From Subjective and Objective Perspective to Reconstruct the High-Quality Tourism Spatial Structure―Taking Gannan Prefecture I

sustainability

Article From Subjective and Objective Perspective to Reconstruct the High-Quality Tourism Spatial Structure—Taking Gannan Prefecture in China as an Example

Libang Ma * , Xiaoyang Li, Jie Bo and Fang Fang College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730000, China; [email protected] (X.L.); [email protected] (J.B.); [email protected] (F.F.) * Correspondence: [email protected]; Tel.: +86-931-7971754

Received: 2 December 2019; Accepted: 29 January 2020; Published: 31 January 2020

Abstract: Spatial relationship is the basic perspective of understanding regions. Tourism spatial structure is the spatial projection of tourism activities, reflecting the spatial attributes and interrelationships of tourism activities. In this paper, taking Gannan Tibetan Autonomous Prefecture as an example, we identified the objective and subjective tourism spatial structure of Gannan Prefecture based on the GIS spatial analysis function and using objective and subjective tourist attractions as the spatial object element. Then, the tourism spatial network was reconstructed. Results are as follows. (1) Both objective and subjective tourist attractions in Gannan Prefecture exhibit aggregated distribution. Among them, the spatial distribution of objective tourist attractions has a significant trend of contiguous aggregation, showing a relatively higher density in the northeastern and southeastern regions, and a lower density in the central and southwestern regions. This is opposite to that of the subjective tourist attractions. (2) The connectivity and accessibility between objective and subjective tourist attractions in Gannan Prefecture are poor, and only a few tourist attractions form a traffic connection with neighboring ones. (3) The objective tourism spatial network of Gannan Prefecture is layered with aggregation, and presents a significant cohesive development trend. This is opposite to the subjective one. (4) Based on the identification results of objective and subjective tourism spatial structures, the objective and subjective core tourism resources as well as tourist attractions should be integrated, and the road transportation system should be constructed and improved. Then, a high-quality tourism spatial network with ‘three poles, three axes and four groups’ was constructed. This study provides a scientific basis for the tourism spatial development, tourist route organization, the layout of tourism service facilities and product, and tourism spatial optimization in specific regions.

Keywords: tourism spatial structure; spatial association; identification; reconstruction; Gannan Tibetan Autonomous Prefecture; China

1. Introduction Since the 1960s, foreign scholars have been deeply worried about the negative impact of tourism on the environment, and have criticized and questioned the development of tourism from diﬀerent perspectives [1]. After entering the 1990s, the rapid development of tourism has led to climate change and environmental degradation and other issues, which have gradually been widely concerned by international organizations, all sectors of society, experts and scholars [1]. Pang et al. research results show that tourism may be one of the biggest economic victims of climate change, and at the same time, tourism is also an important factor causing greenhouse gas emissions [2]. The increasing energy

