Network Analysis of Tourist Flows: a Cross-Provincial Boundary Perspective, Tourism Geographies, 18:5, 561-586, DOI: 10.1080/14616688.2016.1221443

Tourism Geographies An International Journal of Tourism Space, Place and Environment

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Network analysis of tourist flows: a cross- provincial boundary perspective

Hongsong Peng, Jinhe Zhang, Zehua Liu, Lin Lu & Lu Yang

To cite this article: Hongsong Peng, Jinhe Zhang, Zehua Liu, Lin Lu & Lu Yang (2016) Network analysis of tourist flows: a cross-provincial boundary perspective, Tourism Geographies, 18:5, 561-586, DOI: 10.1080/14616688.2016.1221443

To link to this article: http://dx.doi.org/10.1080/14616688.2016.1221443

Published online: 14 Sep 2016.

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Download by: [Nanjing University] Date: 12 October 2016, At: 03:54 TOURISM GEOGRAPHIES, 2016 VOL. 18, NO. 5, 561–586 http://dx.doi.org/10.1080/14616688.2016.1221443

Network analysis of tourist ﬂows: a cross-provincial boundary perspective

Hongsong Peng a, Jinhe Zhanga, Zehua Liua, Lin Lub and Lu Yanga aDepartment of Land Resources and Tourism Sciences, Nanjing University, Nanjing, China; bCollege of Territorial Resources and Tourism, Anhui Normal University, Wuhu, China

ABSTRACT ARTICLE HISTORY In contrast to the extensive research regarding tourist flows on the Received 10 November 2015 international, intranational, interregional, intercity, intracity and Accepted 31 July 2016 tourism-spot scales, little attention has been paid to the cross- KEYWORDS ’ provincial boundary perspective. In view of the fact that China s Tourist-flow network; cross- provincial administrative boundaries have a long history and a provincial boundary tourism profound influence on the society, culture and economy of destination; network analysis; neighboring provinces, this study focuses on tourist-flow networks boundary-shielding effect; in China that cross ‘provincial’ boundaries. Tourist-flow data from a boundary-mediating effect; questionnaire survey and travel-agency-recommended routes were Lugu Lake; China acquired, and the social network analysis method and boundary 关键词 fi effect analysis were adopted for the rst time to study the cross- 旅游流网络; 跨省级行政 boundary tourist flows. Lugu Lake in China was selected for the case 边界的旅游目的地; 网络分析边界屏蔽效应边界 study. The spatial distribution and impact factors of cross-boundary ; ; tourist flows are discussed, and a scientific basis for future 中介效应; 泸沽湖; 中国 collaborations among cross-boundary tourism destinations is provided. The following conclusions are reached. (1) The spatial structure of cross-boundary tourist-flow networks is complex. There is a core–periphery structure, and each node assumes different roles and functions. (2) Cross-boundary tourist flows are significantly influenced by the boundary-shielding effect, and the properties, direction and extent of the effects are diverse and depend on accessibility, resource endowments, resource heterogeneity and the extent of regional integration. (3) Cross- boundary tourist flows are affected by a boundary-mediating effect, and structural holes play a critical role in the boundary-mediating effect and drive the integration of regional tourism.

摘要相比于大量关注国际、国内、区际、城际、城市及景区内部尺度的旅游流研究,很少有研究涉及跨省级行政边界的旅游流这一议题。中国省级行政边界的历史源远流长, 对边界两侧的社会、文化和经济现象产生了深远的影响。鉴于以上事实,本文将视角聚焦于中国跨省级行政边界的旅游流网络。本文通过问卷调查和旅行社网站获取旅游线路数据,首次采用了社会网络分析与边界效应分析的方法研究跨界旅游流,并以中国的泸沽湖地区作为案例,探讨了跨界旅游流的空间分布及影响因素,为未来跨界旅游地合作提供了科学依据。结果表明: (1)跨界旅游流网络的空间结构复杂,并存在核心-边缘结构,各节点在网络中有不同的角色和功能,如旅游核

CONTACT Jinhe Zhang [email protected]; Zehua Liu [email protected]

心、旅游集散中心、旅游通道、重要旅游目的地、一般旅游目的地和边缘旅游目的地; (2)跨界旅游流受边界屏蔽效应的显著影响, 且影响的性质、方向和程度因网络中节点的可达性、资源禀赋、资源异质性及区域一体化程度的差异而不同; (3)跨界旅游流也受边界中介效应的影响,位于结构洞位置的节点是发挥边界中介效应,推动区域旅游一体化的关键。

Introduction Borders are a fundamental spatial feature on all scales. Borders are multidimensional and are composed of not only political but also social and cultural dimensions (van Houtum & van Naerssen, 2002). Political boundaries are geographic lines between two different political entities that objectively hinder connections between factors of production, such as people, material, information and funds (Sofield, 2006; Williams & Hall, 2000). Depending on the political geographic scale, political boundaries have different social, economic and cultural characteristics that can be classified into different levels, such as national, provincial (state) and county (Timothy, 1995, 2002). Compared to national and sub-provincial political boundaries, provincial boundaries are the highest level boundaries that generally influence a cross-boundary area within a country. Generally speaking, whether between countries or administrative regions within a country, the divisions of political boundaries are based mostly on natural geographical conditions such as the terrain and location of mountains and rivers, which have been gradually shaped over time. Because of the consistency of administrative divisions and natural geographic features, it is conducive to consider both in the development of the economy and society. However, during the Yuan Dynasty, Chinese rulers established the provincial system to prevent local separatism, which divided natural geographical units into different provinces. This system has gradually formed an administrative pattern, described as ‘interlocked like dog’s teeth’, that is not conducive to the development and management of regional economies (Xu & Situ, 2005). Similarly, the borders between African countries and the domestic boundaries in the United States and Australia were extended primarily along longitudinal or latitudinal lines, which also inevitably fragmented the natural geographical units (Bill, 2009). Due to the development of the tourism industry, many cross-boundary areas have developed into worldwide tourism destinations, such as the International Peace Garden at the Canada–U.S.A border and the Lower Rio Grande Valley and Big Bend areas at the Mex- ico–U.S.A border (Gelbman, 2008; Timothy & Tosun, 2003; Woosnam, Shafer, Scott, & Timo- thy, 2015). Cross-boundary tourism destinations possess spatial integrity, cultural homology and resource symbiosis. However, because tourism resources and products are constrained by rigid administrative divisions, these cross-boundary tourism destinations face serious problems, such as the complexities of interest groups, local protectionism and the boundary-shielding effect (Yang, Zhang, & Peng, 2011). Such problems become obstacles to communication and economic exchange between cross-boundary areas, and thus the harmonious development of cross-boundary tourism regions has become a serious problem (Yang & Chen, 2008; Yang, Zhang, & Peng, 2011). Furthermore, China’s existing provincial political boundaries that originated in the Yuan Dynasty more than 700 years ago have greatly impacted the human–land relationship in China. In addition, TOURISM GEOGRAPHIES 563 these provincial political boundaries greatly affect the development of tourism destinations (Rowen, 2014). Provincial boundaries have become a bottleneck and hinder the development of regional tourism (Guo, Ding, & Cao, 2008; Yang & Chen, 2008; Yang et al., 2011), which is particularly evident in certain cross-provincial boundary tourism destinations in China, such as Lugu Lake, Dabie Mountain, the Three Gorges and the Shangri-La region (Yang, 2013). Tourist flow is a key issue in tourism geography research and is greatly impacted by political boundaries (Timothy & Kim, 2015). Tourist flows have been examined on different scales, including international, intranational, interregional, intercity, intracity and tourism spots (Bowden, 2003; Shih, 2006; Timothy & Kim, 2015; Yang & Wong, 2013; Zhong, Zhang, & Li, 2011). Less attention has been focused on the network of tourist flows on the cross- provincial boundary scale, particularly in the Chinese context, and the influence of provincial boundaries has yet to be discussed. Boundaries are an important factor in the formation of regional differences and influence tourist-flow networks. More specifically, the characteristics and formation mechanisms of tourist-flow networks are unique to destinations at cross-provincial boundaries. These characteristics and mechanisms are affected not only by destination attributes, geographical distance, spatial structure, accessibility, individual characteristics and crisis events (Amelung, Nicholls, & Viner, 2007; Bonham, Edmonds, & Mak, 2006; De Vita & Kyaw, 2013; Prideaux, 2005; Webber, 2001; Williams & Zelinsky, 1970; Zhang & Jensen, 2007) but also by political boundaries that result in great differences in the economic, cultural and social dimensions of tourism. Therefore, a sound method to study the characteristics and formation mechanisms of cross-boundary tourist flows is urgently needed. Accordingly, this study aims (1) to analyze the spatial structure, network characteristics and roles of nodes of a tourist-flow network on the cross-provincial scale and (2) to measure the nature, direction and extent of the effects of provincial boundaries on destination tourist-flow networks. Consequently, the objectives of this study are twofold. The first objective is to use social network analysis (SNA) and boundary effect analysis (BEA) to analyze the cross-provincial boundary tourist-flow network on a large scale to enrich the scale, methods and content of tourist-flow research. The second objective is to provide policy recommendations for cooperation between cross-provincial boundary tourism destinations.

