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

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

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

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

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

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

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

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

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

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

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

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

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

The rapid development of the Internet industry and the emergence of new business models have reshaped the spatial structure and spatial relationships of cities.The development of Internet enterprises has profoundly reshaped the established venture capital network by establishing investment departments and expanding the ecosystem in the form of venture capital.As venture capital and the Internet industry are mutually attractive and highly geographically coupled, the analysis of the structure and location of the venture capital network of Internet enterprises has become an important foundation for the development of the regional "new economy".This paper examined the spatial dynamics and location selection mechanism of venture capital activities of Chinese Internet companies by constructing a factor flow network, using spatial analysis, network analysis, and negative binomial regression models.The study demonstrated that venture capital investment in Chinese Internet enterprises has developed rapidly over the past 20 years in three phases: initial development from 2001 to 2012, rapid development from 2013 to 2016, and adjustment development from 2017 to 2019, with Internet venture capital showing the characteristics of cumulative development.Compared with traditional venture capital activities, the location choice of Internet venture capital is more concentrated, with a high concentration in a few major cities in the eastern region of China.The three major regions of Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta are the main places of occurrence and association of Internet venture capital activities in China, with a significant tendency of localization of venture capital.The cumulative development of corporate venture capital and the spatial evolution of both investors and financiers have contributed to the formation of a 'core-periphery' structure of the venture capital network.In addition, an assessment of the network density of the cities where the investment and financing occurs showed that the current Internet investment and financing network is still in its developmental stage, and most of the cities are not directly connected, showing a sparse matrix.Three time windows (2012, 2015, and 2017) were selected to analyze the location selection factors of Chinese Internet venture capital in different development periods in terms of enterprise growth environment, investment proximity, and transportation accessibility.The results showed that marketization, policy guidance, investment proximity, and spatial proximity were the main forces shaping the spatial pattern of Internet venture capital activities and had different degrees of influence on the various stages of Internet industry development.This study facilitates an in-depth examination of the spatial distribution characteristics of venture capital activities in the Internet economy and provides a detailed empirical basis for understanding the changes in venture capital investments in Internet enterprises and their locational influences.

Smart city construction will have a profound impact on urban development and regional pattern, and it is necessary to explore its impact on new urbanization and regional market integration, so as to provide empirical evidence for accelerating the construction of smart cities. Based on the panel data of 238 prefecture-level cities from 2005 to 2019, this study considers the establishment of the national smart city pilot as a quasi-natural experiment and includes it in the analysis of the factors influencing market integration. The multi-period difference method was used to empirically investigate the influence of smart city pilot construction on market integration at the prefecture-level city level. The conclusions are as follows. According to the calculation results, the degree of inter-city market segmentation in the whole sample period from 2005 to 2019 showed a trend of shock decline, except that the degree of inter-city market segmentation increased in 2008 and 2009. The benchmark regression results show that although smart city pilot construction can promote economic urbanization, social urbanization, and environmental urbanization of the pilot cities, it is not conducive to market integration among cities. The conclusion that smart city construction will intensify market segmentation remains valid even after a series of robustness tests. The influence of smart city construction on inter-city market integration varies according to city level, city size, and city location, which is mainly manifested in the fact that it significantly inhibits the market integration of low-level cities, small and medium-sized cities, and inland cities, and significantly promotes the market integration of big cities and coastal cities. The influence mechanism shows that narrowing the information infrastructure gap not only helps in playing the role of smart city construction in promoting the development of new urbanization but also promotes market integration among cities. Narrowing the gap in public expenditure is conducive to the promotion of smart city construction for social urbanization and alleviates market segmentation among cities. Promoting the development of secondary industries can enhance environmental urbanization and ease market segmentation. Therefore, in the context of building a national unified large market, the pilot construction of smart cities should pay full attention to the negative effects of the "digital divide" between regions, encourage some localities with conditions to strengthen the construction of smart cities independently, and prevent the "digital divide" between regions from widening. This study further enriches the research on the factors influencing market integration at the city level and simultaneously provides some references for urban digital transformation and the development of new urbanization.

Urban invisible consumption space is a physical consumption space with weak visibility in urban public space, which has become a new channel for urban business and consumption development. It is progressively becoming an important part of the urban business. Analyzing the spatial pattern and influencing factors of urban invisible consumption is an important basis for promoting online and offline commercial development in cities. Taking Changsha City as an example, the invisible consumption space of the city was identified using POI and business information data of Gaode open platform. Using the Gatiss-Ord index and the geographically weighted regression model, we analyzed the spatial distribution of the urban invisible consumption with different levels of Internet participation. The results show that: 1) the main consumption type of invisible consumption space in Changsha is experiential service consumption; 2) the invisible consumption space infiltrated by the Internet, with Wuyi Square and Red Star Business Circle as the main core areas, presents a structural form of "a main core area, an auxiliary core area, strip distribution, and a point-like mixture"; 3) compared with the traditional invisible consumption space, which is less influenced by the Internet, the invisible consumption space infiltrated by the Internet has clear spatial heterogeneity and less spatial autocorrelation. The results show that the urban invisible consumption space tends to cluster in areas with a mature business atmosphere, convenient transportation, high cost-effective rent, and close to residential areas. The spatial distribution of invisible consumption in Changsha presents a basic spatial structure in line with the central place theory, showing a polycentric form of agglomeration and diffusion. Urban invisible consumption space clusters in the local industrial and commercial center. Moreover, the evolution of places shows a trend of infiltration and aggregation to blocks and the location influence is weak, but it is not "no location influence at all". Its distribution characteristics are in line with the theory of flowing space on the scale of urban internal block. However, invisible consumption space use is positively influenced by the Internet, and its potential commercial value lies in promoting and making full use of the existing urban space resources. In addition, for an "online famous city", the offline consumption activities in the network platform are increasing in abundance, and invisible consumption space will become a more important place to perceive the hidden vitality of the city.

