This article aims to investigate the impact of human capital stock on innovation output in China, particularly focusing on its regional heterogeneity, using the panel data of patent application and innovation input among 287 prefecture-level cities from 2007 to 2012 (excluding the data of Hong Kong, Macau and Taiwan). In particular, we use two indicators, the percentage of highly educated talents and the average year of schooling, to capture the level of human capital accumulation. We use the number of patent applications as a proxy for innovation output and population density as a proxy for the level of population concentration. We use fixed-effect models to estimate the linkage between human capital stock and innovation output at the prefecture level and panel quantile regressions to capture the regional heterogeneity. Finding from regressions show that, on average, increased stock of human capital is associated with more innovation outputs, and population concentration is not significantly directly linked to innovation output. The effect of human capital accumulation on innovation output varies from one city to another, and this effect is stronger in cities that situate in the lower rung of innovation hierarchy. The concentration of population is found to strengthen the positive impact of human capital accumulation on innovation, and the increase in urban size is found to promote knowledge spillovers. The moderating effect of population concentration on the relationship between human capital stock and innovation output occurs when the innovation capacity of a city reaches a certain threshold. This moderating effect becomes stronger with an increase in a city’s innovation capacity. Therefore, policymakers are advised to formulate and implement appropriate policies to attract and cultivate talents and to encourage movement of talents, considering the innovation capacity and urban size .
This study measures the spatial correlation network matrix of ‘Internet plus’ based on the revised gravity model, and explores the structural characteristics of spatial correlation network of China’s (excluding Tibet, Hong Kong, Macao and Taiwan) ‘Internet plus’ development through social network analysis methods. We also examine the status, role, type and role of each province (region) in the network, and reveal the influencing factors of spatial correlation network by utilizing the QAP method. The conclusions are as follows: 1) In terms of the overall network characteristics, the degree of interprovincial ‘Internet plus’ connectivity has increased considerably, and the network density has shown a certain degree of expansion. Although the network density presents the increasing trends, the interprovincial ‘Internet plus’ spatial correlation network is not high enough, which needs to be further improved. 2) As far as the network centrality, Beijing and Shanghai are located in the core of the ‘Internet plus’ spatial correlation network, these cities are two major engines promoting the development of ‘Internet plus’. Meanwhile, the role of Guangdong and Jiangsu in the ‘Internet plus’ spatial correlation network has begun to highlight, and gradually moving towards the core area, which is expected to play the central actor role in the ‘Internet plus’ spatial correlation network. And accelerating its internal connections with surrounding areas by virtue of its high efficiency of resource flow and resource acquisition. 3) China’s ‘Internet plus’ development can be divided into four sectors. There exist little changes among these four sectors, only Jiangsu, Fujian, Inner Mongolia, Liaoning and Guangxi have changed during the sample period. In 2016, the first plate includes Beijing, Tianjin, Shanghai, Guangdong and Jiangsu, which belongs to typical ‘net spillover’ type; the second plate covers Zhejiang, Shandong and Fujian, which belonged to a typical ‘broker’ type; The third plate includes Hebei, Hainan, Shanxi, Jilin, Heilongjiang, Anhui and other 16 regions, which belonged to a typical ‘main benefit’, and the fourth plate involves Inner Mongolia, Liaoning, Jiangxi, Henan, Hubei and Guangxi, which belong to the typical ‘two-way spillover’ plate. 4) A complete closed circuit is formed among these four plates. The first plate takes the role of the first radiation center in the network, and presents strong spillover effect on the provinces belong to other three plates. The second plate acts the role of second radiation centers and intermediary bridges, which not only exhibits spillover effects on the third and fourth plates, but also transfers the spillover effects of the first plate to the third and fourth plates. The third plate plays the role of receiver, which receives the spillover effect from the first and second plates. The fourth plate plays a pivotal role in the network, which presents the two-way feedback relationship with the first and second plates. 5) Technological innovation, infrastructure, human capital, market development and opening-up all present positive effects on the spatial correlation network of ‘Internet plus’ development. Especially, China’s ‘Internet plus’ spatial correlation network is inhibited by spatial geographical distance, and the knowledge overflow and flow effect of the ‘Internet plus’ would gradually decrease with the increasing geographic distance.
