Innovation is one of the important ways to promote the high-quality development of the Yellow River Basin. Using the panel data of 79 cities above prefecture level in the Yellow River Basin from 2006 to 2018, this article first constructs an index system to analyze the level of technological innovation and green development of each city, and then deeply explores the mechanism of technological innovation on regional green development through panel econometric model. The results are as follows: 1) From 2006 to 2018, the level of urban technological innovation and green development in the Yellow River Basin has been greatly improved, but the spatial difference is significant, and the overall trend is “downstream > midstream > upstream”. 2) On the whole, technological innovation has no influence on urban green development in the Yellow River Basin, but after adding the quadratic term of technological innovation, there is a obvious positive “U-shaped” nonlinear relationship between them, which also shows the existence of “rebound effect”. 3) The impact of technological innovation on urban green development in the Yellow River basin can be reflected by both direct and indirect effects, but the two effects are just the opposite. That is to say, the promotion of a city’s technological innovation level has a significant “U-shaped” relationship with the urban green development, but it has an inverted “U-shaped” relationship with its neighboring cities. According to the research conclusions, this article puts forward the corresponding policy implications from the direct and indirect effects of urban technological innovation on green development. First of all, it is particularly important for the middle and upper reaches of the Yellow River basin to enhance the ability of technological innovation and strengthen the innovation-driven effect on urban green development; Then, cities in the Yellow River Basin should break the “beggar thy neighbor” phenomenon, so as to strengthen the coordinated development in the Yellow River Basin and give play to the positive spillover effect among cities.

Carbon emission control is the main problem and measure of ecological protection and high-quality development in the Yellow River Basin. Carbon emission at county level research can provide more accurate theoretical support for collaborative governance and sustainable development of the Yellow River Basin. Spatial panel model, spatial autocorrelation analysis and spatial Markov chain with regional background and nearest neighbor as spatial lags were used to analyze the spatiotemporal pattern and spatial effect of carbon emissions in counties of the Yellow River Basin from 2000 to 2017, the results showed that: 1) the carbon emission in the Yellow River basin has increased dramatically since 2000; the high carbon emissions areas, Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia, expands to the outer circle layer and the axial direction, forming the spatial pattern of high in the east and low in the west; 2) there is a phenomenon of “club convergence”; the high carbon emission counties converge in Shandong province and the boundary between Shaanxi, Gansu, Ningxia and Inner Mongolia; the low carbon emission counties converge in the southwest; the comparison between 2000 and 2017 shows that county carbon emission type has strong stability; counties which tranfered from higher carbon emission type to lower carbon emission type are concentrated in the southeast region, while counties that change in the opposite direction are concentrated in Inner Mongolia. 3) high carbon spillover effect and low carbon locking effect are important forces to shape the spatiotemporal pattern and the former is stronger; the regional background enhances “club convergence” and the convergence of surrounded outliers and its acting force was stronger than the nearest neighbor; the probability of carbon emission type transition in insignificant regions increased; 4) the spatial panel model shows that increase of carbon emissions and its spatial effect are promoted by young population structure, large economy, industrial structure dominated by the secondary industry, high living standard and high public expenditure; economy and industrial structure are important driving factors.

With the accelerating process of urbanization and industrialization in China, the coexistence of excessive non-agricultural farmland and low efficiency of urban land use is becoming increasingly serious. So taking the Yellow River Basin as the research object, it is of great theoretical and practical significance to analyze the spatial pattern of urban land economic density in the Yellow River Basin from the provincial, municipal and county level. Taking 8 provinces and regions (Gansu, Qinghai, Shaanxi, Shanxi, Henan, Inner Mongolia, Ningxia and Shandong) in the Yellow River Basin as the research objects, based on the multi-temporal and high-resolution global urban boundary interpretation data in 2018, the urban land economic density of the Yellow River Basin was calculated from the provincial, municipal and county-level scales. The spatial pattern of urban land economic density in the Yellow River Basin was discussed by using the methods of Theil index, global and local spatial autocorrelation analysis. With the help of geographical detector, the influencing factors of urban land economic density were analyzed. The results show that: 1) The economic density of urban land in the Yellow River Basin is generally not high. At the county scale, 68.3% of the counties are lower than the average level; At the municipal level, 57.5% of cities are below the average. 2) There is a significant spatial positive correlation of urban land economic density in the Yellow River Basin. The high value agglomeration areas (HH) are concentrated in the Central Plains urban agglomeration and Shandong Peninsula urban agglomeration, the low value agglomeration areas (LL) are concentrated in the western China such as Shaanxi, Gansu and Ningxia. And the low-quality heterogeneous area is inlaid around the Central Plains urban agglomeration and Shandong Peninsula urban agglomeration. 3) In the whole Yellow River Basin, per capita GDP, population size, local financial investment in science, technology and education, labor density of secondary and tertiary industries and location quality index have a great impact on the economic density of urban land. There are some differences in the upper, middle and lower reaches of the Yellow River Basin. Generally speaking, capital investment intensity, per capita GDP and location quality index have high explanatory powers for the economic density of urban land in the upper, middle and lower reaches.