With the rapid development of information technology and social media platforms, digitization has not only given rise to new types of urban vitality spaces but also profoundly changed traditional spatial forms and constructions through online and offline interactions. This study aims to analyze the characteristics and impacts of the changing virtual and physical vitality space forms in Guangzhou and adduce theoretical references and practical insights for urban development and vitality enhancement. Using POI data and TikTok check-in data, this study conducts a spatial analysis of the spatial characteristics and changes of virtual and physical vitality in Guangzhou. The results show that: (1) The spatial patterns of urban virtual and physical vitality generally show a cyclic structure of decreasing vitality from the central urban area to the peripheral areas, indicating that physical space is the basis for the operation of virtual space. (2) Online and offline digital interactions form three types of vitality spaces: a traditional vitality-enhancing space, traditional vitality-declining space, and new Internet celebrity space, whose spatial distribution characteristics differ, with the new Internet celebrity space showing a scattered and discrete distribution. (3) The impact of mobile short video digital platform on the vitality of different types of spaces is heterogeneous, with a boosting effect for scenic spots, leisure and entertainment spots, and sports and fitness spaces, while the impact on food and beverage spaces is smaller. Scenic spots, leisure and recreation spots, and sports and fitness spaces have less impact on spatial vitality, while commercial shopping has a significant heterogeneous impact on spatial vitality. This study seeks to demonstrate the validity of combining POI data and TikTok check-in data to explore the relationship between digitalization and urban vitality space along the digital and social media dimensions and to enrich the research method of urban vitality space. In addition, this study shows that: First, the impact of digitalization on urban spatial form should be emphasized and the role of digitalization in shaping urban vitality space should be fully recognized. Second, urban planning and construction should focus on the development trend of online and offline interaction and promote the integration and complementation of physical and virtual spaces by introducing digital technologies and means. In addition, different types of vitality spaces afford different development opportunities and challenges in the digital context, so corresponding development strategies should be formulated according to local conditions. Finally, exploring the relationship between digitalization and urban vitality spaces can help to clarify the new urban logic and further enhance urban vitality.

With the rapid development of lifestyle logistics in the service industry, such as e-commerce express and same-city delivery, new challenges have arisen in the planning and implementation of urban logistics freight and related industries. However, studies of existing urban logistics tend to focus on large-scale administrative areas, such as inter-provincial and city clusters, ignoring the importance of intra-city logistics networks. Traditional research methods are mostly based on static data to simulate inter-city logistics links, which makes it difficult to accurately and comprehensively reflect the intra-city logistics factor flows and their linkages. In response to these shortcomings, we adopted a method to track logistics in real time, by using truck GPS "flow data" across the city of Shenzhen. In addition, a community discovery model based on social networks was developed to identify important intra-city logistics nodes. The following conclusions are drawn. 1) There are 12 logistics hub nodes in Shenzhen, including the Songgang Industrial Zone, Yantian Port, and Pinghu Logistics Park, based on the infrastructure of large industrial clusters, advanced technology industrial zones, and integrated logistics parks. 2) Intra-city logistics activities are mainly concentrated on weekdays; in contrast, on weekends, logistics activities are severely inhibited by factors such as frequent activities of residents during holidays and delayed construction of planned roads. 3) The overall internal logistics linkage is weak, with logistics activities concentrated in the Guangzhou-Shenzhen axis, showing a spatial pattern of "dense in the west and sparse in the east", but the existing logistics infrastructure is mostly located in the central part of the city, which results in a misaligned layout of logistics activities heading west and logistics facilities being distributed in the middle. 4) The logistics linkage feature "heterogeneous attraction" is obvious: the logistics activities of different types of nodes tend to flow towards specific areas, gradually forming a logistics corridor with "airport-industrial area-coastal port-logistics park" as the axis. Intra-city logistics relies on the linkage of the corridor and forms a logistics corridor network model, with the corridor as the axis spreading to the periphery. 5) The intra-city logistics spatial organization can be divided into six subgroups, whose edge subgroups basically transfer logistics activities around the core subgroups, constituting a "core-edge" organization pattern. Taking these key results into consideration, we propose planning for high-quality development of intra-city logistics in Shenzhen, from three perspectives: reshaping the logistics space, adjusting planning, and cultivating new growth levels. The results of this study not only help to develop and improve the theoretical and methodological system of urban logistics network and deepen the understanding of intra-city logistics network construction, but also serve as a 'Shenzhen sample' for urban logistics development, with the goal of integrating Shenzhen into the world-class logistics hub of Guangdong-Hong Kong-Macao Greater Bay Area.