Sustainability 2020, 12, 1015; doi:10.3390/su12031015 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 1015 2 of 17 consumption and carbon emission of tourism has become an important factor affecting global climate change, and the carbon emission of tourism has also become one of the important sources of global greenhouse gas emissions [1]. However, overtourism not only intensifies the greenhouse gas emissions, but also threatens the social carrying capacity of tourism destinations to changes the lifestyle of residents and threatens the social welfare [3]. Reasonable tourism planning plays an important role to effectively promote regional economic development, optimize resource allocation, and even improve the well-being of ordinary people [4]. Spatial relationship is the basic perspective of understanding region [5]. In general, the spatial relationship can be abstracted into a network system composed of nodes and edges [6]. This spatial structure reflects the organizational form of human economic and cultural activities in certain geographical regions [7]. Tourism resources are important node-type elements of the tourism spatial structure. The spatial structure of tourism resources has attracted great attentions from researchers. In the 1960s, Leiper and Gunn first proposed the concept of tourism spatial system [8,9]. Then, based on the core-edge theory [10], point-axis theory [11], growing pole theory [12], social network theory, etc., researchers have studied the distribution and function of tourism space [13], the geospatial distribution and relationship of tourism activities [14,15], the spatial organization of tourist attractions [16], the evolution process of tourism spatial model and structure [17], etc. For most of these researches, the tourism spatial structure was constructed by using the objective tourism resource (the tourist attraction, intangible cultural heritage, relic, etc.) as nodes as well as the road as the axis [18]. Great attentions have been paid to the relationship and attribute of network. With the advent of economic globalization and information, the scale of the network is expanding, the complexity of the network is increasing, and the relationship between people and the network is getting closer. The application of Internet technology has accelerated the rapid development of tourism. Tourists express opinions, obtain information and contact with each other through interactive platforms, such as social networking tools, OTA website forums and travel product websites. This has been an important part of Internet users’ online life. Tourists are the main body of tourism activities. Network records, such as travel notes, covering a wide range of data types, including texts, pictures, geographical location information, etc., provide rich tourist information sources. Thus, the tourism digital footprint has gradually formed. American scholar Girardin et al. first proposed and defined the ‘tourism digital footprint’, which is the message and call record sent by tourists during the trip, as well as the text and picture left in the information system, such as the network after the trip [19]. It provides a new perspective for researchers to study the trajectory and consumption behavior of tourists in geographical space. In recent years, using the tourism digital footprint as a data source, many researchers have studied the time and spatial behavior of tourist [20], the perceived image of tourism destination [21,22], and tourism situation in the geographical space of tourism destinations [23], etc. A number of research results have been achieved. However, due to the limitation of data acquisition, the above research constructed the tourism spatial structure only from the perspective of objective tourism resource (the tourist attraction, intangible cultural heritage, relic, etc.) or the travel note, ignoring the comprehensive influence of them on the tourism spatial network and the geospatial characteristics. The construction of the spatial structure of subjective tourism destinations is still in its infancy. In addition, with the advancement of tourism spatial system theory and the application of mathematical analysis method, the spatial structure of tourism resources based on spatial topology theory has attracted more and more attention. According to the flow of tourists and the distribution of tourism resources, how to effectively organize the reasonable tourism spatial structure and tourist route is one of the important issues that need to be solved in tourism development and planning procedures. Based on the analyses of tourism resources, the tourist spatial pattern and their spatial correlation, this paper constructed both the objective and subjective tourism spatial structures using TOP spatial network analysis tools, and conducted preliminary research on the spatial network structure of tourist attractions. Then, the tourism spatial structure was reconstructed with the objective and subjective evaluation results, forming a high-quality tourism spatial pattern. This provides a scientific basis for Sustainability 2020, 12, 1015 3 of 17 the tourism spatial development, tourist route organization, the layout of tourism service facilities and product, and tourism spatial optimization in specific regions. In addition, the research results have certain theoretical and practical significance for enriching the research theory of tourism network as well as guiding the optimization and reconstruction of tourism space. Sustainability 2020, 12, x FOR PEER REVIEW 3 of 17 2. The Overview of Research Region research theory of tourism network as well as guiding the optimization and reconstruction of tourism Gannanspace. Tibetan Autonomous Prefecture is located in the southwestern part of Gansu Province, China. It is between 100 45 and 104 45 east longitude as well as 33 6 and 35 34 north latitude, 2. The Overview of Research◦ 0 Region◦ 0 ◦ 0 ◦ 0 with a total area of 38,521 km2. It is located between the northeastern margin of the Qinghai-Tibet Gannan Tibetan Autonomous Prefecture is located in the southwestern part of Gansu Province, Plateau and the western part of the Chinese Loess Plateau. The terrain is high in the northwest and China. It is between 100°45′ and 104°45′ east longitude as well as 33°6′ and 35°34′ north latitude, with low ina the total southeast, area of 38,521 with km an2. It average is located elevation between the of northeastern 2960 m. There margin are of mainly the Qinghai-Tibet rivers such Plateau as the Yellow River,and Taohe the River,western Daxia part of River the Chinese and Bailong Loess Plateau. River The (collectively terrain is high referred in the tonorthwest as the Threeand low Rivers in the and One River),southeast, belonging with to an the average Yellow elevation River system of 2960 and m. There the Yangtze are mainly River rivers system. such as Gannan the Yellow Prefecture River, has the characteristicsTaohe River, of continental Daxia River seasonaland Bailong climate, River (collect such asively the referred abundant to as sunshine the Three with Rivers low and utilization One rate, insuffiRiver),cient heat belonging with significantto the Yellow vertical River system difference, and the a Yangtze plenty ofRiver precipitation system. Gannan with Prefecture significant has di fference the characteristics of continental seasonal climate, such as the abundant sunshine with low utilization in geographical distribution. The annual average temperature is between 1 and 13 C, the annual rate, insufficient heat with significant vertical difference, a plenty of precipitation with significant◦ precipitationdifference is in between geographical 400 anddistribution. 800 mm, The and annual the average annual temperature average sunshine is between hours 1 and are13 °C, between the 1800 and 2600annual h (Figure precipitation1), statistics is between were 400 obtainedand 800 mm, from and Annalsthe annual of average Gannan sunshine Prefecture hours [ 24are]. between Gannan1800 and Prefecture 2600 hours has (Figure jurisdiction 1), statistics over were one ob citytained and from seven Annals counties. of Gannan At the Prefecture end of 2017, [24]. the permanent resident populationGannan Prefecture was 71.02 has jurisdiction104, the regional over one GDPcity and was seven141.42 counties.108 yuanAt the, andend of the 2017, three the industrial × × structurepermanent ratios wereresident 23.47:13.77:62.76. population was 71.02 In the × 104, whole the regional year, it GDP received was 141.421105.6 × 108104 yuan,tourists and the from home three industrial structure ratios were 23.47:13.77:62.76. In the whole year, it received× 1105.6 × 104 and abroad, achieving a comprehensive tourism income of 51.50 108 yuan, accounting for 82.06% of tourists from home and abroad, achieving a comprehensive tourism× income of 51.50 × 108 yuan, the outputaccounting value for of 82.06% the tertiary of the output industry. value In of 2017, the te thertiary per industry. capita In disposable 2017, the per income capita ofdisposable urban residents was 23,012income yuan, of urban and residents the per capitawas 23,012 disposable yuan, and income the per of capita rural disposable residents income was 6998 of rural yuan, residents statistics about population,was 6998 social yuan, economy statistics and about so population, on in 2017 social were economy obtained and from so Gannanon in 2017 Prefecture were obtained Statistics from Bureau. Gannan Prefecture Statistics Bureau.

FigureFigure 1. 1. StudyStudy region region map. map.

3. Research3. Research Ideas Ideas and and Methods Methods

3.1. Concepts3.1. Concepts and Ideasand Ideas

3.1.1. Concept3.1.1. Concept Definition Definition The touristThe tourist space space is a is natural a natural and and socialsocial region region for for tourists tourists to visit. to visit. The traditional The traditional tourism tourismspatial spatial structurestructure refers refers to the to spatialthe spatial relationship relationship and and combinationcombination of of tourist tourist attractions. attractions. It is a It combination is a combination of Sustainability 2020, 12, 1015 4 of 17 nodes (tourist attractions), passages (traffic lines) and regions (administrative regions) [25], including the combination of distribution pattern, grade and quantity of tourist attractions, and directly triggering the spatial behavior of tourists. This has a profound impact on the development speed, scale, benefits, the time and space arrangement of tourist attractions, as well as the nature, degree and development strategy of the spatial competition in the tourism region [26]. Commonly, the tourism spatial structure is mainly a network system formed on the basis of existing tourist attractions, and does not include the space formed by tourists’ subjective selection of tourism destinations. The high-quality tourism space refers to the collection of the national space carrier composed of the objective and subjective tourism space that creates attractiveness and appeal. It exhibits the comprehensive and unified characteristics of ecology and humanity [27].