Literature review Border tourism Research on border tourism has primarily based on the perspective of cross-national boundaries and has addressed tourism management (Blasco, Guia, & Prats, 2014; Gelbman & Timothy, 2011; Pechlaner, Abfalter, & Raich, 2002; Timothy, 1999; Tosun, Timothy, Parpai- ris, & Macdonald, 2005), supply and demand (Gelbman, 2008; Saxena & Ilbery, 2010; Web- ster & Timothy, 2006), competition and cooperation (Chaderopa, 2013; Felsenstein & Freeman, 2001; Lovelock & Boyd, 2006; Prokkola, 2007; Teague & Henderson, 2006; Wei- denfeld, 2013), and effect factors (Bradbury, 2013; Smith & Xie, 2003) and effects (Kendall & Kreck, 1992). However, there is an increasing need for an improved theoretical understanding of tourism development in border areas (Mansfeld & Korman, 2015). 564 H. PENG ET AL.

Since the 1990s, researchers have increasingly investigated cross-border tourism basic theory, focusing on the concept, function and effects of boundary and development patterns (Gelbman & Timothy, 2011;Sofield, 2006; Timothy & Butler, 1995; Timothy & Tosun, 2003). Timothy (1995) greatly promoted the concept of border tourism and noted that international borders are a frontier field of tourism research. Timothy summarized three attributes of borders and tourism: tourism attraction, travel obstacles and travel transit zones. He suggested that political boundaries could become tourist attractions and studied the nature and type of political boundaries and their effects on border tourism. Hacho- wiak (2006) further considered that borders influenced tourism development in two opposing directions. Hachowiak’s study determined that borders restricted element flows in tourism and hindered regional tourism cooperation and destination spatial extension. However, borders were also the ‘bridge’ between different areas and lines of transition that modify and differentiate tourism and touristic landscapes in the opposite border regions. Several examples of such border types and their functions exist around the world, particularly on US borders (Prokkola, 2008). However, little research has been conducted on the perspective of cross-provincial boundaries and the influence of provincial boundaries has not yet been analyzed.

Tourist flow and its spatial scales A tourist flow is a projection of the trajectory of tourists and related activities in geographical space and is composed of three basic elements: the direction, the rate and the link mode (Bowden, 2003). Current studies primarily focus on the prediction (Hassani, Webster, Silva, & Heravi, 2015; Jackman & Greenidge, 2010; Song & Li, 2008, 2008; Yang, Fik, & Zhang, 2013), characteristics (Pearce, 1995; Zhong et al., 2011), patterns (Campbell, 1967; Lew & McKercher, 2006; Mckercher & Lau, 2008), spatial distribution and agglomeration (Shih, 2006) of tourist flow. Little attention has been focused on the spatial network structure from a cross-boundary perspective. Scale is a core concept in geography research (Holloway & Rice, 2003) and tourist flows have been studied on different scales. On the international scale, Coshall (2000) applied the univariate and bivariate spectral analysis methods to international tourist flows. Bow- den (2003) performed a comparative analysis of cross-national differences in international tourist flows in China. Although those studies were confined to the international scale, they provided a sound framework for future research. The scale most relevant to this study is intranational tourist flows. Although there is little research at this level, Oppermann (1992a, 1992b, 1995) conducted a series of studies regarding intranational tourism within Malaysia. In Oppermann’s research, the functional links and hierarchies of the flow network were identified and it was determined that tourists’ characteristics could influence the pattern of intranational tourist flows. Many recent studies have examined tourist flows on different scales, such as interregional, intercity, intracity and tourism spots (Shih, 2006; Wu & Carson, 2008; Yang & Wong, 2013; Zhong et al., 2011). However, research on tourist flows at the cross-provincial boundary scale, specifically in the Chinese context, is lacking. In addition, current studies have not fully analyzed the measures of the boundary effect, or to be specific, the influence of a provincial boundary on tourist flows. TOURISM GEOGRAPHIES 565

The theory and method of tourist flows Studies regarding tourist flows have primarily adopted mature methods from relevant fields, including metering statistics, GIS analysis, physics theory, regional economics and other related methods. Statistical methods, as a basic means of quantitative analysis, are widely used in the study of tourist flows. Structural time-series models are used to forecast tourist flows (Jackman & Greenidge, 2010; Song & Li, 2008). The Zipf and difference degree indices are used to analyze the spatial structure and characteristics of tourist flows (Zhu & Wu, 2005). GIS analysis methods are widely used as a primary means to analyze the spatial pattern and relations of tourist flows (Wu & Carson, 2008; Yang & Wong, 2013). However, the application of GIS methods is at a preliminary stage, and the use of correlation mod- ules, such as path analysis, buffer analysis and overlay analysis, remains limited. Moreover, because the internal abstract mechanism of tourism flows has strong physical characteristics, physics theory has become the primary method used in studies regarding tourist flows. Among physics theories, the push–pull theory is most widely used to analyze the dynamic mechanism of tourist flows (Chen & Chen, 2015; Kim & Lee, 2002), and the gravity model is used to analyze the temporal evolution of tourism flows (Morley, Rossello, & San- tana-Gallego, 2014). However, these methods are limited in their ability to analyze the direction, rate and link mode of tourist flows from the perspective of a ‘network’ and to further reveal the spatial structure, network characteristics and roles of the nodes of tourist flows. The core concept of the SNA method is to analyze social phenomena from the perspective of a ‘network’ (Asero, Gozzo, & Tomaselli, 2016; Casanueva, Gallego, & Garcıa-Sanchez,

2016). In recent years, scholars have begun to apply the SNA method in research regarding tourism planning, marketing, stakeholders and online networks (Bhat & Milne, 2008; Lee, Choi, Yoo, & Oh, 2013; Racherla & Hu, 2010; Wang & Xiang, 2007). These studies regarding tourism research have used theoretical concepts that are related to networks. However, SNA has not been widely adopted as a mathematical tool for analyzing relational data in tourism studies (Wu, Xiao, Dong, Wang, & Xue, 2012). The unique advantage of the SNA method in the analysis of the formation, evolution and characteristics of a tourist-flow network is the use of relational data between nodes instead of attributed data. Studies regarding tourist flows that utilized the SNA method, including destination networks, tourist movement patterns and tourist mobility, are scarce. Shih (2006) analyzed the network characteristics of drive tourism destinations using node-centrality indicators and structural holes indicators. The utility of network analysis was demonstrated in a study that was conducted by Hwang, Gretzel, and Fesenmaier (2006). In the work of these scholars, the role of different destinations within a tourism network structure emerged from an analysis of trip patterns in which locations were considered nodes and travel between locations was viewed as links among these nodes. Leung et al. (2012) analyzed overseas tourists’ movement patterns in Beijing using density and betweenness indicators. Asero et al. (2016) constructed a tourism network through tourist mobility using scale, density, centrality and structural equivalence indicators; this network revealed that the tourists’ choice defined the role of a destination as ‘central’ or ‘peripheral’ within a network. However, direct analyses that systematically employ SNA indicators to study the spatial structures and network characteristics of tourist flows and the roles of nodes are lacking. 566 H. PENG ET AL.