The spatial organization of MNCs’ global R & D network investment is based on the needs of the current generation required by the demand of MNCs’ open innovation, which further deepens as well as develops MNCs’ traditional global production network investment. In the meantime, it’s a whole new international division of labor measure launched by MNCs in the research and innovation so that MNCs can integrate every country’s preponderant R & D factors to the largest extent, and maintain its lead in its own strategically core and key technologies for a long time when the company is in the face of shorten world knowledge renewal cycle. Based on the concept of generalized complex system, this paper breaks through the tradition that people concentrate more on researching the spatial organization relationship of global R & D network investment spatial organization of MNCs in a few process fields, primarily based on closed-loop thinking, and therefore establishes an analysis framework which can be used to study the whole process+multi system of global R & D network investment spatial organization of MNCs as well. First of all, the article multi-systematically deconstructs the spatial organization of MNCs’ global R & D network investment. Then, based on the results of the above multi-systematical deconstruction, it analyses the whole process of spatial organization of global R & D network investment of MNCs even further within the framework of multi network system. This paper analyzes the spatial organization process of MNCs’ global R & D network investment, which is based on the decomposition analysis of the spatial organization of MNCs’ global R & D network investment. The main conclusions are as the following through the study: 1) What the spatial organization of MNCs’ global R & D network investment composes is a complex giant spatial system, and this spatial organization of MNCs’ global R&D network investment breaks the traditional structure of foreign investment labor division which primarily considers R & D, marketing along with processing and assembling as main parts of labor division. 2) In order to attain the goal of integrating every country’s preponderant R & D factors all over the world, attempting to make a breakthrough of geographical restrictions that innovation mainly stems from a specific country or a specific region is not the purpose of the spatial organization of MNCs’ global R & D network investment. As a matter of fact, the true aim of this spatial organization is to acknowledge the existence of the local innovation system such as regional innovation system, national innovation system and so on. 3) In addition, the spatial organization of MNCs’ global R & D network investment relates to the problems about how to feedback and dock with the traditional spatial organization of MNCs’ global production network investment. 4) The multi-system cooperative governance relationships formed by MNCs’ global R & D network investment space organization in multi stage are not separate mutually but back-and-forth. They alternate with each other, altogether constituting the whole process of MNCs to search, integrate and take advantage of every country’s preponderant R & D factors. This study is expected to be of great significance to unify the knowledge of the systems and processes of MNCs’ global R & D network investment space organization.

The coordination development of urban population and service industry is vitally important for the deepening of urbanization and the improvement of regional competitiveness. However, the mechanism of the spatial coordination between them remains less studied, especially at the regional scale. This study hackled the theories of the spatial coordination between urban population and service in multiple spatial dimensions and conducted an empirical research, taking Fujian Province as the study case. Results showed that: 1) In spatial concentration dimension, Hoover coefficient demonstrated that both of them concentrated in the central city and the service industry concentrated higher. This can be attributable to the advantages of central cities in labor quality and age structure. 2) In spatial agglomeration dimension, spatial agglomeration index showed that both of them gathered around the central cities and the service industry gathered more densely, which reflected the spillover effect of service industry from central cities. 3) In spatial mismatch dimension, the trajectories of the gravity center revealed that, comparing with urban population, service industry is more inclined to the East Coast. This indicated that the eastern coastal area had location advantages in the development of service industry. 4) In spatial coupling dimension, the spatial coupling degree exhibited an upward trend in the process of evolution over time, and a gradual rise from the east coast to the west inland in the spatial pattern. 5) Panel data regression model presented that urban workers’ income, farmers’ income, construction land and fixed asset investment were the driving forces of the spatial coordination between urban population and service industry. At last, this study put forward some policy suggestions for the coordinated development of urban population and service industry in Fujian Province.

Collaborative innovation is of great significance to narrow the innovation gap and accelerate the innovation-driven development of enterprises. Taking high-tech industry of 27 provinces and regions (except Tibet, Qinghai, Ningxia, Xinjiang, Hong Kong, Macau and Taiwan) of China as an example, based on relevant data of China Statistical Yearbook on high technology industry from 2010 to 2017, this article deeply studies the impact mechanism of collaborative innovation on innovation gap from two perspectives: The effect of collaborative innovation on innovation gap and the feedback effect of innovation gap on collaborative innovation. And on this basis, it proposes four hypotheses then analyzes the interactive relationship between collaborative innovation and enterprise innovation gap by means of panel regression model, panel threshold model and panel vector auto regression model. The results show that collaborative innovation has no significant effect on the enterprise innovation gap. The effect of collaborative innovation on enterprise innovation gap has regional heterogeneity. When the level of collaborative innovation is high, the efficiency and speed of collaborative innovation will increase greatly, which is conducive to narrowing the enterprise innovation gap. When the innovation gap is larger, collaborative innovation can narrow the enterprise innovation gap. Collaborative innovation and enterprise innovation gap exist interactive relationship.

Research on the evolution of space and function of tourist destinations in historical cities and their logical relationship can guide the tourism organization and planning management of historic urban areas effectively. Using historical materials, historical maps and Google POI data, the evolution of space and function of Tianshui Historic City since Ming and Qing dynasties has been explored by space syntax. By superimposing the results of spatial syntax analysis with the spatial analysis of GIS that based on residents and tourists elements, the logic relationship between spatial form and tourism function has been investigated. The result shows that: 1) Since the Ming and Qing Dynasties, the tourismification of Tianshui Historic City has experienced the stages of popularization (Ming and Qing dynaties), decline (Republic of China), stagnation (1949-1987) of traditional recreation, the rise of modern tourism and the restoration of traditional recreation (1988-2000) and integration and innovation (2001-present) stage. The traditional urban structure of the historical city is gradually disappearing, but the main axis of the historical axis continues, with the best accessibility, penetration and coordination. The law of ‘space structure inertia’ has an obvious effect. 2) Under the combined effect of different actors, the functions of Tianshui Historic City have changed from traditional functions such as security defense, military politics, bazaars and commerce to industrial production, modern tourism and residential functions, and the recreational function of the city have gradually enhanced. 3) Residents and tourists share public activity space, but the service functions are separated from each other. The coordination of tourist activity space and functional services is better, while the consistency of residents’ public activity space and service facilities is poor. Finally, this article presents suggestions from the street network adjustment and historical city functional layout to promote the development of the historical city.

