk assessments and adaptive planning in coastal cities facing intensifying climate-induced hazards.

Industry and occupation are closely interwoven. Focusing solely on industrial structure cannot adequately address regional development challenges. Multifactor cross-relatedness provides a theoretical foundation for understanding the interplay between regional industrial and occupational relatedness. This study introduces the concept of industry-occupation cross-relatedness to investigate how urban labor skills influence industrial evolution in China, while also analyzing regional and industrial heterogeneity. Specifically, we construct a city-industry panel dataset using Chinese customs import-export data (2000-2015), census data (2000 and 2010), and population sampling survey data (2005 and 2015). This dataset enables us to analyze the structural evolution of the spatial network of industry-occupation interactions. Employing a linear probability model, we examine the impact of industrial relatedness and industry–occupation cross-relatedness on industrial evolution, with a focus on regional and industrial heterogeneity. The main findings are as follows: (1) The cross-relatedness between industries and occupations in Chinese cities has increased over time. Average industrial relatedness density exhibits a significant increase, particularly in eastern regions. Cross-relatedness density shows an increase in regions with moderate cross-relatedness values but a noticeable decline in the northeastern regions. The spatial distribution of the average industrial relatedness density and industry-occupation cross-relatedness density exhibits high consistency. (2) Industry-occupation interaction drives industrial evolution in Chinese cities. A path-dependence effect is evident in industrial evolution. The synergy between industries and occupations enhances regional industrial comparative advantages. The spillover effect of locally related industries strongly supports industrial comparative advantages, second only to the influence of pre-existing industrial foundations. (3) Regional and industrial heterogeneity is notable. Regionally, the probability of previous-stage dominant industries remaining dominant decreases from east to west. Industrial path dependence also declines from east to west, while cross-relatedness has a more significant impact on forming comparative advantages in eastern regions, followed by central regions. Larger cities exhibit a higher probability of path breakthroughs through skill-relatedness. Industrial heterogeneity shows that skill structures contribute the most to the diversification of technology-intensive industries, followed by capital- and labor-intensive industries. This study also provides several policy implications. First, regions should adhere to the principles of economic and social development to formulate reasonable talent demand. Second, vocational education should undergo deeper supply-side structural reforms to better meet industry needs. Finally, cities in different regions should adopt differentiated industrial and labor policies to align with their unique contexts and development stages. Compared with previous research, the marginal contributions of this study are threefold. First, it emphasizes the critical role of human capital as a foundation for high-quality regional industrial development, offering theoretical and methodological insights for promoting employment and addressing structural employment challenges. Second, it explores the bidirectional influence of labor and industry from a multi-factor interaction perspective, advancing research integration in evolutionary economic, industrial, and labor geography. Third, by focusing on coordinated development between industries and occupations, this study provides practical insights for aligning industrial and labor policies, facilitating the deep integration of industrial and talent chains to achieve high-quality development.