Boundary effect In general, the economic development of boundary areas is influenced by the boundary effect, which indicates the ability of an administrative boundary to promote or hinder the flow of economic, social and cultural elements (Coughlin & Novy, 2009). The boundary effect manifests as the boundary-shielding effect and the boundary-mediating effect (Yang, Zhang, & Ye, 2010). The boundary-shielding effect indicates that obstacles to communication and economic exchanges between cross-boundary areas exist. This phenomenon increases transaction costs and impedes the circulation of trade and production factors. In contrast, the boundary-mediating effect indicates that the geographical interac- tion between two different administrative regions is a key route of economic, social and cultural exchange, and its mediating function cannot be replaced by other regions. The boundary effect plays a significant role in cross-boundary tourism regions, particularly in regards to tourism economic cooperation and development (Jin & Lu, 2008). Differ- ent hierarchies, types and scales of regional tourism are significantly influenced by administrative boundaries. Therefore, the extent, direction and nature of the effect are entirely different. Similarly, the boundary effect of cross-boundary tourism destinations also manifests as the boundary-shielding effect and the boundary-mediating effect. The boundary-shielding effect is primarily manifested in three manners: marginal, separated and exclusive. The boundary-mediating effect is primarily manifested in three manners: complementary, adjacent and integral (Yang, 2013). The BEA may be used to measure the extent of regional integration (McCallum, 1995). Relevant research has primarily focused on the boundary-shielding effect and the integra-

tion of boundary regions. Hazledine (2009) used the Canadian air passenger as a case study and determined that the number of seats offered on domestic airline routes was approximately six times greater than the number of seats offered on international flights. This result was consistent with the boundary effects found in studies regarding international merchandise trade (Nitsch, 2000). Okubo (2003) analyzed the border effect in the Japanese market and studied how biased interregional trade compared to international trade. The results from Okubo’s study suggested that the border effect in Japan was con- siderably lower than in the U.S.A and Canada. Smith and Xie (2003) described a method for estimating the impact of the Canada–U.S.A border on travel by American residents to Canada in terms of the ‘distance equivalence’ of the border. On average, the border was equivalent to an additional 1650 km of travel. Similarly, cross-boundary tourist flows are most restricted by the boundary effect, which has not yet been measured. Two deficiencies are apparent in current studies. First, current tourist-flow research using different scales, such as international, intranational, interregional, intercity, intracity and tourism spots, is abundant, whereas research on the cross-provincial boundary scale, particularly in the Chinese context, is relatively inadequate. The number of studies regarding the influence of provincial boundaries on tourist flows is particularly insufficient. Sec- ond, there is extensive research regarding tourist flows using multiple disciplines, but the use of the SNA method in the study of the spatial structure, network characteristics and roles of nodes in cross-boundary tourist flows has been limited and merits further discussion. Therefore, this study aims to use the SNA and BEA (which were used to measure the direction and extent of the influence of the boundary on both sides and which mainly TOURISM GEOGRAPHIES 567 include the boundary-shielding effect and the boundary-mediating effect) methods to study a tourist-flow network on the cross-provincial scale in China and thus fills a gap in the existing research. First, data regarding tourist flows were collected using a questionnaire survey and travel-agency-recommended routes. These data can be divided into two levels: tourist demand and market supply. Second, the SNA and BEA methods were employed to construct a network evaluation index system of cross-boundary tourist flows, including node-structure indicators, network-structure indicators and boundary effect indicators. Lugu Lake, a cross-provincial boundary tourism area, was selected as the study area to analyze the spatial structure, network characteristics, roles of nodes and the boundary effect of tourist flows to reveal the evolution rules and impact factors of cross- boundary tourism and to provide a scientific basis for future collaboration among destinations. The paper is organized as follows. The Methodology section introduces the SNA and BEA methods. The Empirical analysis section analyzes the spatial structure, the network characteristics, the roles of nodes and the boundary effect of the cross-boundary tourist flows of Lugu Lake. Finally, the Conclusion summarizes the findings, discussed the implications of the current study and provides suggestions for future research.

Methodology Social network analysis A social network is an agglomeration of social actors and their relationships that integrate a theoretical framework with a research method (Luo, 2010; Novelli, Schmitz, & Spencer, 2006; Scott, 2000; Scott, Baggio, & Cooper, 2008; Wasserman & Faust, 1994). SNA, derived from graph theory, attempts to describe the structure of relationships (displayed by links) between specific entities (displayed by nodes) and applies quantitative techniques to pro- duce relevant indicators and results for studying the characteristics of an entire network and the position of individual actors within the network structure (Shih, 2006). This study employs the SNA method to analyze cross-boundary tourist flows. First, the scope and nodes of the network were determined. The activity space of tourist flows is the network scope and the related scenic spots and cultural attractions are the nodes (Asero et al., 2016). Second, we determined the linkage relationships of tourist flows, which are defined by touring routes. In Figure 1, for example, the involvement of a tourist in five destinations (labeled A, B, C, D and E) is displayed. The graph indicates that this tourist first visited destination A, and then destinations B, D and C in sequence; but this tourist did not visit destination E. Based on the graph, the asymmetric matrix of this tourist can be constructed such that the rows and columns index destinations in the graph. In the matrix, a 1 in the (i, j)th cell (row i, column j) indicates a direct link from i to j, and a 0 in the cell indicates that a direct link does not exist. The matrix describes what network analysis refers to as sociometric choices, which merely depict the presence or absence of a given type of relationship (Shih, 2006). Third, data were collected and processed. Data were collected from a questionnaire survey and travel-agency-recommended routes. Then, we established the database of tourist flows. Fourth, based on the database of tourist flows, we aggregated the two types of data and established the valued matrix, in which the (i, j) th cell expresses the number of times the tourist routes occur from destination i to 568 H. PENG ET AL.