Taking ninety county-level cities in Zhejiang Province as geographical units, this article uses spatial analysis and negative binomial regression model to examine the structure, spatio-temporal evolution and impact mechanism of Zhejiang city innovative network from 2007 to 2017, with the data of co-application patents based on big text data mining. The study finds that: Firstly, the scale continues to expand and the structure becomes more and more complex in network evolution. In the study period, the scale of Zhejiang city innovative network has been growing from a sparse network to a dense network, but it is still underdeveloped. The network acts as small-world feature which is superior to random network, and the whole network has good accessibility and communication efficiency. Innovative network shows core-periphery pattern which is centralized, localized, and hierarchical, centers around Hangzhou Great Bay Area, leaves western Zhejiang cities in the edge. Secondly, as for impact mechanism, proximity of different dimension has different impact on the level of city innovative cooperation. In addition, city characteristics and network effects also have significant impacts. The intensity of city innovative cooperation in Zhejiang Province is significantly positively correlated with their economic scale, education level and policy support, and the technological gap and the administrative level also play a positive role. Geographical proximity and border proximity are positive factors affecting innovation cooperation. The relationship between cognitive proximity and innovative cooperation is u-shaped, which indicates that when cognitive proximity is at a low level, it is not conducive to innovation cooperation. However, with the increase of inter-city cognitive proximity, its marginal effect on innovative cooperation becomes more and more strong. On the contrary, relationship of innovative cooperation and institutional proximity shows an inverted ‘U-shaped’ curve, indicating that institutional proximity has a positive effect on innovative cooperation level at a lower level, but its effect will become weaker with the improvement of city market system. Technology proximity shows a positive effect, indicating that the similarity of technology structure makes the knowledge interaction among the cities more smoothly. Third, network effects play a positive role in promoting inter-city innovative cooperation. Compared with individual node network characteristics, network structure characteristics formed by cities surrounding the nodes play a more significant role in promoting cooperation. Innovative network more inclined to show the overall effect, so we should focus on the optimization of network structure, taking the advantage of role of core node.

Based on online public data, spatial clustering characteristic, spatial difference and influence factors of catering industry development level are analyzed by some spatial statistic methods such as kernel density estimation and spatial autocorrelation index. The main conclusions are as follows. At first, there are plenty of types of restaurants in Wuhan, the catering maintain obvious local flavor, and there are apparent differences among different types of restaurant. Secondly, customers in Wuhan have given good scores to all three index taste, environment and service, in terms of sorting, the taste score is larger than service score and service score is larger than environment score. Furthermore, all of the three scores have significant positive correlation with consumption price per capita. Thirdly, the high density area of catering service point distribution is along with rail transit line in Wuhan, and has highly related with business circles. At the same time, the three scores of catering service satisfaction have significant spatial aggregation. Fourthly, the hotspot distribution areas of the three scores, which mainly distribute in the traditional commercial and residential mixed areas and surrounding areas of important commercial facilities, are consistent overall, and the number of three types of hotspots is roughly equal. At last, some of the cold spot distribution areas of the three scores are consistent and some of them are not consistent. These cold spots mainly distribute around railway stations, hospitals and schools and old and remote community, and the number of cold spots is different obviously. The research results of this article could give some advices to urban planning, site selection of catering service point and smart travel for consumers.

The modern logistics industry has the advantage of going deep into all industries and is a new engine for economic growth of urban agglomerations. The express logistics industry, as a rapidly developing service industry, has gradually become a leading industry which promotes the transformation of circulation and stimulates consumption upgrading. And it further promotes the integration of urban agglomerations. Studying the spatial layout of express logistics network and exploring the network formation mechanism are of great significance for promoting the coordinated development of express logistics and improving the modern service industry in urban agglomerations. This research selected three major urban agglomerations in China, including the Yangtze River Delta, the Beijing-Tianjin-Hebei, and the Pearl River Delta as the cases, and the social network analysis, random forest classification methods and Quadratic Assignment Procedure methods were applied to analyzing the spatial structure characteristics of e-commerce express logistics networks at county scale and further explore its formation mechanism. The results show that: 1) The network density values are high, indicating that all three major urban agglomerations have formed typical network space structures, and the flow paths of e-commerce express logistics between cities are extensive. In the Yangtze River Delta, the e-commerce express logistics network has formed a hierarchy-shaped spider web structure, and the intensity of inter-county connections gradient decreases from strong to weak with Hangzhou Bay as the center. The network character has formed a “Beijing-Tianjin” dual core structure in the Beijing-Tianjin-Hebei due to administrative barriers. The network has formed a “Sui (Guangzhou)-Guan (Dongguan)-Shen (Shenzhen)” logistics link corridor in the Pearl River Delta and the intensity of inter-county connections increased from the periphery of the region to the east bank of the Pearl River Estuary. In addition, connection intensity in all the above e-commerce express logistics networks show the effect of hierarchical diffusion rather than proximity diffusion. 2) The logistics output-oriented counties are concentrated in the middle of the urban agglomeration, relying on the central city for outward express logistics dispersion; the logistics input-oriented counties are scattered in the periphery. Furthermore, the logistics balanced counties in the Yangtze River Delta are widely distributed; the low logistics input-oriented counties distributed in the northern Beijing-Tianjin-Hebei; and the high logistics output-oriented counties show an obvious centralized distribution pattern in the east bank of the Pearl River Estuary. 3) The formation of e-commerce express logistics networks are mainly influenced by per capita disposable income, permanent resident population and inter-county distance. The results are important to enrich the urban network theory as well as enhance the level of modern service industry and the overall competitiveness of urban agglomerations.

