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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

Understanding the factors influencing the return of high-level scientific and technological talent from outside China's customs borders is important for optimizing the policy practice of introducing high-level talent from abroad. Based on the biographical information of 1,248 high-level scientific and technological talents who had studied or worked outside China's customs borders and then returned to China to obtain the National Outstanding Young Scientist Fund from 2009 to 2020, this study analyzes the characteristics of the spatial pattern of the return of high-level scientific and technological talents from outside China's customs borders and their influencing factors using social network analysis and a nested logit model, respectively. The results reveal the following: The outflow city network for talents from outside China's customs borders centers around Hong Kong (China) and Cambridge (USA), while the inflow city network is centered on Beijing and Shanghai. There are notable regional differences in the distribution of cities where these talents return.Cambridge (USA) is the primary source of returning talents to Beijing and Shanghai, while Singapore is the main source of returning talents to Nanjing. For Guangzhou and Wuhan, Hong Kong (China) serves as the main source of returning talents.At the individual level, academic ties and the level of talent introduction programs significantly influence the choice of cities for returning talents. At the macro level, high salaries, significant investment in science and technology, and a high concentration of top-tier scientific research platforms in Chinese cities are the primary factors attracting high-level talents from outside China's customs borders. Additionally, the quality of educational service facilities and favorable climate conditions also play a significant role in influencing city choices.Individual heterogeneity exists in the influence of urban macro-factors on location choices for talent return. Male talents, those with mobility experience, or those who have resided outside China's customs borders for an extended period tend to place greater emphasis on academic and social ties as well as support from high-level talent introduction programs. Conversely, talents returning later are more influenced by higher salary levels and the availability of top-tier scientific research platforms in the city.Compared with existing studies that primarily focus on analyzing the influence mechanisms of talent return driven mainly by policy factors, this study contributes to revealing the joint impact of government policy factors and market-oriented factors on the return of high-level scientific and technological talents.

In the context of intersecting knowledge economies and globalization, attracting international academic migrants has become a crucial external driver for fostering innovation and sustainable development at national and regional levels. The knowledge production activities of international academic migrants in their host countries are embedded within local institutional and cultural environments, and involve multidimensional interactions that engage in economic and sociocultural geographies. However, limited research has examined how different types of collaborative network embeddedness affect the knowledge production of these migrants and the intricate sociocultural mechanisms underlying these dynamics. This study examined network embeddedness in knowledge production by combining the "buzz-pipeline" framework with strong- and weak-tie models, focusing on the structure and characteristics of three types of collaborative networks: local buzz collaborations, domestic pipeline collaborations, and international pipeline collaborations. In-depth interviews with international academics provided qualitative insights into the sociocultural dynamics that drive network embeddedness. Key findings include: (1) Spatial characteristics: Local buzz collaborations among international academics in China are largely concentrated in cities, such as Suzhou, Ningbo, Shantou, and Shenzhen, underscoring the importance of localized networks. Regional hubs, such as Guangzhou and Shanghai play a pivotal role in facilitating cross-city exchanges within urban clusters. Domestic pipeline collaborations are predominantly found in provincial capitals, with Beijing leading the most. International pipeline collaborations are mainly oriented toward developed countries, particularly in Europe and the United States. (2) Network relationships: Domestic pipeline collaborations positively influence both local buzz and international pipeline collaborations. However, a negative correlation exists between local buzz and international pipeline collaboration, suggesting that scholars engaged in strong local networks may be less likely to establish extensive international ties. (3) Impact of network embeddedness: Both "buzz" (regional cooperation) and "pipeline" (cross-regional and cross-national cooperation) have a significant positive impact on the knowledge production of international scholars. However, in terms of knowledge spillover, domestic pipeline collaborations yielded better results in terms of paper output and quality than that by local buzz and international pipeline networks. Strong-tie local buzz networks are the most effective models for maintaining the research productivity. (4) Social and cultural mechanisms: Sino-foreign cooperative universities play an important role in fostering strong institutional ties, resulting in a "broad weak-tie domestic pipeline and a stable strong-tie local buzz" model that maximizes knowledge production. On one hand, Local weak-tie collaborations serve as bridges between international scholars and key research resources. On the other hand, international collaboration networks favor strong-tie international pipelines because of the difficulties international scholars face in completely embedding them into local cultural and institutional contexts. Simultaneously, a stable international collaboration network, particularly one established prior to their arrival in China, can enhance the competitiveness of academic community in China. Concludingly, this study contributes to the literature by offering a nuanced understanding of the multi-scalar and sociocultural dimensions of collaborative networks that shape the knowledge production of international academic migrants. This study provides valuable policy insights for enhancing the ability of China to attract, integrate, and retain global talent. Strengthening institutional support, fostering deeper local embeddedness, and facilitating cross-regional and international collaborations are essential for consolidating the position of China in the global knowledge economy.

