Network Structure and Influencing Factors of Tourist Flow in the Yangtze River Economic Belt: A Study Based on Travelogues

Network Structure and Influencing Factors of Tourist Flow in the Yangtze River Economic Belt: A Study Based on Travelogues | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Network Structure and Influencing Factors of Tourist Flow in the Yangtze River Economic Belt: A Study Based on Travelogues xiang zhang, junhui Liu, jinsong Wu, Gege Liang, fangyi Cheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6315958/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the high-quality development of the tourism industry and increasing cross-regional cooperation, understanding the evolution and driving mechanisms of tourism flow networks in the Yangtze River Economic Belt (YREB) has become critical. This study constructs a multi-scale tourism flow network across 126 prefecture-level cities using 300,000 travelogues from Ctrip (2015–2019). By integrating social network analysis (SNA) and quadratic assignment procedure (QAP) regression, we decode structural characteristics, node centrality dynamics, and key drivers of network evolution. Key findings include: (1) The network exhibits a multi-nucleated polarization structure centered on Shanghai, Wuhan, and Chongqing, with low overall density and weak connectivity among peripheral cities; (2) Node centrality is highly polarized, emphasizing the agglomeration and radiation capacities of core cities; (3) Primary drivers include the density of 5A/4A scenic spots, tourism infrastructure, per capita GDP, and transportation accessibility. Theoretically, this study advances travel geography by introducing a dynamic, data-driven framework that challenges traditional push-pull theory through digital mediation. By integrating user-generated content (UGC) and multimodal analysis, we pioneer the application of big data to network resilience research, offering insights into algorithmic platforms’ role in reinforcing spatial hierarchies. Our holistic model bridges gaps in multi-scale synergy and multi-factor flow analysis (e.g., tourism, economy, and information flows), providing a foundation for addressing spatial inequalities and informing policies for balanced, sustainable governance. Humanities/Complex networks Social science/Geography Tourism flow network Social network analysis push-pull theory Big data Yangtze River Economic Belt(YREB) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction As a national strategic core region spanning nine provinces and two cities, the Yangtze River Economic Belt(YREB) accounts for about 46.4% of the country's GDP and 42.9% of the country's population. This super economic zone, which extends from the eastern coastal hub to the western inland, is not only an engine driving China's economic growth, but also a treasure house of cultural and natural heritage —— has 24 UNESCO World Heritage sites and more than 1,400 high-level tourist attractions. In recent years, driven by policy frameworks such as "Belt and Road", tourism has become a key force in promoting regional coordinated development and economic transformation and upgrading. However, it is worth noting that the tourism development in this region presents significant unbalanced characteristics, the core-edge structure is prominent, and the fragmentation problem of inter-city cooperation needs to be solved urgently. In the context of promoting coordinated regional development globally, identifying the driving elements of tourism integration such as infrastructure, economic gradient and resource endowment can provide a decision-making basis for narrowing the regional development gap. More importantly, tourism flow is not only an indicator of economic phenomena, but also a spatial carrier of social interaction, cultural communication and environmental pressure. Its research has multiple enlightenments for global sustainable governance. The traditional tourism flow research relies on static statistical data or questionnaire survey, and has limitations such as extensive spatial and temporal resolution and sampling bias. The digital age provides innovative research opportunities for user-generated content (UGC), especially travel big data. Taking the travel data of Ctrip and other platforms as an example, these text information can capture the real-time and fine-grained spatial and temporal trajectory of tourists, and reveal the potential behavior patterns and preference characteristics. It is worth noting that the media application of explosive growth is reshaping the tourism decision-making mechanism, weibo, little red book platform make niche destination for "visibility" breakthrough, travel notes as an important form of UGC, it contains the dynamic data flow can reflect the "edge-moderate" the evolution of the structure, and the perspective of the supply side facilities construction and the interaction of decision-making behavior. The evolution of the tourism pattern of the Yangtze River Economic Belt(YREB) deeply reflects the era trajectory of China's tourism development. The traditional tourism flow presents linear agglomeration characteristics, with Shanghai, Wuhan, Chongqing and other hub cities as nodes, and the marginal cities have been in the development depression for a long time. With the deep transformation of China's tourism market —— the increase of residents' disposable income, diversification of consumption preferences and acceleration of digital transformation —— new spatial forms are being generated. Modern tourists increasingly prefer multi-destination series travel. Under the superimposed effect of transportation network upgrading and digital platform empowerment, this trend has given birth to "highly hub areas" and "moderate regions" with transition functions, forming a network pattern of multi-center polarization development. The analysis of this spatial structure is of great theoretical value to the optimization of international resource allocation and the promotion of cross-regional coordination. Based on the data of 300,000 travel notes from 2015 to 2019, this study constructed a multi-scale tourism flow network in 126 prefecture-level cities along the Yangtze River Economic Belt(YREB). Through the combination of social network analysis (SNA) and secondary assignment program (QAP) regression, the dynamic evolution mechanism of the network, the spatial heterogeneity of the decoding of the tourism network and its driving mechanism are revealed. The research not only makes up for the insufficient analysis of the existing literature on the urban scale, but also innovatively uses UGC big data to reveal the interaction between tourism infrastructure, economic gradient difference and digital influence. The research results can provide policy support for policy makers to enhance network resilience, optimize infrastructure investment priorities, and foster new drivers of balanced development, thus providing solutions for promoting regional cross-basin coordinated development. 2 Literature review 2.1 Research progress of tourism flow measurement method Scholars show a diversified trend in the tourism flow measurement methods. Early studies mostly built an analytical framework based on classical theories (such as spatial diffusion theory and core-edge theory), and the data sources were mainly official statistics and questionnaire survey. With the development of information technology, dynamic network data has become a research hotspot. Scholz Taking tourism-related tweets as the research object, using Twitter to obtain information data to study the spatial layout characteristics of the flow of tourism information elements [ 1 ] . Van der Zee And others use the online comment data of the international tourism platform Kitty Eagle to study the relationship network characteristics generated by the flow of tourists between micro nodes such as scenic spots, restaurants, shops and memorials [ 2 ] . In terms of research methods, social network analysis (SNA) is widely used, supplemented by spatial autocorrelation analysis, modified gravity model and other quantitative tools, emphasizing modelling and dynamic analysis. China's tourism flow measurement has undergone a transition from static to dynamic. Early research relied on statistical yearbook, questionnaire survey and other cross-sectional data. Zhou Huiling et al. calculated the scale of Chinese inter-provincial tourism flow and the advantages, connection and development level of the domestic tourism flow network structure [ 3 ] . In recent years, online big data (such as Baidu index, Weibo data, geotagged photos) has become the mainstream. For example, Duzhen et al. compared [ 4 ] with Baidu search index. Yang Yong and others use the Baidu index to represent the tourism information flow network [ 5 ] . At the method level, the social network analysis takes the lead, supplemented by the gravity model (such as Wang Kai et al. [ 6 ] builds the tourism economic connection network of the urban agglomeration in the middle reaches of the Yangtze River). Tourism flow measurement method has made significant progress in data acquisition and technology application. International research introduced dynamic network data early, and mature method system; China relies on local platforms (such as Baidu Index) to achieve innovation. This study innovatively integrates multi-source data, analyzes the coordination between traditional statistical data and network dynamic big data; optimizes the measurement method of micro scale (such as prefecture-level city), especially in the real-time tracking of tourist behavior track; the comprehensive measurement of cross-factor flow (such as passenger flow, information flow and economic flow) is not comprehensive enough to reflect the complexity of tourism flow network. In the future, it is necessary to strengthen multi-modal data fusion, develop intelligent analysis tools, and promote the development of measurement methods to the dynamic and refined direction. 2.2 Research on the structural characteristics of tourism flow network Research on tourism flow network structure covers multi-scale space. At the macro level, Chung, Bendle and others take countries or regions as nodes to analyze the trend of factor flow between destinations [ 7 , 8 ] . Lozano et al. to discuss the differentiation of entry-exit tourism flow in important countries in the world [ 9 ] . At the micro level, Salas, Han and others analyze the relationship network between nodes such as scenic spots and hotels through tourist trajectory [ 10 , 11 ] . In terms of network structure evolution, Mou et al. use geolabeled photos to build the inbound tourism flow network [ 12 ] . Zhu He Dynamic analysis of the multi-scale structure of China's inbound tourism flow [ 13 ] . The general trend shows that international research has shifted from macro to micro, with diversified data sources (such as social media and online platforms), and focusing on the analysis of network dynamic evolution. Chinese studies focuses on multi-scale regional network characteristics. At the macro level, Ruan Wenqi et al. analyzed the network structure by outbound tourism routes [ 14 ] . At the middle level, Cheng Xuelan et al. studied the network structure of tourism flow in five urban agglomerations along the eastern coast of China [ 15 ] . Du Jieli et al. discussed the structural characteristics of tourism flow in the Guangdong-Hong Kong-Macao Greater Bay Area [ 16 ] . At the micro level, Wang Shuhua and others established a tourism flow network [ 17 ] with 49 high-frequency scenic spots in 17 prefecture-level cities in Henan as nodes. Lin Wenhui et al. analyzed the tourism flow space network of scenic spots in Hangzhou [ 18 ] . The research objects are mainly tourism flow, which has been extended to tourism economic flow in recent years (for example, Li Hua et al. has constructed the key matrix [ 19 ] of China's regional tourism economic connection through gravity model) and information flow (such as the urban information flow network [ 20 ] based on Baidu Index). The research on the network structure of tourism flow presents the characteristics of "scale refinement and multiple objects". International research focuses on cross-country and cross-regional comparison, while China focuses on national strategic areas (such as the Yangtze River Delta and the Yellow River basin). In this study, we study the spatial scale of prefecture-level cities, focus on the overall network structure of national strategic areas (such as the Yangtze River Economic Belt(YREB)), and propose the differences of regional development: passenger flow, future information flow and economic flow, full diachronic analysis, deepening the coordination between static section research and dynamic evolution mechanism. It is necessary to strengthen the integration of cross-factor flow, deepen the multi-scale research of major strategic areas, and introduce time series analysis to reveal the law of network evolution. 2.3 Study on the influencing factors of tourism flow network The theoretical system of the influencing factors of tourism flow network is mature, with the "push-pull" theory [ 21 ] and the spatial interaction theory [ 22 ] as the core framework. In terms of subjective factors, Mansfeld explores the influence of willingness to travel on behavior [ 23 ] . Objective factors covering the economic level (Gundelfinger in Spain to the canary islands route as an example analysis of the price impact on flow [ 24 ] ), transportation convenience (Alderighi analyzed the international tourism transportation convenience has a significant impact on the tourism flow [ 25 ] ), cultural proximity (Antonio Accetturo analyzed the cultural close to the impact of economic exchange and tourism flow [ 26 ] ), etc. In recent years, research has turned to policy and social factors, such as Setareh, to analyze the impact of transaction costs on tourism flow [ 27 ] . Chinese research conducts empirical analysis based on foreign theories. In terms of driving mechanism, Yan Flash proposed the comprehensive model [ 28 ] of "internal drive-pulling force-friction". Gao Jie et al. emphasized the driving effect of economic development level on Macao immigration [ 29 ] . In terms of influencing factors, destination attraction (resource endowment, transportation accessibility) [ 30 ] and tourist source characteristics (revenue, policy) are widely discussed in [ 31 ] . At the method level, quantitative tools such as geographic detector and QAP analysis are popular. For example, Yang Li et al. used QAP to explore the influencing factors of "Belt and Road" tourism information flow [ 32 ] . The study of the influencing factors of tourism flow has formed a combination system of "theory-demonstration". International research focuses on macro mechanisms, while China focuses on regional evidence. The influencing factors in this study are selected based on the traditional analysis systematic framework, but the research on the differentiation driving mechanism of multiple factor flow (such as passenger flow and information flow) is insufficient; network dynamic data is captured to reveal the network dynamic change factors; policy factor analysis is weak, and targeted research on national strategies (such as Yangtze River Economic Belt(YREB)) is scarce. In the future, it is necessary to build a multi-factor flow comprehensive driving model, strengthen policy-oriented analysis, and use real-time data to improve the research dynamics. 