It is helpful to understand the behavior tendencies of different groups of criminals so as to uncover the formation mechanism of urban crime pattern and conduct measures for crime control and prevention precisely. This article focuses on the perspective of criminal offender, based on the data of pickpocket cases in Nanguan District of Changchun City from 2010 to 2016, using logistic regression models to examine the relationships between the demographic characteristics of criminals and their behavioral tendencies. The results shows that: 1) Offenders with the common ‘labels’ in terms of the demographic characteristics or some attributes tend to conduct crime in some specific periods and areas in the city. Generally, the gender, age, education, criminal record, household registration have some influence on the spatial and temporal tendency of the criminal offenders respectively. 2) In the pickpocket cases in Changchun, the male offenders have the spatial and temporal tendency of committing crimes along the street and in summer and autumn, while the female criminals have a tendency to commit crime in the place with denser population and in the daytime (10-21 o’clock). The older offenders tend to commit crimes in the places of commercial areas, public service facilities as well as streets in the rest day and daytime (4-15 o’clock). 3) The local registered criminals tend to commit crimes in the commercial areas, public service facilities and on the street. The offenders with a criminal record are more likely to commit crimes in the areas with denser population and in the day time (10-21 o’clock). It is because the fact that offenders with the common demographic characteristics have the similar spatial and temporal behavior pattern that the crime profile based on the offender characteristics will be useful in understanding the behavior mechanism of criminals, uncovering the relationship between the criminal attributes and the space as well as the time, and providing precise and scientific measurements for crime prevention and control.

Under the mode of global value chain division of labor, the focus of global industrial structure has shifted from manufacturing industry to service industry, and service trade has become a new engine to promote world economic growth. The trade value-added accounting method can truly and effectively restore the trade gains and actual status of a country participating in the global value chain, which is a beneficial supplement to the study of trade network under the traditional gross value accounting system. Based on the complex network theory and trade added value accounting, This research focuses on the spatial evolution and its influencing factors of trade in goods, services and value-added of the world’s 64 economies, applying the related technique tools, such as ArcGIS, Ucinet, and MATLAB. The main conclusions are as follows: 1) The tightness and accessibility efficiency of the three kinds of networks are steadily increasing, and the networking trend of service trade is more significant. The global financial crisis has a great impact on network balance and network link, which is shown in the index of network reciprocity and compatibility. 2) There are significant ‘core-edge’ in spatial pattern. At the regional level, there are two ‘cliques’ in the Asia-Pacific region and the European Union region. The United States, China, Japan and Germany are the pivotal nodes of the connection between the two ‘cliques’. 3) Top1 network shows different evolutionary characteristics among goods, services and value-added trade networks, and we found these three types of organizational structure of networks are shown the shape as ‘star’, ‘snowflake’ and ‘star-chain’ respectively. The rise of China is remarkable in three types of networks. In contrast, the development of service trade lags far behind that of goods trade. 4) The QAP analysis shows that traditional factors have strong explanatory power for the formation of the three types of networks, such as GDP, geographical distance, common language. Meanwhile, Disparities of per capita GDP, Economic Integration Agreement lead to their differences, but their significance level is not high and tends to weaken. Finally, combined with the current international situation, this paper gives some policy suggestions on the development of China’s foreign trade under the background of new era of globalization.

The rational layout of industrial tourism space has important practical significance for the transformation and upgrading of resource-based cities, old industrial areas, and industrial enterprises, improving quality and efficiency, and innovating and developing. Using the nearest neighbor index, thiessen polygons, kernel density estimation, buffer analysis and other methods, the spatial layout characteristics and linkage mode of different types of industrial tourism in Changsha-Zhuzhou-Xiangtan Urban Agglomeration are systematically based on multi-scale urban agglomeration, county (city), region the study. The results show that: 1) Industrial tourism in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration can be divided into three types: industrial tourism demonstration sites, industrial heritage and industrial zones. The spatial distribution types all show agglomeration, and the overall degree of agglomeration is relatively strong, specifically as follows: Industrial Tourism Demonstration Site> Industrial Heritage> Industrial Zone. 2) Industrial tourism has formed a ‘central agglomeration-peripheral dispersion’ spatial layout. The distribution density decreases from the center of the urban agglomeration to the periphery, and the central city decreases to the county and county-level cities. 3) From a multi-scale perspective, the distribution of industrial tourism on the scale of urban agglomeration is distributed in the form of “clusters”, on the scale of counties and cities it is distributed in the form of “isolated islands”, and the degree of agglomeration on the regional scale is the highest, forming a “patchy” clustered area. Based on the above conclusions, combined with the source market, tourism resources, traffic conditions, etc. of the Changsha-Zhuzhou-Xiangtan Urban Agglomeration, point-axis linkage, agglomeration linkage and integrated linkage modes of industrial tourism are proposed.