The research on the efficiency of urban infrastructure is an important part of the scientific evaluation of the quality of infrastructure development. On the basis of constructing the evaluation index system of urban infrastructure input efficiency, with the help of maxdea software and DEA model, this article measures and decomposes the input-output efficiency of China’s municipal infrastructure from 2006 to 2016, and analyzes its spatial correlation pattern by ESDA method. The results show that: 1) The overall input-output efficiency of China’s urban infrastructure is not high, but it shows a growth trend, and the high-efficiency cities are also increasing at an average speed, gradually changing to scale investment, and the technical efficiency increases slowly. 2) The spatial distribution pattern is high in the eastern China, low in the middle and high in the western China. 3) The spatial agglomeration range of urban infrastructure investment efficiency has shifted, and the hot spot distribution is roughly consistent with its spatial distribution.
Roads play an important role in socio-economic development and are the link between geospatial and human socio-economic activities. Road network expansion is an important driving factor that leads to the increase of the fragmentation of regional habitats. How to measure the characteristics of road networks scientifically and quantitatively is a key scientific issue in road ecology. Taking the upper reaches of the Minjiang River as a case, in view of some limitations in the calculation process of the current road network measurement index, a weighted road network linear density index and a sloped road network influence domain area density index are proposed and adopted. Combined with the spatial autocorrelation analysis method and using the grid of 1km×1km as the research unit, this study explores the spatial differentiation pattern of the road network's influence on the area comprehensively from the line and the surface. This study also analyzes the correlation of these road network measurement indexes to assist index selection in road ecological research. The results show that: 1) There are obvious regional differences in the degree of road impact in the study area. Generally speaking, it is decreasing from high-grade roads and urban centers in the middle of the study area to the surrounding areas. The area with a higher degree of road impact shows a ‘two vertical and two horizontals’ pattern. 2) There are spatial agglomeration effects in the influence of roads in the middle, east and south of the study area. Compared to the weighted road networks, the spatial autocorrelation of the linear density of unweighted road networks is smaller, and the differences in the influence of different grades of roads are ignored. In particular, the road network’s impact on the area is underestimated with the unweighted method. In contrast, the weighted road network and line density reflects the actual situation of the impact of different grades of roads. Moreover, the area of road influence of the same grade is not a fixed width, but changes with the slope on both sides of the road. Therefore, the result is more in line with objective reality. 3) According to the improved road impact area (MREZ) divided by taking into account the slope, the area of road impact area in the study area accounts for 39.9% of the total area of the study area. Different grades of roads have different areas of influence, with expressways having the largest area of influence and county roads having the smallest area of influence. Regardless of the division method of REZ or MREZ, the areas occupying 16.9%-17.6% of the study area are affected by multiple grade roads simultaneously. Finally, the six indexes of road network measurement are all significantly correlated. As expected, similar indexes have a large correlation while the different indexes have a small correlation. If only a single linear density or areal density index is used, it will lead to incomplete information. Although the correlation between similar indices is very strong and mutually corroborated, the improved index proposed in this study is more in line with objective facts and has simple calculation. It is recommended that in the future research on the ecological impact of roads, these improved indexes should be taken into consideration. The exploration of the new index of road network measurement in this study helps to accurately and quantitatively evaluate the ecological effect of the road network, and provides a scientific basis for further planning of the road network and its surrounding ecosystems.
In the era of knowledge economy, talent are acknowledged as a key driver for innovation, and thereby determine the vitality and competitiveness of regional economy. The competition of attracting talent initiated by some second-tier cities is increasingly heating up. The central government has repeatedly stressed the importance of reasonable, fair, smooth and orderly mobility of talent to facilitate economic transformation. Against this background, based on the “Graduate Employment Quality Report” and first-hand questionnaire survey data of university graduates from Nanjing, this study adopted chord diagram and map visualization to depict the previous and subsequent migration patterns of university graduates. Furthermore, multinominal logistic regressions were employed to explore the factors underlying graduates’ migration choice. The results show that the the number of people flowing into Nanjing for higher education decreases with the increase in distance. Nearly 84% of the graduates originally come from the eastern and central regions. After graduation, their subsequent migration shows further concentration to the east. Using a measurement of regional circulation, the overall migration path of graduates presents an “east-west asymmetric U-shaped” pattern, with the Yangtze River Delta region constituting the core area for graduates to flow to. The economic development level of the destination of graduates’ subsequent migration is obviously higher than that of their domicile, and the destination is often in close proximity to their domicile. At the individual level, the rank of the university, discipline, social netweok, and graduates’ evaluation on job opportunities all have significant influence on graduates’ migration decisions.