3 Study area, study methods, and data sources 3.1 Study area The Yangtze River Economic Belt(YREB) covers the administrative scope of 9 provinces and 2 cities in the Yangtze River Basin in China, and connects the eastern Yangtze River Delta, the central triangle, and the western Chengdu-Chongqing and central Yunnan cities in the region.Since the 1990s, the Yangtze River Economic Belt(YREB) has been regarded as an important development axis of the national economy (Figure 1).In 2021, the GDP of Yangtze River Economic Belt(YREB) accounts for 46.4% of the country, 42.9% of the total population, 24 World heritage sites and 1424 advanced scenic spots, and broad tourism market prospects. Due to its strong economic strength and great development potential, it has become one of the important engines of China's economic growth [ 33 ] .In this study, 126 cities at prefecture level and above along the Yangtze River Economic Belt(YREB) were selected as the research unit. In order to avoid errors caused by small economic size and administrative system differences, the research unit did not include Xiantao, Tianmen, Qianjiang and Shennongjia. 3.2 Study Methods (1) Construction of tourist flow network Taking 126 cities at prefecture-level and above along the Yangtze River Economic Belt(YREB) as the network nodes, the directional flow relationship between cities is extracted based on the travel tracks of tourists from 300,000 travel notes of Ctrip from 2015 to 2019.1 when there is direct passenger flow between two cities, otherwise 0, forming a dichotomous directed matrix of 126*126 (Table 1 ). Two things to pay attention to: first, pay attention to the direction (order) of the city in the itinerary; second, pay attention to the name of the county and the name of the scenic spot, that is, no prefecture-level city and autonomous prefecture, which are matched to the corresponding cities and states in accordance with the corresponding principle of the territory. Table 1 Example of tourist itinerary information statistics(The 2019 section) Serial Number User ID User Name Travel notes title Node 1 Node 2 Node 3 3 234026781 Flowers and fruits Go south in winter, and experience the different scenery of Jiangnan Ning Po Soochow 11 17005454 Da yongjin -David A 3-drive tour in Ningbo Shanghai Ning Po Shanghai 13 35699560 M18****039 Ningbo Yuyao Siming Lake Sequoia forest said to go Hangzhou Ning Po 22 85965253 Seven days of saint traveling everywhere 2019 latest Cixi self-drive tour (colorful flower season along the water town) Shanghai Hangzhou Ning Po 24 5018839 fangyjwt Ningbo —— delicious city Shanghai Ning Po Shanghai 28 2010700 Seven days of saint traveling everywhere A 2-day tour of Jiulong Lake in October Shanghai Ning Po Shanghai 30 7935092 A bourgeois woman who likes leisure Golden osmanthus fragrance, I once again close to you-Ningbo, Shaoxing, Hangzhou three heavy travel notes Ning Po shaoshing Hangzhou (2) Network structure feature analysis Assessment network overall collaboration level and the rationality of resource allocation, the overall network index based on previous research [ 34 ] network density (reflect connection tightness), network correlation (measure integrity), network hierarchy (characterization of asymmetry), network efficiency (assess redundancy) and core-edge model (identify hierarchy structure) five indicators, calculation formula as shown in Table 2 .The core-edge model refers to the discrete classification method of Borgatti et al. (1999) [ 50 ] through UCINET software. Table 2 Overall network index formula expression Network density Network correlation degree Network rating Network efficiency computational formula \(\:\text{D}=\text{L}/[\text{N}\times\:(\text{N}-1\left)\right]\) \(\:\text{C}=1-\left\{\text{V}/[\text{N}\times\:(\text{N}-1\left)\right]\right\}\) \(\:\text{H}=1-\left[\text{K}/\text{m}\text{a}\text{x}\left(\text{K}\right)\right]\) \(\:\text{H}=1-\left[\text{M}/\text{m}\text{a}\text{x}\left(\text{M}\right)\right]\) commentate N:Number of nodes N:Number of nodes K:Symmetric accessible point log in the network M:Number of redundant connections in the network L:Actual number of associations between nodes V:The number of unreachable points in the network max(K):Maximum possible number of symmetric achievable points in the network max(M):Maximum possible number of redundant connections in the network Identify network key nodes, individual centrality index based on previous research [ 35 ] selection degree centrality (outward / inward, measure node radiation and agglomeration ability), close to the centrality (assess node accessibility), mediation centrality (characterization node control) 3 class index (in Table 3 ), using Gephi space visualization analysis. Table 3 Formula for calculating the centrality of the individual center network Degree centrality Close to centrality Intermediary centrality computational formula \(\:{C}_{D,out\left(ni\right)}=\sum\:_{j-1}^{n}{X}_{y}\) \(\:{C}_{C\left(ni\right)}=\frac{1}{\sum\:_{j-1}^{n}d({n}_{i},{n}_{j})}\) \(\:{C}_{B\left(ni\right)}=\sum\:_{j}^{n}\sum\:_{k}^{n}\frac{{g}_{jk\left(ni\right)}}{{g}_{jk}}(j\ne\:k\ne\:i)\) \(\:{C}_{D,in\left(ni\right)}=\sum\:_{j-1}^{n}{X}_{ij}\) commentate n:Total number of nodes n:Total number of nodes n:Total number of nodes \(\:{C}_{D,out\left(ni\right)}\) :Extroversion degree centrality \(\:{C}_{C\left(ni\right)}\) :The proximity centrality of the i \(\:{C}_{B\left(ni\right)}\) : Mediator centrality of i \(\:{C}_{D,in\left(ni\right)}\) :Introversion degree centrality \(\:d({n}_{i},{n}_{j})\) : The shortest distance of the \(\:{n}_{i},{n}_{j}\) \(\:{g}_{jk\left(ni\right)}\) : The shortest number of paths from j to k \(\:{g}_{jk}\) : The shortest number of paths going from j to k and passing through the node i (3) Identification of network influencing factors Based on the framework of "push-pull theory" [ 36 ] , 8 indicators such as tourism resource endowment (number of 5A / 4A scenic spots), tourism facilities (number of travel agencies and star hotels), regional economy (per capita GDP), traffic conditions (grade highway mileage) are selected to construct the explanatory variable matrix. The secondary assignment procedure (QAP) analysis was used to explore the correlation and regression relationship between variables and tourist flow network through non-parametric displacement test (5000 random substitutions), so as to avoid the multicollinearity problem of traditional regression method, which was specifically realized through the QAP module of UCINET. 3.3 Data sources There is a significant positive relationship between the change of tourist movement trajectory in online travel notes and the spatial footprint of tourist flow obtained from the survey, which is a solid reflection of tourist trail [ 37 ] .Comprehensive analysis of the online travel platform travel data accessibility and comparability and understand the ctrip user group coverage of comprehensive [ 52 ] , this study choose ctrip online travel data as the basic data, through the analysis of network travel travel information, extract the trip can reflect the sequence of space node as travel flow data.Select the Python code crawler method and customize the crawl rules:(1) The climbing cycle is three complete natural years in 2015,2017 and 2019; (2) with 126 "cities" along the Yangtze River Economic Belt(YREB) as the keyword; (3) The network travel data attributes retrieved include travel title, travel destination, personnel composition, cost, travel mode, transportation, travel route and other information.In the end, the original travel notes of the three years were 318,200,307,595 and 300,353 respectively.The data cleaning standards are strict. Excluding travel notes that do not belong to the scope of the research field, travel notes with advertising nature, travel notes with only one tourist destination city, travel notes containing only pictures or unable to restore the process of browsing the route. The final retained travel notes are 113,867,100,987 and 125,331 respectively. The national economic data are from the Statistical Bulletin of National Economic and Social Development, China City Statistical Yearbook and China Tourism Statistical Yearbook in 126 cities at the prefecture level and above. The number of scenic spots refers to the list of A-level tourist attractions published on the official website of the culture and tourism departments (bureau) of provinces and cities. The administrative division data are obtained from the geographic information resource directory service system of the People's Republic of China. The administrative boundaries of 126 cities at or above the prefecture level and above in the Yangtze River Economic Belt(YREB) were obtained from the element classification of the National Basic geographic database (2019 edition). The urban vector coordinate data of Yangtze River Economic Belt(YREB) are derived from the national basic geographic database (2019 edition); the distance between cities is calculated by ArcGIS10.8 projection based on the vector geographic coordinates of Yangtze River Economic Belt(YREB). 4. Structure characteristics of the tourist flow network in the Yangtze River Economic Belt(YREB) 4.1 Network identification of tourist passengers Visual tool Gephi was used to draw the correlation map of the tourism flow network in the Yangtze River Economic Belt(YREB). Each node represents 126 cities in the Yangtze River Economic Belt(YREB), and the curve represents the existence of tourism flow relationship between the two cities. The initial tourist flow network structure is shown in Figure Fig. 2-Figure Fig. 4. According to Fig. 2 to Fig. 4, from 2015 to 2019, the Yangtze River Economic Belt(YREB) connects many node cities; there are many connections between node cities; the number of connecting lines in different node cities varies, and the connection levels of node cities are different; the node cities with connection relationship no longer limit the adjacent range, breaking through the regional effect of "adjacent", and forming a universal connection.This shows that the tourist flow network in the Yangtze River Economic Belt(YREB) has been formed and persisted. The number of nodes in the tourist flow network is increasing and the connection is constantly complicated, but there are still Nujiang, Lincang and other cities that are not connected with other nodes and are in an independent state. This shows that the development of tourist flow in the Yangtze River Economic Belt(YREB) has presented a complex, multi-threaded and widely related network structure. 4.2 Analysis of the overall network structure characteristics Network density From 2015 to 2019, the related development scale of tourist flow showed an upward trend, and the closeness of network connection was strengthened year by year, but there are problems such as low overall level of network density value and low degree of network development. In the theoretical 126 nodes, 100% interconnected states, up to 126,126 paths, or 15,876, may occur. In 2015, the network density of tourist flow was 0.167, that is, there were only 1064 connections between the real city nodes in the corresponding network. In 2017 and 2019, the network density did not exceed the theoretical 0.2, indicating that the connection between passenger flow nodes is not close enough and the connection path is few. The density value of tourist flow network is strengthened year by year, indicating that the relationship of tourists between cities and states is gradually strengthened (Fig. 5 ). Network correlation degree From 2015 to 2019, the correlation value of the tourist flow network is relatively high. Most cities have direct or indirect correlation in the tourist flow network, but there are still isolated points in the network. The correlation value of the tourist flow network shows a trend of first falling and then rising, but the overall gap is not large, and the degree of correlation and cooperation between cities has increased, but the tourist flow network has isolated points, which is still an incomplete network. Network level From 2015 to 2019, the network level of tourist flow gradually increased, indicating that the development of the tourism market is unbalanced, and the advantages of popular tourist cities are increasingly prominent. This may be due to the limited spatial flow range due to personal willingness, tourism attraction, traffic duration, space proximity, and regional membership. Network efficiency From 2015 to 2019, the network efficiency value of tourist flow is at a high level, indicating that the network connection is less redundancy, the connection is relatively loose, the network stability is poor, the regional cooperation is not carried out in depth, the driving effect of tourism is weak, and it is difficult for the network to achieve coordinated development at present. The network efficiency value of tourist flow is declining, reflecting the increasing number of related channels in the Yangtze River Economic Belt(YREB), and the network stability is gradually strengthened. 