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.

Social changes have triggered a drive for change among young people, leading to a spatial reverse mobility trend among university graduates in China, amid shifts in both domestic and international contexts. Using university graduates in Guangzhou as a case study, this study employed a mixed-methods approach, integrating questionnaire surveys, cyber-ethnography, and in-depth interviews. Grounded in spatial and social mobility theories, this study explores the motivations, processes, and outcomes of reverse mobility from the perspectives of structural factors and individual agencies. The findings reveal that: (1) the unequal distribution of spatial resources, shifts in mobility within modern society, and the unique socio-historical environment of Generation Z graduates jointly shape their reverse mobility preferences, with the unequal distribution of spatial resources driving university graduates to consider their location choices from both urban and individual perspectives. Resources available in cities are uneven, and individuals have varying access to these resources. They need to uncover comparative advantages in order to "overtake on a curve." The transformation of societal mobility further facilitates the diversification of movement. Spatially, rapid intercity movement enabled by technology allows some local resources, previously exclusive to first-tier cities, to be more easily accessed across regions. The rapid flow of information further amplifies regional disparities and provides a multifaceted understanding of different cities. Graduates from Generation Z, who are in a unique historical era and social structure, increasingly focus on noneconomic factors such as class mobility, quality of life, and cultural consumption when making employment decisions, no longer simply considering economic opportunities. (2) The factors influencing the reverse migration of university graduates mainly include the realization of their economic, social, and educational cultural capital, as well as considerations of the cost of urban living and the overall environment. According to survey results, university graduates had a clear understanding of the differences between cities. They recognized that first-tier cities offer better matching job opportunities for their fields and higher social status in the future, along with superior cultural resources and public services. However, they are also aware that competition in first-tier cities is intense, the possibility of upward social mobility is relatively low, housing costs are high, and quality of life is lower compared to that in non-first-tier cities. (3) The interviews further confirmed that reverse spatial mobility to lower-tier cities does not necessarily signify a decline in the social status of graduates. Many adapt well to new locales, embedding themselves both spatially and culturally and constructing new social networks that afford a quality of life that is not easily attainable in larger cities. This study developed a new framework for understanding the mechanisms underlying university graduates' reverse mobility by systematically examining pre-migration motivations and post-migration local negotiations and adaptation. By taking a more comprehensive view encompassing economic, non-economic, structural, and agentic factors, this research deepens our understanding of man-milieu interactions during the social transition period. It offers insights into local development and talent attraction strategies and provides policy recommendations to promote balanced urban development in a highly mobile society.

Urbanization in China has entered a new phase that emphasizes both scale expansion and quality improvement. This has led to demands on urban functional structures and rational urban planning. Street space serves as a vital spatial carrier for meeting urban residents' needs, such as travel, shopping, and leisure. It is comprised of urban roads and their ancillary facilities, buildings along the route, and many other elements. However, existing studies typically focus on the traffic function of urban roads, overlooking other functional aspects of street space as complex public activity areas, thereby hindering the optimization of street space quality. Therefore, there is a need to propose a classification method for the urban street space functions. Given the proliferation of taxi trajectory data and street view imagery, street space can be described in detail from the citizens' perspectives. Therefore, this study proposes a street space classification method that integrates taxi trajectory and street view imagery to delineate urban functions. The dynamic travel characteristics of urban residents in the street space are constructed using the taxi trajectory, including the number of trajectories passing by the street, the number of origin points on the street, and the number of destination points on the street. The physical environment characteristics of the street are constructed using the street view image, which contains single element street features, combined element street features, and overall element street features. Subsequently, based on residents' dynamic travel characteristics and the physical environmental characteristics of street space, the K-Means method is utilized to allocate street spaces with similar urban functions into the same clusters. Taking Bao'an District of Shenzhen as a case study, it was found that clustering with K=3 yielded the most interpretable results. Subsequently, based on street characteristics and auxiliary POI information (including POI density and POI enrichment index), street spaces were successfully classified into three urban functional types: commercial, traffic, and residential. Further analysis was conducted by integrating street view images, the Gaode map, and the 24-hours distribution of the trajectory, which validated the reliability of the classification results, achieving an experimental accuracy of 81%. Additionally, this study constructed two sets of ablation experiments for a comparative analysis of the effectiveness of the characteristics extracted in this study. The results of street space function classification based on taxi trajectory data and street view image data showed classification accuracies of 67% and 34%, respectively, which are lower than the classification effect of combining the two data sources. The results of street space classification based on feature selection from taxi trajectory data indicated that the classification accuracy considering only the weekday taxi trajectory features was 69%, and considering only the taxi trajectory features during T08:00-22:00 on both weekdays and weekends was 73%, which is lower than the classification effect of using three types of trajectory features throughout the entire day. By integrating the characteristics extracted from multiple data sources, the methods took into account both pedestrian and vehicle traffic functions, as well as the diversity of the streetscape environment and residents' activities. The identification results can provide references for the design and quality optimization of street space, as well as detailed investigations into urban functional areas such as residential, commercial, and traffic street space.