Figure 1. A simple graph and matrix. destination j. Finally, based on the valued matrix, an appropriate cutoff value was selected to dichotomize the cells of the valued matrix and apply the binary data to the indicators and graphs of the network analysis. SNA programs are primarily based on the dichotomized matrix using Netdraw software. To analyze the characteristics of the tourist-flow network, the numerical matrix (valued matrix) must be translated into a dichotomized matrix by selecting an appropriate cutoff value after repeated testing and selection. Tradi- tionally, an appropriate cutoff value is used to identify the typical characteristics of the nodes in the tourist-flow network and to avoid problems that are associated with either very small or very large connections. As a result of the dichotomizing process, the (i, j)th cell of the valued matrix is assigned a 0 when the number of tourist routes from node i to node j is below the selected cutoff value; otherwise, the cell is assigned a 1 (Shih, 2006). Thus, the dichotomized matrix is constructed (Lee et al., 2013). Then, the tourist-flow network diagram is constructed using Netdraw software to evaluate the network characteristics, the spatial structure, and the roles of nodes. The tourist flow network-evaluation index system primarily includes node-structure and network-structure indicators (Casanueva, Gallego, & Sancho, 2013; Lee et al., 2013;Luo& Zhong, 2015;Shih,2006). Node structures are measured by node-centrality indicators and structural holes indicators (Casanueva et al., 2013; Shih, 2006), which are primarily used to analyze the role and function of nodes in the network. The network structure is evaluated based on the size, diameter, density, network centralization and core–peripheral model (Asero et al., 2016; Casanueva et al., 2016;Leungetal.,2012). This model helps to analyze the characteristics (using indicators such as size, diameter, density and network centralization) and spatial structure of the network (using the core–peripheral model) by measuring the entire or local tourism network structure of the area. The contents and functions of these indicators are discussed in detail in future sections of this study. Network analysis uses the concept of the centrality of nodes in their network to acquire the positional features of individual nodes within networks (Scott, 2000). Freeman (1979, 1980) identified three forms of centrality: degree centrality, closeness centrality and betweenness centrality. Degree centrality is the simplest and most intuitive of the three forms and measures centrality in terms of the number of nodes to which a particular node connects (Lee et al., 2013). Thus, degree centrality measures the extent to which a particular node is directly connected to all other nodes without considering the control ability to other nodes. In TOURISM GEOGRAPHIES 569 addition, degree centrality may be classified as either in-degree or out-degree centrality. The use of these two indicators corresponds to the investigation of tourist-flow network because inward and outward connections of a node represent the receipt and transmis- sion of numerous tourism routes, respectively. Comparing the in-degree and out-degree of a given node may reveal whether the focal node is a ‘beginning’, ‘core’ or ‘terminal’ node for various routes (Shih, 2006). Closeness centrality considers not only the direct links of a node but also its position in the network and takes into account indirect ties based on the distance between the node and other nodes in the network (Casanueva et al., 2016). Closeness centrality focuses on the proximity of a node to all other nodes in the network and is a global measurement tool that considers the proximity of all network members, not only connections to neighboring nodes as with degree centrality. In a directed network, closeness centrality may be classified in terms of either ‘in-closeness’ or ‘out-closeness’ based on inward and outward connections, respectively. This indicator reflects the concept that a node is central if it can quickly interact with all other nodes. In the context of tourist-flow networks, when a node can reach other nodes and it is close to these reachable nodes, its closeness centrality will be high because it is more central and closer to all of the other nodes, and vice versa. Betweenness is the third concept of node centrality and measures the extent to which a particular node lies between various other nodes within a set of nodes (Scott, 2000). The betweenness of a node describes the ability of a given node to control interactions between pairs of other nodes within a network. When applying betweenness to the tourist-flow network, a particular node with high betweenness centrality is a highly critical intermediary between pairs of other nodes because most tourists will stop at this node while traveling between other various nodes. Structural holes represent a competitive advantage for a node with linkages spanning different groups resulting from efficacious and non-redundant connections (Burt, 1992). A structural hole refers to the fracture phenomenon among tourist flow network nodes. Tour- ism nodes such as A, B and C are linked, but if B has no connection to C, then a ‘structural hole’ exists between B and C, and A is the node in the structural hole. Structural holes may be measured by their effective size, efficiency and constraints (Burt, 1992; Casanueva et al., 2013; Shih, 2006). Among these factors, effective size is a non-redundant factor in the network. If the effective size of a node i variesfrom1,allnodesenjoystronglinkstoeach other, up to the observed node of i’s links in the network, indicating that network nodes do not link to one another. The ratio of the effective size divided by the number of the node’s total links measures efficiency and varies from a minimum and approaches 0, indicating high link redundancy and, therefore, low efficiency, to a maximum of 1, indicating that every link in the network is non-redundant. Constraint is the extent to which a node is directly or indirectly dependent on other nodes via crisscrossing connections with the absence of structural holes. A particular node in the tourist-flow network that possesses numerous structural holes represents more opportunities to broker tourist flows than other nodes and indicates that it is in a non-substitutable location. However, this condition could potentially congest tourist flows due to the lack of substitute nodes and routes (Shih, 2006). More specifically, the size of the network is a measure of the number of nodes or elements that compose the network (Asero et al., 2016). The diameter of the network is the longest geodesic distance between two nodes (Casanueva et al., 2016). Density is the number of links that actually exist in a network in relation to the number that are theoretically possible. Analytically, density is the coefficient between the number of existing links 570 H. PENG ET AL. and the number of possible links calculated on the basis of network size (Baggio, Scott, & Cooper, 2010). Network centralization measures the degree of centralization of a tourist-flow network and can be classified into three levels: degree-network centralization, closeness-network centralization and betweenness-network centralization. Similarly, degree-network centralization and closeness-network centralization are separated into in-degree and out-degree centralization, respectively (Hwang et al., 2006). The core–peripheral model is used to reflect the position of nodes in the network and determine which nodes are in the core and periphery positions (Asero et al., 2016;Hu& Racherla, 2008; Hwang et al., 2006).

Boundary effect analysis Although the SNA method can explore the spatial structure, network characteristics and roles of the nodes of cross-boundary tourist flows, it does not identify the direction and extent of the boundary effect on the tourist-flow network and internal nodes. Specifically, this study constructs whole boundary effect (WBE) indicators and node boundary effect (NBE) indicators to measure the boundary effect of cross-boundary tourist flows using the density and node centrality index measured by network analysis. This study measures the WBE by analyzing the difference in density between the local network and the overall (cross-boundary) network. To measure the effect of boundary on tourist flows between a specific node and other nodes on both sides of the boundary, NBE indicators are established and measured by analyzing the difference between local centrality and boundary centrality. According to specific indicators and formulas that follow, the BEA may be used to measure the influence of boundaries on tourist flows and reveal the extent and direction of these influences.

Whole boundary effect The WBE is calculated using the following formula:

WBE D ðDin ¡ DcrossÞ=Din£100%; (1) where Din is the network density within the local area; Dcross is the overall density in the cross-boundary area and WBE is generally between 0 and 1. As WBE approaches 1, the boundary-shielding effect increases; as WBE approaches 0, the boundary-shielding effect decreases. If WBE is less than 0, the boundary-mediating effect is greater than the shielding effect.

Node boundary effect The NBE is deﬁned as

NBE D ðCin ¡ CcrossÞ=Cin£100%; (2) where Cin is the local centrality and indicates the ratio of direct, existing relationships and theoretical relationships between a speciﬁc node and other nodes inside the boundary; Ccross is the boundary centrality, which indicates the ratio of the direct, existing relationships and theoretical relationships of a speciﬁc node with other nodes outside the boundary. NBE is generally between 0 and 1. As the NBE approaches 1, the boundary-shielding TOURISM GEOGRAPHIES 571 effect of the node increases and as the NBE approaches 0, the shielding effect of the node decreases. If the NBE is less than 0, the mediating effect of the node is greater than the shielding effect.