4.3 Characteristic analysis of individual central network nodes Degree centrality From 2015 to 2019, from the three regional levels, the central spatial distribution of the tourist flow network presents a multi-center pattern in the upstream, midstream and downstream. There are few middle and high value areas, the central gap between cities and states is significant, and the radiation effect of the core areas is relatively small, which may be due to the tendency of tourists to popular tourist cities.The passenger flow is mainly concentrated in the upstream and downstream, which may be mainly due to the higher level of economic development in the downstream region, tourists can enjoy more perfect travel conditions, while the upstream cities and states have rich tourism resources, strong attraction; some midstream cities become transit hubs for tourists, but the number is relatively small. From the city level, in the 2015–2019 tourist flow network, the inward and outward oriented node cities are always Shanghai, Wuhan, Suzhou, Hangzhou, Nanjing, etc. (Fig. 6), indicating that these node cities occupy a core position in the tourism market, with a broad tourist source market and strong tourism attraction. The degree of centrality of each node city continues to increase, indicating that the passenger flow of the nodes is gradually rising, and the tourism market is gradually expanding. However, more than 80% of the node center values are distributed in the [0,300] section, indicating that the passenger flow is unbalanced, and most tourists are concentrated in a few high-order node cities. At the same time, with the increasing standard deviation between extroversion and introversion, the competitiveness of the tourism market is also improving, and the attraction of popular cities is becoming stronger. Close to centrality From 2015 to 2019, from the three regional levels, the tourist flow network is close to the central distribution, with a multi-center radiation development structure in the lower, middle and upstream, and an overall strong and locally weak development pattern. The tourist flow is close to the central group development trend is obvious, the high value area is mainly concentrated in the upstream and downstream areas, the low value area is distributed in Shaoyang and Loudi. The upstream and downstream tourist flow are more closely connected, and the unique tourism resources of the upstream and the developed economic level of the downstream promote the flow of tourists. From the city level, the node cities, including Shanghai, Chongqing, Suzhou, Wuhan, Nanjing, Chengdu and so on (Fig. 7). The passenger flow market is frequently directly connected, has strong accessibility and accessibility, and is not limited by other nodes. In the high-order node cities, the outward value close to the centrality is less than the inward value, indicating that the tourist flow agglomeration ability of these cities is strong. Introverted in 2015 and outward close to the centrality are below the mean of nodes to 8, respectively is Loudi, Xiaogan, cross, at, great, Suining, Shaoyang, Ezhou, reduced to four in 2019, Loudi, Xiaogan, Dazhou, Suining, passenger network node direct correlation in the strengthening trend, completely dependent on the edge of the development of other nodes reduce the number of actors. Intermediary centrality From 2015 to 2019, the network intermediary of tourist flow presents a "point-axis" development mode, with Chongqing, Chengdu, Guiyang, Shanghai, Nanjing, Hangzhou, Suzhou, Wuhan, Changsha, Hefei and so on as the central points, and the "development axis" (Fig. 8). The node cities with high intermediary center are Shanghai, Chongqing, Chengdu, Wuhan, Nanjing, Suzhou, Kunming, etc., indicating that these cities have always been popular tourism distribution centers, acting as an important intermediary role. The intermediary center of Shanghai in Shanghai is increasing year by year, indicating that Shanghai has more and more control over other nodes in the whole passenger flow network, and its ability of resource allocation. This is mainly due to Shanghai's convenient transportation hub, superior geographical location and developed transportation network. From 2015 to 2019, there are 102,98 and 104 nodes with less than the mean, indicating that most of the nodes are strongly dependent in the network. 4.4 Core- -edge characteristics of the tourist flow network In this study, Network- -Core / Periphery module 0000000000000 pieces of the "core-edge" structure and density matrix [ 51 ] between "core" and "edge" in Ucinet software.Using the Arc GIS map layered display, the partition results of the core and the edge are visualized in two types of colors, as shown in Fig. 9. From the perspective of spatial distribution, the tourist flow network is in the development period, the number of cities and states in the core area of the tourist flow network is increasing, and the intensity of tourist flow between the cities and states in the Yangtze River Economic Belt(YREB) is increasing.In 2015, the "core-edge" structure of the tourist flow network is relatively loose, and the connection between cities and states is not closely close. Except for the downstream node cities Shanghai, Suzhou, Nanjing and Hangzhou, the midstream node cities Wuhan and Changsha, and the upstream node cities Chongqing, Chengdu and Kunming, other cities and states all belong to the edge areas.In 2017, the number of cities and states in the core area of the tourist flow network increased, and the tourist contact between cities and states along the Yangtze River Economic Belt(YREB) increased. Downstream Shanghai, Suzhou, Nanjing, midstream Wuhan, Changsha, upstream Chongqing and other cities are in the core region.In 2019, the flow of tourist flow and tourists between cities and states is getting closer, and the connection of tourist flow network keeps increasing. The core area is mainly distributed in the provincial capitals with good tourism resources or high economic level. Table 4 The "core-edge" density matrix of the tourist flow network in the Yangtze River Economic Belt(YREB) In 2015 In 2017 In 2019 Core Area Marginal Zone Core Area Marginal Zone Core Area Marginal Zone Core Area 27.533 1.237 44.8 2.674 44.304 3.916 Marginal Zone 1.24 0.184 2.458 0.22 3.342 0.25 Degree of Fitting 0.552 0.582 0.662 From the density matrix (Table 4 ), there is a significant difference in the tourist flow network density between the core areas and the marginal areas of the Yangtze River Economic Belt(YREB).From 2015 to 2019, the "core" to "core" network has the highest density, indicating that tourists have frequent flow between the core tourism areas, because the pursuit of well-known tourist destinations is consistent;"Core" to "edge" and "edge" to "core" network density is not big, and growing year by year, the core area and edge area, tourist flow range from popular tourist city to the surrounding tourist city scope expanded, one is due to the pull effect of tourism core, on the other hand is due to the edge area tourism construction gradually strengthen;The network density from "edge" to "edge" is the smallest, and the degree of connection between nodes is only 0. 059,0.067 and 0. 082 respectively, but it does not reach the overall network density of 0.118,0.128 and 0.135, indicating that the tourist flow in marginal tourism areas is very little and the tourist attraction is extremely low. 5. The influencing factors of the tourist flow network structure in the Yangtze River Economic Belt(YREB) 5.1 Selection of indicators of influencing factors The "push-pull" theory was first proposed as [ 38 ] by British scholar E. G. Ravenstein in the 1880s. D. J Bgane further summarized the causes of population migration and formed the "push-pull theory".Because the theory involves the population flow, describes the tourists from tourists to destination of popular for, so Dann will push —— pull theory applied to the tourism research field [ 39 ] , that the psychological factors is the inner thrust of tourism activities, and the various attributes of tourist destination is the external pull to attract tourists.Mo Kun (2014) applied the theory of push and pull to the empirical analysis of the factors affecting the willingness of pension tourism in [ 40 ] .According to the previous results of the study [ 41 ] , the common points and differences of the characteristics of the pull of the tourist attractions and the thrust reflected in the tourist psychology, in order to explain the common and different characteristics of the tourist flow network structure.On the basis of comprehensive consideration, the following 7 main influencing factors and 8 indicators are selected (Table 5 ). In addition to the spatial proximity indicators, other indicators are from the statistical year of prefecture-level cities, Statistical Bulletin of National Economic and Social Development, China City Statistical Yearbook, China Tourism Statistical Yearbook, official websites of municipal and state governments, etc. Table 5 The influencing factors of tourist flow network structure Influencing factors Impact indicators Reference documentation Push pull effect Tourism resource endowment Number of 5A and 4A tourist attractions Zhang Kai et al(2013) [ 42 ] Pull Basic tourism facilities The number of travel agencies, star-rated hotels Dong Intro et al(2018) [ 43 ] Pull Regional economic foundation per capita GDP Wang Kai et al(2019) [ 44 ] Pull Industrial capital structure Value-added value of the tertiary industry Shi Jianzhong et al(2022) [ 45 ] —— Social fixed capital amount Yang Yong et al(2022) [ 46 ] Convenient transportation The mileage of grade roads Ni Weiqiu et al(2018) [ 47 ] Pull population size Permanent resident population at the end of the year Overland, etc(2023) [ 48 ] Push Geospatial distance Space adjacent Li Hangfei et al(2017) [ 49 ] Push 5.2 QAP correlation analysis QAP correlation analysis to explain the correlation between independent variables and tourist flow network. In the correlation analysis, the most important indicator is the significance, which only proves significance if the significance value is below 10%.QAP related analysis shows that the number of 5A and 4A scenic spot, travel agencies and star hotels, the per capita GDP, social fixed capital and the added value of the third industry, grade highway mileage, at the end of the permanent population, space proximity of the matrix and tourist flow matrix through significant level test, the influencing factors and tourism flow network are positively correlated.It shows that the differences between the number of scenic spots, the number of travel agencies and above, the number of star hotels, per capita GDP, the added value of social fixed capital and the tertiary industry, the mileage of grade highways, the permanent resident population and the spatial proximity have a correlation with the tourist flow. In addition, the flow of tourism factors tends to choose areas with high level of resource endowment, good service facilities and level, developed economy and convenient transportation. 5.3 The QAP regression analysis Table 6 Results of QAP regression model fitting between independent variables and tourist flow networks quota tourist flow ༲2 0.574 The adjusted༲2 0.562 Number of random substitutions 5000 They was standardized using the range method to eliminate the influence of the dependent variable dimension. According to the model fitting results (Table 6 ), it can be seen that the fit of QAP regression analysis model of tourism flow in the Yangtze River Economic Belt(YREB) is relatively high, and the adjusted evaluation coefficient R2 is 0.562, which has passed the significance test, indicating that 56.2% of the network information of tourist flow in the Yangtze River Economic Belt(YREB) can be explained, and the regression analysis results are good.The results of regression analysis of influencing factors of tourist flow network structure are shown In the tourist flow network, the influencing factors with the highest significance level are the tourism resource endowment level, the service level of tourism facilities, the regional economic foundation and the transportation convenience degree.First, the tourism resource endowment can explain the structure of the tourist flow network at a significant level of 1%. In different cities, the difference between the number of 4A and 5A scenic spots has some connection to the flow of tourists. Due to the large difference in tourism resources in different cities, the spatial correlation of tourism flow is high, and the pulling effect on each city is also strong.Secondly, the service level of tourism facilities can explain the characteristics of tourist flow network at a significant level of 1%. Travel agencies belong to the attribute data representing the regional tourism economy. Usually, the number of travel agencies in the areas with developed tourism economy will also increase accordingly. At the same time, the difference in the number of star hotels can drive the flow of tourists between different cities along the Yangtze River Economic Belt(YREB) to a certain extent. Therefore, the service level of tourism facilities has a positive effect on accelerating the flow of tourist passengers.Thirdly, the convenience of traffic is tested under the significance level of 1%, indicating that traffic factors are one of the most important factors affecting the structure of tourist flow network, which explains that many cities with rich and unique tourism resources in the upper reaches of the Yangtze River Economic Belt(YREB) are in a marginal position in the passenger flow network.Finally, an important index of tourism development level in a region is the level of tourism economy, which can reflect the development and utilization degree of tourism resources in the region and promote the flow of tourist flow. In addition, people's way of thinking will also affect the development of tourism. The open concept of people, positive and enterprising, can promote the development of regional tourism. Table 7 Results of QAP correlation analysis and regression analysis of factors affecting tourist flow network structure Independent variable Measuring indicators tourist flow correlation coefficient regression coefficient Tourism resource endowment Number of 5A and 4A tourist attractions 0.004 ** 0.000 *** Basic tourism facilities The number of travel agencies, star-rated hotels 0.012 ** 0.000 *** Regional economic foundation per capita GDP 0.011 ** 0.000 *** Industrial capital structure Value-added value of the tertiary industry 0.071 * 0.106 Social fixed capital amount 0.098 * 0.113 Convenient transportation The mileage of grade roads 0.030 ** 0.000 *** population size Permanent resident population at the end of the year 0.066 * 0.064 * Geospatial distance Space adjacent 0.069 * 0.067 * Note: * and * * correlation pass the test at the significant levels of 10% and 5%, respectively, and *, * * * and * * * indicate that the regression coefficients pass the test at the significant levels of 10%, 5% and 1%, respectively. 