3.1.2. Research Ideas As a common economic activity, tourism occurs and develops using the tourism spatial system, including the destination system, the source system and the travel system, as the substance carrier [28]. Scenic spots, intangible cultural heritage, ruins, relics, etc., are important types of tourist attractions, and objective constituent elements of the functional system of tourism destinations. They have a special status in the tourism spatial structure, and are the objective basis for the formation of tourism space [29]. Travel note is the main way of tourism promotion, and the basis for subjective identification of the quality of tourism space. With the continuous popularization and promotion of new public media, such as Weibo and WeChat, the objective perception of tourists has an important impact on the system of tourism resources, especially the tourism spatial system [30]. The spatial pattern of tourist attractions, intangible cultural heritages, ruins, relics, etc., determines the overall form of spatial distribution, reflects the spatial attributes and interrelationships of tourism activities. In addition, it is the basis for the formation and development of objective tourism spatial structure, and has a profound impact on the development speed, scale, benefits, the time and space arrangement of tourism resources [31]. The subjective perceptions of tourists about the destinations are expressed through social platforms, which has certain influences on the choice of potential tourists, directly affecting the decision-making process of tourism [32]. Thus, it changes the cognition of the original objective spatial structure of tourists, and results in the formation of a new subjective one [22,33,34]. With the constant changes in people’s travel behaviors, traditional tourism space can no longer meet the needs of tourists. Especially, in order to seek excitement and freshness, the young tourists prefer the subjective tourism space during the choice of tourism destinations [35]. The emergence of new media has affected the spatial allocation of tourism destinations and the development of related tourism elements, which in turn affects the spatial behavior decision-making and destination choice of tourists. Based on this, this paper identified the tourism spatial structures under the objective tourist attraction and the perception of tourists from the objective and subjective levels, respectively. Objective tourism space is composed of local inherent tourism resources, which is not determined by human behavior and perception, such as Labrang temple, Sanko Grassland, Gahai Lake etc. subjective tourism space is composed of tourists’ perception and user experience. Then, the tourism spatial structure was reconstructed [36,37].

3.2. Data and Source

3.2.1. Tourist Attractions Data and its Source The tourist attractions mainly include scenic spots, intangible cultural heritages, ruins, relics, etc. The relevant data are from Chinese Intangible Cultural Heritage Website, Gansu Provincial People’s Government Website, Bureau of Culture, Radio, Film, and Television, Press and Publication of Gannan Tibetan Autonomous Prefecture, statistical data of cultural centers of the city and county in Gannan Prefecture (one city and seven counties), ‘Gansu Cultural Relics Statistical Yearbook’ between 2008 and 2018, and ‘Gansu Tourism Statistics Yearbook’ between 2008 and 2018. According to the quality classification standard of tourist attractions in China, the tourist attractions are divided into five grades, Sustainability 2020, 12, 1015 5 of 17

Sustainability 2020, 12, x FOR PEER REVIEW 5 of 17 with the advantages and disadvantages of 5A, 4A, 3A, 2A and A respectively. As of September 2018, thereSeptember are 27 national 2018, there A-level are tourist27 national attractions A-level in tour Gannanist attractions Prefecture, in Gannan including Prefecture, six tourist including attractions six at 4A-level,tourist 10 attractions tourist attractions at 4A-level, at 10 3A-level, tourist attractions and 11 tourist at 3A-level, attractions and 11 at tourist 2A-level. attractions A total at of 2A-level. 518 projects of intangibleA total of cultural518 projects heritage of intangible listed in thecultural national, heritage provincial, listed in state,the national, and county provincial, levels, state, including and six nationalcounty intangible levels, including cultural heritage six national projects intangible (Labrang cultural Temple heritage Music ‘Duder’,projects (Labrang Gannan TibetanTempleFolk Music Songs, Zhuoni‘Duder’, Baron Gannan Drum Dance,Tibetan Zhouqu Folk Songs, Dance, Zhuoni Gannan Baron ‘Nan-mute’ Drum Dance, Tibetan Zhouqu Opera, Dance, Gannan Gannan Tibetan ‘Nan- Thangka, Zhuonimute’ Lintan Tibetan Tao inkstoneOpera, Gannan Making, Ti Gannanbetan Thangka, Tibetan Medicine), Zhuoni Lintan 48 projects Tao inkstone at provincial Making, level, Gannan 192 projects at stateTibetan level Medicine), and 518 projects 48 projects at county at provincial level. There level, are192 544projects items at of state cultural level ruinsand 518 and projects relics, at including county six at thelevel. national There level, are 544 28 atitems the provincialof cultural level,ruins and 510relic ats, including the county six level at the (Figure national2a). level, 28 at the provincial level, and 510 at the county level (Figure 2a).

