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.

The problem of missing persons is a major global challenge, which causes serious harm to their families and societies. For this study, we collected 9,193 U.S. missing-persons records for the years 1996-2021 from the Doe Network platform. We used mathematical statistics and Moran's I index to analyze the socio-demographic characteristics, spatio-temporal distribution and its evolution patterns. Then the geodetector was applied to conduct an in-depth analysis of the influencing factors in socio-cultural, economic and demographic aspects. Based on above findings, a sociological theory of the formation mechanism of the missing-person phenomenon in the United States was proposed. Major findings included: (1) With age increasing, the number of missing persons initially increased and then dropped gradually. The highest missing rate was found among adolescents (13-18 years old) and adults (19-59 years old). Although more males than females were reported missing, the high-incidence period of males lagged slightly behind that of females. The high missing rate among adolescent females was linked to sexual crimes, including sex trafficking and rape, while that of adult men tended to be caused by family discord or debt problems. Among racial groups, black people faced the greatest risk of going missing. (2) From 1996, the number of missing-person incidents initially showed a wave upward trended and then fell sharply, after peaking in 2017, because of a series of immigration regulations. In 2020, it declined dramatically again, due to COVID-19. Influenced by the temperature, school holidays, and festivals, most people were reported missing during the months of June, August, and December. Only few missing incidents happened between February and April. (3) Spatially, at the state level, the missing population distribution decreased from the coastal border area to the inland area; over time, areas with a great number of missing-person incidents advanced simultaneously from the eastern and western coastal areas and the southern US-Mexico border to US inland areas. At the county level, they were concentrated on the edge and scattered internally. (4) Missing-person incidents were caused by the interaction of multiple factors; regional population mobility, fertility rate, and the number of vulnerable people had a positive impact on numbers of missing people, while per capital GDP had a negative impact. The power of population-based environmental factors was significantly enhanced after be interacted with social and economic factors, on explaining the missing-person spatial distribution, all of which were above 80%. (5) The underlying mechanism of missing-person incidents could be understood from the perspective of "social anomie". In other words, the disconnect between social goals and means led to social anomie, which then induced deviant behavior, including abduction, murder, and running away from home, increasing the likelihood of missing-person incidents. Finally, we offered suggestions for disappearance prevention and further study directions. The findings provided a basic understanding of the missing-person phenomenon, contributing to global scientific information, which could aid in preventing missing-person incidents.

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

The crime of human trafficking is an abnormal (involuntary, passive) phenomenon of population migration (disappearance, persecution); it has attracted great attention from the public and academic community because of its resultant social harm and far-reaching impacts on individuals and families. Constrained by the concealment, dispersion, variability, and complexity of the crime of human trafficking in China, the perspectives of earlier research topics were relatively clustered and limited. During the last 10 years, geographers have gradually achieved certain new understandings and progress through continuous exploration. From the perspective of the related sub-disciplines of geography, this study focuses on the main progress, existing issues, future trends, and crime governance paths to review studies on the crime of human trafficking in China. The results indicate the following: (1) Regarding the interdisciplinary situation: The crime of human trafficking is a social pain point of common concern among multiple disciplines. Geography has the advantage of being a latecomer and its integration with other disciplines will help understand the problem in depth and solve it systematically. Geographers will have a broad stage for future research in the field of human trafficking crimes. (2) Regarding the research objects: The earlier studies on the crime of child trafficking in China in the international context were actually subject studies of human trafficking in the Chinese criminal law context. The crime of human trafficking in China is unique compared to other countries and other types of crimes. (3) Regarding the research data: In the past, the data sources mainly comprised non-governmental organizations and individuals. The current data sources show the co-occurrence of non-governmental and official sources and the trend of the integration of offline and online availability. The integration and utilization of multi-source data will be the main path to future studies. (4) Regarding field investigation: In the context of the COVID-19 pandemic's impact and the upgrading of related family tracing means, field investigation has opened up new ways, and online investigations (online interviews, participatory observation in live broadcasting rooms, etc.) have become complementary or alternative channels of traditional field investigations and surveys. (5) Regarding patterns and trends: Based on the update and verification of available data, it is found that the stability of the spatiotemporal pattern of trafficking crimes and the dependence on the main routes in China, and cross-border and inter-provincial border areas, are worthy of attention. Future research trend will shift from being independent to comprehensive—from a quantitative study to a qualitative or mixed study; from case numbers to individuals, families, and their social networks; from the source area to the bridging of source, flow, and sink areas; and from a type of human trafficking to multi-type comparisons of missing persons. (6) Regarding measures and suggestions: Combined with the existing research knowledge and current crime trends, this study presents overall strategies and specific paths for dealing with the crime of human trafficking and assisting the abducted and their relatives.