6. Conclusion and discussion 6.1 Conclusion A multi-scale analysis of the tourism flow network within the Yangtze River Economic Belt(YREB) (YREB) reveals key insights into the interaction of spatial hierarchy, regional inequality and tourism dynamics in the digital age. First of all, in the Yangtze River Economic Belt(YREB) area, the flow of tourist flow presents a complex multi-line, multi-chain and multi-level correlation structure, which breaks through the geographical restrictions of "adjacent" and "near", and forms a network structure of universal connection between nodes. There is a general correlation between the overall network nodes of the tourist flow, and the close degree of correlation shows a good trend, but the overall network is at a low density, the network connection is not close enough, there are isolated points, the network structure is not complete and stable, and the network development still has a large room for improvement. Secondly, from the perspective of degree centrality, the tourist flow network shows the center of Shanghai, Nanjing, Wuhan and Changsha in the middle reaches; it shows the characteristics of higher upstream and lower reaches and smaller in the middle reaches. Shanghai, Hangzhou, Chongqing, Suzhou, Kunming, Nanjing, Wuhan, Chengdu and so on, are among the top 10 cities in terms of the tourist flow network. Shanghai is not only the core tourist source and also a well-known tourist destination. Hangzhou, Chongqing, Chengdu, Kunming, Nanjing and Wuhan have gradually developed into typical inward-oriented cities, with relatively few tourists exported. The centrality of the bottom ten cities is 0, and the tourism popularity and travel potential are insufficient. From the perspective of close centrality, the strong and weak development pattern of the high-value areas are mainly concentrated in the upstream and downstream regions, and the low-value areas are distributed in Shaoyang and Loudi in the middle reaches. The absolute center of the tourist flow is Shanghai, Chongqing, Suzhou, Wuhan, etc., which directly realizes the interaction with the tourism market, and the tourist flow agglomeration ability is strong. Other cities show a balanced situation. From the perspective of intermediary centrality, the network intermediary centrality of tourist flow presents a "point-axis" development mode. The tourist flow is mainly controlled by Shanghai, Chongqing, Chengdu, Wuhan, Nanjing, Kunming, Suzhou and other cities, which are popular tourism distribution centers, while other nodes have a low sense of existence, and form a "development axis" from the east to the west. Finally, the tourist flow network of the Yangtze River Economic Belt(YREB) presents an obvious and stable core-edge structure. The Yangtze River Economic Belt(YREB) has formed a multi-core polarization structure with the node cities in the core areas of the three major urban agglomerations as the core. The core areas are mainly the downstream Yangtze River Delta urban agglomeration, the Wuhan and Changsha urban agglomeration in the middle reaches, Chongqing and Chengdu in the Chengdu-Chongqing urban agglomeration, etc. The marginal areas are mainly composed of other cities and prefectures except the core cities of the three urban agglomerations. The number of 5A and 4A scenic spots, the number of travel agencies and star-rated hotels, the per capita GDP, the mileage of grade roads, the permanent resident population at the end of the year, the number of Internet access households and the space proximity have an impact on the network structure of tourist flow. Among them, the first four factors are the main reasons for the difference in the tourist flow network. 6.2 Discussion By combining user-generated content (UGC) with social network analysis, this study provides a dynamic framework for the decoding of spatial inequality in the digital age. It challenges the static interpretation of the "push and pull" theory by redefining the "pull factor" as a digital intermediary structure, by strengthening the existing hierarchy of the "push and pull" theory, and then makes the following discussion: (1) Cultivate new core nodes. Focus on cultivating tourism cooperation alliances in the middle and upstream regions and take the upstream and downstream tourism cooperation and development as the top priority. Tourism cooperation alliances with the upstream and midstream Guiyang, Kunming, Hefei, Jiujiang and other node cities as the core can be built to enhance the radiation effect. We should attach importance to the planning and development of tourism elements in edge node cities, pay attention to the search for typical tourism resources, develop differentiated and personalized tourism routes, and enhance the intrinsic value of the developed tourism resources in the Yangtze River Economic Belt(YREB). Efforts should be made to improve the quality of tourism services, improve the experience and satisfaction of tourists, improve the revisit rate of tourists, and promote the balanced development of tourist flow. (2) Strengthen regional cooperation and driving role. By establishing an integrated or integrated center integrating resource exchange, tourist demand exchange and market supply exchange, the tourism resources of the upper, middle and lower reaches regions will be integrated, the connection between the central node cities in the upper, middle and lower reaches of the Yangtze River Economic Belt(YREB) will be strengthened, and the coordinated development of the upper, middle and lower reaches will be promoted. Strengthen the connection between the node cities within different regional urban agglomerations, and strengthen the construction of large-scale transportation infrastructure and transportation points to improve the non-central node cities in the central medium and low value level or low value cluster areas. Some non-central node cities should strengthen tourism publicity, innovate marketing methods, build information transmission platforms, promote joint marketing, improve the visibility of tourism resources, and make use of regional differences and unique advantages to attract potential tourists. (3) Form a comprehensive tourism space network pattern. Reasonable planning of tourist routes, improve the overall network contact density. It is necessary to give full play to the advantages of diversified natural resources and historical and cultural tourism resources in the middle and lower reaches of the Yangtze River Economic Belt(YREB), focus on high-quality projects, connect the famous tourist cities in the upper, middle and lower reaches into lines, and expand the surrounding valuable tourist attractions into the main line, with point and line and surface. Starting from different needs, types and regions, we will actively develop new growth points of innovative cultural tourism and actively cultivate new tourism attractions. Adhering to the concept of all-region tourism development, we will break through the restrictions of administrative divisions of 9 provinces and two cities, form a common force for development at the regional level, strengthen the tourism cooperation in the entire spatial region of the Yangtze River Economic Belt(YREB) at the overall level, strengthen regional ties, and enhance the high-quality competitiveness of tourism. We will promote resource allocation between regions and give greater space for tourism cooperation. We will encourage the promotion of diversified cooperation models in the tourism industry and cooperate with local governments to build tourism infrastructure. All localities should respect the regional layout of tourist flow networks, not only highlight the driving role of core groups, but also promote the balanced development of the whole network. Declarations Ethical approval ： This article does not contain any studies with human participants performed by any of the authors. Informed consent ： This article does not contain any studies with human participants performed by any of the authors. Author Contribution "A.B. and F. wrote the main manuscript text and C.D.E prepared figures. All authors reviewed the manuscript." Data Availability All data obtained/ generated has been provided.Data would be made available upon reasonable request from the corresponding author. References Scholz J, Jeznik J Evaluating Geo-Tagged Twitter Data to Analyze Tourist Flows in Styria, Austria[J]. 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Chongqing University, Chongqing, pp 45–123 Ma Weina C (2024) Jingying. —— Take Ctrip as an example [J]. Mod Bus, (21):35–38 Additional Declarations No competing interests reported. Supplementary Files 2015CoreedgeMatrix.xlsx 2015Characteristicanalysisofindividualcentralnetworknodes.xlsx 2015Coreedge.xls 2015intercitytourismflow.xlsx 2017Characteristicanalysisofindividualcentralnetworknodes.xlsx 2017Coreedge.xls 2017intercitytourismflow.xlsx 2019Characteristicanalysisofindividualcentralnetworknodes.xlsx 2019CoreedgeMatrix.xlsx 2017CoreedgeMatrix.xlsx 2019Coreedge.xlsx 2019intercitytourismflow.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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07:51:56","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":8808,"visible":true,"origin":"","legend":"","description":"","filename":"2017CoreedgeMatrix.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6315958/v1/7c92bf140b0ee025272230d2.xlsx"},{"id":84676664,"identity":"dcc0778b-f1fb-44ad-b38f-cdae6c3d9c67","added_by":"auto","created_at":"2025-06-16 07:43:56","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11149,"visible":true,"origin":"","legend":"","description":"","filename":"2019Coreedge.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6315958/v1/7f09e1d0a62842bd103b5994.xlsx"},{"id":84677147,"identity":"1de46b29-7f19-42f5-821d-df4f56fa5387","added_by":"auto","created_at":"2025-06-16 07:51:57","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":48257,"visible":true,"origin":"","legend":"","description":"","filename":"2019intercitytourismflow.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6315958/v1/0977c167f59128738f699fda.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Structure and Influencing Factors of Tourist Flow in the Yangtze River Economic Belt: A Study Based on Travelogues","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs a national strategic core region spanning nine provinces and two cities, the Yangtze River Economic Belt(YREB) accounts for about 46.4% of the country's GDP and 42.9% of the country's population. This super economic zone, which extends from the eastern coastal hub to the western inland, is not only an engine driving China's economic growth, but also a treasure house of cultural and natural heritage \u0026mdash;\u0026mdash; has 24 UNESCO World Heritage sites and more than 1,400 high-level tourist attractions. In recent years, driven by policy frameworks such as \"Belt and Road\", tourism has become a key force in promoting regional coordinated development and economic transformation and upgrading. However, it is worth noting that the tourism development in this region presents significant unbalanced characteristics, the core-edge structure is prominent, and the fragmentation problem of inter-city cooperation needs to be solved urgently.\u003c/p\u003e \u003cp\u003eIn the context of promoting coordinated regional development globally, identifying the driving elements of tourism integration such as infrastructure, economic gradient and resource endowment can provide a decision-making basis for narrowing the regional development gap. More importantly, tourism flow is not only an indicator of economic phenomena, but also a spatial carrier of social interaction, cultural communication and environmental pressure. Its research has multiple enlightenments for global sustainable governance. The traditional tourism flow research relies on static statistical data or questionnaire survey, and has limitations such as extensive spatial and temporal resolution and sampling bias. The digital age provides innovative research opportunities for user-generated content (UGC), especially travel big data. Taking the travel data of Ctrip and other platforms as an example, these text information can capture the real-time and fine-grained spatial and temporal trajectory of tourists, and reveal the potential behavior patterns and preference characteristics. It is worth noting that the media application of explosive growth is reshaping the tourism decision-making mechanism, weibo, little red book platform make niche destination for \"visibility\" breakthrough, travel notes as an important form of UGC, it contains the dynamic data flow can reflect the \"edge-moderate\" the evolution of the structure, and the perspective of the supply side facilities construction and the interaction of decision-making behavior.\u003c/p\u003e \u003cp\u003eThe evolution of the tourism pattern of the Yangtze River Economic Belt(YREB) deeply reflects the era trajectory of China's tourism development. The traditional tourism flow presents linear agglomeration characteristics, with Shanghai, Wuhan, Chongqing and other hub cities as nodes, and the marginal cities have been in the development depression for a long time. With the deep transformation of China's tourism market \u0026mdash;\u0026mdash; the increase of residents' disposable income, diversification of consumption preferences and acceleration of digital transformation \u0026mdash;\u0026mdash; new spatial forms are being generated. Modern tourists increasingly prefer multi-destination series travel. Under the superimposed effect of transportation network upgrading and digital platform empowerment, this trend has given birth to \"highly hub areas\" and \"moderate regions\" with transition functions, forming a network pattern of multi-center polarization development. The analysis of this spatial structure is of great theoretical value to the optimization of international resource allocation and the promotion of cross-regional coordination.\u003c/p\u003e \u003cp\u003eBased on the data of 300,000 travel notes from 2015 to 2019, this study constructed a multi-scale tourism flow network in 126 prefecture-level cities along the Yangtze River Economic Belt(YREB). Through the combination of social network analysis (SNA) and secondary assignment program (QAP) regression, the dynamic evolution mechanism of the network, the spatial heterogeneity of the decoding of the tourism network and its driving mechanism are revealed. The research not only makes up for the insufficient analysis of the existing literature on the urban scale, but also innovatively uses UGC big data to reveal the interaction between tourism infrastructure, economic gradient difference and digital influence. The research results can provide policy support for policy makers to enhance network resilience, optimize infrastructure investment priorities, and foster new drivers of balanced development, thus providing solutions for promoting regional cross-basin coordinated development.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research progress of tourism flow measurement method\u003c/h2\u003e \u003cp\u003eScholars show a diversified trend in the tourism flow measurement methods. Early studies mostly built an analytical framework based on classical theories (such as spatial diffusion theory and core-edge theory), and the data sources were mainly official statistics and questionnaire survey. With the development of information technology, dynamic network data has become a research hotspot. Scholz Taking tourism-related tweets as the research object, using Twitter to obtain information data to study the spatial layout characteristics of the flow of tourism information elements \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Van der Zee And others use the online comment data of the international tourism platform Kitty Eagle to study the relationship network characteristics generated by the flow of tourists between micro nodes such as scenic spots, restaurants, shops and memorials\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In terms of research methods, social network analysis (SNA) is widely used, supplemented by spatial autocorrelation analysis, modified gravity model and other quantitative tools, emphasizing modelling and dynamic analysis. China's tourism flow measurement has undergone a transition from static to dynamic. Early research relied on statistical yearbook, questionnaire survey and other cross-sectional data. Zhou Huiling et al. calculated the scale of Chinese inter-provincial tourism flow and the advantages, connection and development level of the domestic tourism flow network structure\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In recent years, online big data (such as Baidu index, Weibo data, geotagged photos) has become the mainstream. For example, Duzhen et al. compared \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003ewith Baidu search index. Yang Yong and others use the Baidu index to represent the tourism information flow network\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. At the method level, the social network analysis takes the lead, supplemented by the gravity model (such as Wang Kai et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003ebuilds the tourism economic connection network of the urban agglomeration in the middle reaches of the Yangtze River).