Empirical analysis Case-study area Lugu Lake includes a lake area of approximately 48.45 km2 and is located at the junction of Lijiang City in the Yunnan Province and the Liangshan Yi Autonomous Prefecture in the Sichuan Province. Lugu Lake is approximately 230 km from Xichang City, the capital of Liangshan Yi Autonomous Prefecture and approximately 200 km from Lijiang City. Tourists traveling through the Sichuan area to visit Lugu Lake primarily travel from the Sichuan Province and Chongqing. These tourists are primarily self-service tourists and treat Xichang City as a passageway. Tourists traveling through the Yunnan area to visit Lugu Lake are primarily from the Yangtze River Delta, the Pearl River Delta and Beijing City, are primarily on group tours and treat Kunming City, Lijiang City and the Diqing Autonomous Prefecture as a passageway (Liu, 2006). Early in their infancy, the Mosuo People primarily lived on agriculture due to a lack of convenient transportation. In the 1980s, increasing numbers of anthropologists, novelists, journalists and tourists began to draw attention to the unique plateau scenery and the Mosuo culture. After the 1990s, Lugu Lake became a scenic spot and began to receive tourists. Tourism then developed rapidly. It is one of the most attractive tourist destinations in

China and has enjoyed a high proﬁle and a strong reputation for tourism.

Collection, sorting and arrangement of information Because the number of foreign tourists in Lugu Lake is relatively small, the data for this study were related only to domestic tourist flows. The data for this case study were collected from questionnaire surveys and travel-agency-recommended routes and were divided into the tourist-demand level and the market-supply level. Because many Chinese tourists arrange travel through travel agencies, data collection from these two sources improved the accuracy of the data. First, the scope of the study area and tourism nodes was determined. The survey was conducted from 12 August, 2012, to 24 August 2012. Survey respondents were inter- viewed face to face. We visited government institutions, major enterprises and most of the important attractions on both sides of the boundary to acquire relevant background information. To ensure the quality of the questionnaire, a pilot research was conducted on 12 August. We amended the questionnaires according to feedback from the interviews. Finally, we determined the spatial scope of cross-boundary tourist flows in Lugu Lake. Figure 2 presents 19 major tourism nodes. Second, the tourist-flow data were collected. The tourist-flow data included questionnaire data and travel-agency-route data. The questionnaire data were considered primarily from the perspective of tourists’ needs. To properly determine the tourism node, the travel-agency-route data were used to improve the data from the supply perspective. The travel-agency-route data were primarily routes that contained higher level tourism nodes, 572 H. PENG ET AL.

Figure 2. Study area and the 19 nodes. such as Yulong Snow Mountain, Lugu Lake and Shangri La, and rarely contained lower level tourism nodes. In contrast, the questionnaire data contained lower level tourism nodes and provided data from this aspect. The questions of the survey encompassed soci- odemographic information, access passageways, the purpose of tourism, modes of travel, length of stay, and the rate and direction of tourist flows. Tourists were asked to list tourism nodes that they had traveled to and planned to travel to in an order, then 19 tourism nodes were determined as the result of the tourist interviews. Compared with these prior- ity tourism nodes proposed to the tourists, it was more reasonable to determine the tourism nodes from the tourist interviews. To guarantee the validity of the questionnaire, we selected the Town of Lugu Lake, the Walking Marriages Bridge in Sichuan Province, and the Luoshui Wharf and the Lige Viewing Deck in Yunnan Province as the locations to be investigated. Questionnaires were collected in locations where tourists could be easily approached, such as restaurants and inns. To avoid subjectivity when selecting respondents, every fifth tourist who completed the questionnaire was selected as an individual to be investigated. If the tourist did not cooperate or certain other practical difficulties existed, we postponed the investigation until the next fifth respondent, until all questionnaires were distributed successfully. A total of 464 valid questionnaires were collected. Travel agency routes were collected online at websites where travel agencies promoted touring routes. The online recommended routes of the reception travel agency in Kunming, Lijiang and Xichang, and the online recommended routes of the primary tour-organizing agency in the tourist market, including Sichuan Province, Yunnan Prov- ince and Chongqing City were collected. A total of 355 recommended routes from 72 travel agencies were collected, including 193 routes in Yunnan Province and 162 routes in TOURISM GEOGRAPHIES 573

Sichuan Province. The study included all routes that referred to Lugu Lake in the marketing material. Finally, considering tourist demand (as indicated by questionnaire data) and market supply (as indicated by travel-agency-route data), this study merged the data to expand the sample size, and the evaluation database (the valued matrix) was then established. To avoid the problem of mismatching two types of data (Beritelli, 2011), regression correlation analyses were performed on the N £ N matrix using the quadratic assignment procedure (Beritelli & Laesser, 2011). The correlation coefficient was 0.83, denoting a statistically significant correlation. This correlation indicated that the data merge was feasible because certain respondents used the travel agency and the merge was closely related to the type of regional tourism resources and the spatial structure and distribution of the infrastructure. Finally, the numerical matrix for the cross-boundary tourist-flow network was constructed based on the evaluation database of the Lugu Lake cross-boundary tourist-flow network and included the data for 684 tourist flows. After the tests were repeated, a mean cutoff value of 12 for connecting times between the nodes in the network was selected. This value reflected the characteristics of most cross-boundary tourist activities and route preferences and was representative and scientifically feasible. The analysis was conducted using Ucinet 6 software (Borgatti, Everett, & Freeman, 2002),whichisanSNAprogramthat enabled the computation of node-structure indicators and network-structure indicators.

Sample analysis As displayed in Figure 3, the number of tourists and tourist income has increased rapidly in recent years. The sample analysis revealed that the tourists were primarily from the Sichuan Province (45.2%), Chongqing City (13.4%), the Yunnan Province (10.8%) and the Guangdong Province (5.7%). As displayed in Table 1, sightseeing tourism played a vital role (62.8%). Self-service travel (74.4%) and travel-agency-organized travel (25.6%) were dominant patterns. Most tourists traveled by tour bus (38.0%) or a self-driven car (33.3%); other tourists traveled by train (27.8%), airplane (14.9%) or bus (18.6%). Approximately, 90.1% of tourists had traveled to Lugu Lake for the ﬁrst time. Most tourists (74.7%) stayed

Figure 3. Tourist arrivals and tourism revenue of the Lugu Lake Scenic Area. Source: Administration of Lugu Lake Scenic Area in Sichuan Province and Yunnan Province. 574 H. PENG ET AL.

Table 1. Basic travel characteristics of the sample. Tourism purpose Sightseeing 62.8% Leisure 26.3% Other 10.9% Tourism pattern Travel agency-organized travel 25.6% Self-service travel 74.4% Means of transportation Tour bus 38.0% Self-driven car 33.3% Train 27.8% Airplane 14.9% Bus 18.6% Number of visits One time 90.1% Two times 7.7% More than three times 2.2% Duration of stay One day 13.2% Two days 74.7% More than three days 12.1% Travel companion Relatives and friends 50.8% Colleagues/classmates/ coworkers 34.7% Tours 11.0% Alone 3.5% Access passageway Ninglang County 48.9% Yanyuan County 51.1% Note: ‘Means of transportation’ is a multiple choice, and it employs the method of repeat count. Source: Questionnaire survey. for two to three days and then visited surrounding attractions. Many tourists (50.8%) traveled with relatives and friends. Ninglang County in Lijiang City and Yanyuan County in the Liangshan Prefecture were the primary access passageways, accounting for 48.9% and 51.1% of travel, respectively.

Results and discussion Characteristics of the tourist flow network Based on the valued matrix, the cross-boundary tourist-flow network graph of Lugu Lake was constructed using the Netdraw software. As displayed in Figure 4, the size of the line represents the scale of the tourist flows. According to the principle of equal proportion, the connection scale of tourist flows between the nodes is divided into three categories: a strong connection represents 111–165 tourist flows, a secondary connection represents 56–110 tourist flows and a weak connection represents 1–55 tourist flows. From an overall perspective, the cross-boundary tourist-flow network of Lugu Lake is complex and the entire network is loose. A fracture phenomenon exists in the overall cross-boundary area and Lugu Lake acts as a bridge in the network. However, from a local perspective, the local network is intensive and even diverges with more than one center, such as Yulong Snow Mountain, Shangri-La, the Old Town of Lijiang in the Yunan Province, the Qionghai Lushan Scenic Area and Luoji Mountain in the Sichuan Province.