Figure 2. (a) Spatial distribution of objective tourism space, (b) Frequency of subjective tourism space Figure 2. (a) Spatial distribution of objective tourism space, (b) Frequency of subjective tourism space in travelin travel notes. notes. 3.2.2. Tourists’ Perception Data and its Source 3.2.2. Tourists’ Perception Data and its Source Tourists’Tourists’ perception perception data data mainly mainly refers refers toto thethe onlineonline travel travel notes notes about about tourism tourism destinations destinations in in GannanGannan published published on on well-known well-known travel travel websites websites and forums forums by by touris tourists.ts. This This note note has has a significant a significant impactimpact on potentialon potential tourists. tourists. This This paper paper selectsselects Ma Honeycomb Honeycomb and and Ctrip Ctrip website website for fordata data collection. collection. Ma HoneycombMa Honeycomb is a is largest a largest travel travel social social network network in in China China at at present,present, which contains a a large large number number of travelof travel notes notes and photosand photos published published and and taken taken by by real real travel travel usersusers [38]. [38]. Ctrip Ctrip website website is largest is largest online online traveltravel service service provider provider in in China, China, with with a a large large numbernumber of registered registered users. users. Online Online travel travel information information preservationpreservation is relatively is relatively complete, complete, and and information information update update speed speed isis fast.fast. Therefore, it it is is suitable suitable to to be a samplebe a sample source source database database [39]. [39]. The The data data of of Ma Ma Honeycomb Honeycomb and Ctrip Ctrip webs websiteite can can effectively effectively cover cover differentdifferent types types of touristsof tourists and and enrich enrich data data samples. samples. Through the the location location information information of two of two website website users, it is found that their users are mainly domestic users [40]. According to the availability of data, users, it is found that their users are mainly domestic users [40]. According to the availability of data, this paper sets the perception of domestic tourists to shape the subjective tourism space from June this paper sets the perception of domestic tourists to shape the subjective tourism space from June 2013 to June 2019. The data of this article is from the Ma Honeycomb and Ctrip website. ‘Gannan’ is 2013the to search June 2019. keyword, The dataand search of this results article are is fromarranged the Maaccording Honeycomb to the update and Ctrip time website.of Gannan ‘Gannan’ Travel is the searchNotes. In keyword, this paper, and the search time period results of are crawling arranged online according travel notes to the is between update timeJune of2013 Gannan and June Travel Notes.2019. In The this data paper, from the travel time notes period have of some crawling proble onlinems, such travel as information notes is between duplication, June information 2013 and June 2019.missing The data and frombeyond travel the research notes have scope. some Thus, problems, in this paper, such following as information criteria duplication,were adopted informationto select missingdata andfrom beyond the original the researchtravel notes. scope. (1) Thus,The time in thisthat paper,tourists following traveled in criteria Gannan were must adopted be between to select dataJune from 2013 the and original June travel2019. (2) notes. Travel (1) notes The must time thatinclude tourists information traveled about in Gannan Gannan must tourist be attractions. between June 2013In and the Junedata 2019.collection, (2) Travel according notes to mustthe above include criteria, information 874 travel about notes Gannan(442 from tourist Ma Honeycomb attractions. and In the data432 collection, from Ctrip according website) to were the aboveselected criteria, as samples 874 travelfor the notes data (442analysis from using Ma Honeycombthe web crawling and 432 fromtechnology Ctrip website) to identify were the selected frequency as samples of each scenic for the sp dataot in analysisthe travel using notes the(Figure web 2b). crawling The text technology is pre- processed to generate the analysis text. Then the word segmentation and mergence were performed, to identify the frequency of each scenic spot in the travel notes (Figure2b). The text is pre-processed and the final document was generated. The word frequency, semantic network and sentiment to generate the analysis text. Then the word segmentation and mergence were performed, and the analysis were analyzed using ROST WordParser software to analyze the tourism destination image finaland document generate wasvisual generated. images of Thetourist word attractions. frequency, The semanticspatial location network of the and tourism sentiment destination analysis was were analyzed using ROST WordParser software to analyze the tourism destination image and generate visual images of tourist attractions. The spatial location of the tourism destination was detailed with Sustainability 2020, 12, 1015 6 of 17

Google Earth. In addition, the coordinates of tourist attractions with large area were replaced with its particle coordinates.

3.3. Research Methods In this paper, the Gannan tourism spatial structure were studied from two directions: the spatial pattern and spatial relationship.

3.3.1. The Research Method of Tourism Spatial Pattern (1) Kernel density index Kernel density estimation is based on a moving cell by using kernel function to calculate the density of elements in a cell. Based on the non-parametric method of surface density estimation, the degree of spatial aggregation of tourist attractions and tourism destinations was analyzed. Kernel Density Estimation (KDE) uses the kernel function and assigns different weight values to points in the search region to make the distribution smoother and obtain the density value of unknown region. Events have a higher probability of occurrence in point-intensive region than in point-sparse region [41]. n 1 X x f (x, y) = k i (1) nh2 h i=1 where f (x,y) is the density estimation at the (x,y); n is the number of observations; h is the bandwidth, and the value is 40 km; k is the kernel function; and xi is the distance from the ith observation position. (2) Nearest neighbor index Both tourist attractions and tourism destinations can be approximated as point targets, and their distribution patterns can be classiﬁed into the random distribution, uniform distribution and aggregated distribution. Combining the relevant research results with the purpose of this paper, the nearest neighbor index was used to identify the spatial structure [42,43]. The nearest neighbor distance index R is the ratio of the average Euclidean distance of each point in the geospatial location from the target to the average distance in the random distribution [44]: 1 1 rE = q = (2) n 2 √D 2 A r R = i (3) rE where rE represents the theoretical nearest distance of objective and subjective tourism space, A represents the area of research region, n represents the number of objective and subjective tourism space points, D represents the point density, and ri represents the actual average nearest neighbor distance of spatial distribution of objective and subjective tourism space. R is expressed as the nearest index of objective and subjective tourism space in Gannan Prefecture. When R = 1, the point set is randomly distributed; when R > 1, the point set tends to be uniformly distributed; when R < 1, the point set tends to be aggregated distribution. (3) Semi-variogram Since the spatial distribution type of points measured by the nearest neighbor index analysis method has the controversy on the deﬁnition criteria, the semi-variogram was used to verify the tourism spatial structure of Gannan Prefecture [45]. The semi-variogram value r(h) can be calculated by:

N(h) 1 X 2 r(h) = [Z(xi) Z(xi + h)] (4) 2N(h) − i=1 where Z(x) is the locations random variable, Z(xi) is the sample value of Z(x) at the space point xi, Z(xi+h) is the sample value of Z(x) at the distance from h at xi, and N(h) is the total number of sample point pairs when the separation distance is h. Sustainability 2020, 12, 1015 7 of 17