\u003c/p\u003e \u003cp\u003eTourism flow measurement method has made significant progress in data acquisition and technology application. International research introduced dynamic network data early, and mature method system; China relies on local platforms (such as Baidu Index) to achieve innovation. This study innovatively integrates multi-source data, analyzes the coordination between traditional statistical data and network dynamic big data; optimizes the measurement method of micro scale (such as prefecture-level city), especially in the real-time tracking of tourist behavior track; the comprehensive measurement of cross-factor flow (such as passenger flow, information flow and economic flow) is not comprehensive enough to reflect the complexity of tourism flow network. In the future, it is necessary to strengthen multi-modal data fusion, develop intelligent analysis tools, and promote the development of measurement methods to the dynamic and refined direction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research on the structural characteristics of tourism flow network\u003c/h2\u003e \u003cp\u003eResearch on tourism flow network structure covers multi-scale space. At the macro level, Chung, Bendle and others take countries or regions as nodes to analyze the trend of factor flow between destinations\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Lozano et al. to discuss the differentiation of entry-exit tourism flow in important countries in the world\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. At the micro level, Salas, Han and others analyze the relationship network between nodes such as scenic spots and hotels through tourist trajectory\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In terms of network structure evolution, Mou et al. use geolabeled photos to build the inbound tourism flow network\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Zhu He Dynamic analysis of the multi-scale structure of China's inbound tourism flow\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The general trend shows that international research has shifted from macro to micro, with diversified data sources (such as social media and online platforms), and focusing on the analysis of network dynamic evolution. Chinese studies focuses on multi-scale regional network characteristics. At the macro level, Ruan Wenqi et al. analyzed the network structure by outbound tourism routes \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. At the middle level, Cheng Xuelan et al. studied the network structure of tourism flow in five urban agglomerations along the eastern coast of China\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Du Jieli et al. discussed the structural characteristics of tourism flow in the Guangdong-Hong Kong-Macao Greater Bay Area\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. At the micro level, Wang Shuhua and others established a tourism flow network\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003ewith 49 high-frequency scenic spots in 17 prefecture-level cities in Henan as nodes. Lin Wenhui et al. analyzed the tourism flow space network of scenic spots in Hangzhou\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The research objects are mainly tourism flow, which has been extended to tourism economic flow in recent years (for example, Li Hua et al. has constructed the key matrix\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003eof China's regional tourism economic connection through gravity model) and information flow (such as the urban information flow network\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003ebased on Baidu Index).\u003c/p\u003e \u003cp\u003eThe research on the network structure of tourism flow presents the characteristics of \"scale refinement and multiple objects\". International research focuses on cross-country and cross-regional comparison, while China focuses on national strategic areas (such as the Yangtze River Delta and the Yellow River basin). In this study, we study the spatial scale of prefecture-level cities, focus on the overall network structure of national strategic areas (such as the Yangtze River Economic Belt(YREB)), and propose the differences of regional development: passenger flow, future information flow and economic flow, full diachronic analysis, deepening the coordination between static section research and dynamic evolution mechanism. It is necessary to strengthen the integration of cross-factor flow, deepen the multi-scale research of major strategic areas, and introduce time series analysis to reveal the law of network evolution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study on the influencing factors of tourism flow network\u003c/h2\u003e \u003cp\u003eThe theoretical system of the influencing factors of tourism flow network is mature, with the \"push-pull\" theory\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eand the spatial interaction theory\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e as the core framework. In terms of subjective factors, Mansfeld explores the influence of willingness to travel on behavior\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Objective factors covering the economic level (Gundelfinger in Spain to the canary islands route as an example analysis of the price impact on flow\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e), transportation convenience (Alderighi analyzed the international tourism transportation convenience has a significant impact on the tourism flow\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e), cultural proximity (Antonio Accetturo analyzed the cultural close to the impact of economic exchange and tourism flow\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e), etc. In recent years, research has turned to policy and social factors, such as Setareh, to analyze the impact of transaction costs on tourism flow\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Chinese research conducts empirical analysis based on foreign theories. In terms of driving mechanism, Yan Flash proposed the comprehensive model\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003eof \"internal drive-pulling force-friction\". Gao Jie et al. emphasized the driving effect of economic development level on Macao immigration\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In terms of influencing factors, destination attraction (resource endowment, transportation accessibility)\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e and tourist source characteristics (revenue, policy) are widely discussed in\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. At the method level, quantitative tools such as geographic detector and QAP analysis are popular. For example, Yang Li et al. used QAP to explore the influencing factors of \"Belt and Road\" tourism information flow \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study of the influencing factors of tourism flow has formed a combination system of \"theory-demonstration\". International research focuses on macro mechanisms, while China focuses on regional evidence. The influencing factors in this study are selected based on the traditional analysis systematic framework, but the research on the differentiation driving mechanism of multiple factor flow (such as passenger flow and information flow) is insufficient; network dynamic data is captured to reveal the network dynamic change factors; policy factor analysis is weak, and targeted research on national strategies (such as Yangtze River Economic Belt(YREB)) is scarce. In the future, it is necessary to build a multi-factor flow comprehensive driving model, strengthen policy-oriented analysis, and use real-time data to improve the research dynamics.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Study area, study methods, and data sources","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Study area\u003c/h2\u003e\n \u003cp\u003eThe Yangtze River Economic Belt(YREB) covers the administrative scope of 9 provinces and 2 cities in the Yangtze River Basin in China, and connects the eastern Yangtze River Delta, the central triangle, and the western Chengdu-Chongqing and central Yunnan cities in the region.Since the 1990s, the Yangtze River Economic Belt(YREB) has been regarded as an important development axis of the national economy (Figure 1).In 2021, the GDP of Yangtze River Economic Belt(YREB) accounts for 46.4% of the country, 42.9% of the total population, 24 World heritage sites and 1424 advanced scenic spots, and broad tourism market prospects. Due to its strong economic strength and great development potential, it has become one of the important engines of China\u0026apos;s economic growth \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.In this study, 126 cities at prefecture level and above along the Yangtze River Economic Belt(YREB) were selected as the research unit. In order to avoid errors caused by small economic size and administrative system differences, the research unit did not include Xiantao, Tianmen, Qianjiang and Shennongjia.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Study Methods\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e(1) Construction of tourist flow network\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTaking 126 cities at prefecture-level and above along the Yangtze River Economic Belt(YREB) as the network nodes, the directional flow relationship between cities is extracted based on the travel tracks of tourists from 300,000 travel notes of Ctrip from 2015 to 2019.1 when there is direct passenger flow between two cities, otherwise 0, forming a dichotomous directed matrix of 126*126 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Two things to pay attention to: first, pay attention to the direction (order) of the city in the itinerary; second, pay attention to the name of the county and the name of the scenic spot, that is, no prefecture-level city and autonomous prefecture, which are matched to the corresponding cities and states in accordance with the corresponding principle of the territory.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eExample of tourist itinerary information statistics(The 2019 section)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eSerial Number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eUser ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eUser Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eTravel notes title\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eNode 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eNode 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eNode 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\"\u003e\n \u003cp\u003e234026781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eFlowers and fruits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eGo south in winter, and experience the different scenery of Jiangnan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eSoochow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\"\u003e\n \u003cp\u003e17005454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eDa yongjin -David\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eA 3-drive tour in Ningbo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\"\u003e\n \u003cp\u003e35699560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eM18****039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNingbo Yuyao Siming Lake Sequoia forest said to go\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eHangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\"\u003e\n \u003cp\u003e85965253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eSeven days of saint traveling everywhere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e2019 latest Cixi self-drive tour (colorful flower season along the water town)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eHangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"char\"\u003e\n \u003cp\u003e5018839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003efangyjwt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNingbo \u0026mdash;\u0026mdash; delicious city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"char\"\u003e\n \u003cp\u003e2010700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eSeven days of saint traveling everywhere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eA 2-day tour of Jiulong Lake in October\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48.0348px;\"\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"char\"\u003e\n \u003cp\u003e7935092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"left\"\u003e\n \u003cp\u003eA bourgeois woman who likes leisure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"left\"\u003e\n \u003cp\u003eGolden osmanthus fragrance, I once again close to you-Ningbo, Shaoxing, Hangzhou three heavy travel notes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"left\"\u003e\n \u003cp\u003eNing Po\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"left\"\u003e\n \u003cp\u003eshaoshing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 48.0348px;\" align=\"left\"\u003e\n \u003cp\u003eHangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e(2) \u003cstrong\u003eNetwork structure feature analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\"\u003eAssessment network overall collaboration level and the rationality of resource allocation, the overall network index based on previous research\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003enetwork density (reflect connection tightness), network correlation (measure integrity), network hierarchy (characterization of asymmetry), network efficiency (assess redundancy) and core-edge model (identify hierarchy structure) five indicators, calculation formula as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.The core-edge model refers to the discrete classification method of Borgatti et al. (1999)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e through UCINET software.