The Belt and Road Initiative aims to achieve common development and prosperity for all countries. Building a scientific and reasonable geopolitical risk assessment system is an important prerequisite for participating countries to prevent and resolve geopolitical risk. Taking 64 countries in the six economic corridors of the Belt and Road Initiative as the assessment object, this study builds a geopolitical risk assessment system based on post-transaction costs. It analyzes the geopolitical risk level, spatial and temporal distribution characteristics, and influencing factors using the full array polygon graphical indicator method, Global Moran's I, and the spatial Durbin model. The research results show that: 1) From the time dimension, the geopolitical risks of participating countries show a trend of first rising and then falling, reaching a peak in 2015. 2) From the perspective of spatial distribution, high-risk areas are mainly concentrated in the Middle East and South Asia, while medium to high risk areas are concentrated in Indochina and the Arabian Peninsula. Most of the low to medium risk countries are Western Pacific island countries, while low-risk countries are mainly in Central Europe. The clustering characteristics of geopolitical risks are obvious. The results of Global Moran's I show that from 2011-2020, the geopolitical risk concentration area was initially located in the Middle East and South Asia, and then in 2015, Europe and East Asia also experienced high geopolitical risks. By 2020, it was still mainly concentrated in the Middle East and Eastern Europe, however, generally, the regions with high geopolitical risk will still be mainly concentrated in the Middle East and Eastern Europe. 3) The research results on the factors influencing geopolitical risks indicate that political stability, economic freedom, economic growth, increased education expenditure, and better natural resources have a significant inhibitory effect on geopolitical risk, while increases in the unemployment rate, population size, and oil resources, will to some extent, promote the generation of geopolitical risks. The indirect effect results show that political stability, economic freedom, and the unemployment rate of the host country have a significant impact on surrounding countries. Based on this, we believe that countries participating in the Belt and Road Initiative need to improve their government governance capabilities, accelerate their modernization transformation, effectively utilize the resources, funds, and technologies brought about by the Belt and Road Initiative. Furthermore, actively integrate into the regional economic cooperation framework of the Belt and Road Initiative and improve their ability to manage geopolitical risks. This study enriched the evaluation system of geopolitical risks. During the construction of the evaluation indicators, the results emphasized the sudden and violent characteristics of geopolitical risks, further explained the violent confrontation and economic game existing in geopolitical risks, and effectively enriched the literature on the spatial characteristics and influencing factors of geopolitical risks of countries participating in the Belt and Road Initiative. It should be pointed out that there are still some limitations in this study. This study is based on countries participating in the Belt and Road Initiative and there are deficiencies in the discussion of geopolitical risk for countries not in the region. Future research can be based on a global perspective, further enriching the evaluation index system of geopolitical risk, and conducting more in-depth research on the spatial transmission path and geopolitical risk avoidance measures.

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.

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.

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.

The digital economy, an essential engine for the high-quality development of China's economy, has the potential to become a breakthrough in promoting the rapid recovery of tourism. From a spatial perspective, this study used panel data from 284 prefecture-level and higher cities in China from 2011 to 2019 and constructed a spatial Durbin model (SDM) to empirically test the spatial effect and mechanism of the digital economy on tourism development. (1) Digital economy and tourism development showed significant positive global spatial autocorrelation during the study period. Hotspots of the digital economy have long been located in southeastern coastal areas, and cold spots in central and western China have shrunk significantly. Tourism development hotspots are mainly distributed in the Yangtze River Delta urban agglomerations and in Yunnan, Guangxi, Guizhou, and Chongqing. Cold spots were distributed in the central and western cities of the Shandong Peninsula and gradually expanded southward. (2) In China, the digital economy has a significant direct effect and positive spatial spillover effect, which was confirmed by a series of robustness tests were conducted. From the perspective of different regions, although the direct effect was significantly positive in all regions, the influence coefficient in the eastern region was significantly larger than that in the central, western, and northeastern regions. The spatial spillover effect is entirely significant in the eastern region, partly significant in the central and northeastern regions, and not significant in the western region, indicating that "digital segregation" exists in the western region. (3) The positive spatial spillover effect of the digital economy on tourism development is optimal at 300 km. Subsequently, the spatial spillover effect followed the law of geographical distance attenuation. The spatial spillover effect reaches the critical point of the practical effect at 800 km and almost disappears at 1500 km. (4) Among the digital economy components, digital infrastructure, digital industry development, and digital inclusive finance can significantly promote local tourism development. However, only digitally inclusive finance has a significant positive spatial spillover effect, and the effects of the remaining components are insignificant. This study constructs an analytical framework for the spatial effects of the digital economy on tourism development and conducts rigorous empirical research to compensate for the limitations of current research from a local perspective. This study also examined the spatial effects of various components of the digital economy, which helped identify the source of the impact of the digital economy on tourism development more accurately. In addition, the regional heterogeneity and distance attenuation law of the spatial effect of the digital economy on tourism development were analyzed, and customized policy implications were proposed based on the research conclusions. Overall, this study has essential reference value for achieving high-quality tourism development and expanding the scope of digital economy application.

Based on the space of flows theory, this study adopts China Smart Logistics Network big data to build China's e-commerce express logistics network, and explores the spatial structure characteristics of the e-commerce express logistics network, summarizes the regularity of the express logistics flows, finally reveals the formation mechanism of the network through complex network analysis, machine learning algorithms and other methods. The results are as follows: From the node dimension,the spatial inequality characteristics of the importance of e-commerce express logistics in Chinese cities are significant. Taking Heihe-Tengchong Line(Hu Line) as the boundary, the most important cities in the network are distributed within the four major urban agglomerations east of the boundary. The results based on random forest method show that express logistics export-oriented cities form the "e-commerce express logistics export belt" in the southeast coast. Macau and Taiwan receive express logistics input from Beijing-Tianjin-Hebei Urban Agglomeration and Yangtze River Delta Urban Agglomeration respectively on a small scale while Hong Kong plays an important role in logistics distribution function in the network as a high-equilibrium express logistics area. Additionally, from the dimensions of edges and overall network, the network density value is 0.927 0, and the average least connections value is 1.1375, indicating a wide network coverage and relatively complete express logistics routes between cities. Besides, China's e-commerce express logistics network has a small-world effect and high efficiency of the factor flows. A diamond-structured network is also formed with Shanghai, Guangzhou, Chongqing, and Beijing as the four core nodes. In comparison, the Yangtze River Delta Urban Agglomeration is more balanced in the development of e-commerce express logistics; Beijing-Tianjin-Hebei and Chengdu-Chongqing Urban Agglomerations are more dependent on the internal core cities; Guangdong-Hong Kong-Macao Greater Bay Area has the lowest network cohesion, and the express logistics links among Hong Kong, Macao and the other nine cities in the Pearl River Delta are weak. Overall, the network formation is influenced by the development of urban agglomerations. Driven by information technology, traditional and new infrastructure construction, etc., the network is less dependent on the distance factor. Express logistics elements mainly follow the hierarchical diffusion mechanism. This research expands the application of logistics big data in the field of urban network research, reveals the structural characteristics and formation mechanism of China's e-commerce express logistics network, helps enrich the theory of "space of flows", and is also of great significance for understanding the city correlation under the digital economy and the shaping of urban space by modern logistics elements, and promoting the digital transformation and high-quality development of express logistics.

