Research on the Spatiotemporal Evolution of China's Intercity Network Structure from the Perspective of the Relationship between Listed Companies and Customers

The connection between enterprises is an important part of urban connection. Strengthening the analysis of urban functional network based on the connection between enterprises is of great significance to enrich the theoretical research of urban network. Based on the trade relationship data between listed companies and their top five customers from 2010 to 2020, this paper constructs China's urban network, and analyzes the spatio- temporal evolution characteristics of urban network based on the perspective of trade links between enterprises. The research shows that: ① From 2010 to 2020, the urban network scale shows the characteristics of first rising and then falling, and the overall network density is low, ranging from 0.014 to 0.018. The center of gravity of the network presents the trend of "S" - shaped spatial trajectory change and overall southward movement.This feature is consistent with the trend of China's economic center moving southward in recent years. The overall spatial structure of the network changes from coastal to "T" - shaped structure. This feature is consistent with the "T" strategy of China's land development. ② The network traffic is concentrated in a few node cities. The total amount of capital in and out of the top 20 cities accounts for 71.9% of the total capital flow. Beijing and Shanghai are the absolute core of the network. The provincial capitals or sub provincial cities such as Hangzhou, Wuhan, Shenzhen and Guangzhou assume the function of regional centers. Foshan, Qiqihar, Nantong and other manufacturing developed cities are important nodes. It indicates that trade links are more likely to occur in cities with high administrative levels or developed industries. ③ The Pearl River Delta has the highest network density, which is between 0.324 and 0.334. The Yangtze River Delta has the highest total trade flow, which is 78.35 billion yuan. Although the networking level of urban agglomeration in the middle reaches of the Yangtze River and Chengdu Chongqing urban agglomeration is relatively low, they have become an important force to promote the evolution of network structure. ④ The COVID-19 has had a significant impact on the trade flow and network structure of the overall network. The network associations have been further divided and reorganized. The Guangzhou Shenzhen associations have been significantly strengthened. It shows that Guangzhou and Shenzhen have a strong combination effect. The Shanghai associations have been significantly weakened. The research results have a certain reference value for promoting the construction of domestic big cycle and unified big market. © 2023 Journal of Geo-Information Science. All rights reserved.

Spatiotemporal Evolution of SARS-CoV-2 Alpha and Delta Variants during Large Nationwide Outbreak of COVID-19, Vietnam, 2021.

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions.

Tracing production carbon emission transfer through global value chains: Towards a top gainer principle.

This study proposes the top gainer principle (TGP) and builds a calculation model based on the TGP to measure production carbon emissions transfer (PCET) in the context of global value chains. Compared with embodied carbon research, the innovative TGP model establishes a traceability mechanism based on the difference between responsibility and actual emissions from the perspective of the value chain, avoiding the endless debate between producer and consumer responsibility, which makes the TGP model more reasonable and fairer. In addition, using long-term input-output data, this study measures spatiotemporal patterns and the network evolution of global PCET. The results show that the total amount of global PCET has increased, and the regions with high outflows of PCET mainly include East Asia, North America, Central and Western Europe, and Russia. Among these regions, the United States and China accounted for the largest proportion of PCET outflow. By contrast, South America and Africa are typical low-outflow regions. From North America via central Europe, Turkey, Iran, South Asia to China, is a "W"-shaped high net outflow belt. The overall concentration of the global PCET network first decreased and then increased, and the network structure evolved into a bipolar network group with China and the United States as the core. Under the shock of the COVID-19 pandemic, the network structure showed a trend towards decentralization. This study suggests that efforts should be made to strengthen the responsibility of major countries, enhance the supervision of lead firms, establish a carbon emission transfer compensation system within value chains, and promote the development and spread of carbon emission reduction technologies to facilitate the reduction of global carbon emissions.