The current risk assessment of single landslides and debris flow disasters ignores the increasing supply, accumulation, and superposition amplification effects of disasters from top to bottom, resulting in a serious underestimation of the risk of landslide-debris flow disaster chains. This study takes the "2010.9.21" mega-landslide debris flow disaster in the Magui River Basin in Gaozhou, western Guangdong as a case study. A landslide-debris flow disaster chain risk assessment index system, guided by the cumulative amplification effect, was established from the perspective of disaster chain initiation, transmission, and cumulative amplification. A comprehensive index model was used to scientifically evaluate the risk of the disaster chain, and actual investigation results were used for verification. The results are as follows: 1) The landslide-debris flow disaster chain in the Magui River Basin is characterized by multi-ditch collection, high impact force, and major terrain fluctuation. The landslide in the starting area is directly transformed into a debris flow during the instability process and flows into the debris flow branch ditch over a short distance. Several debris-flow branches received landslides along the path, converging into the main ditch. After potential energy accelerates through the circulation area, the flow rushes out of the ditch, leading to a large area of fan-shaped accumulations in the low- and slow-terrain areas, causing serious damage to residential houses and farmland. 2) A total of one small watershed unit carries an extremely high risk, accounting for 2.04% of the total number of small watersheds. The extremely high-risk area covers 3.64 km², accounting for 2.24% of the total area. It is mainly distributed in a small watershed east of Liutang Village. There were eight small watersheds in high-risk areas, accounting for 16.33% of the total small watershed number. The dangerous area covers an area of 20.50 km², accounting for 12.62% of the total area. Most watersheds are concentrated in Langlian Village, Shenshui Village, Makeng Village, and northern Longkeng Village in the Middle East region of Liutang Village. The number of small watersheds in the medium-risk area was 18, accounting for 36.73% of all the small watersheds, and the total area covered by dangerous area was 81.22 km², accounting for approximately 44.90% of the total study area. The medium-risk areas were widely distributed within the scope of the study, especially in the southern part of Longkeng Village, most of the small watersheds of Liutang Village, the southern part of Langlian Village, Magui Village, Chengdong Village, Gancheng Village, the central area of Daxi Village, Houyuan Village, and Shanxin Village. There were 22 small watersheds in the low-risk area, accounting for 48.98% of the total number of small watersheds. The low-risk area covers 57.07 km², accounting for 35.13% of the total study area. It is mainly distributed in the small watersheds of Shanxin Village, Houyuan Village South, Yadong Village South, and Zhoukeng Village in the northeast; Daxi Village in the west; Hemudong Village in the central region; and Longkeng Village in the south. 3) The evaluation results of this study were verified using actual investigation data, which showed high consistency with field survey results, thereby confirming the credibility of the method employed in this study. The index system and evaluation approach for the risk assessment of mass landslide-debris flow disaster chains proposed in this paper can serve as a reference for risk studies of landslide-debris flow disaster chains in South China and other similar areas.

The impact of international geopolitics on transportation network patterns is an important topic in economics and transportation geography. Previous studies have often overlooked the diversity of domestic crude oil transportation among countries due to limitations in statistical data, focusing mainly on national-level node selection. Additionally, the evolution of network characteristics is predominantly analyzed through long-term descriptive approaches, lacking specific contextual analyses of network evolution. This study investigates changes in the maritime crude oil transportation network along the Belt and Road Initiative (BRI) routes against the backdrop of the Russia-Ukraine conflict, offering new evidence for research in this field. Using AIS(Automatic Identification System) ship trajectory big data and complex network analysis methods, this study analyzes the overall characteristics, node importance, core-periphery structure, and clustering of the maritime crude oil transportation network along the BRI routes from 2019 to 2022. Furthermore, it examines the impact of maritime network changes on the stability of crude oil imports to China. Our findings reveal several key points. 1) The closeness, strength, and accessibility of network connections between ports show an initial increase followed by a decreasing trend. The direction of the overall network characteristic changes in the periods 2019-2020 and 2020-2022 are opposite, with a greater magnitude in the latter period. In recent years, particularly following the Russia-Ukraine conflict, the scale-free nature of the network has continuously increased, accompanied by an increase in the concentration of crude oil shipping connections. This concentration, notably evident towards export destinations, reflects a shifting pattern in the crude oil supply demand landscape, spatially manifested as China replacing some of its crude oil shipping connections with the Middle East, thus reducing its reliance on Russian crude oil shipments. 2) The comprehensive importance of export ports has become more prominent, with a slight decrease followed by a significant increase in recent years. The importance of ports in Russia's Far East region has notably increased, reflecting a shift in Russia's crude oil export center eastward after the Russia-Ukraine conflict. The network structure transitioned from single-core to multi-core to single-core with export ports occupying more central layers. 3) Initially, there was a continuation of the core-periphery and clustering structures, but later, there was significant structural reorganization. In 2020, the core-periphery structure and clustering in terms of core ports, geographical distribution, and cluster size were largely the same as corresponding clusters in 2019; however, by 2022, a noticeable structural reorganization emerged. 4) Changes in maritime networks significantly and heterogeneously affect China's crude oil import stability. At the network level, import stability initially increases and then decreases, with the decline in the later period far exceeding that in the earlier period. At the port level, compared to ports around Bohai Bay and the Yangtze River Delta, ports along the southeastern coast, Pearl River Delta, and southwestern coast were more affected by the Russia-Ukraine conflict in terms of crude oil import stability. China responded to the risk of instability in its crude oil import network against the backdrop of the Russia-Ukraine conflict by adjusting its sources and proportions of imports from different ports. This study provides scientific evidence for a deeper understanding of the impact of geopolitical events on China's oil imports and the formulation of national energy security strategies.