Cybergeography, an emerging subfield of human geography, has received increasing attention over recent decades. In particular, the digital transition of cities and the rapid rise of the digital economy have provided an impetus to the development of Cybergeography in China. This study attempts to provide a literature review of the research progress in Cybergeography in China over the past two decades regarding its disciplinary characteristics, main branches, and evolutionary paths. Through a bibliometric analysis and knowledge graphs based on a large number of Chinese articles (8,735) in geographic journals from the database (CNKI), we concluded that Chinese Cybergeography is mainly encompassed in the fields of urban geography, economic geography, tourism geography, geographical information science, and other disciplines, and the main institutional contributors include the Nanjing University, Institute of Geographic Sciences and Resources of the Chinese Academy of Sciences, East China Normal University, Hebei Normal University. Chinese Cybergeography can be classified into five independent but interconnected sub-areas: (1) urban network analysis based on information flows; (2) online consumption behavior and their spatial impacts on urban space; (3) internet visibility and tourism flow; (4) smart cities and communities; and (5) digital economy and its interactions with spatial organizations. Chinese Cybergeography has experienced four phases: the embryonic stage during the initial 21^st century, fast development period (2006-2013), flourishing period (2014-2019), and transition period (since the outbreak of COVID-19). Chinese Cybergeography has evidently become highly diversified and interdisciplinary through this period, with its research focus expanded from the early stages of "informatization level" and "regional differences" to hot topics, such as "flow space," "digital economy," and "smart city." Thereafter, we pointed out that Chinese Cybergeography has achieved fruitful achievements in the past 20 years and even has international leadership in some fields; however, compared with the rich and colorful theoretical establishments in the West, various problems and challenges are still present. For example, a relatively old-fashioned disciplinary thinking based on the absolute space concept currently exists, while limited attention has been paid to research on virtual societies and metaphor space, as well as the critical discourses on cultural and social consequences of digital transition. Furthermore, the big data method also tends to be overused in existing research, whereas the fieldwork-based approach has largely been neglected. Finally, we provide prospects for future research on Chinese Cybergeography by proposing that, in response to the digital and intelligent transformation in today's world, there is an urgent research agenda to establish China-characterized Cybergeography by incorporating Western establishments in theories and Chinese demands in practice. On the one hand, enriching the current research perspective is necessary by incorporating epistemologies from critical and humanistic geography. On the other hand, Chinese Cybergeography needs to keep up with the development and changes in social practices to continuously expand the research scope, such as focusing on the impacts of emerging digital technologies (such as artificial intelligence and metaverse) on urban and regional development. Therefore, Chinese Cybergeography can aid decision makers in promoting urban digital transformation, development of the digital economy, and coordinative development among different regions and cities.

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.

Agriculture has been making a new turn toward digitization in developed areas. However, in the underdeveloped ethnic regions of western China, does digitization represent a valuable opportunity or a serious challenge? In this article, Litang County, Sichuan Province, located in the western high plateau region, was taken as an example, and text analysis of multi-source data was performed, including of policy documents, planning texts, website information of agricultural companies and government departments, and materials based on semi-structured interviews with local authorities and leading agricultural companies, with the purpose of discussing the dynamic mechanism of agricultural digitization in underdeveloped western counties. It was found that agricultural digitization in Litang has the following three typical characteristics: 1) full-chain digitization, that is, a closed-loop structure of data is formed through enterprise data centers, local data management platforms, and e-commerce platforms from various links such as vegetable and fruit breeding, planting, condensation logistics, and product sales; 2) multi-agent promotion, that is, agricultural companies, retailers, government departments and their agents, as well as social organizations, cooperate and promote each other in agricultural digital infrastructure construction, data technology application and data management, and agricultural resources integration; 3) coordination within and outside the county, namely timely response to the national rural revitalization strategy, corresponding poverty alleviation policies and local planning goals and action plans, coordination between the matching aid units and local departments and institutions in terms of the dual goals of economic benefits and poverty alleviation, and coordination between agricultural companies, social organizations, platform institutions and farmers, and other stakeholders within and outside the county. This study contributes to the establishment of a four-dimensional analysis framework of natural environment-market-institution-technology based on the institutional analysis of economic geography and enriches the research on economic geography in the field of agricultural digitalization. The main conclusions are as follows. First, the special natural environment in the high plateau area has shaped the constraint conditions and advantages of agricultural development in Litang, which encourages local companies to adopt a differentiation strategy, enter outside segment markets, and create a new evolutionary path using biological and digital technology. Second, the application of digital technology is not only beneficial to meet the needs of remote consumers for production information but also to provide feedback on timely changes in market demand for local agricultural companies, which is conducive to the adjustment and optimization of the decisions of agricultural producers. Technology investment increases production costs, which can be achieved by entering middle- and high-end markets to increase revenue. Third, the digital transformation of leader companies is not only the result of responding to or embedding national and local agricultural digitization strategies, but also puts forward potential requirements for the adjustment and optimization of local institutions and production organizations. Government and leadership companies jointly promote agricultural digitalization. In addition, the national matching poverty alleviation policy enables local governments to obtain scarce resources such as technology, capital, and market opportunities through external support, effectively reducing the risks and costs of digital transformation for leader companies, farmers, and other local production actors and promoting the spatial agglomeration of digital elements and resources through the external economies of the production network. However, it is crucial to take advantage of external support to cultivate local entrepreneurial talent and increase human capital for sustainable local development.