\u003cbr\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverall network index formula expression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork density\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork correlation degree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork rating\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork efficiency\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecomputational formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{D}=\\text{L}/[\\text{N}\\times\\:(\\text{N}-1\\left)\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}=1-\\left\\{\\text{V}/[\\text{N}\\times\\:(\\text{N}-1\\left)\\right]\\right\\}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}=1-\\left[\\text{K}/\\text{m}\\text{a}\\text{x}\\left(\\text{K}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}=1-\\left[\\text{M}/\\text{m}\\text{a}\\text{x}\\left(\\text{M}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ecommentate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN:Number of nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN:Number of nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK:Symmetric accessible point log in the network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM:Number of redundant connections in the network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL:Actual number of associations between nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV:The number of unreachable points in the network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax(K):Maximum possible number of symmetric achievable points in the network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax(M):Maximum possible number of redundant connections in the network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIdentify network key nodes, individual centrality index based on previous research\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003eselection degree centrality (outward / inward, measure node radiation and agglomeration ability), close to the centrality (assess node accessibility), mediation centrality (characterization node control) 3 class index (in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), using Gephi space visualization analysis.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFormula for calculating the centrality of the individual center network\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eDegree centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eClose to centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003eIntermediary centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 61px;\"\u003e\n \u003ctd style=\"height: 109px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ecomputational formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D,out\\left(ni\\right)}=\\sum\\:_{j-1}^{n}{X}_{y}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 109px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{C\\left(ni\\right)}=\\frac{1}{\\sum\\:_{j-1}^{n}d({n}_{i},{n}_{j})}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 109px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{B\\left(ni\\right)}=\\sum\\:_{j}^{n}\\sum\\:_{k}^{n}\\frac{{g}_{jk\\left(ni\\right)}}{{g}_{jk}}(j\\ne\\:k\\ne\\:i)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D,in\\left(ni\\right)}=\\sum\\:_{j-1}^{n}{X}_{ij}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 157px;\" rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ecommentate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003en:Total number of nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003en:Total number of nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n \u003cp\u003en:Total number of nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 61px;\"\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D,out\\left(ni\\right)}\\)\u003c/span\u003e\u003c/span\u003e:Extroversion degree centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{C\\left(ni\\right)}\\)\u003c/span\u003e\u003c/span\u003e:The proximity centrality of the i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{B\\left(ni\\right)}\\)\u003c/span\u003e\u003c/span\u003e: Mediator centrality of i\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 61px;\"\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{D,in\\left(ni\\right)}\\)\u003c/span\u003e\u003c/span\u003e:Introversion degree centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d({n}_{i},{n}_{j})\\)\u003c/span\u003e\u003c/span\u003e: The shortest distance of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{i},{n}_{j}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 61px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{jk\\left(ni\\right)}\\)\u003c/span\u003e\u003c/span\u003e: The shortest number of paths from j to k\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 48px;\"\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 48px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{jk}\\)\u003c/span\u003e\u003c/span\u003e: The shortest number of paths going from j to k and passing through the node i\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e(3)\u0026nbsp;\u003cstrong\u003eIdentification of network influencing factors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBased on the framework of \u0026quot;push-pull theory\u0026quot;\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, 8 indicators such as tourism resource endowment (number of 5A / 4A scenic spots), tourism facilities (number of travel agencies and star hotels), regional economy (per capita GDP), traffic conditions (grade highway mileage) are selected to construct the explanatory variable matrix. The secondary assignment procedure (QAP) analysis was used to explore the correlation and regression relationship between variables and tourist flow network through non-parametric displacement test (5000 random substitutions), so as to avoid the multicollinearity problem of traditional regression method, which was specifically realized through the QAP module of UCINET.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Data sources\u003c/h2\u003e\n \u003cp\u003eThere is a significant positive relationship between the change of tourist movement trajectory in online travel notes and the spatial footprint of tourist flow obtained from the survey, which is a solid reflection of tourist trail \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.Comprehensive analysis of the online travel platform travel data accessibility and comparability and understand the ctrip user group coverage of comprehensive\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e, this study choose ctrip online travel data as the basic data, through the analysis of network travel travel information, extract the trip can reflect the sequence of space node as travel flow data.Select the Python code crawler method and customize the crawl rules:(1) The climbing cycle is three complete natural years in 2015,2017 and 2019; (2) with 126 \u0026quot;cities\u0026quot; along the Yangtze River Economic Belt(YREB) as the keyword; (3) The network travel data attributes retrieved include travel title, travel destination, personnel composition, cost, travel mode, transportation, travel route and other information.In the end, the original travel notes of the three years were 318,200,307,595 and 300,353 respectively.The data cleaning standards are strict. Excluding travel notes that do not belong to the scope of the research field, travel notes with advertising nature, travel notes with only one tourist destination city, travel notes containing only pictures or unable to restore the process of browsing the route. The final retained travel notes are 113,867,100,987 and 125,331 respectively.\u003c/p\u003eThe national economic data are from the Statistical Bulletin of National Economic and Social Development, China City Statistical Yearbook and China Tourism Statistical Yearbook in 126 cities at the prefecture level and above. The number of scenic spots refers to the list of A-level tourist attractions published on the official website of the culture and tourism departments (bureau) of provinces and cities. The administrative division data are obtained from the geographic information resource directory service system of the People\u0026apos;s Republic of China. The administrative boundaries of 126 cities at or above the prefecture level and above in the Yangtze River Economic Belt(YREB) were obtained from the element classification of the National Basic geographic database (2019 edition). The urban vector coordinate data of Yangtze River Economic Belt(YREB) are derived from the national basic geographic database (2019 edition); the distance between cities is calculated by ArcGIS10.8 projection based on the vector geographic coordinates of Yangtze River Economic Belt(YREB).\n\u003c/div\u003e"},{"header":"4. Structure characteristics of the tourist flow network in the Yangtze River Economic Belt(YREB)","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Network identification of tourist passengers\u003c/h2\u003eVisual tool Gephi was used to draw the correlation map of the tourism flow network in the Yangtze River Economic Belt(YREB). Each node represents 126 cities in the Yangtze River Economic Belt(YREB), and the curve represents the existence of tourism flow relationship between the two cities. The initial tourist flow network structure is shown in Figure Fig.\u0026nbsp;2-Figure Fig.\u0026nbsp;4.\u003cp\u003eAccording to Fig.\u0026nbsp;2 to Fig.\u0026nbsp;4, from 2015 to 2019, the Yangtze River Economic Belt(YREB) connects many node cities; there are many connections between node cities; the number of connecting lines in different node cities varies, and the connection levels of node cities are different; the node cities with connection relationship no longer limit the adjacent range, breaking through the regional effect of \u0026quot;adjacent\u0026quot;, and forming a universal connection.This shows that the tourist flow network in the Yangtze River Economic Belt(YREB) has been formed and persisted. The number of nodes in the tourist flow network is increasing and the connection is constantly complicated, but there are still Nujiang, Lincang and other cities that are not connected with other nodes and are in an independent state. This shows that the development of tourist flow in the Yangtze River Economic Belt(YREB) has presented a complex, multi-threaded and widely related network structure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Analysis of the overall network structure characteristics\u003c/h2\u003e\u003cstrong\u003eNetwork density\u003c/strong\u003eFrom 2015 to 2019, the related development scale of tourist flow showed an upward trend, and the closeness of network connection was strengthened year by year, but there are problems such as low overall level of network density value and low degree of network development. In the theoretical 126 nodes, 100% interconnected states, up to 126,126 paths, or 15,876, may occur. In 2015, the network density of tourist flow was 0.167, that is, there were only 1064 connections between the real city nodes in the corresponding network. In 2017 and 2019, the network density did not exceed the theoretical 0.2, indicating that the connection between passenger flow nodes is not close enough and the connection path is few. The density value of tourist flow network is strengthened year by year, indicating that the relationship of tourists between cities and states is gradually strengthened (Fig.\u0026nbsp;5 ).\u003cp\u003e\u003cstrong\u003eNetwork correlation degree\u003c/strong\u003e From 2015 to 2019, the correlation value of the tourist flow network is relatively high. Most cities have direct or indirect correlation in the tourist flow network, but there are still isolated points in the network. The correlation value of the tourist flow network shows a trend of first falling and then rising, but the overall gap is not large, and the degree of correlation and cooperation between cities has increased, but the tourist flow network has isolated points, which is still an incomplete network.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNetwork level\u003c/strong\u003e From 2015 to 2019, the network level of tourist flow gradually increased, indicating that the development of the tourism market is unbalanced, and the advantages of popular tourist cities are increasingly prominent. This may be due to the limited spatial flow range due to personal willingness, tourism attraction, traffic duration, space proximity, and regional membership.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNetwork efficiency\u003c/strong\u003e From 2015 to 2019, the network efficiency value of tourist flow is at a high level, indicating that the network connection is less redundancy, the connection is relatively loose, the network stability is poor, the regional cooperation is not carried out in depth, the driving effect of tourism is weak, and it is difficult for the network to achieve coordinated development at present. The network efficiency value of tourist flow is declining, reflecting the increasing number of related channels in the Yangtze River Economic Belt(YREB), and the network stability is gradually strengthened.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Characteristic analysis of individual central network nodes\u003c/h2\u003e\u003cstrong\u003eDegree centrality\u003c/strong\u003e From 2015 to 2019, from the three regional levels, the central spatial distribution of the tourist flow network presents a multi-center pattern in the upstream, midstream and downstream. There are few middle and high value areas, the central gap between cities and states is significant, and the radiation effect of the core areas is relatively small, which may be due to the tendency of tourists to popular tourist cities.The passenger flow is mainly concentrated in the upstream and downstream, which may be mainly due to the higher level of economic development in the downstream region, tourists can enjoy more perfect travel conditions, while the upstream cities and states have rich tourism resources, strong attraction; some midstream cities become transit hubs for tourists, but the number is relatively small.\n\u003c/div\u003e\n\u003cp class=\"Section2\"\u003eFrom the city level, in the 2015\u0026ndash;2019 tourist flow network, the inward and outward oriented node cities are always Shanghai, Wuhan, Suzhou, Hangzhou, Nanjing, etc. (Fig.\u0026nbsp;6), indicating that these node cities occupy a core position in the tourism market, with a broad tourist source market and strong tourism attraction. The degree of centrality of each node city continues to increase, indicating that the passenger flow of the nodes is gradually rising, and the tourism market is gradually expanding. However, more than 80% of the node center values are distributed in the [0,300] section, indicating that the passenger flow is unbalanced, and most tourists are concentrated in a few high-order node cities. At the same time, with the increasing standard deviation between extroversion and introversion, the competitiveness of the tourism market is also improving, and the attraction of popular cities is becoming stronger.