In the context of global climate change and accelerated urbanization, coastal cities in China are facing increasing risks from compound disasters caused by the co-occurrence of extreme rainfall and high tide levels. These risks pose substantial threats to urban development and the safety of residents' lives and property. Therefore, it is essential to reasonably calculate the designed co-occurrence probability of rainfall and tide levels under different standards for the planning and design of flood control and drainage systems in coastal cities. In this study, we selected 105 drainage zones in Guangzhou, China with the aim of analyzing the spatial distribution characteristics and co-occurrence risk of extreme rainfall and high tide levels. Based on tide level and elevation data from Guangzhou, the 105 flood-prone zones were divided into 37 areas unaffected by high tide levels and 68 areas affected by high tide levels. Rainfall sequences and corresponding tide-level sequences for each zone were selected using the peak-over-threshold sampling method. On this basis, the designed combinations of rainfall and tide levels under different joint return periods were calculated using Copula functions and the co-frequency method, and their spatial distribution characteristics were analyzed. Our results show that, influenced by factors such as rainfall volume and elevation, the joint return periods of extreme rainfall and high tide levels for 50-year, 100-year, and 200-year events were approximately 30~35 years, 50~58 years, and 74~94 years, respectively. This indicates that the designed return periods for extreme rainfall and high tide levels individually were lower than their corresponding joint return periods, highlighting the obvious amplification effect of the co-occurrence of rainfall and tide levels. The designed storm intensity generally decreased from north to south, reflecting the spatial variability of rainfall patterns across the city. The probability of a 100-year daily rainfall event coinciding with a 100-year high tide level in Guangzhou showed an increasing trend from north to south, underscoring the heightened vulnerability of the southern regions to compound flooding. Additionally, areas in Guangzhou farther from the estuary were less affected by high tide levels than the central and southern regions, resulting in relatively lower risks of rainfall–tide level co-occurrence. This spatial heterogeneity emphasizes the need for region-specific flood control strategies. Our findings provide valuable insight into the spatial distribution and risk of compound flooding in Guangzhou, China. By quantifying the joint probabilities of extreme rainfall and tidal events, we offered a scientific basis for optimizing flood control and drainage infrastructure. The results of this study can guide policymakers and urban planners in developing targeted measures to mitigate the impacts of compound disasters, thereby enhancing the resilience of coastal cities to climate change and urbanization. This study not only contributes to the understanding of flood risks in Guangzhou but also provides a methodological framework that can be applied to other coastal cities facing similar challenges. The research outcomes serve as a critical reference for the planning and design of flood control and drainage systems in Guangzhou, offering practical solutions to reduce the risks posed by compound disasters and to safeguard urban development and public safety.