Tourism flow research is the core topic of tourism geography. With the development of urban tourism and the transformation of functions, the micro-scale tourist flow has an increasingly significant impact on the internal spatial structure of cities. This study takes the popular tourist city of Xi’an as the research area, collects online travel notes data, uses social network analysis and GIS spatial analysis methods to explore the structure of the tourist flow network and its different characteristics under different travel times. The main conclusions are as follows: 1) Under different travel time constraints, tourists’ travel behaviors show significant temporal heterogeneity. 2) The node structure of Xi’an tourist attractions has significant hierarchical and scale differences. With the increase of travel time, the hierarchical structure system of tourist nodes presents the characteristics of “strong and strong” association with high-level attractions as the hub. The tourist gathering function of high-level attractions has improved significantly. 3) Location proximity, proximity to popularity and convenient transportation are the key factors for the formation of attractions group.

Based on GDELT event data, quantitative analysis of ASEAN’s attitudes towards China events in 2013-2019 was made using tone index, event frequency, event memorability, tone equilibrium, and kernel density estimation. We also explored the spatial evolution characteristics of conflict events, tracked the events, and constructed a network of event relationships. Conclusions were as follows: ASEAN’s coverage of China dominated by cooperative events. ASEAN conflicted event tone equilibrium was relatively discrete and these events would be improved in a quarter, while cooperation and conflict events had a positive impact on the occurrence of similar events in the semimonthly cycle. ASEAN risk pattern was dominated by large events and complemented by local events, while the evolution of the risk pattern tended to be concise. ASEAN cooperation events were mainly about leaders meeting, judicial exchanges, infrastructure cooperation, international aid etc. Conflict events were mainly about disputes in the South China Sea, border security, overseas crimes, and terrorist attacks. Philippines and Myanmar were key country nodes. Some events had significant influence on China, such as the meeting between the leaders of the Philippines, Laos, Indonesia and Cambodia, the territorial sovereignty issues of the Philippines, Vietnam and Indonesia, the border risks of Myanmar etc. It is recommended that China should strengthen long term cooperative relations with ASEAN, enhance the design of cooperation frameworks and strategic docking between governments, strengthen sub-regional cooperation, promote the development of new mechanisms for cooperation in the South China Sea, and prevent the prominent risks of countries.

Urban networks have been widely examined using infrastructure connection and firm connection data. In particular, urban networks constructed based on firm connection data have been used to depict the circulation of capital, information, personnel, and products between cities. Existing studies on firm connection networks rely on either inter- or intra-firm relationships. However, there exist various important extra-firm relationships, such as those between firms and governments, research institutions, and non-profit organizations. This study innovatively incorporates the extra-firm relationships between governments and firms into urban network construction to provide new insights into urban network research. Based on the transaction data of Chinese government procurement activities in 2018, this study provides an in-depth analysis of spatial connection pattern and structure characteristics of supply-demand network in Northeast China by using the methods of GIS and Social Network Analysis, and thoroughly discusses the influencing factors on this basis. The results of the study are as follows: 1) The spatial connection structure presents a connection pattern with Changchun as the radiation center and the Harbin-Dalian Corridor as the main axis in the network for central projects. While network for local projects mainly show the characteristics of intra-provincial connections with the 3 capital cities of Harbin, Changchun and Shenyang as divergence centers, and the scope of government and firm activities between cities is relatively limited in network for local projects. At the same time, the number of participating cities and linkages between the supply-demand network for local projects and central projects is obviously different, and the coverage of local project network is wider. 2) No matter network for central or local projects, the 3 capital cities of the Northeast China are in the absolute center of the local supply-demand network. The degree centrality of Harbin, Changchun, and Shenyang is much greater than that of other cities in the region, and the net outdegree is always positive. With multiple cohesive subgroups, there are many small groups in the networks of Northeast China. 3) Affected by the type of budget, there are obvious differences in the structure of urban network for central and local projects; At the same time, the economic strength of a city could affect the position of a city in the network by indirectly influencing the economic activities of its agents in the network, that is, the stronger the city’s strength is, the greater the centrality is in the network; However, in actual procurement activities, the behavior of government and firms will be restricted by geographical distance and administrative boundary; Finally, since government procurement activities are one of the government’s macro-control measures, some national policies can directly affect the strength of government-firm linkages, and thus effectively guide the coordinated development of urban supply-demand networks.

This article introduced the ‘input-output’ efficiency theory and constructed the ‘SEE-PSR’ framework for the analysis of social-ecosystem vulnerability in the village area in Qingpu District of Shanghai. The data envelope model, spatial autocorrelation model, multivariate Logistic regression model, geographical detector and hierarchical cluster model were used to analyze the spatial differences of social-ecosystem vulnerability and its geographical impact mechanism in 184 administrative villages in this area. The results were as follows: 1) The ‘input-output’ efficiency model based on entropy weight aggregation crossover was more reliable and accurate for the evaluation results of village social-ecosystem vulnerability. The vulnerability of the social-ecosystems in the administrative villages showed a trend of gradual decline from east to west, with the average value of vulnerability of 0.583, and the vulnerability of social subsystems became an important reason to limit the decrease of the vulnerability of the social-ecosystems in the region. 2) Geographic factors still had an important impact on the spatial differentiation of social-ecosystem vulnerability in developed villages. The distance from the center of Shanghai, the distance from the Dianshan Lake, the distance from the center of Qingpu District and the area of water area were the four dominant geographical factors affecting the vulnerability of social-ecosystem in this area. The geographical influence presented the spatial difference of system structure, the substitution of type attribute and the transformation of degree. 3) According to the cluster analysis of the influence of geographical factors, the spatial coupling types of social-ecosystem vulnerability factors were divided into 10 types. The multi-geographic factor coupling influence classes were the main decision type, which presented the multi-cyclic regional decision patterns which were dominated by the central multi-factor and co-exist by the single factor on both sides. According to different types, a feasible way to regulate the vulnerability of regional socio-ecological systems was proposed. The results of this study can provide scientific references for urban suburban rural space reconstruction and regional sustainable development with ‘strict protection and vigorous development’ conflict.rban suburban rural space reconstruction and regional sustainable development with ‘strict protection and vigorous development’ conflict.rban suburban rural space reconstruction and regional sustainable development with ‘strict protection and vigorous development’ conflict.