The Fourteenth Five-Year Plan of China has proposed the acceleration of the construction of a strong transport nation. The Outline for the Construction of a Powerful Transportation State emphasizes that it is necessary to focus on the general objective of the construction of a powerful transportation state and create a "123 traveling and transportation circle in the country" and reach the new standard of 1-hour commuting metropolitan area, 2-hour connecting urban agglomeration, and 3-hour coverage of the major cities in the country, which determines the importance of the accessibility of urban agglomeration in the strategy. The high-speed railway network in the twin-city economic circle of the Chengdu-Chongqing region enhances inter-city accessibility and has a spillover effect on socioeconomic development. Starting with HSR network accessibility, in this study, the evolution of the accessibility pattern after the opening of the HSR in 2015, 2020, and 2025 was analyzed using the shortest inter-city travel time, weighted average travel time, and daily accessibility index. Based on the gravity model used to measure the economic reinforcement effect generated by HSR network accessibility, the spatial Durbin model was used to explore the spillover effect of HSR network accessibility on the tertiary industry's economic development from 2015 to 2020. The study results demonstrate that the HSR network improves the level of urban accessibility, narrows the gap of accessibility level between cities, weakens the regional accessibility circle structure, and exhibits significant corridor effect. The HSR network generates a significant spatiotemporal convergence effect. The direction of spatiotemporal convergence of core cities is to expand uniformly to their surroundings, and the direction of expansion of edge cities is mainly to spread along newly opened HSR lines in the form of a belt. The improved accessibility of the HSR network will strengthen regional economic ties, narrow the gap between the attractiveness levels of non-core cities, and enhance the twin-core phenomenon. Increased accessibility is conducive to the economic development of the tertiary industry in neighboring cities, with an increase of 1 percentage point in the accessibility of the high-speed rail network in the neighboring region, leading to an increase of 0.3088 in the local tertiary industry. An increase in the tertiary economic level of neighboring cities will inhibit the development of the local tertiary economy, which may be because the Chengdu-Chongqing region is now more competitive than cooperatives in terms of urban relations; therefore, regional development is still in the siphoning stage. The possible contribution of this study is reflected in the use of accessibility as the core research and explanatory variable to explore the economic spillover effects of accessibility of high-speed rail networks in typical case regions. It aims to reveal the impact of high-speed rail network accessibility on the economy and industry, summarize the theory of spillover effect, and provide a theoretical reference for high-speed rail construction, regional planning, and economic layout optimization.

