The characteristics of the spatial network structure of tourist flow in a high-speed transportation era are the fundamental issue that needs to be explored in the spatial organization and management of the national tourist flow. Through modification of relative parameters of the gravity model close to reality to calculate the inter-provincial tourist flow, and with application of the complex network analysis method to measure the superiority, connectivity and development level of the inter-provincial tourist flow spatial network structure in China, the research reveals the following characteristics of the spatial network structure: the structure of the inter-provincial tourist flow spatial network in China is stable, with tourist flows converging in Southeast China, asymmetric flow, coexistent spatial connectivity structures of proximity type and skipping type, and significant hierarchical structure. In practical terms: (1) Seen from the superiority of the inter-provincial tourist flow spatial network in China, main tourist destinations and main target tourism markets are overlapping, the tourism flow largely converges within the region along Shanghai (East)-Guangdong (South)-Sichuan (West)-Hebei (North); (2) Seen from the connectivity level of the inter-provincial tourist flow spatial network in China, three eastern provinces of Jiangsu, Zhejiang and Shandong are main converging places with the superior tourism flow from various regions, among which Jiangsu and Zhejiang even have created a mutually beneficial relationship; while Tibet is at the bottom of the list, especially the flow from Hainan; (3) Seen from the development level of the inter-provincial tourist flow spatial network in China, the entropy is fairly high, and the structure is stable; the tourism flow presents asymmetry at a certain level, and the spatial converge of superior tourism flow is obvious; and there is basically no group-organizing phenomenon within the spatial network; the inter-provincial tourist flow spatial network China has two types of structure, namely “close mass connection structure” and the “enclave spanning connection structure”. The former is represented by Jiangsu, Zhejiang and Shanghai, and the latter by Jiangsu and Guangdong; the inter-provincial tourist flow spatial network in China is of hierarchical nature, roughly presenting a pattern of eastern-central-western gradient weaving; and the status of unbalanced development will remain unchanged for a relatively long time.

The pattern of Chinese tourism industry is the result of comprehensive development of regional tourism, while tourism investment is a driving factor for regional tourism development. In order to investigate extent and mechanism of tourism investment and its impact on tourism development, the paper analyzed evolution of tourism investment during 2000-2015 in China, based on the fixed assets and human resources investment data in hotels, tourist attractions and travel agencies. Hotels, tourist attractions and travel agencies are known as the three core departments in tourism industry. Conclusions can be concluded as followings. 1) The evolution of tourism investment present characteristic of coordination with tourism development. Thus, the fixed assets investment showed increase tendency in slow speed and the human resource investment firstly increased and then decreased. At the same time, tourism investment in hotels was the largest, which was followed by tourist attractions, and tourism investment in travel agencies was the least. From the perspective of spatial difference, the Eastern China held advantage over the Central and Western China. Meanwhile, the spatial difference of tourism departments among provinces was obvious. Hotels present apparent mostly and travel agencies held small difference. 2) According to the impact index of tourism investment, it was higher for hotels than tourist attractions and travel agencies. The difference of impact index among provinces presented that there were two investment effects, namely the ‘time abrasion effect’ and ‘the performance cumulative effect’, about tourism investment in the three core departments of tourism industry. The time abrasion effect played an important role in the hotels’, tourist attractions’ fixed assets investment and the human resources investment in the whole tourism industry. While the performance cumulative effect only exerts an influence on the fixed assets investment in travel agencies. 3) Based on ‘the spatial traction effect’, the tourism investment led the pattern of tourism development transferring from discrete nuclear polarization to continuous regional diffusion. The paper, at last, analyzed mechanism of the tourism investment’s spatial influence on tourism development and found that the investment was the drive of regional tourism development, and the circulatory accumulation mechanism promoted the sustainable increase of tourism industry.

Cohesive subgroup constitutes a bridge connecting individual cities and urban network. This paper aims to analyze the cohesive subgroups and their mechanisms in the urban network in China. First, data on headquarter and branch locations of China's top 500 public companies in 2016 are subjected to ownership linkage model to approximate the urban network, resulting in a 294×294 valued urban network. Second, four measures of cohesive subgroup analysis, i.e. cliques, k-cores, lambda sets and core-periphery techniques are employed to generalize about the link strengths between cities. Finally, the influencing factors of the cohesive subgroups in the urban network are examined by using quadratic assignment procedure, and the mechanisms are explored under a conceptual framework of urban network growth. Three main findings are concluded. First, the four measures of cliques, k-cores, lambda sets and core-periphery techniques all indicate the presence of cohesive subgroups, revealing the hierarchical structure of link strengths in the urban network in China. The cohesive subgroups are mainly composed of core cities of urban agglomerations, and the cities in the eastern and central regions have more active economic ties compared to the cities in the western region. Second, key resources possessed by cities, such as economic scale, political resources, and knowledge capital, are important factors underlying the formation of cohesive subgroups. Links are more likely to occur between cities with larger economies, richer political resources and more intensive knowledge capital. Temporal distance, geographical location and path dependence also have a profound influence on the spatial pattern of cohesive subgroups. Third, network homophily and path dependence are the dynamic mechanisms underlying the development of cohesive subgroups, and the key resources and location advantages of cities will be further translated into network competitiveness. In the network environment, China's urban governance system and urbanization policies need to be adjusted accordingly. The Chinese government needs to promote network cooperation between cities on a larger spatial scale, and actively respond to the widening economic gap between cities under the network environment.

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.

Structural characteristics and power hierarchy are important elements of urban networks. This paper studied the selection of China's automobile industry supply chain system in 2012 by using "China automobile industry enterprise information Daquan", "China industrial enterprise database" and "China automobile supplier network", and analyzed the characteristics of China's urban network from the perspective of "industry-location". The results showed that: Firstly, based on the supply chain system of automobile industry, China's urban network showed obvious structural characteristics of "low density, multi-core, high clustering, less convergence". Secondly, there existed a "paradox" between the structural characteristics and power levels of cities in the urban network, which means that the network status depended not only on the number of linked cities, but also on the spatial attributes and capital capacity of the associated networks. Thirdly, the urban network power level included not only the leading core cities such as Shanghai and Chongqing, but also the central intensive cities such as Guangzhou and Wuhu, and the power gateway cities such as Suzhou and Chengdu. The result suggested that the "alter-based centrality" and "alter-based power" could not only effectively reveal the real power attribute of China's urban network nodes, but also kept more in line with the unbalanced law of geographical space of economic phenomena. Fourth, leading core cities, including Chongqing, Shanghai, Tianjin, Changchun, Beijing, Shiyan, etc., did not completely take over the six major automotive agglomerations in China, among which Yangtze River Delta region was at the highest level in the power hierarchy whereas the Pearl River Delta region was at the bottom. Finally, it should be noted that the supply chain system of automobile industry was only a special situation between "urban agents", and its research conclusions could not be infinitely copied and promoted, and could not replace the relevant conclusions of other factor flows.