In view of the inadequacy of the analysis of green development and excessive input of factors in the traditional industrial water resources efficiency measurement, the concept of industrial green water input congestion was defined under the framework of total factor productivity, and the congestion level of industrial green water resources input in 30 provinces and regions in Chinese mainland (except for Tibet, Hong Kong and Macau) was evaluated from 2000 to 2018 by FGL model, and then the structural changes of congestion inefficiency and pure technology inefficiency were analyzed based on global inefficiency decomposition. Results showed that, congestion of water resources input was a common problem in green industrial development in various provinces and regions, and the national average level of congestion showed an inverted “U” shape, with a trend from low congestion to deepening dispersion. The congestion levels varied greatly between provinces and regions, and the provinces and regions with higher level of congestion were mainly distributed in the Northeast Old Industrial Base and the central and western China. The nature of industrial green water resource input congestion could be seen as an imbalance in the allocation structure of input factors, while the growth rate of capital, labor, and industrial water resource input factors in the eastern China was relatively balanced, but there was a significant gap between the growth rate of capital input and other input factors in the central China, and the similar phenomena also appeared in the western China, where the growth rate of water resources input was higher than other input factors. So, the unbalanced input factor structure was an important cause of congestion. The overall global inefficiency was caused by the combination of pure technical inefficiency and congestion inefficiency, of which nearly 77% of provinces and regions were pure technical inefficiency. However, the existence and development trend of congestion inefficiency provided warning information that cannot be ignored for industrial resource allocation. Congestion inefficiency in Hebei, Liaoning, and Jiangsu in the eastern China, Jilin and Heilongjiang in the central China, and Xinjiang and Inner Mongolia in the western China should be adjusted urgently. Therefore, both industrial green transformation and water resources management needed to pay more attention to congestion issues.

Based on the theoretical analysis framework of livelihood vulnerability of coastal fishermen under the background of vessel buyback and fishermen transfer programs, this article takes the transfer fishermen in Zhoushan City of Zhejiang Province as the research object, uses the fuzzy comprehensive evaluation method to compare and analyze the changes of livelihood vulnerability of transfer fishermen under different livelihood modes and different periods of transferring the production, and empirically tests the specific factors that affect the changes of livelihood vulnerability of converted fishermen. The results showed that, although the level of livelihood exposure decreased after the conversion, the sensitivity increased and the adaptive capacity decreased significantly, which ultimately increased the livelihood vulnerability level of the converted fishermen, indicating that the overall livelihood status showed a deterioration trend. The changes of livelihood vulnerability of the converted fishermen under different livelihood modes were different. After the conversion, the livelihood vulnerability of the fishermen engaged in recreational fishery decreased, while that of the other four livelihood modes increased. The vulnerability level of fishermen’s livelihood has increased in two different periods, but the increase in the level of fishermen’ livelihood vulnerability before 2015 was relatively smaller. Education level, subsidy for ship reduction and conversion, time of conversion to production and skills training had significant negative effects on the change of livelihood vulnerability. Among them, the subsidy for ship reduction and conversion had the least impact on the change of livelihood vulnerability, which indicated that the subsidy mainly based on funds has not yet played a role in the reconstruction of sustainable livelihood.

The spatial evolution of industries is a simultaneous process with the spatial development of regional economy. This article applies spatial statistical methods to analyzing the spatial development and transformation of China’s industries. The research found that from 2003 to 2016, the spatial centers of the three industries in China all moved westward, and the secondary industry had the largest moving distance. The spatial dispersion of the primary industry is developing and the spatial aggregation degree is decreasing. The manufacturing industry is aggregating from coastal areas to inland, and its spatial aggregation degree is higher than that of service industry. The service industry is aggregating in coastal areas and its spatial centralization is increasing while its spatial aggregation degree increases unceasingly. At present, the secondary and tertiary industries of the economy both show east-west expansion and north-south contraction spatially, and the both sectors of the economy are more inland after 2011-2012. They jointly affect the spatial development and transformation of the national economy in China. The spatial optimization of industrial layout is an important means to promote the spatial transformation and development of economy. In the years of 2003 and 2010, the coordination feature of spatial coupling between the secondary and tertiary industries of the economy was high in east and low in west, while in 2016, the coordination feature of spatial coupling between the two was high in west and low in east. In 2003, the distribution of secondary sector of the economy was closer to the eastern seaboard than that of tertiary sector, and the spatial pattern difference between the two sectors was mainly in the southwest. In 2010, the spatial pattern of tertiary sector was closer to the coastal area than secondary sector and the spatial difference between the two sectors was mainly in the west and south. In 2016, the spatial pattern of secondary sector was more to the southwest, and its spatial difference with the tertiary sector is transferred to the northern part of the spatial ellipse. The Yangtze River Economic Belt has always been one of the most important industrial corridors in China. In particular, the proportion of the secondary industry in Chengdu-Chongqing region and major cities in the middle and lower reaches of the Yangtze River has increased significantly, while the tertiary industry is also growing continuously. It is suggested to accelerate the development of service industry clusters in the middle and lower reaches of the Yangtze river and Chengdu-Chongqing region where the secondary and tertiary industries are well developed, and vigorously promote the in-depth integration of manufacturing and service industries through the hub role of networked cluster organization.