To expand micro-scale research on the urban network of cold chain logistics enterprises, enrich cold chain logistics channel theories, guide marginal counties to improve their network status, further promote the rational allocation of cold chain logistics resources at various scales, and promote the coordinated development of regional cold chain logistics, this study constructed three levels of county network respectively from the inside of the cold chain logistics corridors,within and beyond the corridors and at a national scale. Using Social Network Analysis, Motif analysis, geodetectors, and other models, it gradually analyzed the county network structure of cold chain logistics enterprises in 2020 from the overall to the local level and discussed the driving mechanism of the network structure. The results show that: 1) from an overall perspective, the network structure at all scales presents a core-periphery layout, and the core counties with archipelagic distribution are mainly located in Beijing Tianjin Hebei, Yangtze River Delta, and Pearl River Delta. The difference is that network development inside and outside the corridors and at the national scale is not yet mature, and the network status and node relevance of counties at various scales are quite different; 2) from a local perspective, the core counties within the corridors mainly establish a dual relationship with the core counties in the same city, build a ternary closed and mutually beneficial group relationship with other counties, and develop multi-corridor multi-core communities. The outer core counties of the inner and outer scales of the corridors are mostly formed near the hub of the corridor, often occupying the structural hole position of the inner and outer links of the corridors and are closely linked with other marginal counties in the main agricultural production areas. The core counties at the national scale radiate mainly to the outside world. They often choose the eastern coastal open cities or ports as windows to absorb multiple investments, leading to economic circles and economic belt associations; and 3) from the perspective of the core-periphery layout driving mechanism, economic factors, such as consumption level, are the common driving force of all scales; spatial factors, such as geographical location, lead to differences between scales; and aviation facilities do not show an obvious driving force. The theory of urban network driving mechanisms is still applicable to the study of county units. The formation of the county network structure stems from the functional differentiation and locational separation behavior of cold chain logistics enterprises. Driving mechanisms, such as scale preference, endowment preference, and network proximity effect, show heterogeneity in the role of network structures at various scales.

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.

In recent years, the rapid rise of the e-commerce industry has promoted the rapid development of e-commerce villages. Dayuan Village, which is located on the urban-rural fringe of Guangzhou, has developed into one of the largest and most influential e-commerce villages in China. Studying the mechanism of the formation, development, and transformation of Dayuan Village's e-commerce industry is necessary to explore the evolution mechanism of the digital industry, especially in the urban-rural fringe. Based on the theoretical perspective of "Actor-network Theory", the paper analyzes the network evolution and dissidence elimination process gradually by using in-depth interviews and participant observation to analyze this evolving process. The results show that the development of Dayuan e-commerce village has experienced three evolutionary stages: e-commerce agglomeration, e-commerce industry regularization, and e-commerce upgrading. The different OPPs in the three stages are anchored by traditional e-commerce, local government, and emerging e-commerce. With the formation of key actors, other heterogeneous actors, such as land, housing, express delivery, logistics, and emerging e-commerce platforms were enrolled based on the consensus of goals. Dayuan Village developed from a traditional urban village to a traditional e-commerce village and then transformed into a new e-commerce village, a process that reflects the simultaneous upgrading of local-network mobilization and global-network attachment in village development, increasing the resilience of the village's development. With the joint efforts of heterogeneous actors, the talent pool of Dayuan E-Commerce Village has grown from lack to abundance, social communication from isolation to integration, and business management from deficiency to enhancement. In addition, this study also emphasized, innovatively, the upgrading mechanism in the transformation process of the e-commerce village through the "local-global" framework in the Actor-Network Theory. New short video and live streaming platforms that are enrolled in the network not only further mobilize the activation of local actor networks but also further connect the village and its various assets to the external network and then participate in the global network. Thus, the transition from a traditional e-commerce village to an emerging one is a comprehensive upgrading process for both global and local networks. By analyzing the evolution of the actor network of Dayuan Village from a traditional e-commerce village to an emerging e-commerce village, this paper fills a gap in the study of the upgrading process of e-commerce villages to some extent and firstly uses the "global-local" framework in the actor network to analyze this upgrading process. The case of the development and transformation of the Dayuan e-commerce village illustrates the importance of different key actors for local development in different stages. Market forces and entrepreneurs play an important role in the formative stages of e-commerce villages, whereas the government and associations can regulate and mediate them in the later stages of explosive and rough growth to make e-commerce villages more sustainable. Therefore, the identification of development stages, stage OPPs, and key actors at different stages in the local planning and development process can help in completely exploiting the agency of different actors at different stages.

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.