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClose to centrality\u003c/strong\u003e From 2015 to 2019, from the three regional levels, the tourist flow network is close to the central distribution, with a multi-center radiation development structure in the lower, middle and upstream, and an overall strong and locally weak development pattern. The tourist flow is close to the central group development trend is obvious, the high value area is mainly concentrated in the upstream and downstream areas, the low value area is distributed in Shaoyang and Loudi. The upstream and downstream tourist flow are more closely connected, and the unique tourism resources of the upstream and the developed economic level of the downstream promote the flow of tourists.\u003c/p\u003e\n\u003cp\u003eFrom the city level, the node cities, including Shanghai, Chongqing, Suzhou, Wuhan, Nanjing, Chengdu and so on (Fig.\u0026nbsp;7). The passenger flow market is frequently directly connected, has strong accessibility and accessibility, and is not limited by other nodes. In the high-order node cities, the outward value close to the centrality is less than the inward value, indicating that the tourist flow agglomeration ability of these cities is strong. Introverted in 2015 and outward close to the centrality are below the mean of nodes to 8, respectively is Loudi, Xiaogan, cross, at, great, Suining, Shaoyang, Ezhou, reduced to four in 2019, Loudi, Xiaogan, Dazhou, Suining, passenger network node direct correlation in the strengthening trend, completely dependent on the edge of the development of other nodes reduce the number of actors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntermediary centrality\u003c/strong\u003e From 2015 to 2019, the network intermediary of tourist flow presents a \u0026quot;point-axis\u0026quot; development mode, with Chongqing, Chengdu, Guiyang, Shanghai, Nanjing, Hangzhou, Suzhou, Wuhan, Changsha, Hefei and so on as the central points, and the \u0026quot;development axis\u0026quot; (Fig. 8). The node cities with high intermediary center are Shanghai, Chongqing, Chengdu, Wuhan, Nanjing, Suzhou, Kunming, etc., indicating that these cities have always been popular tourism distribution centers, acting as an important intermediary role. The intermediary center of Shanghai in Shanghai is increasing year by year, indicating that Shanghai has more and more control over other nodes in the whole passenger flow network, and its ability of resource allocation. This is mainly due to Shanghai\u0026apos;s convenient transportation hub, superior geographical location and developed transportation network. From 2015 to 2019, there are 102,98 and 104 nodes with less than the mean, indicating that most of the nodes are strongly dependent in the network.\u003c/p\u003e\n\u003ch2\u003e4.4 Core- -edge characteristics of the tourist flow network\u003c/h2\u003e\n\u003cp\u003eIn this study, Network- -Core / Periphery module 0000000000000 pieces of the \u0026quot;core-edge\u0026quot; structure and density matrix\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003ebetween \u0026quot;core\u0026quot; and \u0026quot;edge\u0026quot; in Ucinet software.Using the Arc GIS map layered display, the partition results of the core and the edge are visualized in two types of colors, as shown in Fig.\u0026nbsp;9.\u003c/p\u003e\n\u003cp\u003eFrom the perspective of spatial distribution, the tourist flow network is in the development period, the number of cities and states in the core area of the tourist flow network is increasing, and the intensity of tourist flow between the cities and states in the Yangtze River Economic Belt(YREB) is increasing.In 2015, the \u0026quot;core-edge\u0026quot; structure of the tourist flow network is relatively loose, and the connection between cities and states is not closely close. Except for the downstream node cities Shanghai, Suzhou, Nanjing and Hangzhou, the midstream node cities Wuhan and Changsha, and the upstream node cities Chongqing, Chengdu and Kunming, other cities and states all belong to the edge areas.In 2017, the number of cities and states in the core area of the tourist flow network increased, and the tourist contact between cities and states along the Yangtze River Economic Belt(YREB) increased. Downstream Shanghai, Suzhou, Nanjing, midstream Wuhan, Changsha, upstream Chongqing and other cities are in the core region.In 2019, the flow of tourist flow and tourists between cities and states is getting closer, and the connection of tourist flow network keeps increasing. The core area is mainly distributed in the provincial capitals with good tourism resources or high economic level.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe \u0026quot;core-edge\u0026quot; density matrix of the tourist flow network in the Yangtze River Economic Belt(YREB)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIn 2015\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIn 2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIn 2019\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarginal Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree of Fitting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eFrom the density matrix (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), there is a significant difference in the tourist flow network density between the core areas and the marginal areas of the Yangtze River Economic Belt(YREB).From 2015 to 2019, the \u0026quot;core\u0026quot; to \u0026quot;core\u0026quot; network has the highest density, indicating that tourists have frequent flow between the core tourism areas, because the pursuit of well-known tourist destinations is consistent;\u0026quot;Core\u0026quot; to \u0026quot;edge\u0026quot; and \u0026quot;edge\u0026quot; to \u0026quot;core\u0026quot; network density is not big, and growing year by year, the core area and edge area, tourist flow range from popular tourist city to the surrounding tourist city scope expanded, one is due to the pull effect of tourism core, on the other hand is due to the edge area tourism construction gradually strengthen;The network density from \u0026quot;edge\u0026quot; to \u0026quot;edge\u0026quot; is the smallest, and the degree of connection between nodes is only 0. 059,0.067 and 0. 082 respectively, but it does not reach the overall network density of 0.118,0.128 and 0.135, indicating that the tourist flow in marginal tourism areas is very little and the tourist attraction is extremely low.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. The influencing factors of the tourist flow network structure in the Yangtze River Economic Belt(YREB)","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Selection of indicators of influencing factors\u003c/h2\u003eThe \u0026quot;push-pull\u0026quot; theory was first proposed as\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003eby British scholar E. G. Ravenstein in the 1880s. D. J Bgane further summarized the causes of population migration and formed the \u0026quot;push-pull theory\u0026quot;.Because the theory involves the population flow, describes the tourists from tourists to destination of popular for, so Dann will push \u0026mdash;\u0026mdash; pull theory applied to the tourism research field\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, that the psychological factors is the inner thrust of tourism activities, and the various attributes of tourist destination is the external pull to attract tourists.Mo Kun (2014) applied the theory of push and pull to the empirical analysis of the factors affecting the willingness of pension tourism in\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.According to the previous results of the study\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, the common points and differences of the characteristics of the pull of the tourist attractions and the thrust reflected in the tourist psychology, in order to explain the common and different characteristics of the tourist flow network structure.On the basis of comprehensive consideration, the following 7 main influencing factors and 8 indicators are selected (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition to the spatial proximity indicators, other indicators are from the statistical year of prefecture-level cities, Statistical Bulletin of National Economic and Social Development, China City Statistical Yearbook, China Tourism Statistical Yearbook, official websites of municipal and state governments, etc.\u003cbr\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe influencing factors of tourist flow network structure\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInfluencing factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference documentation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePush pull effect\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTourism resource endowment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of 5A and 4A tourist attractions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhang Kai et al(2013)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic tourism facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe number of travel agencies, star-rated hotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDong Intro et al(2018)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional economic foundation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eper capita GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang Kai et al(2019)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIndustrial capital structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue-added value of the tertiary industry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShi Jianzhong et al(2022)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial fixed capital amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYang Yong et al(2022)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConvenient transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe mileage of grade roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi Weiqiu et al(2018)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePull\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epopulation size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent resident population at the end of the year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverland, etc(2023)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePush\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeospatial distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpace adjacent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi Hangfei et al(2017)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePush\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 QAP correlation analysis\u003c/h2\u003e\n \u003cp\u003eQAP correlation analysis to explain the correlation between independent variables and tourist flow network. In the correlation analysis, the most important indicator is the significance, which only proves significance if the significance value is below 10%.QAP related analysis shows that the number of 5A and 4A scenic spot, travel agencies and star hotels, the per capita GDP, social fixed capital and the added value of the third industry, grade highway mileage, at the end of the permanent population, space proximity of the matrix and tourist flow matrix through significant level test, the influencing factors and tourism flow network are positively correlated.It shows that the differences between the number of scenic spots, the number of travel agencies and above, the number of star hotels, per capita GDP, the added value of social fixed capital and the tertiary industry, the mileage of grade highways, the permanent resident population and the spatial proximity have a correlation with the tourist flow. In addition, the flow of tourism factors tends to choose areas with high level of resource endowment, good service facilities and level, developed economy and convenient transportation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 The QAP regression analysis\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of QAP regression model fitting between independent variables and tourist flow networks\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003equota\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003etourist flow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e༲2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe adjusted༲2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of random substitutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"Section2\"\u003eThey was standardized using the range method to eliminate the influence of the dependent variable dimension. According to the model fitting results (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), it can be seen that the fit of QAP regression analysis model of tourism flow in the Yangtze River Economic Belt(YREB) is relatively high, and the adjusted evaluation coefficient R2 is 0.562, which has passed the significance test, indicating that 56.2% of the network information of tourist flow in the Yangtze River Economic Belt(YREB) can be explained, and the regression analysis results are good.The results of regression analysis of influencing factors of tourist flow network structure are shown\u003cp\u003eIn the tourist flow network, the influencing factors with the highest significance level are the tourism resource endowment level, the service level of tourism facilities, the regional economic foundation and the transportation convenience degree.First, the tourism resource endowment can explain the structure of the tourist flow network at a significant level of 1%. In different cities, the difference between the number of 4A and 5A scenic spots has some connection to the flow of tourists. Due to the large difference in tourism resources in different cities, the spatial correlation of tourism flow is high, and the pulling effect on each city is also strong.Secondly, the service level of tourism facilities can explain the characteristics of tourist flow network at a significant level of 1%. Travel agencies belong to the attribute data representing the regional tourism economy. Usually, the number of travel agencies in the areas with developed tourism economy will also increase accordingly. At the same time, the difference in the number of star hotels can drive the flow of tourists between different cities along the Yangtze River Economic Belt(YREB) to a certain extent. Therefore, the service level of tourism facilities has a positive effect on accelerating the flow of tourist passengers.Thirdly, the convenience of traffic is tested under the significance level of 1%, indicating that traffic factors are one of the most important factors affecting the structure of tourist flow network, which explains that many cities with rich and unique tourism resources in the upper reaches of the Yangtze River Economic Belt(YREB) are in a marginal position in the passenger flow network.Finally, an important index of tourism development level in a region is the level of tourism economy, which can reflect the development and utilization degree of tourism resources in the region and promote the flow of tourist flow. In addition, people\u0026apos;s way of thinking will also affect the development of tourism. The open concept of people, positive and enterprising, can promote the development of regional tourism.