3.3.2. The Research Method of Tourism Spatial Association (1) The connectivity The connectivity refers to the degree of development of the transportation network. It is used to investigate the association of tourist flows among tourist attractions in Gannan. The main indicators are the β index and the s index [46]. The β index is the average number of connection of nodes in the transportation network, which reflects the level of network connectivity. For the multi-node tourism region, the more traffic lines connecting different tourist attractions, the higher the level, as well as the higher the connectivity. Then, it is more convenient for tourists to travel among tourist attractions. The higher index is the requirement and guarantee for the optimization of the spatial network structure of tourist attractions in the tourism region. The formula is β = m/n, where β represents the connectivity of the traffic network, m represents the number of edges in the traffic network, i.e., the number of direct connections between the two nodes, and n represents the number of vertices in the transportation network, i.e., the number of nodes. Commonly, the value of β is between 0 and 3. The greater the value of β, the better the connectivity of the network. The γ index is the ratio of the actual number of connections among nodes in the transportation network to the theoretical maximum one, reflecting the connectivity of a regional transportation network. The formula is γ = m/3(n-2). The value of γ index ranges from 0 to 1. When γ = 0, there is no connection in the network, that is, the nodes are not connected to each other. When γ = 1, the maximum number of connections is achieved, that is, the connectivity of network is excellent. (2) Accessibility The accessibility reflects how easy it is to move between nodes in a transportation network, that is, the degree of patency from one node to the other, characterizing the speed of traffic between tourist attractions [47]. The accessibility index is the average distance of the shortest path from one vertex to all other vertices in the network. The formula is:

Xn Ai = Dij/n (5) i=1 where Ai represents the accessibility index of vertex i in the network; Dij represents the shortest distance from vertex i to vertex j; The cumulative sum represents the distance from vertex i to all other vertices; n is the number of nodes. The smaller the value of Ai, the higher the accessibility of the node.

3.3.3. The Research Method of Tourism Spatial Network Through the tourism spatial pattern and association research, the high-quality tourism nodes and the connection strength among nodes in Gannan are obtained, respectively. Based on this, TOP network spatial analysis and complex network tools are used to explore the spatial structure of tourism network and identify the high-quality tourism space in Gannan [48,49]. In this paper, the connection strength among nodes was calculated using the distance of transportation network and the level of node in the network: xa xb i × j Wij = p γ (6) Dij where Wij represents the connection strength between nodes i and j; xi and xj are levels of nodes i and j, respectively, xi and xj are levels of nodes i and j respectively, which are national, provincial, state, and county levels, and the values are 4,3,2,1; when considering the scale indicator, since only one indicator is selected, a = b = 1; p is a constant. The main function of the model is to distinguish the magnitude of gravity among nodes, so the p value is 1. γ is the distance attenuation coeﬃcient, which commonly has the value of 2; Dij represents the transportation distance between the two nodes i and j, which can be calculated through OD Cost Matrix tool of ArcGIS 10.2 software. Sustainability 2020, 12, x FOR PEER REVIEW 8 of 17

where Wij represents the connection strength between nodes i and j; xi and xj are levels of nodes i and j, respectively, xi and xj are levels of nodes i and j respectively, which are national, provincial, state, and county levels, and the values are 4,3,2,1; when considering the scale indicator, since only one indicator is selected, a = b = 1; p is a constant. The main function of the model is to distinguish the magnitude of gravity among nodes, so the p value is 1. γ is the distance attenuation coefficient, which Sustainability 2020, 12, 1015 8 of 17 commonly has the value of 2; Dij represents the transportation distance between the two nodes i and j, which can be calculated through OD Cost Matrix tool of ArcGIS 10.2 software. BasedBased on on the the ArcGIS ArcGIS platform platform and and using using VBA VBA programming, programming, the the maximum maximum value value of of tourism tourism connectionconnection strength strength of of each each node node is is determined determined (The (The tourism tourism connection connection among among nodes nodes is is the the line line connectingconnecting the the nodes nodes in thein the network. network. The The data data is binarized. is binarized. If there If there is a connectionis a connection between between nodes nodesi and i j,and the tourismj, the tourism connection connection will be judgedwill be to judged be 1; If to there be 1; is noIf there such connection,is no such theconnection, tourism connectionthe tourism willconnection be judged will tobe be 0). judged Then, theto Top1be 0). network Then, canthe beTop1 obtained network by connectingcan be obtained corresponding by connecting nodes. Thecorresponding top 3 (Top3 network)nodes. The and top the 3 top(Top3 5 (Top5 network) network) and the tourism top 5 connection (Top5 network) accessibility tourism are connection used to buildaccessibility TOP network. are used The to build reason TOP for network. choosing The these reason three for types choosing of networks these three is that types they of cannetworks reﬂect is thethat spatial they organizationscan reflect the of touristspatial attractions organizations with of di ﬀtouristerent scales. attractions The connection with different of TOP scales. tourism The networkconnection results of TOP in the tourism direction. network This isresults because in the the direction. maximum This network is because connection the maximum direction network of the tourismconnection node directionk originates of fromthe tourism the node nodeg, but k thatoriginates of node fromg not the necessarily node g, resultsbut that from of thenode node g notk. Thus,necessarily the TOP results network from is the a directed node k. network Thus, the [50 TOP]. network is a directed network [50].

4.4. Result Result Analysis Analysis

4.1.4.1. Analysis Analysis of of Objective Objective and and Subjective Subjective Tourism Tourism Spatial Spatial Pattern Pattern