Technological innovation is considered an important source of national economic development, but existing studies have found that there are few independent research and development (R&D) activities in Chinese enterprises, and the achievement transformation of R&D investment is still facing great difficulties. As a new technical means, Internetization is regarded as an important driving force for promoting enterprise innovation in the information technology era. However, few empirical studies have conducted detailed analyses of the relationship between Internetization and enterprise innovation, ignoring the fact that Internetization can quickly transfer information. Therefore, based on the invention patent data of listed enterprises in the computer, communication, and other electronic equipment manufacturing industries in Guangdong Province from 2012 to 2020, and dividing Internetization into two types: Information Internetization and Application Internetization, this study analyzes the basic characteristics of enterprise Internetization and its impact mechanism on enterprise innovation in Guangdong Province by using inverse distance weight interpolation, panel regression, intermediary effect, and test and panel quantile models. The results show that Guangzhou and Shenzhen are the main innovation centers in Guangdong Province, of which Shenzhen is the main core and Guangzhou is the sub-core, effectively improving innovation ability in the surrounding areas. Second, the Internetization level of enterprises in Guangdong is increasing annually. Enterprises with high Internetization levels are mainly distributed in the Pearl River Delta. Simultaneously, the information Internetization level of enterprises in Guangdong was generally higher than the application Internetization level. Next, increasing the degree of enterprise Internetization contributes to an increase in innovation output. At different innovation levels, the impact of Internetization on enterprise innovation output shows a "U" shape, and Internetization can improve the innovation level of enterprises by improving the scale and quality of R&D investment, as well as effectively reducing negative externalities. Finally, as a new technical means, the Internet can more effectively promote the optimization of enterprise management structure and improvement of internal communication efficiency, and promote the improvement of innovation efficiency by reducing negative externalities, compared with "informatization" and other means. Simultaneously, the innovation-driving effect of different types of Internetization forms shows different characteristics. The innovation impact of information Internetization is effective, inexpensive, and can improve R&D investment. The cost of Application Internetization is higher, making it more suitable for large enterprises.

As an important force to guide future economic and social development, strategic emerging industries are the core of China's modern industrial system that helps in achieving high-quality economic development. The study sample includes 1,109 most representative listed enterprises in strategic emerging industries and their associated 19,540 enterprises. This study has depicted the characteristics of the spatial distribution of China's strategic emerging industries from the perspectives of industrial clustering and industrial networks and provides an in-depth analysis of cluster structure, network centrality, network heterogeneity, and their spatial differentiation. This study has four main findings. First, in the eastern region, the core and supporting enterprises of strategic emerging industries are relatively balanced and the industry clusters are more complete, while in the central and near-western regions, there are relatively few core enterprises and they are mainly concentrated in a few provincial capitals. In the northwest and northeast regions, there are not enough core and supporting enterprises to support the development of strategic emerging industry clusters. Second, China's strategic emerging industry network has a diamond-shaped network pattern with the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing regions at the apex, and several vertically linked industry networks that have been formed by core cities. Third, the core companies' key investments in China's strategic emerging industries are concentrated in three key segments of the industry chain: manufacturing, R&D, and information (20.42%, 19.50%, and 17.56%, respectively). Based on this, two major industrial networks-the R&D and information services network and the manufacturing network and three national hubs-Beijing, Shenzhen, and Shanghai, were formed. Beijing is the core node of the R&D and information services network, whereas Shenzhen and Shanghai are the core nodes of the production and manufacturing networks. In addition, Hangzhou is gradually becoming an information service center city with national influence, while Guangzhou, Chengdu, Wuhan, and Xi'an remain regional information service centers and manufacturing leading centers. Finally, there is a significant positive correlation between the size of industry agglomerations and the centrality of the industry network. When the cluster size is small, cluster size expansion primarily supports the expansion of the breadth of industrial network linkages, whereas when the cluster size is large, it primarily supports the increase in linkage intensity. More importantly, when industry agglomerations reach a certain size, the rapid increase in the strength of industrial network linkages is more often due to small and medium-sized supporting enterprises than due to large core enterprises. This study provides a comprehensive understanding of the characteristics and spatial patterns of China's strategic emerging industries as well as the problems faced by different regions. Moreover, an analysis of the heterogeneity of industrial networks, especially the differences between the core node cities of different networks, also demonstrates the variability of the impact of different types of industrial networks on different cities and regions.

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.

Landslide No.3 in Fei'e Mountain is located in the Shunde District of Foshan City, Guangdong Province, and its' lithology is mainly composed of pre-Cretaceous Baizushan Formation (K₁b) argillaceous siltstone. In this study, engineering geological drilling, geophysical exploration, geological mapping, and indoor testing were used to determine that it is a medium-scale bedding rocky landslide with a typical double-layer deep sliding surface. The maximum length of the landslide body is approximately 220 m in the longitudinal (south-west) direction and approximately 230 m in the horizontal (north-west) direction; the maximum thickness is approximately 32m, and the attitude of sliding surface is 230°∠12°-17°. Landslide body tensile cracks, including nine large-scale tensile cracks, are very well-developed. The longest crack is approximately 120 m long and has a crack opening width of 0-13 cm, with a height difference between the two sides of the crack (rupture wall) of 0-0.2 m. The ground of the leading edge of the landslide was uplifted and cracked, with a maximum uplift height of approximately 1.7 m. The landslide shear outlet was clearly visible and exhibited well-developed scratches. The scratch direction was the same as the main slide direction of the underlying landslide. Landslide deformation severely cracked the building structure and obstructed the drainage channel. There was a loose residual soil layer on the surface of the slope of Landslide No.3, and many fractures and joints were present in the lower bedrock. During rainfall, rainwater penetrated the deep part of the slope along the rock layer surface, joints, and fractures, which greatly increased the bulk density of the rock and soil mass, and softened the argillaceous siltstones, which greatly decreased their shear strength. The excavation of the slope formed a steep surface, which reduced the load at the foot of the slope and thus reduced the anti-sliding force. During long-term seepage, the rock and soil mass near the landslide face was softened to form a weak zone mixed with joints and stratigraphic phases. During long periods of heavy rain, the weak zone became soaked, soft, and plastic, which reduced its shear strength. When downward force increased, the effective anti-sliding force of the weak zone was greatly reduced, resulting in a landslide. During this process, Landslide No.3 developed two slip surfaces. The maximum buried depths of slip surfaces 1 and 2 (corresponding to landslides 1 and 2) were 32 and 15.5 m, respectively, which means that landslide 2 overlaid landslide 1 and slip surface 1 creeping occurred before that of slip surface 2. The trailing edge of slip surface 1 developed a fissure, the characteristics of which are described above. As the fracture surface was not fresh, its' development time is unknown. As in the sliding process, landslide 2 first formed a continuous sliding surface, and its' sliding rate was slightly greater than that of landslide 1, landslide 2 was the first to cut out from the steep ridge of the landslide's front edge. As a result of the shearing action of landslide 2, landslide 1 developed multiple vertical cracks. Rainwater seeping down these cracks further lubricated slip surface 1, which resulted in drum mounds and cracks in the leading edge of the landslide. Slip surface 1 subsequently formed a continuous sliding surface, and Landslide No.3 entered the uniform deformation stage. Timely emergency measures prevented landslide deformation damage and halted landslide progression before entering the accelerated deformation stage. Considering its double-layer slip surface structure, a comprehensive combination of slope cutting, an anchor (cable), lattice beam, double-row prestressed anchor-pulling anti-slip pile, three-dimensional mesh grass greening, interception, drainage, and a hairy stone retaining wall was used to prevent and control the landslide. Long-term monitoring results showed that these methods had a high rectification effect and successfully controlled landslide deformation and displacement.