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of QAP correlation analysis and regression analysis of factors affecting tourist flow network structure\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMeasuring indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003etourist flow\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecorrelation coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eregression coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTourism resource endowment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of 5A and 4A tourist attractions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e\u003cstrong\u003e***\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic tourism facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe number of travel agencies, star-rated hotels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e\u003cstrong\u003e***\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegional economic foundation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eper capita GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e\u003cstrong\u003e***\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIndustrial capital structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue-added value of the tertiary industry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial fixed capital amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConvenient transportation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe mileage of grade roads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003csup\u003e\u003cstrong\u003e**\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003csup\u003e\u003cstrong\u003e***\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epopulation size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermanent resident population at the end of the year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeospatial distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpace adjacent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNote: * and * * correlation pass the test at the significant levels of 10% and 5%, respectively, and *, * * * and * * * indicate that the regression coefficients pass the test at the significant levels of 10%, 5% and 1%, respectively.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion and discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e6.1 Conclusion\u003c/h2\u003e\nA multi-scale analysis of the tourism flow network within the Yangtze River Economic Belt(YREB) (YREB) reveals key insights into the interaction of spatial hierarchy, regional inequality and tourism dynamics in the digital age. First of all, in the Yangtze River Economic Belt(YREB) area, the flow of tourist flow presents a complex multi-line, multi-chain and multi-level correlation structure, which breaks through the geographical restrictions of \"adjacent\" and \"near\", and forms a network structure of universal connection between nodes. There is a general correlation between the overall network nodes of the tourist flow, and the close degree of correlation shows a good trend, but the overall network is at a low density, the network connection is not close enough, there are isolated points, the network structure is not complete and stable, and the network development still has a large room for improvement.\u003cbr /\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecondly, from the perspective of degree centrality, the tourist flow network shows the center of Shanghai, Nanjing, Wuhan and Changsha in the middle reaches; it shows the characteristics of higher upstream and lower reaches and smaller in the middle reaches. Shanghai, Hangzhou, Chongqing, Suzhou, Kunming, Nanjing, Wuhan, Chengdu and so on, are among the top 10 cities in terms of the tourist flow network. Shanghai is not only the core tourist source and also a well-known tourist destination. Hangzhou, Chongqing, Chengdu, Kunming, Nanjing and Wuhan have gradually developed into typical inward-oriented cities, with relatively few tourists exported. The centrality of the bottom ten cities is 0, and the tourism popularity and travel potential are insufficient. From the perspective of close centrality, the strong and weak development pattern of the high-value areas are mainly concentrated in the upstream and downstream regions, and the low-value areas are distributed in Shaoyang and Loudi in the middle reaches. The absolute center of the tourist flow is Shanghai, Chongqing, Suzhou, Wuhan, etc., which directly realizes the interaction with the tourism market, and the tourist flow agglomeration ability is strong. Other cities show a balanced situation. From the perspective of intermediary centrality, the network intermediary centrality of tourist flow presents a \"point-axis\" development mode. The tourist flow is mainly controlled by Shanghai, Chongqing, Chengdu, Wuhan, Nanjing, Kunming, Suzhou and other cities, which are popular tourism distribution centers, while other nodes have a low sense of existence, and form a \"development axis\" from the east to the west.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the tourist flow network of the Yangtze River Economic Belt(YREB) presents an obvious and stable core-edge structure. The Yangtze River Economic Belt(YREB) has formed a multi-core polarization structure with the node cities in the core areas of the three major urban agglomerations as the core. The core areas are mainly the downstream Yangtze River Delta urban agglomeration, the Wuhan and Changsha urban agglomeration in the middle reaches, Chongqing and Chengdu in the Chengdu-Chongqing urban agglomeration, etc. The marginal areas are mainly composed of other cities and prefectures except the core cities of the three urban agglomerations. The number of 5A and 4A scenic spots, the number of travel agencies and star-rated hotels, the per capita GDP, the mileage of grade roads, the permanent resident population at the end of the year, the number of Internet access households and the space proximity have an impact on the network structure of tourist flow. Among them, the first four factors are the main reasons for the difference in the tourist flow network.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003e6.2 Discussion\u003c/h2\u003e\n\u003cp\u003eBy combining user-generated content (UGC) with social network analysis, this study provides a dynamic framework for the decoding of spatial inequality in the digital age. It challenges the static interpretation of the \"push and pull\" theory by redefining the \"pull factor\" as a digital intermediary structure, by strengthening the existing hierarchy of the \"push and pull\" theory, and then makes the following discussion:\u003c/p\u003e\n(1) Cultivate new core nodes. Focus on cultivating tourism cooperation alliances in the middle and upstream regions and take the upstream and downstream tourism cooperation and development as the top priority. Tourism cooperation alliances with the upstream and midstream Guiyang, Kunming, Hefei, Jiujiang and other node cities as the core can be built to enhance the radiation effect. We should attach importance to the planning and development of tourism elements in edge node cities, pay attention to the search for typical tourism resources, develop differentiated and personalized tourism routes, and enhance the intrinsic value of the developed tourism resources in the Yangtze River Economic Belt(YREB). Efforts should be made to improve the quality of tourism services, improve the experience and satisfaction of tourists, improve the revisit rate of tourists, and promote the balanced development of tourist flow.\u003c/div\u003e\n\u003cp class=\"Section2\"\u003e(2) Strengthen regional cooperation and driving role. By establishing an integrated or integrated center integrating resource exchange, tourist demand exchange and market supply exchange, the tourism resources of the upper, middle and lower reaches regions will be integrated, the connection between the central node cities in the upper, middle and lower reaches of the Yangtze River Economic Belt(YREB) will be strengthened, and the coordinated development of the upper, middle and lower reaches will be promoted. Strengthen the connection between the node cities within different regional urban agglomerations, and strengthen the construction of large-scale transportation infrastructure and transportation points to improve the non-central node cities in the central medium and low value level or low value cluster areas. Some non-central node cities should strengthen tourism publicity, innovate marketing methods, build information transmission platforms, promote joint marketing, improve the visibility of tourism resources, and make use of regional differences and unique advantages to attract potential tourists.\u003c/p\u003e\n\u003cp class=\"Section2\"\u003e(3) Form a comprehensive tourism space network pattern. Reasonable planning of tourist routes, improve the overall network contact density. It is necessary to give full play to the advantages of diversified natural resources and historical and cultural tourism resources in the middle and lower reaches of the Yangtze River Economic Belt(YREB), focus on high-quality projects, connect the famous tourist cities in the upper, middle and lower reaches into lines, and expand the surrounding valuable tourist attractions into the main line, with point and line and surface. Starting from different needs, types and regions, we will actively develop new growth points of innovative cultural tourism and actively cultivate new tourism attractions. Adhering to the concept of all-region tourism development, we will break through the restrictions of administrative divisions of 9 provinces and two cities, form a common force for development at the regional level, strengthen the tourism cooperation in the entire spatial region of the Yangtze River Economic Belt(YREB) at the overall level, strengthen regional ties, and enhance the high-quality competitiveness of tourism. We will promote resource allocation between regions and give greater space for tourism cooperation. We will encourage the promotion of diversified cooperation models in the tourism industry and cooperate with local governments to build tourism infrastructure. All localities should respect the regional layout of tourist flow networks, not only highlight the driving role of core groups, but also promote the balanced development of the whole network.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"A.B. and F. wrote the main manuscript text and C.D.E prepared figures. All authors reviewed the manuscript.\"\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data obtained/ generated has been provided.Data would be made available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e \u003cli\u003e\u003cspan\u003eScholz J, Jeznik J Evaluating Geo-Tagged Twitter Data to Analyze Tourist Flows in Styria, Austria[J]. Int J Geo-Information 2020, 9(11):681\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan der Zee E, Bertocchi D (2018) Finding patterns in urban tourist behaviour: A social network analysis approach based on TripAdvisor reviews[J], vol 20. Information Technology \u0026amp; Tourism, pp 153\u0026ndash;180. 14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuiling Z (2020) Structure structure of China based on modified gravity model [J]. 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Mod Bus, (21):35\u0026ndash;38\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tourism flow network, Social network analysis, push-pull theory, Big data, Yangtze River Economic Belt(YREB)","lastPublishedDoi":"10.21203/rs.3.rs-6315958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6315958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the high-quality development of the tourism industry and increasing cross-regional cooperation, understanding the evolution and driving mechanisms of tourism flow networks in the Yangtze River Economic Belt (YREB) has become critical. This study constructs a multi-scale tourism flow network across 126 prefecture-level cities using 300,000 travelogues from Ctrip (2015\u0026ndash;2019). By integrating social network analysis (SNA) and quadratic assignment procedure (QAP) regression, we decode structural characteristics, node centrality dynamics, and key drivers of network evolution. Key findings include: (1) The network exhibits a multi-nucleated polarization structure centered on Shanghai, Wuhan, and Chongqing, with low overall density and weak connectivity among peripheral cities; (2) Node centrality is highly polarized, emphasizing the agglomeration and radiation capacities of core cities; (3) Primary drivers include the density of 5A/4A scenic spots, tourism infrastructure, per capita GDP, and transportation accessibility. Theoretically, this study advances travel geography by introducing a dynamic, data-driven framework that challenges traditional push-pull theory through digital mediation. By integrating user-generated content (UGC) and multimodal analysis, we pioneer the application of big data to network resilience research, offering insights into algorithmic platforms\u0026rsquo; role in reinforcing spatial hierarchies. Our holistic model bridges gaps in multi-scale synergy and multi-factor flow analysis (e.g., tourism, economy, and information flows), providing a foundation for addressing spatial inequalities and informing policies for balanced, sustainable governance.\u003c/p\u003e","manuscriptTitle":"Network Structure and Influencing Factors of Tourist Flow in the Yangtze River Economic Belt: A Study Based on Travelogues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 07:43:49","doi":"10.21203/rs.3.rs-6315958/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2b7f452-4413-41bb-9a0d-ca7674dac490","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50049803,"name":"Humanities/Complex networks"},{"id":50049804,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-04-16T13:27:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 07:43:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6315958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6315958","identity":"rs-6315958","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}