Spatiotemporal evolution and suitability of apple production in China from climate change and land use transfer perspectives

Apple production in China, the world's largest apple producer and consumer, is challenged by a huge and growing population coupled with rapid industrialisation and urbanisation. China's apple output has increased continuously over the past 42 years with distinctive spatial differences. Herein, changes in the spatial patterns of apple production increases, and their potential impact factors in China are described at the provincial level. Between 1978 and 2019, the centre‐of‐gravity of apple production shifted southwest towards the upper reaches of the Yellow River, the main water source for agricultural irrigation in North China. Analysis of absolute and relative growth of apple output reveals that the Loess Plateau, characterised by fragile habitat and low land productivity, has gradually become a major contributor to apple production. Despite annual increases in apple output, apple production system has become more fragile and unstable overtime, especially in the Shaanxi‐Gansu region where apple cultivation is prevalent. With continuous changes in policy, the amount of forest transfer (i.e. the area of other land use types converted to forest) has significantly affected the impact of standardised precipitation evapotranspiration index on apple production increases in China. Thus, to prevent the degradation of new forests, a differentiated management and protection system should be implemented for apple planting sub‐regions. This should include altering subsidy policies on apple production, enhancing soil erosion control in the Loess Plateau and strengthening ecological management of forests and grassland.

What Affects the Economic Resilience of China's Yellow River Basin Amid Economic Crisis-From the Perspective of Spatial Heterogeneity.

This paper contributes to the study of regional economic resilience by analyzing the dynamic characteristics and influence mechanisms of resilience from the perspective of spatial heterogeneity. This paper focuses on the resistance and recoverability dimensions of resilience and analyzed the dynamic changes in economic resilience in China's Yellow River Basin in response to the 2008 economic crisis. The multi-scale geographical weighted regression model was utilized to examine the effect of key factors on regional economic resilience. Our findings show the following: (1) The resistance of the Yellow River Basin to the financial crisis was high; however, the recoverability decreased significantly over time. (2) The spatial heterogeneity of driving factors was significant, and they had different effect scales on economic resilience. Related variety, government agency, environment, and opening to the global economy had a significant effect on economic resilience only in a specific small range. Specialization, unrelated variety, and location had opposite effects in different regions of the Yellow River Basin. (3) Specialization limited the area's resistance to shock but enhanced the recoverability. Related variety improved regional economic resilience. Unrelated variety was not conducive to regional resistance to shock and had opposite effects on the recoverability in different regions. (4) Government agency and financial market promoted regional economic resilience. Environment pollution and resource-based economic structure limited regional economic resilience. Opening to the global economy and urban hierarchy limited regional resistance to shock, but strong economic development had the opposite effect of improved regional resistance. The location in the east of the Yellow River Basin enhanced the recoverability; however, the location in the west limited the recoverability.

The Analysis of Patterns of Two COVID-19 Outbreak Clusters in China.

Since the emergence of COVID-19, there have been many local outbreaks with foci at shopping malls in China. We compared and analyzed the epidemiological and spatiotemporal characteristics of local COVID-19 outbreaks in two commercial locations, a department store building (DSB) in Baodi District, Tianjin, and the Xinfadi wholesale market (XFD) in Fengtai District, Beijing. The spread of the infection at different times was analyzed by the standard deviation elliptical method. The spatial transfer mode demonstrated that outbreaks started at the center of each commercial location and spread to the periphery. The number of cases and the distance from the central outbreak showed an inverse proportional logarithmic function shape. Most cases were distributed within a 10 km radius; infected individuals who lived far from the outbreak center were mainly infected by close-contact transmission at home or in the workplace. There was no efficient and rapid detection method at the time of the DSB outbreak; the main preventative measure was the timing of COVID-19 precautions. Emergency interventions (closing shopping malls and home isolation) were initiated five days before confirmation of the first case from the shopping center. In contrast, XFD closed after the first confirmed cases appeared, but those infected during this outbreak benefitted from efficient nucleic acid testing. Quick results and isolation of infected individuals were the main methods of epidemic control in this area. The difference in the COVID-19 epidemic patterns between the two shopping malls reflects the progress of Chinese technology in the prevention and control of COVID-19.

Study on Agglomeration, Evolution and Autocorrelation Effects of Spatio-temporal of COVID-19 Epidemic in Prefecture-level Cities in China during Government's Strict Control Period