4.1.1.4.1.1. Kernel Kernel Density Density Analysis Analysis DensityDensity maps maps ofof objective objective and and subjective subjective tourist tourist attractions attractions are aregenerated generated using using ArcGIS's ArcGIS’s Kernel KernelDensity Density Estimation Estimation (KDE) (KDE) (Figure (Figure 3). 3).There There is is si significantgnificant regional didifferencefference betweenbetween spatial spatial distributionsdistributions of objectiveof objective and subjectiveand subjective tourist attractionstourist attractions in Gannan in Prefecture. Gannan ThePrefecture. spatial distributionThe spatial ofdistribution objective tourist of objective attractions tourist has attractions a significant has trend a significant of contiguous trend of aggregation, contiguous showingaggregation, a relatively showing highera relatively density higher in the density northeastern in the andnortheastern southeastern and southeastern regions, and aregions, lower densityand a lower in the density central in and the southwesterncentral and southwestern regions. Three regions. regions Three with significant regions with high significant densities ofhigh the densities maximum of valuethe maximum 2.35 are formedvalue 2.35 in Xiahe are formed County in andXiahe Hezuo County City, and Lintan Hezuo County City, Lintan and Zhuoni County County, and Zhuoni as well County, as Zhouqu as well County.as Zhouqu The County. density The gradually density decreases gradually from decreases the center from the to the center periphery, to the periphery, and a medium-density and a medium- corridordensity iscorridor formed is amongformed countiesamong counties (Figure (Figure3a). This 3a). is This opposite is opposite to that to that of the of the subjective subjective tourist tourist attractions.attractions. They They present present a a relatively relatively higher higher density density in in the the central central region, region, and and a a lower lower density density in in the the peripheryperiphery region. region. The The regions regions with with high high density density exhibit exhibit point point distribution, distribution, except except for for the the central central part, part, whichwhich exhibits exhibits the the surface surface distribution. distribution. In In addition, addition, the the maximum maximum density density of of them them is is only only 0.11. 0.11.

Figure 3. (a) Density map of objective tourism space, (b) Density map of subjective tourism space. Figure 3. (a) Density map of objective tourism space, (b) Density map of subjective tourism space. 4.1.2. Nearest Neighbor Analysis 4.1.2. Nearest Neighbor Analysis According to the objective and the subjective tourist attractions, the nearest distances among the tourist attractions are 5.15 km and 4.56 km, respectively. Using ArcGIS, the actual linear distances between objective as well as subjective tourist attractions and their nearest tourist attractions are 0.21 km and 1.74 km, respectively. In addition, their nearest neighbor indices are 0.0415 and 0.3822, respectively. Both the objective and subjective nearest neighbor indices are <1, and the distribution type belongs to the aggregated pattern, indicating that the tourist attractions are relatively aggregated in the region. The level of aggregation of objective tourist attractions is higher than that of the subjective one. Sustainability 2020, 12, 1015 9 of 17

4.1.3. Semi-Variogram Analysis In ArcGIS 10.2, this paper selects spherical model, exponential model, Gaussian model and power function model. Through comparison of several fitting models, it is found that Gaussian model has the highest fitting accuracy. Finally, the semi-variogram model parameters and distribution types of spatial distribution of objective and subjective tourism spatial distribution are obtained in Gannan Prefecture (Table1). The nugget value C0 represents the spatial heterogeneity of the random part, and the spatial variability value C0/(C0 + C) reflects the degree of spatial variability caused by the random factor.

Table 1. The parameters and spatial distribution types of semi-variogram model of the objective and subjective tourism.

Range Nugget Sill Value Nugget Coeﬃcient Distribution Parameters Model (A) Value (C0) (C + C0) (C0/(C + C0)) Type objective 46.9900 0.0926 0.9519 0.0973 Gaussian model aggregation subjective 59.2500 0.7125 1.2012 0.5932 Gaussian model aggregation

As shown in Table1, the optimal semi-variogram models for the spatial distribution of objective and subjective tourism in Gannan Prefecture are Gaussian models. Their spatial patterns exhibit aggregated distribution. The variability of objective tourism spatial structure is relatively small, and that of subjective one is great. This indicates that the variation of objective tourism spatial structure is weakly affected by random factors, mainly due to the spatial autocorrelation. The variation of subjective one is mainly caused by random factors, and the travel note has a significant effect on the tourism destination choice.

4.2. The Analysis of Objective and Subjective Tourism Spatial Association

4.2.1. Connectivity Analysis According to the spatial distribution network of objective tourist attractions in Gannan Prefecture, the number of connections between two nodes m is 98, the number of nodes n is 101, as well as β = 0.9703 is calculated according to the formula. The number of connections between two nodes in the subjective tourism destinations m is 35, the number of nodes n is 35, as well as β = 1.0 is calculated according to the formula. The connectivity of transportation network among objective or subjective tourist attractions in Gannan Prefecture is not high, and the density of traﬃc routes among tourist attractions is relatively low. γ index is similar to β index, and reﬂects the connectivity of a regional transportation network. The objective and subjective γ indices calculated according to the formula are 0.33 and 0.36, respectively, both of which are low. This further indicates that the connectivity among objective or subjective nodes is low. Only a few tourist attractions are connected with their neighbor ones through transportation network. Thus, the overall transportation infrastructure in Gannan Prefecture is poor, and there are few transportation routes. This is an important factor hindering the tourism development.

4.2.2. Accessibility Analysis According to the formula, the average accessibility indices of the objective 101 nodes and the subjective 35 nodes in Gannan Prefecture are 170.63 km and 161.56 km, respectively. The overall accessibility is not high. The accessibilities of objective 59 nodes and the subjective 18 nodes are smaller than their average values, respectively, accounting for 57.42% and 51.42% of the total nodes, respectively. The accessibility is relatively good. As shown in Figure 4, the objective tourist attractions with good accessibility are mainly distributed in the northeastern region, such as the Lintan County andSustainability Zhuoni2020 County,, 12, 1015 followed by the surrounding Hezuo City, Luqu County and Diebu County.10 The of 17 overall accessibility of western and southern regions is poor. Zhouqu County has the worst accessibility,inevitably constrained, and the objective which reduces tourist theattractions overall accessibilityhave low accessibility. of tourism networkThe tourism of the development entire Gannan is inevitablyPrefecture. constrained, The spatial distributionwhich reduces of subjective the overall accessibility accessibility is consistent of tourism with network that of of the the objective entire Gannanone. The Prefecture. tourist attractions The spatial with thedistribution accessibility of subjecti smallerve than accessibility the average is valueconsistent are mainly with distributedthat of the objectivein the northeastern one. The tourist and central attractions regions. with The the Maqu accessibility County andsmaller Zhouqu than Countythe average in the value southern are mainly region distributedhave the worst in the accessibility. northeastern Therefore, and central it is region necessarys. The to improveMaqu County the transportation and Zhouqu facilitiesCounty in the westernsouthern and region southern have the regions, worst especially accessibility. in the Therefore, Maqu County it is necessary and Zhouqu to improve County, the and transportation enhance the facilitiestourism competitiveness.in the western and southern regions, especially in the Maqu County and Zhouqu County, and enhance the tourism competitiveness.