The rapid development of information technology has triggered an explosion of data, marking the era of big data. A wide range of transportation big data has been used in urban space and travel behavior studies since the beginning of this century. Mobile phone signaling data in particular have many advantages: they have prevalent spatial and temporal coverage, high tracking stability, satisfactory resolution, and low cost. The description of urban phenomena and the analysis of their forming mechanisms using mobile phone signaling data are thoroughly studied by previous research. The next course of action is to tackle specific urban problems. This study summarizes the application progress of mobile phone signaling data in job-housing relationships and travel behavior studies, discusses the application prospects of mobile phone signaling data in transportation carbon emissions research based on past applications and the existing literature on low-carbon transportation, and proposes a research framework and several future directions for studies using mobile phone big data to examine job-housing relationships, travel behavior, and transportation carbon emissions. We first provide a brief introduction to the features of mobile phone signaling data in comparison with other commonly used data types, including their type, content, and spatial-temporal resolution. We then review the existing applications in job housing and travel research. Regarding the jobs-housing relationship, prior studies employ mobile phone signaling data to detect the spatial distribution of workplaces and residences of urban dwellers, analyze jobs-housing relationship features and urban spatial structure characteristics, and examine the factors influencing jobs-housing relationships. Regarding travel behavior, studies employ mobile phone signaling data to identify stays and trips, infer trip modes, detect trip routes, and explore the universal laws of human mobility. Next, we also discuss how mobile phone signaling data can be applied to transportation carbon emissions research. Indeed, mobile phone signaling data can be used in the calculation of transportation carbon emissions and analysis of the relationships between urban spatial structure, individual travel behavior, and transportation carbon emissions, and its wide coverage and large sample size can be exploited to fill research gaps and problems that have yet to be resolved using traditional traffic datasets. Finally, we present a research framework underlining the indirect and direct effects of the jobs-housing relationship and travel behavior on transportation carbon emissions. We also propose future directions in study contents and methodological innovations by recommending long time-series longitudinal studies, large-scale comparative studies, and new population and transportation phenomena. We further recommend fusing multi-source big and small data, incorporating machine learning algorithms into traditional statistical analyses, and constructing digital twin models. Examining the jobs–housing relationship, travel behavior, and transport carbon emissions using mobile phone signaling data is essential for clarifying the interactions between urban and regional structures, travel behavior characteristics, and transport carbon emissions. It has important implications for emissions reduction and sustainable development in the context of proposing carbon peaking and carbon neutrality goals.

The construction and development of the metro have reshaped the activity spaces of residents and provided them with abundant opportunities to access various resources. However, existing literature often overlooks the impact of increased access to resources due to the metro system on housing prices. This study, based on residential transaction data from 2017 to 2019 obtained from Beike and metro line and station data from Chengdu, employed network analysis to define a 15-minute activity space for 56,999 residential samples in 2,609 neighborhoods around metro stations, serving as the basis for decomposing resource allocation. Subsequently, an XGBoost model was developed to explore the nonlinear impacts of accessibility and resource allocation at the neighborhood and cross-regional levels on housing prices. The results show that 1) the closeness centrality of the nearest metro station primarily affected housing prices. 2) Additionally, the relative importance of resource allocation on housing prices at the neighborhood and cross-regional levels was 34.60% and 19.55%, respectively, highlighting the significance of resource allocation at the cross-regional level. The development of the metro has reshaped residents' activity spaces, increasing access to various resources. The value of resources obtained through the metro has significantly affected residents' willingness to pay. Furthermore, resource allocation at the two levels reflected a different impact on housing prices. At the neighborhood level, the relative importance of factories and comprehensive hospital facilities was 8.02% and 7.47%, respectively. Meanwhile, the relative importance of parks and comprehensive hospital facilities were 4.85% and 3.86%, respectively, at the cross-regional level. 3) The relationship between accessibility, resource allocation characteristics, and housing prices is complex and nonlinear. Regarding accessibility, the travel time to the nearest metro station and housing prices exhibited a roughly U-shaped relationship, whereas the closeness centrality of the nearest metro station positively affected housing prices. In terms of resource allocation characteristics, different facilities had varying degrees of impact on housing prices at the neighborhood and cross-regional levels. Specifically, parks and primary and secondary schools at the neighborhood level showed an overall positive impact on housing prices, while factories, comprehensive hospitals, and shopping services at the neighborhood level generally suppressed housing prices. Additionally, business and financial service facilities at the neighborhood level showed an inverted U-shaped relationship with housing prices. At the cross-regional level, comprehensive hospital facilities and primary and secondary school facilities had opposite effects on housing prices compared to park facilities and shopping services. The impact of factories at the cross-regional level on housing prices was unstable. Business and financial service facilities at the cross-regional level demonstrated an inverted U-shaped relationship with housing prices, with a suppressive effect when the count of these facilities exceeded 423. The study findings provide valuable insights into sustainable metro development and rational resource allocation.