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.

Against the backdrop of digital technology integration and digital transformation, the digital economy has become an important new economic development in China. The digital economy changes and subverts the traditional production and organization modes of industries, breaks regional and spatial barriers, and realizes a highly interconnected network structure. Therefore, it is critical to study the digital economy from a network analysis perspective. In previous studies, the interlocking network model and the headquarters-branch model have been adopted to construct enterprise networks; however, the networks based on these models have some deficiencies in comprehensiveness and authenticity. An enterprise equity network built based on the investment relationships between enterprises can reflect the actual contact network status of the enterprise. Based on the above background, this study analyzed the evolutionary characteristics of the digital economy network in the Yangtze River Delta area through an enterprise equity network and explored its influencing factors and mechanisms. First, we constructed the digital economy networks of the Yangtze River Delta area for 2010, 2015, and 2020 using equity investment data between enterprises and a directed correlation network model. Second, with the indicators of degree centrality, degree centralization, an investment source, and others, the spatial and temporal evolution characteristics of digital economy networks were described from two aspects: network patterns and investment relationships. Furthermore, this study explored the possible factors affecting digital economy networks in the Yangtze River Delta area through a Quadratic Assignment Procedure. This study has three main findings: 1) From 2010 to 2020, various types of digital economy networks in the Yangtze River Delta area showed the characteristics of hierarchical diffusion and node agglomeration. Each industry forms an expansion of the network framework around the core node in the initial stage and then expands to the surrounding areas through secondary nodes. Shanghai and Hangzhou were the most important network nodes in the digital economy. 2) The number of net inflow areas for digital economy investment in the Yangtze River Delta was increasing, whereas the number of net outflow areas was decreasing. Hangzhou was the main net inflow city, while Shanghai and Ningbo were the main net outflow cities. Simultaneously, the source of investment in research units has changed from inside the province to outside, and there are clear interprovincial differences. 3) Cognitive proximity, the difference in the number of digital economy enterprises, the difference in the proportion of secondary industries, the difference in information facilities, and the early network foundation all have significant positive effects on the establishment of digital economy networks. System proximity, the difference in the proportion of tertiary industries, the time difference in the establishment of digital economy enterprises, and the gap in innovation ability restrict the formation and development of digital economy networks. Geographical distance is no longer the main factor influencing the formation of digital economic networks. Based on the above conclusions, this study proposes some suggestions for promoting the development of the digital economy from the perspectives of industrial agglomeration, facility and policy allocation, and path dependence.

With the progress of information technology and the transformation of the global economy, the digital economy is increasingly showing rapid growth and is becoming a key force in restructuring global factor resources, the global economic structure, and the global competitive landscape. E-sports, which is an emerging cultural industry and sport, has great significance in promoting cultural exchanges among countries and enhancing their respective national soft power. Presently, owing to its professionalization, internationalization, and ecologization, e-sports enables broader and multidimensional connections between game participants. However, e-sports cooperation networks based on virtual communities have not yet received widespread attention. Therefore, this study uses the information database of the participating teams of three international e-sports events, namely, the League Of Legends World Championship, The International DOTA2 Championships, and the CS: GO Major, to explore the structure of transnational e-sports team networks and their evolution from a theoretical perspective of virtual communities. This study uses the social network analysis and the gravitational model methods to reveal the multidimensional proximity and national attributes that influence the e-sports cooperative network patterns. The results show that first, the spatial evolution of the global e-sports cooperation network shows rapid expansion and low density, weak association, and strong dynamic network characteristics. The number of nodes increases rapidly while the network density shows a fluctuating decrease. This indicates that the development of Internet technology and the increasing popularity of e-sports have drawn increasingly more countries to participate in international e-sports activities, and the node connection of the e-sports cooperation network tends to be decentralized as a whole. Second, the global e-sports cooperation network has evolved into five associations representing geographical regions: the European associations with Denmark, Sweden, Finland, and Germany as the core, the Asia-Pacific associations with China and South Korea as the main partners, the Commonwealth of Independent States associations with Russia and Ukraine as the main partners, the Latin American associations with Peru and Argentina, as the main partners, and the Intercontinental associations with the United States and Canada as the main partners. Third, the spatial structure of the global e-sports cooperation network is influenced by the interrelationship between countries and their respective industrial bases. Social and organizational proximities drive the formation of e-sports cooperation networks, whereas geographical and cultural proximities do not significantly affect e-sports team cooperation. The interaction between geographical proximity and social proximity on the intensity of e-sports cooperation reflects a substitution effect; scientific research expenditure, e-sports revenue, and e-sports strength are the key elements affecting countries' importance in e-sports cooperation networks. Conversely, economic scale and general factors such as economic size and education level do not have significant effects on global e-sports team cooperation. This reflects the uniqueness of the e-sports industry in a digital economy. This study contributes to the research on the reconfiguration of industrial organization networks driven by the digital economy. Furthermore, this study provides a reference for making China's e-sports industry internationally competitive by improving its e-sports training system.