This article explored the evolution characteristics and its causes regarding spatial network structure of China’s provincial tourism efficiency from 2011 to 2016 by applying Super-DEA model and social network analysis method comprehensively. The results show that: 1) From 2011 to 2016, the average value of China’s provincial tourism efficiency is 0.739, showing a slight decline as a whole, and the spatial distribution characteristic of ‘Eastern region>Central region>Northeastern region>Western region’ is roughly presented. 2) During the research period, the spatial correlation network of China’s provincial tourism efficiency is presented to be multi-threaded, dense and complicated, and the spatial network structure is not stable yet. The network density of tourism efficiency has decreased, but it still presents a rigid and hierarchical network structure. 3) Guangdong, Jiangsu and Shandong Provinces have the highest priority and play the role of leaders in the spatial network structure. However, Hainan Province is partial to a corner, which is at the end of the transmission of tourism production factors, and its connectivity with other provinces is weak. The core-periphery structure of the whole spatial network tends to be cohesive group. 4) Tourism investment level, the distance among capitals and informatization development level jointly drive the evolution and optimization of spatial network structure of China’s provincial tourism efficiency.

As important carriers of industrial development and economic growth, development zones are of great significance to realize China’s industrial transformation and regional coordinated development. China’s development zones are dominated by technology-intensive industries that account for more than 50%, with obvious advantages in agricultural and sideline products, equipment manufacturing, electronic information industry and biomedicine. Then the eastern, central and western regions are dominated by technology-intensive, labor-intensive and capital-intensive industries, respectively. The industrial agglomeration degree of the development zones shows a reverse trend of expanding as the scale shrinks, while urban agglomerations and central cities are their most important carriers. The agglomeration patterns of the three types of industries are similar at the regional and provincial scales, but the agglomeration patterns at the prefecture scale are becoming more complicated, especially in the northeast, southwest and northwest regions of China. In addition, the industrial agglomeration of the development zone has obvious spatial dependence, and the prefecture scale is stronger than the provincial scale. There is a significant co-agglomeration effect between the industries in the development zones, and the industrial attributes have a great impact on them. At the provincial scale, there is a wide range of co-agglomeration effect among industries. For example, there are strong co-agglomeration effects between textile & garment, petrochemical and other types of industries. However, the equipment manufacturing, electronic information and other technology-intensive industries have a strong co-agglomeration effect at the prefecture scale. Based on the above research findings, this paper further suggests the cultivation of labor-intensive and capital-intensive industrial clusters such as textile, clothing and petrochemical industries on a provincial scale. At the prefecture scale, we should focus on the development of technology-intensive industrial clusters such as electronic information.

Taking the urban agglomerations of the Yangtze River Delta, the Pearl River Delta and the middle-south of Liaoning Province as examples, using the relevant data of cities at the prefecture level and above in each urban agglomerations from 2009 to 2018, and with the aid of social network analysis and spatial measurement methods, this paper empirically studies the influence of high-speed railway construction on the spatial correlation pattern and economic growth effect of urban agglomeration. The results show that in terms of spatial association form, through the construction of high-speed railway, the node center of cities under the jurisdiction of each urban group has been improved and the relationship between cities is more closely. However, the ranking sequence of cities in urban groups varies, forming a spatial association sequence structure with different characteristics In the aspect of economic growth effect, high-speed railway construction promotes the coordinated economic development of urban agglomeration, and the higher the economic development level and spatial correlation degree of urban agglomeration, the higher the positive economic growth power of high-speed railway construction; The spatial spillover effects of high speed rail on economic growth in the Pearl River Delta, central and southern Liaoning and Yangtze River Delta Economic Zone were significantly positive and decreased in turn.

The symbiotic relationship between producer services and manufacturing leads to the co-agglomeration of two sectors. As most literature focuses on their functional connections, studies from a spatial perspective are limited. Relying on the firm-level data from tianyancha.com, this paper analyzes co-agglomeration patterns and characteristics between six manufacturing sub-sectors and five producer service sub-sectors in Guangzhou, China. We utilized kernel density, Moran’s I, and geo-detector to explore the spatial similarities between those sub-sectors and the underlying mechanisms. Kernel density was employed to visualize the collaborative agglomeration directly. Global and local bivariate Moran’s I statistics were employed to explore the spatial autocorrelation. By employing the geographic detector, we further examined the underlying mechanism of varying co-agglomeration patterns. Major findings are as follows: 1) Manufacturing in Guangzhou is more scattered relative to produce services, with several agglomeration centers in the peripheral regions. 2) The distributions of manufacturing and producer services are spatially similar. Such similarities also vary across sub-sectors. Among these six sub-sectors, technology service companies have the most similar manufacturing distribution, with a high level of polycentricity. On the other hand, financial companies have the lowest similarity to the spatial layout of manufacturing companies. 3) The high-high cluster areas of producer service and manufacturing are located near suburbs such as Panyu District and Baiyun District. The low-low cluster areas are concentrated in distant suburbs like Conghua District and Zengcheng District. The high-low and low-high clusters are located in central urban areas such as Tianhe, Yuexiu, and Haizhu districts, suggesting a spatial mismatch of producer service and manufacturing in Guangzhou’s urban center. 4) Technology service has the strongest impact on all six manufacturing sub-sectors’ spatial configurations, with the highest spatial similarity. In contrast, the cluster of financial firms is unlikely to leads to the agglomeration of manufacturing companies. This divergence is highly associated with the trajectory of the city’s industrial development, functional characteristics of service industries, and spatially varying flexibility of private enterprises.