The spatio-temporal evolution of major public infectious epidemics during government's strict control period in prefecture-level city can effectively reflect china's comprehensive emergency prevention and control capabilities. Based on statistical data including number of active cases, total confirmed, deaths of COVID-19 in 312 cities in China from January 24 to March 5, 2020, this paper uses methods including exploratory spatial data analysis, optimized hot spot analysis, spatial Markov chain, spatial panel data model to analyze spatio-temporal evolution characteristics of COVID-19 epidemic in China under government's strict control.The study found that: (1) The number of active cases of COVID-19 in China experienced characteristics of "rapid growth and diffusion, basic control, gradual decline, and complete control in some areas" and reached its peak on February 17, with an average daily growth rate of 17.5% during rising period and an average daily decline rate of 5.1% during falling period, and the epidemic change characteristics of most cities are similar to Nationwide's situation;(2) The high population mobility during Spring Festival transportation period is main reason for rapid expansion of epidemic. The Baidu's migration scale index for the 14 days prior to Wuhan closure was significantly correlated with total confirmed cases of COVID-19 in some cities;(3) The method called optimized hot spot analysis has identified that spatial distribution of hot spots of epidemic is stable and mainly distributed in 36 cities with Wuhan as the center and a radius of about 350 kilometers, while no statistically significant cold spot cities were identified;(4) The results of Markov chain transfer probability matrix analysis of active cased of COVID-19 in 312 cities show that various types are more stable and the probability of maintaining original type is greater than 0.85. The average probability of downward transfer is significantly higher than the probability of upward transfer. The probability of each type of transition changes significantly under the influence of different spatial lag types;(5) The estimation results of the spatial panel data model show that the number of active cases of COVID-19 in cites has spatial-temporal autocorrelation. This paper analyzed spatio-temporal evolution characteristics of COVID-19 epidemic during government's strict control period at prefecture-level city level from multiple perspectives, the focus of COVID-19 prevention and control is to reduce its spatio-temporal autocorrelation effects, this study provides a decision-making reference for government's current and future response to major public infectious epidemics. 2021, Science Press. All right reserved.

Spatiotemporal evolution and influencing factors of Coronavirus disease 2019 in Jiangxi province

Objective To analyze the epidemiological characteristics, spatiotemporal evolution and influencing factors of coronavirus disease 2019 (COVID-19) in Jiangxi Province. Methods  Text analysis was used to extract epidemiological information, ArcGIS 10.3 was performed to capture the evolution, spatial analysis method was applied to explore the spatiotemporal characteristics, and Partial Least Square (PLS) estimation was used to analyze the influencing factors of the epidemic distribution.  Results  In Jiangxi Province, 930 cases have been confirmed in total, with young and middle-aged people accounting for the most (60.40%), and the service industry, migrant workers and labors accounted for the largest proportion. According to the activity track and contact history, the cases can be divided into three types: Imported, mixed and diffuse. The diffuse cases account for 85.48% of the total. The development of the epidemic can be divided into three stages: Import period, diffusion period and control period. The spatial distribution of the epidemic showed the pattern of “southwest-northeast” and “the northern part of Jiangxi was more heavily affected than the southern and middle part of Jiangxi, with high primary ratio in Nanchang-Xinyu”. Population concentration, the intensity of communication and the distance from the worst-hit area were the main factors affecting the distribution of the epidemic.  Conclusions  The gender distribution of confirmed cases was balanced, with young and middle-aged people as the main group. The epidemic had great influence on service industry. The epidemic developed with the pattern of “rapid increase followed by slow decrease”, and with significant spatial heterogeneity. Population concentration and mobility as well as the overall epidemic pattern were the key factors affecting the epidemic distribution. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Spatiotemporal evolution, pattern of diffusion, and influencing factors of the COVID-19 epidemic in Hainan Province, China.

Within 1 month after the first case occurred in Hainan Province, China, the number of confirmed cases rose to 168, and there was no increase in almost 3 months. As the southernmost province and a famous tourist destination in China, its regular economic exchanges and high-intensity population movements may affect the spread of the epidemic. It is of great theoretical and practical significance to investigate the spatiotemporal evolution, the pattern of diffusion, and factors influencing the coronavirus disease 2019 (COVID-19) epidemic in Hainan Province. Basic and geographic information of confirmed COVID-19 cases was obtained from government websites and other official media. We examined the groups of infection and calculated the diffusion ratio to demonstrate the trend of the epidemic. Map drawing, spatial analysis, and partial least squares regression were used to express the spatiotemporal evolution, the pattern of diffusion, and factors affecting the epidemic. Furthermore, we have made recommendations on the formulation and adaptation of possible future preventive steps. Results show that the COVID-19 epidemic in Hainan Province has substantial spatial heterogeneity but minimal distribution. The tourist city and central city have formed a dual-core pattern for the spread of the epidemic, which could extend to other similar regions. Population density, mobility, and level of urban development have been the major factors of epidemic distribution in the study area.