The rise of urban network research provides a new perspective to analyze the spatial structure of urban agglomeration. The Yangtze River Delta urban agglomeration, one of the most developed and the most active area of human migration in China, has gradually formed a network development pattern, and it is of great significance to study its network spatial structure. This article used the Tencent’s population migration data to construct a directed weighted urban network. The directed alternative centrality (DAC) and power (DAP) were used to measure the characteristics of urban network spatial structure in the Yangtze River Delta. Then the related influencing factors on DAC and DAP of the cities were studied. The results showed that the Yangtze River Delta urban agglomeration has formed a multi-core and hierarchical network structure. Specifically, Shanghai was the core city facing the whole Yangtze River Delta, while Suzhou (Jiangsu), Nanjing, Hangzhou, Hefei, Wuxi, and Ningbo were the core cities facing the local area. There were several spatial development patterns including “urban integration” and “core-periphery” patterns. For example, the population flow connection between Shanghai and Suzhou (Jiangsu) showed that there was an obvious urban integration development mode. The population flow connections between Hangzhou and Shaoxing, Wuxi and Changzhou showed a kind of primary urban integration development patterns, while that between Hefei and Lu’an presented a significant core-periphery development model. The connection between Shanghai, Wuxi, and Ningbo and some cities with population diffusion revealed that there was a development model of “strong core-general core-periphery”. According to the results of DAC, DAP, and the population hinterlands, there were 7 different development types of the 41 cities in this region. The population hinterlands of core cities were generally overlapped and competed. For example, the population hinterlands of Shanghai covered the entire region, and those of Soochow and Nanjing were mainly in Jiangsu and some cities in Anhui Province. The population hinterlands of Hangzhou mainly covered cities in the northern Zhejiang and southern Anhui. Generally, the migration of people from edge cities, such as Lu’an, Huainan, Yancheng, and Quzhou, tends to go to more than one core city, which may lead to fierce competitions among core cities, such as Shanghai, Suzhou (Jiangsu), Nanjing, and Hangzhou. Correlation analysis showed that the city’s economy, administrative level, employment opportunities and income level had great impacts on the DAC and DAP of one city, and the accessibility had some impacts on the DAP of the cities. The study further summarized the spatial structure and development characteristics of the directed urban network, and could provide supports for achieving a high-quality and coordinated development of the Yangtze River Delta urban agglomeration.
With the deepening of ‘the Belt and Road’ and the accelerating pace of Chinese enterprises going global, the Overseas Economic and Trade Cooperation Zones (OETCZs) had become an important platform to undertake the overseas investment of Chinese enterprises and the production capacity cooperation between countries. At present, the case analysis of OETCZs were gradually enriched, but less attention is paid to the ‘Garden within Garden’ development mode of OETCZs. How to overcome the risks brought by the heterogeneity of the host country’s institutional and cultural environment, and how to improve their competitiveness in the context of inclusive globalization are the key issues that need to be solved in the development process of OETCZs by constructing the development mode of ‘Garden within Garden’. This paper takes the successful China-Indonesia Economic and Trade Cooperation Zone as an example to discuss the successful development experience of China’s OETCZs and the development mode of ‘Garden within Garden’ under the background of ‘the Belt and Road’. As one of the first batch of state-level OETCZs set up by Chinese enterprises in Indonesia, the China-Indonesia Economic and Trade Cooperation Zone is a typical ‘Garden within Garden’ development mode. Through on-the-spot investigation and interview method, this paper constructs the framework to analyze the ‘Garden within Garden’ development mode, focuses on the analysis of the development process and characteristics of China-Indonesia Economic and Trade Cooperation Zone from the three dimensions of building the cooperation network among multiple actors, embedding in the local institutional and cultural environment, and providing high-quality environment of the park. On this basis, the analysis framework of the ‘Garden within Garden’ development mode which is helpful to summarize the successful experience of OETCZs from the theoretical aspect, and provide case study and theoretical reference for the construction of OETCZs in the future.