Under the influence of climate change, drought poses a novel and urgent challenge to sustainable development in the humid regions of southern China. Therefore, it is essential to estimate future drought changes and population exposure comprehensively. Using CMIP6 climate models and population forecast data, we estimated drought variations and population exposure in the Xijiang River Basin of Guangxi from 2021 to 2100 under three scenarios of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The findings are as follows: (1) By employing Taylor diagrams to evaluate the multi-model ensemble mean method (MME) of 18 CMIP6 climate models, we found that the method effectively simulated temperature and precipitation in the Xijiang River Basin, reducing the uncertainty associated with single-model simulations. Under all future scenarios, temperature and precipitation in the Xijiang River Basin are projected to increase, with effects of climate change becoming more pronounced in this region. (2) Using the Standardized Precipitation Evapotranspiration Index (SPEI), we observed a significant increasing trend in aridification in both historical and future periods. Significant differences and complex changes in the rate, occurrence time, frequency, intensity, and other drought characteristics were observed under various scenarios. Droughts are expected to be alleviated under low-emission scenarios but intensify comprehensively under high-emission scenarios. (3) The spatial variability of drought in the Xijiang River Basin will differ significantly under different scenarios. In low-emission scenarios, the intensity and extent of droughts nearly disappear in the long-term. Under medium-emission scenarios, the intensity and extent of droughts may increase. Drought events in this region are severe and worsen comprehensively, under high-emission scenarios, and the long-term impact will be extensive and serious. Drought events in this region are influenced by global climate change and are closely linked to the specific socioeconomic development path of the area. (4) Future, population exposure to drought will be highly correlated with different emission scenarios in the Xijiang River Basin. Under low-emission scenarios, the total population affected by droughts decreased. However, under medium-emission scenarios, the population exposed to each drought level will substantially increase in the medium- to long-term, and the spatial distribution will be more complex. In high-emission scenarios, although the exposure of populations may decrease in the short-term owing to extreme weather events, it will sharply increase in the medium- to long-term, especially with a sharp rise in exposure to severe droughts in the long-term. Climate change is the main factor affecting population exposure to drought; however, emission strategies are fundamental drivers, and population growth and structural changes cannot be ignored. Therefore, emission reduction measures play a key role in mitigating the risk of drought under the impact of global climate change. It is urgent to promote the transformation of low-carbon development models, strengthen regional coordination, and formulate adaptive strategies. This study provides scientific evidence for water resource management and drought response strategies in the Xijiang River Basin, and is of great significance for regional sustainable development.

With the acceleration of climate change and urbanization in recent years, extreme rainstorms and urban flooding have increasingly threatened urban safety. Their impact on cultural, commercial, and tourism industries is widespread and significant, often leading to traffic paralysis, closure of tourist attractions, business shutdowns, and passenger stranding. In severe cases, this can endanger personal safety and result in significant economic losses. Shanghai, a representative coastal tourist city in China, is highly prone to rainstorm-induced flooding events from June to October each year due to the Meiyu front, extreme rainstorms, and typhoons. Conducting flood inundation simulations in Shanghai during the flood season is essential to identify high-impact urban flood areas and evaluate flood effects on densely populated cultural, commercial, and tourism hubs. This study used daily rainfall data from Shanghai between 1990 and 2020 to construct nine rainstorm scenarios based on three flood season periods (Meiyu, midsummer, and autumn) and three rainfall thresholds (maximum, 99th, and 95th percentiles). Using the SCS-CN and Mike21 hydrodynamic models for urban rainstorm flood simulations, a fuzzy comprehensive evaluation index system was developed based on a combination of Analytic Hierarchy Process(AHP) and Entropy Weighting Method (EWM) to assess the impact of flooding on Shanghai's cultural and tourism cluster areas. Results indicate the following: (1) Shanghai experiences the highest impact from rainstorm-induced flooding in the midsummer period. In the 95th percentile scenario, suburban areas experience minor flooding, whereas in the maximum value scenario, central urban areas experience a significant increase in flooding impact. (2) Control rules effectively improved the rationality and adaptability of the flood impact evaluation system. Resident and transient populations are key factors in evaluating flood impact. The flood impacts in Shanghai's cultural and tourism clusters showed significant spatial and temporal gradient characteristics, with medium-to-high- and high-impact areas primarily concentrated in the central urban cultural and tourism clusters. (3) Midsummer had the largest medium-to-high and high-impact zones, reaching 3.1 km² (8.79% of the total area), followed by the Meiyu period, whereas the autumn period has the smallest impact. (4) During midsummer, the largest proportion of high-impact areas was found in street- and road-type clusters, followed by waterfront leisure and comprehensive cultural tourism clusters, with areas accounting for 27.52%, 8.30%, and 6.44%, respectively. Cultural and tourism clusters should strengthen early warning, regulation, and preventive measures based on seasonal variations, especially during midsummer, when effective countermeasures must be implemented to reduce flooding impacts on visitor experience and regional safety. This study provides valuable insights for urban flood forecasting, early warning, and emergency response, as well as recommendations for sustainable development of the urban cultural, commercial, and tourism industries.