With China's entry into the era of industry and information, an increasing number of industrial enterprises have withdrawn from the historical stage owing to technical bottlenecks, environmental pollution, and lack of resources. The Party and Government attach great importance to the protection, utilization, and research of industrial heritage. This study takes 164 national industrial heritage groups since 2020 published by the Ministry of Industry and Information as the research object. This paper discusses the spatial and temporal distribution characteristics of national industrial heritage and analyzes its influencing factors using the spatial analysis methods of nuclear density and concentration trend evolution of the geographic information system platform. The main results of this study are as follows: 1) The spatial distribution of national industrial heritage shows remarkable positive spatial autocorrelation, and this correlation increases over time; There are more industrial heritages in the southeast and less in the northwest, and the spatial pattern of "three cores, two pairs, sheet distribution" is formed. 2) From the perspective of heritage types, light industry is mainly distributed in the Yangtze River Basin and coastal areas, while heavy industry is more dispersed. 3) The distribution of industrial heritage in the province is unbalanced, with three types of areas. Type I is in the northeast and first-tier areas, such as the southeast coastal areas in China. Type II is in third-class areas like northwest China, while the others are type III. 4) From the perspective of time evolution, the spatial distribution center of industrial heritage has four obvious turns. Before the founding of New China, the span of the Heritage Center in different periods was small, and the span became larger after that. 5) Most industries in ancient China were influenced by natural geography and the socioeconomic environment. Although the industries in modern China were influenced by the above factors, they were more influenced by historical and political factors such as nationalism, the Westernization Movement, and major national strategies. National industrial heritage is concentrated in the southeast coastal and middle and lower reaches of Yangtze River plain areas with good natural environment, high population density, high economic level and smooth information flow, while the number of distributions in Guangdong and northwestern areas is less, and areas such as Guangxi and Ningxia are even blank. On the one hand, due to the backward industrial development of Inner Mongolia, Xinjiang, Tibet, and other provinces in the western region, only a few meet the requirements for the selection of national industrial heritage. On the other hand, "Renewal" has become the theme of the development and planning of times in the process of rapid urbanization in Guangdong Province, one of the birthplaces of modern industry in southern China. A large number of industrial heritage sites have been abandoned and demolished, and the protection and development of these sites are far backward. Therefore, each province should fully understand the importance of protecting and selecting national industrial heritage and take this as an opportunity to accelerate this process within their respective jurisdictions. Each province ought to reasonably link development and protection to better promote the organic renewal and activation of cities.

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.

The issue of "spatial justice" has become a hot topic among urban research and planning scholars in China. An analysis of 1516 English-language documents in the Web of Science (WOS) from 2000-2021 was conducted through CiteSpace. Knowledge maps of keyword clustering, core author groups, and research institutions were mapped to reveal the hotspots and trends of foreign spatial justice research. The results reveal the following: (1) Foreign literature issuance exhibits a phased upward trend, divided into three stages of exploration (S1), stabilization (S2), and explosion (S3). The publication volume increased steadily in S1, contending and flourishing around the theme of space deprivation, exclusion and poverty. The publication volume has increased significantly in S2 compared with S1, the connotation of spatial justice is gradually clear and complete, environmental justice has received significant attention, and the influence of process and procedural justice is increasing. The spatial justice research has explosively grown in S3, focusing on the value effect and practical significance of spatial justice in the post-globalization era and stock development period. (2) A total of three hotspots emerged in foreign spatial justice research: The rise of research targeting youth and children, who have become one of the main actors and are motivated by awareness and environmental change to actively participate in the fight for justice on a global scale. Environmental justice research is booming with divergent and extended content, focusing on waste trade and climate change. The public and green spaces of the city have become research hotspots as the pursuit of spatial justice value turns to high quality and sustainability. (3) Trends in spatial justice research abroad include integration of environmental justice and urban space, as well as scale synthesis and thematic expansion driven by technological progress. In general, the maturation of foreign spatial justice research provides an important reference for the theoretical construction and practical application of spatial justice in China. How to connect with the international frontier, form research results with local characteristics, and effectively implement them in current spatial practice in the context of new urbanization is an urgent issue to be solved.