Based on the data of e-sports enterprises from 2003 to 2016, using the Herfindahl-Hirschman index, spatial autocorrelation analysis and spatial panel econometric model, the geographic agglomeration characteristics of China’s e-sports industry and its influencing factors are quantitatively analyzed. The research fidnding shows: 1) The development of China’s e-sports industry is advancing through twists and turns, and has significant stage characteristics. Since the entry of e-sports games into China in 1998, it has a history of more than two decades. It has experienced many iconic events such as germination, officialization, twists and turns, recovery, and rebirth. It has finally been recognized by the public and the official. After being officially listed in China’s sports events in 2003, it experienced three stages of “volatility development-power accumulation diffusion-concentrated outburst”, which was characterized by difficult exploration, legal and correct name, and entering life. 2) The development of China’s e-sports industry is spatially dependent, and the evolution of industrial agglomeration has undergone various structural models. The spatial distribution state of the e-sports industry presents a spatial dependence from random to agglomeration, and its evolutionary model is constantly changing, showing an overall two-level agglomeration center and undergoing a structural evolution from “dual-core” to “single-core”. The focus of development has generally moved to the Guangdong-Hong Kong-Macao Greater Bay Area. 3) Analysis of driving factors shows that factors such as economic level, industrial structure, human resources, government policies, innovation environment, economic extroversion, and user scale all affect the geographic agglomeration of the e-sports industry. Among them, the influence of industrial structure, human resources and economic level on the agglomeration of e-sports industry is particularly significant. The high economic development level, excellent completion structure and good human resources are more conducive to the development and agglomeration of e-sports industry.
As an important part of modern transportation system, the huge traffic flow of expressway truly reflects the regional connection and has a profound impact on the social economy, people’s life and spatial structure. The massive expressway toll data collection is a significant support to analyse and reveal the traffic flow characteristics and the spatial structure evolution. According to the Jiangsu expressway toll collection data in 2011, 2014 and 2017, this paper selects car traffic to construct the O-D flow network and county connection network, so that the key toll stations and distribution characteristics of network degree are identified effectively, and the spatial pattern of car flow and the features of its network group structure are revealed. It is found that the spatial distribution of car flow shows a notable difference between the south and north of Jiangsu, with high-value points around the toll stations of central cities, provincial boundaries and cross-river bridges. The O-D flow network has an obvious scale-free feature, while the community structure is characterized by distribution along the expressway routes. The county connection network can be divided into 8 communities, with central cities as the cores.
The eco-environmental effects and spatial heterogeneity characteristics of land use transformation of ‘ecological-productive-living’ is an important basis for guiding regional territorial spatial development and eco-environmental protection. This study used remote sensing land use data for 1998, 2008 and 2018 to construct a land classification system of ‘ecological-productive-living’ in the Yellow River Delta (YRD), China. The characteristics of land use transformation of ‘ecological-productive-living’, the eco-environmental effects and the main influencing factors were quantitatively analyzed by the transition Tupu, eco-environmental quality index, the gravity center migration model and the Geodetector statistical method. The results show that: 1) Land use transformation in the YRD was mainly manifested as a substantial increase in the proportion of productive land (+13.50%), a decrease in the proportion of ecological land (?17.19%) and small increase in the proportion of living land (+3.69%). From 1998 to 2008, main types of transformation are the conversion from ecological land to productive land. From 2008 to 2018, there is no absolute advantage types of land use transformation, and the transformation process is complicated. 2) The eco-environmental quality index for the YRD increased from 0.390 in 1998 to 0.395 in 2018, with the spatial distribution of the index mainly showing areas of higher quality. The eco-environmental qualities of the central part and the estuary of the YRD showed continuous increases, whereas those of the southeast and northeast coastal areas decreased. 3) The main factors affecting the pattern of eco-environmental quality in the YRD was shown to be vegetation coverage, micro-geomorphic types and soil types, with the contribution rates of location factors and socio-economic factors becoming weaker with an improvement in eco-environmental quality. The results of this study showed a rising contribution rates of ecological protection efforts to the pattern of eco-environmental quality in the YRD.