Node-structure analysis Ucinet software was utilized to calculate the node structure of the cross-boundary tourist- ﬂow indicators of Lugu Lake, which is displayed in Table 2. Each tourism node has TOURISM GEOGRAPHIES 575

Figure 4. Tourist-flow network graph of Lugu Lake. concentration or radiation relationships with an average of 7.16 other nodes and the mean values of out-closeness centrality and in-closeness centrality are 59.58 and 59.87, respectively. Each tourism node acts as a mediator 4.18 times, indicating close relationships among the nodes. Variances are often used in statistics and refer to the degree of dispersion of a group of data; in the case of an identical sample size, a higher variance indicates larger fluctuations in the data. However, in this study, the variances of in-closeness centrality, out-closeness centrality and betweenness centrality are high: 74.86, 88.59 and 34.50, respectively. This implies that there are considerable tourist flows moving between the Old Town of Lijiang and Yulong Snow Mountain. These nodes form the core tourism nodes, whereas the Lashihai Nature Reserve, Hailuo Valley and Daocheng-Yading have peripheral roles in the network, as displayed in Figure 4. The questionnaire results indicated that most cross-boundary tourists gather in and distribute from Kunming, Lijiang and Xichang, and accounted for 92.14% of the total number of tourists. As displayed in Table 2, the node-centrality indicator revealed that

576 .PN TAL. ET PENG H.

Table 2. Indicators of node structure. Degree centrality Closeness centrality Structural holes Tourism nodes Out-degree In-degree Out-closeness In-closeness Betweenness centrality Effective size Efficiency Constraint Old Town of Lijiang 15.00 14.00 85.71 81.82 24.54 10.21 0.64 0.23 Yulong Snow Mountain 10.00 13.00 69.23 78.26 9.08 8.17 0.58 0.27 Lugu Lake 10.00 9.00 69.23 66.67 12.05 5.84 0.58 0.33 Shangri-La 8.00 9.00 64.29 66.67 9.95 6.15 0.62 0.34 Old Town of Dali 9.00 8.00 60.00 58.07 2.14 4.65 0.67 0.36 Erhai Lake and Cangshan Mountain 8.00 8.00 58.07 58.07 1.70 4.50 0.45 0.37 Kunming city 9.00 6.00 62.07 60.00 2.55 3.83 0.43 0.39 Shuhe District 6.00 9.00 60.00 66.67 3.06 4.80 0.48 0.39 Qionghai Lushan Scenic 7.00 6.00 62.07 58.07 4.66 3.54 0.51 0.47 Lufeng Dinosaur Valley 9.00 3.00 62.07 50.00 0.28 3.75 0.38 0.38 Xinhua China Folk Culture Village 8.00 4.00 60.00 51.43 0.83 3.58 0.40 0.43 Butterfly Spring 6.00 6.00 54.55 54.55 0.38 2.67 0.33 0.47 Shilin Scenic Area 5.00 7.00 52.94 62.07 1.18 3.17 0.45 0.47 Xishuangbanna 4.00 8.00 51.43 64.29 2.11 4.33 0.54 0.43 Hailuogou Valley 5.00 6.00 48.65 52.94 2.34 3.18 0.53 0.51 Lashihai Nature Reserve 4.00 7.00 56.25 60.00 0.58 2.36 0.34 0.51 Xichang Satellite Launch Center 5.00 4.00 50.00 45.00 0.57 2.56 0.51 0.61 Luoji Mountain 5.00 4.00 58.07 45.00 0.91 1.89 0.38 0.65 Daocheng-Yading 3.00 5.00 47.37 58.07 0.53 2.00 0.40 0.65 Note: The closeness centrality varies from 0 up to 100, the effective size varies from 1 up to the observed node of a specific node’s links in the network, and the efficiency and constraint vary from 0 up to 1. But the degree centrality and betweenness centrality depend on the case. Besides the constraint indicators, the higher is the indicator value, the better is the result. Source: Calculated results of the binary data of tourist flows using Ucinet software. TOURISM GEOGRAPHIES 577 cross-boundary tourists primarily chose high-level tourism destinations and tourism areas with favorable traffic conditions. The degree centrality and closeness centrality of the Old Town of Lijiang and Yulong Snow Mountain are at very high levels; these two nodes make up the tourism center (main destination in a multiple destination itinerary) (Lew & McKercher, 2002) and the tourist center (distribution center) of the cross-boundary tourist-flow network. Abundant tourism resources and readily available transportation attract tourists to Yunnan and Sichuan. In the cross-boundary tourist-flow network, Lugu Lake and Shangri-La are the secondary tourism centers and the secondary tourist centers that act as mediators to other nodes. More specifically, the Lugu Lake Scenic Area across the Sichuan Province and the Yunnan Province has an inherent mediating advantage in terms of tourist resources’ spatial structure and travel route organization. The betweenness centrality is 12.05. Shangri-La is situated close to Lugu Lake, an important tourist destination in the Yunnan Province. Shangri-La is an important tourist center and is closely linked to certain tourist destinations (e.g. Kunming and Xishuangbanna) and other key travel source markets (e.g. Chongqing and Chengdu). The betweenness centrality is 9.95. Dali Town, Erhai Lake, Cangshan Mountain, Kunming and the Shuhe District are important tourist destinations and travel channels that have high-level centrality indicators. Most tourists from the Sichuan Province travel via the Qionghai Lushan Scenic Area which is the only secondary tourist center in the Sichuan Province and a key destination in the cross-boundary tourist-flow network. Lufeng Dinosaur Valley, the Xinhua China Folk Culture Village, the Butterfly Spring, the Shilin Scenic Area and Xishuangbanna are all general tourist destinations. The tourist flows primarily connect with Kunming, Dali, Lijiang and Xishuangbanna, and have minor probabilities of intervening in the cross-boundary network. The Lashihai Nature Reserve, another lake tourist destination, is affected by Lugu Lake’s shielding effect and attracts few tourists. The Xichang Satellite Launch Center, Luoji Mountain and Dao- cheng-Yading are located in the southwestern section of the Sichuan Province and are a great distance from the core tourism nodes in the Yunnan Province. Therefore, these destinations exist in the periphery of the cross-boundary network. Among the structural holes indicators, certain nodes (e.g. Old Town of Lijiang, Yulong Snow Mountain, Lugu Lake, Shangri-La and Dali Town) have a high-level effective size and efficiency and a low level of constraint. In the network, these nodes have more opportunities and locational advantages (Table 2). However, the shortage of alternative tourism nodes and lines may cause a congestion problem in tourist flows.

Network-structure analysis The characteristics of tourism products and the tourism-market structure indicate that there are 19 nodes in the cross-boundary tourist-flow network of Lugu Lake and the diameter of this network is 10. To maximize efficiency, most tourists travel over a long period of time and travel across several nodes on a large scale; this phenomenon is most obvious in the Yunnan Province. The density of the cross-boundary tourist-flow network is 0.40 and the standard deviation is 0.49. Theoretically, a network constructed of 19 tourism nodes should possess 342 link relationships. However, only 136 link relationships exist and thus, the network density is low. Moreover, the standard deviation is high; a large number of tourist flows are confined to nodes within the same province and there are few connections across provinces. The degree-network centralization and closeness-network centralization are both high, which indicates that the network structure is not sound and the 578 H. PENG ET AL.