In recent years, the number of Chinese university graduates has been progressively increasing, and their employment situation has become increasingly poor under the influences of the COVID-19 epidemic and downward economic pressure. The employment issue of university graduates has attracted considerable attention. The migration of university graduates is synthetically affected by multiple factors including individual-, family-, and city-level factors. A systematic theoretical framework of the influencing mechanism of the migration of university graduates, which combines these factors, is lacking. Moreover, extant studies have scarcely investigated the migration of university graduates from the overall perspective of family capital, let alone different factors considered by graduates with different family capital levels in the migration decision-making process. The role of human capital in the relationship between the family capital and migration of graduates still remains unknown. To address this gap, based on first-hand survey data on the migration of new graduates from 78 universities in eight cities in 2022, this study employs a nested logit model to examine the influencing factors on the intercity migration of university graduates from both the individual and city levels, and reveals the dual influencing mechanism of family capital (economic, cultural, and social capital) and human capital (education qualification, university type, student cadre status, academic records, certificates, and internship experience). The results demonstrate that: 1) Family capital exerts a significantly positive impact on the intercity migration of university graduates. Graduates with higher levels of family capital are more inclined to move to higher-level cities. Compared with graduates whose parents have an annual income level below 90 thousand RMB and highest education qualification below a college degree, graduates whose parents have an annual income level above 90 thousand RMB and highest education qualification above a college degree are more likely to flow to first-tier and second-tier cities. 2) The influence of family capital on the migration of graduates presents significant heterogeneity. When choosing employment cities, graduates with a higher level of family capital pay more attention to urban economic and amenity factors. Compared with graduates with lower levels of family capital (represented by lower parental income levels, lower educational qualification levels, and parents' non-managerial or professional occupations), graduates with higher levels of family capital (opposite to their counterparts) are more affected by income levels, living costs, environmental quality, medical resources, and cultural resources, in selecting employment cities. 3) The human capital of graduates plays both a positive mediating role and a certain degree of a negative moderating role in the impact of family capital on the migration of graduates. All the variables of human capital play a positive mediating role, that is, family capital positively influences the migration of graduates by affecting their human capital; however, some variables of human capital (student cadre status, academic records, certificates, and internship experience) play a negative moderating role, that is, the human capital accumulated in the university can weaken the impacts of family capital on the migration of graduates. By introducing a spatial perspective, this study provides not only empirical evidence for the response to the social concern about whether getting good jobs depends on family background or personal efforts, but also scientific references for promoting the full employment of graduates and guiding the rational talent flows.