The quality of urbanization structure based on the orderly coordination and coupling of internal systems is an important basic component of urbanization quality, which should be included in the existing urbanization quality analysis framework that based on urbanization development benefits. The former is called the foundation and supporting urbanization Quality I, the latter is called the benefit of urbanization quality II, which is mutually causal. Based on the perspective of urbanization structure quality, this article uses the improved coupling coordination model to comparative analysis the urbanization structure qualities and their driving forces between Chongqing and Chengdu that are the core cities of Southwest Economic Core Area (Chengdu-Chongqing urban agglomeration area) from 2005 to 2017, in order to provide a reference for the integrated construction of the “Chengdu-Chongqing Twin Cities Economic Circle”. The results show: 1) The qualities of urbanization structure in Chongqing and Chengdu are relatively high, and their are continuously in growing, and Chengdu ’s urbanization structure quality is significantly higher than that of Chongqing. Which is closely related to the spatial organization structures formed by topography, development stage, and urban positioning, of Chongqing ’s “modern metropolis in the main urban area + vast rural areas in non-main urban areas” spatial organization structure and Chengdu’s modern metropolitan spatial organization structure. 2) The driving factors of urbanization show the characteristics of diversification, stages and differences. In which, the administrative force and market force played important roles during the period, and followed by the internal forces and the external forces, while the external forces in the whole process is continuous weakening, reflecting that China’s urbanization is an endogenous development progress. 3) Based on the average values of the urbanization subsystem and its system coupling and coordination analysis, can effectively avoid the distortion and inaccuracy caused by the differences in index selection of each urbanization subsystem. Finally, this article puts forward some suggestions for the urbanization sustainable of the two cities.
With the frequent occurrence of social crises and natural disasters, improving resilience has become an important means for socio-ecological systems to cope with external disturbances. Scientific evaluation of socio-ecological resilience has important practical significance for local decision-making. Based on the variable fuzzy recognition model, the article analyzed the spatio-temporal evolution of socio-ecological systems resilience in 12 poverty-stricken counties in the Dabie Mountain of Anhui Province from 2008 to 2017. Then, by using the obstacle degree model, the article revealed the main influencing factors and characteristics of resilience. The results showed that: 1) In terms of temporal changes, the resilience of the socio-ecological system showed a steady upward trend, but the overall level was not high. Among them, the resilience of the social subsystem had risen slightly, and there had been a sudden change in some years; the resilience of the economic subsystem had increased year by year, with obvious time periods; the resilience of the ecological subsystem had a ‘rising-falling-rising’ fluctuation repeatedly, but the overall trend was downward. 2) In terms of spatial dynamic changes, the resilience of the socio-ecological system generally showed a ‘very low-lower-medium’ upward trend. The socio-ecological system resilience changed from differentiation to convergence among 12 counties, and changes was faster among central counties, slower among north and south counties. To social subsystem, except for some adjacent counties, the spatial correlation of the resilience was relatively weak, and the resilience in north area improved faster than in the south. To economic subsystem, the change of their resilience was correlated spatially, improved continuously, changed consistently among 12 counties, and the resilience in south area improved faster than in the north. To ecological subsystem, the resilience was correlated partly among 12 counties, and south area was slower than north. 3) For socio-ecological system resilience, tourism development, Man-land relationship and ecological environment were the main influencing factors, especially tourism development was the most; economic development, medical education, resource endowment and poverty alleviation policies were secondary influencing factors. More closer the counties were, more higher the similarity of influencing factors were, but as the complexity of the terrain changed, the similarity decreased from north to south. Some problems still deserve further study: 1) In the socio-ecological system of tourism destination, how to quantitatively separate the impact of tourism and other factors, and explore the effect of tourism development on the resilience of socio-ecological system more carefully? 2) In order to evaluate the effect of tourism development, how to compare socio-ecological system resilience between poverty-stricken areas and non poverty-stricken areas, tourism areas and non tourism areas? 3) Tourism development in poverty-stricken areas started relatively late, and how to explore more effective methods to reveal the law of socio-ecological system resilience in poverty-stricken areas in a limited time scale?
Due to the lack of advanced technology and talent innovation elements, the evolution process and formation mechanism of industrial cluster innovation network in underdeveloped areas vary a lot from those in economically developed areas. How to break through the bottleneck restrictions of talent and technology and how to scientifically build its innovation network become to be very critical in the process of high-quality transformation. In order to enrich the research content and paradigm of traditional industrial cluster innovation network, as well as provide a theoretical basis for local governments in underdeveloped areas to enhance the development of cluster innovation, this article takes Gaogou Town in Anhui Province, a national cable town in underdeveloped areas has been selected as a case, and takes the traditional cable industry cluster has been taken as the object for this research. Based on patent cooperation and field survey data, with the help of UCINET, a quantitative analysis of the innovation network of the Gaogou cable industry cluster is carried out in this article to reveal the composition and evolution characteristics of the innovation network, and to explore its evolution mechanism as well from the perspective of multidimensional proximity theory. The research shows that: 1) The innovation network of industrial clusters is composed of three major elements; network main body, network connection and network structure, which are interrelated and develop synergistically; 2) the innovation network of traditional industry cluster in towns area has been formed for a long time, and has experienced the initial stage, networking stage and development stage. During above the period, the innovation subject changed from single to diversified, the mode of connection turned from informal to formal, the strength of connection grew from weak to strong, and the connection structure developed from sporadic sites to network. 3) Due to the limitation of regional scope, industrial nature and economic foundation, geographical proximity plays an important role in the formation of innovation network of traditional industrial clusters in towns; Cognitive proximity is in backward state, and its function is restricted by spatial proximity to a certain extent; The self-innovation ability of traditional industries in towns is quite weak, and the innovation network mainly depends on the government and cooperative organizations, and the role of institutional proximity and organizational proximity needs to be further highlighted.