In the knowledge economy era, technological innovation has become increasingly crucial in forming international competitive advantages and driving national economic development. However, the global distribution of technological innovation remains uneven, with a sustained "core-periphery" structure. From the perspective of evolutionary economic geography, technological development paths are closely linked to the local knowledge base, making it generally difficult to overcome spatial constraints. Consequently, the path-dependent nature of national technological development may reinforce the disadvantageous position of late-developed countries in global technological progress, further widening the technological development gap. In this context, exploring how latecomer countries can achieve innovation breakthroughs spatially becomes essential. From the viewpoint of talent mobility, we employ a global patent database and data on the stock of highly skilled mobile talent to construct a country-time-level econometric model to analyze the impact of birthplace diversity among highly skilled talent on the ability of destination countries to achieve place-breaking innovations. Additionally, we conduct group regression based on multidimensional proximity (geographic, cultural, and institutional) to analyze the varying roles of different talent groups. Our findings reveal that greater diversity in the birthplaces of mobile talent significantly increases the likelihood of destination countries accessing new technological fields unrelated to their existing knowledge base. This suggests that the diversity of talent's country of origin positively influences place-breaking innovations in the destination country. Moreover, we find that multidimensional proximity affects the mechanism by which talent diversity drives place-breaking innovations. Specifically, talent diversity has a stronger impact on breakthroughs between geographically and culturally distant countries, while institutional distance appears less significant. This is likely because mobile talent from geographically distant countries brings more differentiated knowledge and skills, helping destination countries overcome geographical constraints on technological development. In contrast, between culturally closer countries, mobile talent faces fewer communication and integration barriers, which enhances the positive effects of their birthplace diversity. The results suggest that, when introducing highly skilled migrants, countries should focus not only on the scale and quality of talent but also the diversity of talent origins. Actively recruiting highly skilled individuals with diverse skills and differentiated knowledge can foster technological development, help overcome geographical constraints, and achieve technological catch-up. Additionally, countries should cultivate supportive linguistic, cultural, and social environments to ease the cross-cultural adaptation challenges faced by highly skilled immigrants. Simultaneously, countries should promote interaction among diverse talent groups, facilitate communication with local talent, and build affiliation networks to better leverage the benefits of talent diversity. These insights provide important guidance for latecomer countries in designing immigration and innovation policies and offer a new direction for future research. Future studies should explore non-proprietary innovation behaviors, delve into the integration and exchange among different talent groups at the micro level, and examine the underlying mechanisms using both quantitative and qualitative methods.

High-Speed Rail (HSR) stations can influence the expansion of the surrounding construction land. However, relevant studies face three main limitations: influence scope estimation lacking a theoretical foundation, less focus on whether the impacts of HSR stations on construction land expansion vary, and misjudgment of the drivers of HSR stations on construction land expansion. To address these research questions, this study first conducts a literature review to theoretically analyze the influence of HSR stations on the surrounding construction land expansion and then identifies the ideal curve for the influence distance of HSR on construction land expansion based on location theory and distance decay theory. Using the 24 stations of the Hainan Roundabout Railway (HRR) as an example, we revealed differences in the influence of various HRR stations on construction land expansion through GIS technology, buffer analysis, and nonlinear fitting to quantitatively analyze the expansion of construction land around HRR stations, identifying the impact range and direction of different HRR stations on the expansion of construction land. Building on the identification of heterogeneous impact results, the study further employed Geodetector to analyze the factors and reasons for the differentiated results of construction land expansion around different HRR stations from four dimensions: attributes of the socioeconomic environment, location conditions, HRR station attributes, and natural conditions. The results show that: (1) after the construction and operation of each HRR station, the surrounding construction land has expanded; the Hainan Eastern Ring HSR (the East Ring) has increased 1.70 km² around each station per year and the Hainan Western Ring HSR (the West Ring) has increased 1.25 km² around each station per year. (2) The changing trend of construction land expansion around 20 of 24 HRR stations conforms to the ideal curve, with the impact range of construction land expansion concentrated within 0.5-3.5 km, and the influence intensity of impact ranging from 0.06 to 6.64 km². (3) The impact directions of construction land expansion around 20 HRR stations are mainly in three types of directions: "HSR-main urban area," "HSR-town center," and "HSR-scenic spot." This is because the expansion of construction land around HRR stations is not only influenced by the spillover effects of the stations, but also by the traction effect of the main urban areas, town centers, or tourist areas where the HRR stations are located. The stations along the East Ring of Hainan mainly expanded towards the main urban areas, whereas the stations along the West Ring of Hainan mainly expanded towards town centers. (4) Differences in the scope of the influence of each HRR station on the surrounding construction land expansion were mainly related to several variables, ordered as follows: socioeconomic environment, location conditions, attributes of the HRR station, and natural conditions. The GDP density of the towns where the HRR stations were located had the highest impact intensity at 0.51, followed by population density at 0.49, whereas the average elevation had the lowest impact intensity at 0.12. This study analyzed the mechanism and ideal curve of construction land expansion around HSR stations, establishing a logical basis for studying the spillover effects of HSR stations. In addition, this study analyzes the various impacts of HSR stations on the expansion of surrounding construction land and the reasons for these differences, providing a scientific basis for the current operation and future location of HSR stations. This study also offers methodological insights into the impacts of other infrastructures on the expansion of construction land in surrounding areas.