Typhoons are among the most destructive natural disasters affecting China's coastal regions, often resulting in substantial economic loss and casualties. The annual average Direct Economic Loss (DEL) caused by typhoon disasters in China exceeds 60 billion yuan, accounting for 10%-30% of the DEL caused by all disasters each year. Consequently, the accurate assessment and prediction of typhoon-induced DEL are essential for improving disaster mitigation strategies and optimizing resource allocation. Rapid development of artificial intelligence and the growth of multi-source spatiotemporal big data have introduced data-driven methods for assessing disaster losses. These methods have the advantage of using large samples to improve adaptability and consider more risk factors. In this study, DELs of 30 typhoon events in Fujian Province at the county level and a total of 911 samples were collected from 2009 to 2021 to establish an assessment model. Owing to the large range of the DEL in different districts and counties during the same typhoon, the logarithm of the DEL was used as the model output. This study included three steps for constructing the model. First, 24 influencing factors of typhoons, including disaster-inducing factors, disaster-forming environmental factors, and disaster-bearing body exposure factors, were calculated using the Pearson correlation coefficient and variance inflation coefficient to analyze the multicollinearity effect, and 20 key factors were selected to assess the DEL. Second, a LightGBM-based model is developed using the selected indicator factors as model inputs. Of the 911 samples, 734 were used to train the model, and 177 were used for validation. Finally, Super Typhoon Meranti was used as a case study to evaluate the applicability of the model in the dynamic DEL assessment of a typhoon. This study evaluated predictive performance of the model using five indicators: the Pearson correlation coefficient (R), coefficient of determination (R ²), mean squared error, mean absolute error, and median absolute error. The importance of LightGBM factors shows that the maximum daily wind speed, river network density, maximum daily precipitation, cumulative precipitation, and GDP per unit area are the primary determinants of typhoon-induced economic losses in Fujian Province. In the training set, R between the predicted results of the model and the actual loss was 0.836, and R ² was 0.66, indicating good fitting ability. In real-world applications, the proposed model effectively captured the spatial distribution of losses from Typhoon Meranti, demonstrating its potential for disaster loss prediction. This study provides valuable insights into typhoon risk assessment and emergency management in Fujian Province and other coastal areas. We sorted the relevant research literature and found that economic loss assessment is more difficult than population, housing, and other loss assessments because economic loss is a comprehensive statistical indicator in China. Therefore, we drew on the method of processing DEL as logarithms from the literature. By comparing with other studies, the results of this study can improve model performance in terms of data quality inspection and sample size.

As China transitions to high-quality economic development, the innovation-driven growth model places increasing demand on the high-tech industry, highlighting the critical role of high-tech talent in enhancing regional competitiveness. Existing studies have primarily focused on talent distribution patterns at national or provincial levels, with limited exploration at the city level. Furthermore, most studies concentrate on the distribution patterns and influencing factors at a single stage, treating talented individuals as a homogeneous group without a comparative analysis of the differences in talent distribution across employment stages, educational qualifications, or school levels. To address these gaps, in this study, we employed big data on high-tech talent resumes spanning 2003 to 2021 using spatial analysis techniques and spatial econometric models to examine the spatial distribution patterns and influencing factors of high-tech talent across different employment stages at the urban scale. Additionally, we explored the heterogeneity of the influencing factors at various educational qualifications and institutional levels. The results reveal the following. First, high-tech talents in China are predominantly clustered in economically developed eastern coastal regions, provincial capitals, municipalities, and urban agglomerations. Compared with the initial employment stage, the spatial concentration of high-tech individuals intensified during the current employment stage. The number of dense clusters in the eastern coastal regions decreased, whereas the number of non-dense clusters in the western and northeastern regions increased. A strengthened "high-high" clustering pattern around central cities in urban agglomerations reflects the increasing preference for high-tech talents in such areas. Second, the factors influencing the distribution of talented individuals differ significantly across employment stages, and are shaped by economic conditions, amenities, talent policies, and administrative hierarchies. Economic factors, particularly income levels, play a dominant role during the initial employment stage, whereas amenities become more influential in the current stage, reflecting a growing focus on quality of life as material needs are met. Talent policies significantly affected talent distribution at both stages. Third, the distribution of high-tech individuals exhibits heterogeneity across educational qualifications and university tiers. High human capital groups, such as postgraduate degree holders and graduates from "double first-class" universities, demonstrate a stronger preference for amenity factors, particularly during the current employment stage. These findings have important implications for understanding talent distribution dynamics, optimizing talent allocation, and fostering supportive environments for talent development. This underscores the urgent need for effective management of high-tech talent mobility, formulation of targeted and actionable talent policies, and enhancement of talent governance modernization.