Figure 4. (a) Accessibility distribution map of objective tourism space, (b) Accessibility distribution Figure 4. (a) Accessibility distribution map of objective tourism space, (b) Accessibility distribution map of subjective tourism space. map of subjective tourism space. 4.3. The Identification of Objective and Subjective Tourism Spatial Structure 4.3. The identification of Objective and Subjective Tourism Spatial Structure The Top1 network exhibits the ‘neighbor connection’ between the core tourist attractions and its surroundingThe Top1 network tourist exhibits nodes, forming the ‘neighbor several connection’ tourism spatial between aggregated the core tourist units. Theattractions Top3 network and its surroundingreflects the interweaving tourist nodes, connections forming several among thetouris internalm spatial nodes aggregated of the aggregated units. The unit Top3 as well network as the reflectsnearest the connection interweaving among connections aggregated among units. the The inte corernal aggregated nodes of the unit aggregated and the sub-core unit as aggregatedwell as the nearestunit are connection particularly among prominent. aggregated Based onunits. this, The the co Top5re aggregated network reflects unit and the the ‘preferential sub-core aggregated connection’ unitfeature are amongparticularly nodes prominent. of aggregated Based units. on this, the Top5 network reflects the ‘preferential connection’ feature among nodes of aggregated units. 4.3.1. The Identification of Objective Tourism Spatial Structure 4.3.1. The Identification of Objective Tourism Spatial Structure With the expansion of the network scale, the objective tourism spatial network of Gannan PrefectureWith isthe layered expansion with aggregation,of the network and presentsscale, the a significantobjective cohesivetourism developmentspatial network trend of (Figure Gannan5). PrefectureThe number is layered of nodes with with aggregation, the connectivity and presen greaterts a than significant 0 in the cohesive Top1 network development is 47, accounting trend (Figure for 5).46.54% The number of the total of nodes number with of the connection connectivity edges. greater This than indicates 0 in the that Top1 the primarynetwork connectionis 47, accounting of the forobjective 46.54% tourism of the total network number in Gannan of connection Prefecture edges. is mainly This indicates concentrated that onthe these primary 47 nodes. connection Twospatial of the objectiveregions with tourism relatively network high in level Gannan of aggregation, Prefecture the is Lintan-Zhuonimainly concentrated and Diebu-Zhouqu on these 47 nodes. aggregated Two spatialregions, regions are formed. with Inrelatively the Top3 high network, level theof numberaggregation, of tourist the Lintan-Zhuoni nodes with the connectivityand Diebu-Zhouqu greater aggregatedthan 0 increases regions, to 85. are Most formed. (84.16%) In of the the Top3 tourist ne nodestwork, participates the number in the of network, tourist nodes and the with network the connectivityscope continues greater to expand. than 0 increases The level to of 85. aggregation Most (84.16%) of the oforiginal the tourist two nodes core aggregatedparticipates regions in the network,increases significantly.and the network In the scope Top5 continues network, theto expand number. The of tourist level nodesof aggregation with the connectivityof the original greater two corethan aggregated 0 continuously regions increases increases to 94,significantly. and the network In the Top5 scope network, further extends the number to the of global tourist scale nodes to the west and north. In addition, a sub-core aggregated region occurs besides the original two core aggregated regions. Network connections are closer, and the connectivity is higher. Sustainability 2020, 12, x FOR PEER REVIEW 11 of 17

with the connectivity greater than 0 continuously increases to 94, and the network scope further extends to the global scale to the west and north. In addition, a sub-core aggregated region occurs Sustainabilitybesides the2020 ,original12, 1015 two core aggregated regions. Network connections are closer, and 11the of 17 connectivity is higher.

Figure 5. (a,b) The spatial structure of top1 network of tourism contact intensity, (c,d) The spatial Figure 5. (a,b) The spatial structure of top1 network of tourism contact intensity, (c,d) The spatial structure of top3 network of tourism contact intensity, (e,f) The spatial structure of top5 network of structure of top3 network of tourism contact intensity, (e,f) The spatial structure of top5 network of tourism contact intensity. tourism contact intensity. 4.3.2. The Identification of Subjective Tourism Spatial Structure 4.3.2. The Identification of Subjective Tourism Spatial Structure The subjective tourism spatial network system presents a significant spatial aggregated distribution The subjective tourism spatial network system presents a significant spatial aggregated with a radial development trend (Figure5). In the Top1 network, the number of nodes with the distribution with a radial development trend (Figure 5). In the Top1 network, the number of nodes connectivity greater than 0 is 23, accounting for 65.71% of the total number of connection edges. with the connectivity greater than 0 is 23, accounting for 65.71% of the total number of connection This indicates that the primary connection of the subjective tourism network in Gannan Prefecture is edges. This indicates that the primary connection of the subjective tourism network in Gannan mainlyPrefecture concentrated is mainly on concentrated these 23 nodes, on these forming 23 node a significants, forming aggregateda significant region. aggregated In the region. Top3 In network, the the number of tourist nodes with the connectivity greater than 0 increases to 34, and 91.74% of the tourist nodes participates in the network. Contrary to the case of spatial aggregation of objective tourist nodes, the core of the subjective network is located in the central and northwestern regions, forming two core aggregated regions. In the Top5 network, the number of tourist nodes with the connectivity greater than 0 increases to 35. The network connection is closer, and the connectivity is Sustainability 2020, 12, x FOR PEER REVIEW 12 of 17