Table 3. Indicators of network centralization. Network centralization Sub-indexes Value Degree-network centralization Out-degree 45.99% In-degree 40.12%

Closeness-network centralization Out-closeness 56.80% In-closeness 47.69%

Betweenness-network centralization Betweenness-network centralization 21.49% Note: The indicators vary from 0% up to 100%. The lower is the indicator value, the lower is the degree of the tourist- flow network centralization. Source: Calculated results of the binary data of tourist flows using Ucinet software. entire network is constrained by several key tourism nodes. As displayed in Table 3, degree-network centralization and closeness-network centralization are both high and indicate that the degree of centralization of the tourist-flow network is high, which sug- gests a core–periphery structure. The core–peripheral model results confirm the core–periphery structure of the cross- boundary tourist flow network of Lugu Lake. The core and periphery include 14 and 5 nodes, respectively. The density of the Yunnan core area is 0.55, including Lugu Lake, whereas the density of the Sichuan periphery area is 0.70 not including Lugu Lake. The network density is lower in core areas than in periphery areas, reflecting the large number and wide scope of the tourism nodes in the core areas. By contrast, the density of tourist flows in the same administrative region of Sichuan and Yunnan is significantly greater than the entire network density (0.40). The development of cross-boundary tourism has an obvious boundary-shielding effect. When the boundary dimension increases, the density of the entire network decreases and cooperation in the cross-boundary area becomes more difficult. In addition, the density between the core area and the periphery area is only 0.16 and Lugu Lake is just on the boundary area of cross-boundary tourism destinations. Consequently, sparse structural holes between two closely linked internal networks are formed and strengthen the structural hierarchy of the network. However, this hierarchy may inspire certain individuals or organizations to link together and use their com- bined resources to change the network structure and develop a strong competitive advantage. Therefore, as a cross-boundary tourism destination in the Sichuan and Yunnan Provinces, Lugu Lake has great potential for tourism cooperation.

Boundary effect analysis

Whole boundary effect. Tourist flows in the Sichuan and Yunnan areas are influenced by the boundary-shielding effect. The WBE is 56.52% from Sichuan to Yunnan and 27.27% from Yunnan to Sichuan, both at significant levels; the boundary-shielding effect of the Sichuan area is greater than that of the Yunnan area. This indicates that tourist flows between Sichuan and Yunnan are constrained and difficult to integrate into the cross- boundary network. This analysis was confirmed by clique analysis,1 which determined that the group of nodes in the tourist-flow network appeared to be geographical divisions.

Node-boundary effect. As displayed in Table 4, the local centrality is generally high and reflects the influence of the boundary-shielding effect on cross-boundary tourist flow. The TOURISM GEOGRAPHIES 579

Table 4. Node boundary effect of Lugu Lake. Tourism nodes Local centrality (%) Boundary centrality (%) Node boundary effect (%) Old Town of Lijiang 100.00 40.00 60.00 Yulong Snow Mountain 69.23 20.00 71.11 Lugu Lake 38.46 100.00 ¡160.01 Shangri-La 46.15 40.00 13.33 Old Town of Dali 69.23 0.00 100.00 Erhai Lake and Cangshan Mountain 61.54 0.00 100.00 Kunming city 69.23 0.00 100.00 Shuhe District 38.46 20.00 48.00 Qionghai Lushan Scenic 80.00 23.08 71.15 Lufeng Dinosaur Valley 69.23 0.00 100.00 Xinhua China Folk Culture Village 61.54 0.00 100.00 Butterﬂy Spring 46.15 0.00 100.00 Shilin Scenic Area 38.46 0.00 100.00 Xishuangbanna 30.77 0.00 100.00 Hailuogou Valley 80.00 7.69 90.39 Lashihai Nature Reserve 30.77 0.00 100.00 Xichang Satellite Launch Center 80.00 7.69 90.39 Luoji Mountain 80.00 7.69 90.39 Daocheng-Yading 40.00 7.69 80.78

Old Town of Lijiang, the Qionghai Lushan Scenic Area, the Hailuogou Valley, the Xichang Satellite Launch Center and Luoji Mountain are all tourism centers and tourist centers within their local network. The local centrality is high. However, the local centrality of Xish- uangbanna and the Lashihai Nature Reserve are low because tourist flows are primarily affected by the boundary-radiating effect of high-level nodes in their local areas and the linkage with other nodes is lower. In great contrast to the local centrality, the boundary centrality of the other 18 nodes is generally low, with the exception of Lugu Lake. Lugu Lake is an exception because it acts as the linkage for tourist flows between the Sichuan and Yunnan Provinces, and its node boundary effect is ¡1.60, which indicates that its boundary-mediating effect is greater than the boundary-shielding effect. However, the node-boundary effect of the other 18 nodes is between 0 and 1, which indicates the other nodes are subject to the boundary-shielding effect and the difference is significant. This result further implies that the connection of cross-boundary tourist flows is not strong. Among the nine tourism nodes (e.g. Dali Town, Erhai Lake, Cangshan Mountain and Kunming City), tourist flows primarily occur within the local areas and the node boundary effect is high (100%). Conversely, the node-boundary effect of the Old Town of Lijiang (60.00%), Shangri-La-la (13.33%) and the Shuhe District (48%) is relatively low primarily because these nodes act as tourist channels and may enhance cooperation between cross-boundary areas in the future.

Conclusion Tourist flows into a destination are affected by many factors, such as resources, spatial structure, accessibility and tourist preferences. In a cross-boundary tourism area, the administrative boundary is a key factor influencing the destination that is reflected in the spatial movements of tourists and the tourist-flow network. Based on a network perspective, this study analyzed tourist flows on the cross-provincial scale using SNA and BEA, constructed an evaluation index system and discussed the characteristics and spatial structure of the network and the roles of the nodes. This study enriches border tourism 580 H. PENG ET AL. research and from a methodological perspective, is a bold attempt in the study of tourist flows with respect to scale and method. First, in contrast to studies for which data are entirely obtained from a tourist-sampling survey and purely based on social considerations (Asero et al., 2016; Shih, 2006), this study incorporates data regarding travel-agency-recommended routes. These data reflect the physical condition of the network, including resources, spatial structure and accessibility, and thus research inferences are based on both social and physical phenomena. Second, this study analyzes tourist flows on a cross-provincial scale and extends the scale of current tourist-flow research, which has primarily focused on international, intranational, interregional, intercity, intracity and tourism-spot scales (Bowden, 2003; Coshall, 2000; Shih, 2006; Wu & Carson, 2008; Yang & Wong, 2013; Zhong et al., 2011). Compared to national and sub-provincial boundaries, the provincial boundary is the highest level boundary that most generally influences the border area within a country. Provincial boundaries greatly impact the pattern, formation and evolution of tourist flows but are rarely discussed. Thus, a BEA (McCallum, 1995) was conducted to measure the influence of the provincial boundary on tourist flows in terms of their properties, direction and extent. The results demonstrate that cross-boundary tourist flows are significantly influenced by the boundary-shielding effect. The properties, direction and extent of the effects are diverse and are related to accessibility, resource endowments, the heterogeneity of resources and the extent of regional integration. Moreover, the boundary-mediating effect is verified in certain nodes in a cross-boundary tourist-flow network, which may enhance cooperation of cross-boundary areas in the future. Additionally, in terms of research methods, there has been extensive research on tourist flows using multiple disciplines (Jackman & Greenidge, 2010; Morley et al., 2014; Song & Li, 2008; Wu & Carson, 2008; Yang & Wong, 2013), but systematic studies regarding the spatial structure, network characteristics and roles of nodes of tourist flows from the perspective of a ‘network’ using the SNA method have been limited. The SNA method has unique advantages in the analysis of the formation, evolution and characteristics of tourist-flow networks; these network evaluation indicators primarily include node-structure indicators and network-structure indicators (Casanueva et al., 2013; Lee et al., 2013; Luo & Zhong, 2015; Shih, 2006). Strategically, this SNA approach could facilitate the evaluation of tourist-flow networks in geographic regions. The results of the empirical analysis included in this study verify the applicability of the SNA method. Specifically, the role of network nodes can be effectively judged by measuring the node-centrality indicators and structural hole indicators by conducting a node- structure analysis (Casanueva et al., 2013; Shih, 2006). The empirical results show that these nodes can act as different functions in the cross-boundary tourist-flow network of Lugu Lake (e.g. tourism centers, secondary tourism centers, tourist centers, secondary tourist centers, tourist channels, important tourist destinations, general tourist destinations and peripheral tourist destinations). Moreover, network-structure analysis is helpful in analyzing the characteristics and spatial structure of a network (Asero et al., 2016; Casa- nueva et al., 2014; Leung et al., 2012). The empirical results indicate that the structure of a cross-boundary tourist-flow network is complex and align with earlier results that indicate the core–periphery structure is a key feature of a tourism network (Asero et al., 2016). Notably, a large number of tourist flows move to and from tourism centers or secondary tourism centers, weakening the positions of peripheral tourist destinations. As a regional growth pole, a tourism center and a secondary tourism center may attract and pull tourist TOURISM GEOGRAPHIES 581