Research on urban spatial networks based on "flow" data has become a new paradigm in the assessment of urban spatial connections and the delineation of metropolitan influence areas in urban geography and territorial spatial planning. Research on urban connections in Chongqing focuses primarily on districts and counties within the city's administrative region, utilizing passenger or cargo flow data to study the spatial structure of the network within Chongqing. However, few studies have been conducted on cross-provincial administrative regions between Chongqing and its neighboring areas, which does not align with the actual influence of Chongqing's metropolitan area. Based on highway traffic passenger flow data, this study employs social network and GIS spatial analysis methods to study the urban network spatial structure between Chongqing and its neighboring areas from the perspective of passenger flow connections. The results indicate the following: (1) Chongqing's central urban area serves as the absolute core of the urban network, with Changshou, Jiangjin, and Bishan as important nodal cities. Fuling, Bishan, and Changshou exhibit notable accessibility within the network, while Fuling, Qianjiang, Jiangjin, and Wanzhou play prominent intermediary roles. There are no prominent regional nodes outside Chongqing's administrative area, and the growth poles for the development of the Chengdu-Chongqing Economic Circle need further cultivation. (2) The passenger flow network between Chongqing and its neighboring areas exhibits a three-tiered axial connection, with the overall network displaying a distinct radial characteristic. The urban clusters in northeastern Chongqing form a distinct band-shaped axis along the Yangtze River with Chongqing. The urban clusters in southeast Chongqing and their neighboring areas exhibit radial axes, with relatively weak connections to the central urban area. Some areas in Guang'an and Dazhou have overcome provincial administrative boundaries, and the network hierarchy is distributed according to "4(level 1)+15(level 2)+18(level 3)." (3) The cohesive subgroups between Chongqing and its neighboring areas demonstrate a high degree of geographical proximity, forming a "core-periphery" structure. This reflects the significant influence that factors such as spatial distance and road extensions exercise on the road passenger transport network. Conducting research on the spatial structure of cross-administrative urban networks from the perspective of highway passenger flow holds significant theoretical and practical value for enriching research on the spatial structure of cross-administrative metropolitan areas and promoting the linkage between Chongqing's metropolitan area and its surrounding regions.

With the rapid development of urban regionalization and networking of high-speed transport, intercity travel has increasingly played a key role in China's economic and social development and socioeconomic functional connections. However, amidst global change and uncertainty, the event disturbance-oriented theory and empirical research on intercity travel is still insufficient to improve the ability of transportation systems to cope with disturbances. Since uncertainty is prevalent in transport operations, improving Intercity Travel Behavior Resilience (ITBR) and grasping the spatiotemporal pattern of demand-side intercity travel fluctuation to restrain risk is essential for resilient transport construction. Based on related theories and analysis methods of spatial interaction and intercity travel, this study refines the definition of ITBR. A measurement model of ITBR was constructed based on long-term intercity travel data and the general properties of disturbance events. Furthermore, COVID-19 disturbance was used as a case study to reveal the adaptive pattern of intercity travel and the spatiotemporal pattern of ITBR over three years. The results show that the evaluation of ITBR based on seasonal and holiday trends reveal spatiotemporal patterns of intercity travel fluctuations influenced by disturbance events. The impact of the COVID-19 pandemic on intercity travel is as follows: peak of the national pandemic > peak of the Omicron variant > peak of the multipoint fluctuation. The intensity of intercity travel decreased linearly with an increase in distance, and intercity travel during the three stages is lost by 0.86, 1.03, 1.15 percentage points, respectively, with an increase of 50 km. The average intercity travel distances of residents in these three stages were shortened by 52.55, 65.31, and 105.16 km, respectively. The value of ITBR decreased from the multipoint fluctuation period to the national pandemic period because of the Omicron outbreak. Overall, ITBR showed a gradual increasing trend during the study period. Meanwhile, ITBR in these three stages was characterized by obvious spatial differentiation and regional agglomeration. Compared to existing research, this study further expands existing research focusing on intra-city travel behavior resilience by exploring ITBR on the regional scale.