Taking Zhejiang province as an example, based on county-level economic and social statistics, we established an index system for main factors driving the development of the digital economy. We used entropy, spatial autocorrelation, a Geographically Weighted Regression (GWR) model, and cluster analysis to explore the spatial differences between the development level and driving factors of the digital economy in Zhejiang. First, we observed spatial differences in the development of the digital economy in Zhejiang, with the level being high in the northeast and low in the southwest. Second, the influence of each driving factor represents strong spatial characteristics, and industrial innovation was the most important factor, with a distribution pattern of high in the east and low in the west. As a secondary driving factor, government input is consistent with the spatial layout of the economic factors. Commercial culture is strong in the south and weak in the north. Third, we observed multiple combinations of characteristics in the spatial differences of the four driving factors to establish the mechanism that drives the development of the digital economy in Zhejiang: the local digital economy/new business type is driven by the coupling of industrial innovation atmosphere and consumer demand, formed by developed market advantages. The three main bodies of consumers, government, and enterprises have formed a virtuous circle, promoting each other through the digitization of traditional industries and emerging digital industries, leading the development in the surrounding counties. Fourth, under the action of the four factors, three types of regional development types and 13 subcategories have generally been formed in Zhejiang: enterprise-consumer-led, business-government-led, government-consumer-led. Certain places, such as Hangzhou and Ningbo, are leading the implementation of the concept of digital development, creating the core of the digital economy in Zhejiang; Jiaxing and Jinhua are relying on transportation hub cities or global wholesale centers to rapidly develop intelligent logistics and transportation. In addition, with the support of the government, the later-developing areas are improving their infrastructure and developing the digital industry represented by ecology and tourism. For this reason, in the process of empowering digital economy development, complete attention should be paid to the development path of ecology, tourism, and other suitable digital industries in underdeveloped areas under the leadership of the government. We observed that enterprises and consumers create emerging digital industries in the leading areas of the digital economy in Zhejiang and then promote the digital transformation and upgrading of industries and governments. However, in the later-developing areas dominated by policy investment, the development of the digital economy is mostly oriented toward meeting the needs of industries and consumers, and the force driving emerging digital industries is weak.

Urban creative networks, as distinct forms of production networks, differ from inter-firm-based urban networks. It is featured by flexible production, "temporary cooperation" and being "people-centered." This study attempts to address the concept of the urban creative network in theory, and based on the basic database of Chinese digital music, it integrates the social network, Geographical Information System (GIS) spatial analysis, and econometric model to empirically analyze the structural evolution characteristics and influencing factors of the creative cooperation network of Chinese singers in the past 40 years, which enriches the theoretical and empirical study of urban networks from the perspective of creative individual connections. The results suggest the following: (1) The scale of Chinese urban digital music collaborative networks grows rapidly and shows the characteristics of low density and a "small world", and network density is negatively correlated with network scale, with obvious social network properties. (2) The collaborative network as a whole presents a "core-periphery" structure and the development trend of polycentricity and high-centricity cities are mainly located in the eastern region, with relatively low centrality in the central and western cities, and gradually forms a triad structure of Beijing-Hong Kong-Taipei. (3) The evolution of the digital music cooperation network is characterized by both path dependence and path creation, and the main form of network extension is hierarchical diffusion, with the early high-intensity links between Hong Kong and Taiwanese cities gradually being replaced by mainland cities. (4) The coverage of city ties in the cooperative network expands with an uneven spatial distribution, and high-intensity ties are mainly concentrated in the eastern cities. Over time, the core cities' control over network resources through inter-high-intensity ties weakened. (5) The cooperative network has an obvious community structure, and the communities of this coproduction network appear to be expanding, grouping, and hierarchical, with dual-core and multi-core models as its main spatial organization modes. (6) A negative binomial regression model analysis shows that the scale of the urban economy, industrial structure, and the level of the network economy have significant impacts on the network structure, while the roles of human capital, opening up, urban administrative level, and traffic accessibility are not evident, indicating that the Chinese music cooperation network has obvious endogenous mechanisms and local embeddedness. The findings of this study provide in-depth theoretical and policy insights for understanding intercity interactions and promoting the development of creative industries under the influence of the creative (digital) economy. First, a study on city networks based on the production networks of digital creative industries should focus on the characteristics of creative production networks and the role of creative actors (people). Second, policymakers should fully understand the characteristics of digital creative production sector actors and production modes when formulating policies for the development of digital creative industries and choose development paths that fit the advantages of local resources to realize industrial development. Finally, we propose a future research agenda for urban creative networks.

As a typical representative of advanced producer services, banks can be used to characterize the spatial structure of urban networks through the headquarters-branch connections and the implied capital flow process, which can guide the exploration of financial market connectivity and coordinated regional development. In this paper, the financial linkages of 26 representative banks in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) were transformed from city scale to county scale urban network using the interlocking network model, and the structural characteristics and influencing mechanism of urban network are discussed using the Social Network Analysis and Geographically Weighted Regression model. The results show that: 1) The Pearl River Estuary in the GBA is a dense area of financial linkages. The level of financial linkages on the east bank of the Pearl River is generally higher than that on the west bank of the Pearl River, and the hierarchical expansion to the peripheral space is continuous or progressive. 2) The clustering and accessibility of the overall network perform well. The whole network presents a typical "small world" effect. Guangzhou Tianhe, Guangzhou Yuexiu, Foshan Nanhai, Foshan Shunde, and other nodes are of high gradability and proximity, and they are also important intermediaries for financial connections in the GBA. 3) Most of the nodes with high gradability have higher effective scale and efficiency, and have the advantage of structural holes. The peripheral spatial nodes have a high restriction degree. The network mainly relies on some nodes in the core or sub-core subgroups to generate financial linkages, while the financial linkages among most nodes of the edge subgroup are weak. 4) The variables negatively correlated with the total number of linkages are population density, economic development level, and government control behavior. The variables that are positively correlated with the total number of linkages are the degree of transportation convenience and social consumption ability. The relationship between the demand for financial services and the level of openness to the outside world and the total linkages is complex and has bidirectional effects. An additional contribution of this paper is as follows: 1) The preference for location choice of APS enterprises represented by banks is higher for geographical agglomeration in local markets, and the financial relationship at the county scale is more important for the location choice of bank branches within cities. 2) The differentiated urban network structure characteristics revealed by the heterogeneity of bank branches provide a reference value for the appeal of different types of bank branches.

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

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.