Based on a household survey data of nearly one thousand displaced households in the suburbs of Nanjing, this paper studies their employment changes, and the timing and reasons for their changes, using an event history model. The results show that: 1) More than 90% of low-income urban residents have experienced employment changes after displacement, and the employment changes were concentrated in the first four years after their forced move, with 1.5 changes per capita during the study period. The changes in employment is the result of a combination of factors, which varies across employment changes. 2) Individual socio-economic attributes such as gender, age, education, and commute before and after displacement have significant effects on employment change. Respondents who were older at the time of move, lower in education, female, had a shorter commute before displacement, had a longer commute after displacement, and had worked in a city center the previous year had a greater probability of employment change. 3) Factors such as the late family movement year, more employment opportunities in the community, and the opening of new subway lines have clearly induced individual employment changes. In other words, the availability and accessibility of jobs has a significant impact on employment change. 4) Occupation in the previous year has a significant impact on whether employment changes. Self-employed persons are less likely to experience job changes than staffers, and the former have relatively stable employment. In addition, the number of years the family has stayed on the individual employment has a significant impact. The longer residents stay, the lower the probability of employment change. In other words, the employment status of forced movers tends to become more stable over time.
Taking Zhengzhou as an example, the hot spot analysis results of POI elements are analyzed by spatial weighted overlay method based on the residents’ dependence and element attribute values, and the results show the static structure of urban polycentric space. This paper expresses residents’ travel preferences by using the theory of word vector and data field, analyzes the attraction strength of urban sub-centers to dynamic targets by using trajectory data, and explores the dynamic interaction relationships of polycentric networks. The results show that: 1) The static elements of the city are in a circle spatial distribution form with a centralized core and scattered peripheries. The density of various elements gradually decreases towards the periphery with Erqi Square as the center. On one hand, affected by the construction policies and plans of new urban districts, some sub-centers have formed the ability of attracting and gathering. Their functional positioning, industrial planning and surrounding traffic play an important role in regional development. On the other hand, although the planning prospects of some remote sub-centers are broad, they are far from forming the decentralization effect that the sub-centers should have. 2) The attraction strength of sub-centers to dynamic targets is not balanced. Although the polycentric attraction strength and spatial interaction shown by the trajectory of taxis and electric bicycles are different, they all reflect the typical overflow structure of this city. Various urban elements and populations gradually disperse from the overly dense core urban area to the periphery. The core urban area is still the main direction of spatial interaction. Therefore, it is necessary to create a good environment for the development of polycentric regions through policy guidance, strengthen the infrastructure and transportation facilities construction of characteristic functional centers, enhance the attraction of peripheral sub-centers, and achieve balanced development of urban space.