The spatial decay of retail patronizing behavior in trade areas exists objectively, but there is a controversy about the power law and exponential law and no conclusion on the distance-decay mode has been reached to date. Exploring spatial decay modes and parameters will help to optimize urban commercial space structure and provide scientific advices. Taking examples of 14 commercial centers in Shanghai, this study aims to analyze and validate the distance-decay law and its coefficients of retail travel behavior, based on mobile phone signaling data in 2013. We compare and examine the distance decay modes and coefficients of varying scale commercial centers in different leisure time. The results show that both power and exponential distance-decay modes have high goodness of fits, the adjusted R² is greater than 0.6 for most of commercial centers. In comparison, power law function fits better slightly, but the advantage is not significant, the goodness of fit of the former is only slightly higher about 0.05 than the later. Meanwhile, the distance is still a key factor affecting retail travel behavior of urban residents, and distance-decay coefficient decreases with the size of commercial centers increasing, especially on weekday. Power-law distance decay coefficient is between 1 and 2 for commercial centers, which has strong spatiotemporal heterogeneity. Such as, the distance decay coefficient of Zhenru commercial center is up to 2.084, while that of East Nanjing road commercial center is 1.010. So the radiation capacity of commercial centers will be too overestimate or underestimate if given coefficient 1 or 2 is adopted. Another conclusion of this research is that the constraint effect of leisure time on retail travel behavior cannot be ignored. Compared with weekend, the distance decay parameter on weekday is larger, and the smaller the commercial center size is, the more significant the distance decay is. The coefficient differences between weekend and weekday for East Nanjing road and Xujiahui commercial centers are smaller, which is less than 0.3. While there are significant differences in varying leisure time for Zhenru and Zhonghuan commercial centers.

According to different data sources and different technical routes, the land types and building heights of Guangzhou’s main urban areas in the large scale in the six periods of 1835-2017 were interpreted and restored. Focusing on the spatial differentiation of urban heights, the migration of height centers of gravity, and the rise of urban heights, the evolution of urban heights in Guangzhou’s main urban areas since 1835 was analyzed. Research shows that: 1) 1835-1907 belonged to the siege structure under the leadership of the king. The height is between 4 and 5 m, with little difference between the center and the periphery. The evolution of urban heights since 1928 has the characteristics of a modern city. 2) Although the scope of the main urban area, the geometric center, and the CBD center of each era have undergone major migrations, the height of the city has gradually decreased from 1928 to 2017, and can continue to strengthen. 3) In 1990, there was a difference in the building height of the land use type, and the land use types tended to be diversified. The difference in height between the function types increased the combination type of urban buildings. 4) During the eastward migration along the north bank of the Pearl River, the height of the center of gravity was small and the plane expanded prominently in 1928-1960; However, the subsequent 1990-2017 eras showed strong vertical uplift. The building enters the stage of modern high-rise buildings. The structure and evolution of Guangzhou’s urban building heights depend on the combined effects of the geographical pattern, urban planning, and market forces of the ‘near the mountain and by the river’ plain cities.

This article takes the main urban area of Taiyuan city as an example. Firstly, according to the ecosystem service theory, the positive and negative ecosystem regulation service values provided by different land use types in Taiyuan are measured and calculated respectively. Then, the spatiotemporal evolution characteristics of urban complex ecosystem regulation services are analyzed comprehensively. Finally, from the direct cause (land use change) and the root cause (space urbanization, population urbanization, industrial space organization and location condition optimization, government policies and planning), the mechanism of urban ecosystem regulation service capacity change in Taiyuan is explained. The results showed that: 1) From 2004 to 2016, Taiyuan composite ecosystem presented inverted environmental Kuznets curve of ecological regulation service value, the overall urban ecology developed well, and the ability of environmental ecological regulation service gradually increased; Spatially, the value of ecological regulation services presents a pattern of patches and checkerboard. On the whole, the value of regional complex ecosystem regulation services gradually increases from the central urban area of Taiyuan city to the surrounding areas. 2) The main driving mechanism for the change of the regulation capacity of the complex ecosystem in Taiyuan: the change of land use attribute is the direct cause of the change of ecosystem regulation services; Space urbanization, population urbanization, industrial space organization and space planning, and relevant government policies are the root causes.

The county-to-district reform expands urban space and functions, accelerates the flow of factors and the allocation of resources, and thus affects the urban real estate prices. Using the propensity score matching method and difference-in-difference method to test and analyze the effect and mechanism of the county-to-district reform on China's real estate market. The study shows that: 1) The county-to-district reform has a significant restraining effect on real estate price, and the effect is short-term; 2) In terms of cities, Type I large cities and mega cities will have a more significant restraining effect on house price rises after the county-to-district reform; 3) The stronger the urban expansion is, the stronger the restraining effect on the real estate price is, while the population inflow and infrastructure construction reduces the restraining effect on the housing price.