flows and elements from peripheral tourist destinations. However, these regions also act as a bridge that links cross-boundary tourist flows and distributes tourist flows to surrounding areas through a trickle-down effect (Hirshman, 1958), which implies that the core tourism destination may also transfer tourist flows from other directions to peripheral tourism destinations. This increase in flows may cause congestion problems and affect the accessibility of the nodes in the tourist-flow network. Furthermore, it is understood that regional disparity exists between the nodes of tourism destinations with regard to resource endowment, location, accessibility, infrastructure allocation and degree of market development. Such disparities may not be conducive to the coordinated development of tourism destinations, and the boundaries of various scales that are discussed in this study are only one of those factors. Therefore, the methodology and results are not only applicable to countries such as China, where provincial administrative boundaries have a long history and profound influence on the society, economy and culture on both sides of the boundaries, but may also be applicable to other multiple destination tourism (e.g. drive tourism) (Lew & McKercher, 2002; Shih, 2006) and cross-boundary destinations on other scales, such as cross-national, state or regional scales. However, this should be verified by future empirical studies. As a practical implication, identifying of the characteristics, the spatial structure and the role of the nodes in a tourist-flow network is useful for tourism planning and destination- management strategies for planning tourism facilities, managing tourism routes and defining marketing strategies. This utility is more evident in the case of cross-boundary areas such as Lugu Lake, where tourist flows, space behavior and regional tourism cooperation are very complex. In this regard, this study contributes to existing studies by provid- ing insights into cross-boundary destination areas and offering government planners and tourism service providers’ insights on how tourists may perceive a tourism area. Specifi- cally, based on this study, we would like to suggest that government planners and tourism service providers identify the characteristics of the tourist-flow network, and the roles and functions of different nodes in the network before they invest facilities and activities in cross-boundary tourism destinations. The facilities and services in tourism center and tourist center to promote information about nodes of tourist flows may help visitors to identify routes to access tourism destination effectively. Traffic-related facilities and services are also essential to avoid congestion problems in the tourist flows at such nodes. Further- more, increased knowledge regarding the tourist flows among tourism destinations in the cross-boundary areas can result in more focused marketing of tourism products. However, this study has some limitations. The SNA method is not sufficient for all tourism-related research problems or practical problems. Based on the panel data, the SNA method fails to reveal the space–time evolution process and the mechanism of tourist flows. Therefore, future research should increase the analysis of cross-boundary stakeholders, such as tourism enterprise, government and residence, and identify the driving mechanism of the formation and evolution of tourist-flow networks. Such analyses will also facilitate the characterization of the current conditions of tourist-flow networks.

Note

1. Clique analysis is a module in the software of social network analysis. It can be used to identify node sets that have a positive, direct and close relationship among members in a tourist-ﬂow network. 582 H. PENG ET AL.

Acknowledgements

We would like to express our sincere appreciation to Prof. Alan A. Lew and Prof. Shaul Krakover with their valuable advices and suggestions to improve this study. I am also indebted to the anonymous referees for helpful comments.

Disclosure statement No potential conﬂict of interest was reported by the authors.

Funding

This work was supported by the National Natural Science Foundation of China [grant number 41271161]; [grant number 41171115].

Notes on contributors

Hongsong Peng is a Ph.D. candidate in the Department of Land Resources and Tourism Sciences at Nanjing University Nanjing, China. His research interests include tourism geography, tourism environmental impact and tourism planning.

Jinhe Zhang is a professor and director of the Department of Land Resources and Tourism Sciences at Nanjing University, China. His research interests include tourism geography, tourism environmental impact and human geography.

Zehua Liu is a lecturer in the Department of Land Resources and Tourism Sciences at Nanjing Univer- sity, China. His research interests include tourism geography and tourism statistics.

Lin Lu is a professor and director of the Center for Tourism Research and Planning, School of Territo- rial Resources and Tourism at Anhui Normal University, China. His research interests include tourism geography and tourism management.

Lu Yang is a master student in the Department of Land Resources and Tourism Sciences at Nanjing University, China. Her research interests include environmental impact assessment and tourism planning.

ORCID

Hongsong Peng http://orcid.org/0000-0002-4520-7078

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Appendix 1. Some formulas used in social network analysis process.

Indictors Explanation Formulas Xl In-degree The sum of the number of nodes j in the network (1 to l) CD;inðniÞ D rij;in centrality that connect inwardly (from node j to node i). j D 1 Xl Out-degree The sum of the number of nodes j in the network (1 to l) CD;outðniÞ D rij;out centrality that connect outwardly (from node i to node j). j D 1 Closeness The inverse of the sum of the geodesic distances from C ðn Þ D P 1 c i l ð ; Þ centrality node i to all the other nodes in the network (1 to l). j D 1d ni nj Xl Xl g ðn Þ Betweenness The sum of the node i’s estimated probabilities of C ðn Þ D jk i ; j 6¼ k 6¼ i B i g centrality standing along any geodesic that all pairs of nodes j k jk (nodes j and k, excluding node i) in the network have P selected. l ½ ð Þ ¡ ð Þ D i D 1 CD n CD ni Degree- The variability in the degree centrality scores of all nodes CD l2 ¡ 3l C 2 network in the network. P centralization l ½ ð Þ ¡ ð Þ D i D 1 CC n CC ni £ð ¡ Þ Closeness- The variability in the closeness centrality scores of all CC ðl ¡ 2Þðl ¡ 1Þ 2l 3 network nodes in the network. P centralization l ½ ð Þ ¡ ð Þ D i D 1 CB n CB ni Betweenness- The variability in the betweenness centrality scores of all CB l3 ¡ 4l2 C 5l ¡ 2 network nodes in the network. centralization

Note: rij;in is one of the inward connections of node i; rij;out is one of the outward connections of node i; l is the number of nodes in the network; dðni; njÞ is the geodesic distance, which is deﬁned as the length of the shortest path between nodes i and j; gjk is the number of geodesics between nodes j and k; gjkðniÞ is the number of geodesics linking the two nodes that contain node i; CDðn Þ is the maximum value of degree centrality of the nodes; CCðn Þ is the maximum value of closeness centrality of the nodes; CBðn Þ is the maximum value of betweenness centrality of the nodes. Source: Casanueva et al. (2016), Hwang et al. (2006), Lee et al., (2013), Scott (2000) and Shih (2006).