The northern Shaanxi is a key region for the distribution of red beds and Danxia landform in China, it has significant differences with Danxia landform in southeast China and abroad. However, few research was concerned with this issue. Based on field investigation and analysis of geologic literature and rock samples, this study focuses on the development of Danxia landform in the northern Shaanxi from the aspects of geologic background, lithologic characteristics, geomorphic features, and evolution process. Then, a comparison with Danxia landform in the humid southeast China and foreign countries was conducted to summarize their commonalities and differences. It reveals that Danxia landform in the northern Shaanxi is developed in a large inland depression basin, the Ordos Basin. The dominant red beds for the development of Danxia landform is Luohe Formation, which is mainly composed of medium to fine-grained sandstone and was deposited in an arid desert during Early Cretaceous, and featured by large cross-bedding. Experimental analysis shows that the Luohe sandstones are well-sorted and well-rounded but poorly cemented, typical features of aeolian sandstone such as disc-shaped impact craters are found on surfaces of quartz particles. The most remarkable geomorphic feature of Danxia landform in the northern Shaanxi is that there is a Quaternary loess cap on the top, forming a type of covered Danxia. In terms of landform evolution, Danxia landform in the northern Shaanxi is generally in young stage, featured by a combination of plateau and canyons, with closely spaced gullies and continuous Danxia cliffs developed, but there are few isolated individual landforms. The developmental process of Danxia landform in the northern Shaanxi could be divided into four stages: 1) Red beds deposition during Early Cretaceous; 2) Tectonic uplift and the development of paleo-Danxia landform during Late Cretaceous; 3) Intermittent uplift since Paleogene and covered by loess in Quaternary. As for comparison with Danxia landform in other regions at home and abroad, Danxia landform in the northern Shaanxi has many differences with those in the humid Southeast China, but it has more similarities with Danxia landform in the western United States. The current theories based on Danxia landform in the humid southeast China cannot well reflect these regional differences, more further comparative research on Danxia landform in and outside China should be conducted, so as to improve our understanding on Danxia landform.
With the development and popularization of the Internet, illegal and criminal activities have gradually penetrated cyberspace. Cyber fraud, as a typical cybercrime, has been causing property losses and seriously threatening social stability. Based on 25 597 first-instance written judgments of cyber fraud cases obtained from the China Judgments Online Database (excluding Hong Kong, Macau and Taiwan), this study used Natural Language Processing (NLP) method to extract county-level cyber fraud cases in China from 2017 to 2020 and analyzed the spatial-temporal distribution of cyber fraud. The results of spatial autocorrelation analysis show that cyber fraud mostly happens in the southeast coastal areas, including Jiangsu, Shanghai, Zhejiang, Fujian, and Guangdong. Over time, the clusters of cyber fraud cases in Anhui and Henan gradually disappeared in 2019, while some other noticeable clusters appeared in Hunan, Chongqing, etc. Nationwide, cyber fraud criminals mainly come from Fujian, Hubei, Henan, Guangdong, and Hunan. There are significant differences in the distribution patterns of cyber fraud criminals in different provinces. Cyber fraud criminals inflow to Jiangsu and Zhejiang are relatively scattered, coming from Fujian, Jiangxi, Hunan, Hubei, Henan, Guangdong, Anhui, etc. Cyber fraud criminals inflow to Guangdong, Fujian, and Henan are comparatively concentrated, mainly come from local province and neighboring provinces.
Through specific analysis of the three dimensions of night light development index (NLDI), cultural diversity and the rank-size rule, the human development level of the Qinghai-Tibet Plateau in different periods is evaluated. The results show that: 1) Since the 21st century, human development in the Qinghai-Tibetan Plateau has been improved year by year, but the overall level of human development is still below average level. There are some spatial differences in the level of human development within the region. The night light intensity in the southeast is relatively high, showing the concentrated population distribution. 2) The areas with high ethnic diversity in the Qinghai-Tibetan Plateau mainly extend and expand along the Tibetan-Yi Corridor and Hexi Corridor. The night light intensity in the multicultural blending area is high, and the overall level of human development is on the rise. 3) The rank-size rule is used to analyze the urban scale constructed by night light intensity. A prominent problem in the urban system of the Qinghai-Tibet Plateau lies is that the maximum urban scale is still too small.
Based on the meteorological data from 8 weather stations in the Alxa Plateau, the wind erosion climatic factor index (C factor) was calculated using the function given by the UN Food and Agriculture Organization, and the spatio-temporal variation of wind erosion climatic erosivity was analyzed. The results showed that the C factor of Alxa Plateau ranged from 15.0 to 160.0 and the average was 67.7. On the spatial, C factor decreased from Guaizi Lake to Southeast and southwest respectively; the C-factor, at Guaizi Lake station was 156, while the C factor, on the southern edge of Tengger Desert in the southeast of Alxa Plateau was 20-30 and that of Heli Mountain in the southwest was 30-35. The seasonal variation of C factor was distinct. On the whole, the largest value was in spring, the second in summer and the smallest in autumn. The sum of C factor in spring and summer accounted for 62.6% of the whole year. Mann Kendall (M-K) test showed that the wind erosion climatic erosivity had abrupt change in 1990. The annual C factor of Guaizi Lake station increased markedly, while other stations decreased significantly. Wind speed was the decisive factor of wind erosion climate erosivity in the Alxa Plateau.