Top3 network, the number of tourist nodes with the connectivity greater than 0 increases to 34, and 91.74% of the tourist nodes participates in the network. Contrary to the case of spatial aggregation of Sustainabilityobjective tourist2020, 12 nodes,, 1015 the core of the subjective network is located in the central and northwestern12 of 17 regions, forming two core aggregated regions. In the Top5 network, the number of tourist nodes with the connectivity greater than 0 increases to 35. The network connection is closer, and the connectivity higher. Two relatively large core aggregated regions are formed in Luqu county and Xiahe county, is higher. Two relatively large core aggregated regions are formed in Luqu county and Xiahe county, and the connection between these two regions are closer. and the connection between these two regions are closer. 4.4. The Reconstruction of High-Quality Tourism Spatial Pattern 4.4. The Reconstruction of High-Quality Tourism Spatial Pattern According to the identification results of objective and subjective tourism spatial structure, a ‘groupAccording tour’ spatialto the identification layout pattern results combining of objective ‘point’, and ‘axis’ subjective and ‘surface’ tourism is spatial formed structure, (Figure6). a Then,‘group the tour’ objective spatial regional layout tourismpattern combining spatial pattern ‘point’, of ‘four ‘axis’ poles, and two ‘surface’ axes and is formed three groups’ (Figure (Figure 6). Then,6a) isthe formed. objective The regional four growingtourism spatial poles referpattern to theof ‘fou intangibler poles, two cultural axes heritageand three sites groups’ mainly (Figure based 6a) on is Lintan-Zhuoniformed. The four (the growing first level), poles therefer Labrang to the intangible Temple, Milarepacultural heritage Buddha sites Pavilion mainly and based Zhouqu on Lintan- Folk CultureZhuoni (the secondfirst level), level). the Two Labrang tourism Temple, development Milarepa axes Buddha refer to Pavilion the Religious and Zhouqu Folklore Folk Sightseeing Culture Belt(the insecond the Northern level). Two Region tourism and thedevelopment Adventure-Experience axes refer to Beltthe Religious of Canyon Folklore and Forest Sightseeing in the Southern Belt in Region.the Northern The three Region groups and refer the toAdventure-Experience tourism groups of Hezuo-Xiahe Belt of Canyon Tibetan and Buddhism, Forest in Lintan-Zhuoni the Southern FolkRegion. Customs The three and groups Diebu-Zhouqu refer to tourism folk Customs. groups The of Hezuo-Xiah tourism spatiale Tibetan network Buddhism, is mainly concentrated in sixLintan-Zhuoni counties (cities) Folk in Customs the northeastern and Diebu-Zhouqu region. The fo spatiallk Customs. connections The tourism between spatial Luqu network County is ormainly Maqu concentrated County and in other six countiescounties (cities)(cities) are in weak.the northeastern In addition, region. the subjective The spatial regional connections tourism spatialbetween pattern Luqu ofCounty ‘four poles,or Maqu two County axes and and two other groups’ counties is formed (cities) (Figure are 6weak.b). The In fouraddition, growing the polessubjective include regional two first-level tourism spatial growing pattern poles of of ‘four Labrang poles, Temple two axes and and Langmu two groups’ Temple, is formed as well (Figure as two second-level6b). The four growing growing poles poles of include Sanko Grasslandtwo first-le andvel Gahaigrowing Lake. poles Two of tourism Labrang development Temple and axes Langmu refer toTemple, the Religious as well Folkas two Sightseeing second-level Belt growing in the Western poles of Grassland Sanko Grassland and the Landscape and Gahai Tourism Lake. Two Tour tourism Belt in thedevelopment Eastern Region. axes refer The two to the groups Religious refer to Folk tourism Sightseeing groups of Belt Xiahe-Hezuo-Lintan in the Western Grassland Tibetan Buddhism and the andLandscape folk customs Tourism ecology, Tour Belt the in Luqu-Diebu the Eastern Region grassland. The ecology two groups canyon refer forest. to tourism The tourism groups of spatial networkXiahe-Hezuo-Lintan is mainly concentrated Tibetan in theBuddhism northern and and folk central customs regions. ecology, The spatial the Luqu-Diebu connections grassland between Zhouquecology canyon County forest. or Diebu The Countytourism locatedspatial network in the southeastern is mainly concentrated regions and in other the northern counties and (cities) central are weak.regions. Neither The spatial the objective connections nor the subjectivebetween Zhou tourismqu spatialCounty networks or Diebu can County fulfill the located requirements in the ofsoutheastern overall tourism regions development. and other counties Therefore, (cities) it is necessaryare weak. to Neither reconstruct the objective the tourism nor spatialthe subjective pattern oftourism Gannan spatial Prefecture networks on thecan basisfulfill of the objective requirements and subjective of overall networks, tourism development. providing reference Therefore, for it the is overallnecessary tourism to reconstruct development. the tourism spatial pattern of Gannan Prefecture on the basis of objective and subjective networks, providing reference for the overall tourism development.

Figure 6. (a) Objective tourism spatial network; (b) subjective tourism spatial network. Figure 6. (a) Objective tourism spatial network; (b) subjective tourism spatial network. The reconstruction of the high-quality tourism spatial pattern is devoted to the comprehensive The reconstruction of the high-quality tourism spatial pattern is devoted to the comprehensive manifestation of the social, economic and ecological beneﬁts of tourism, highlighting the sustainability. manifestation of the social, economic and ecological benefits of tourism, highlighting the In the process of reconstruction, based on the objective and subjective tourism spatial structures, sustainability. In the process of reconstruction, based on the objective and subjective tourism spatial we should integrate the objective and subjective core tourism resources as well as tourist attractions, structures, we should integrate the objective and subjective core tourism resources as well as tourist improve the road traﬃc system, and construct a tourism spatial network of ‘three poles, three axes and attractions, improve the road traffic system, and construct a tourism spatial network of ‘three poles, four groups’ (Figure7). The three growing poles are Labrang Temple-Sanke Grassland Large-Scale three axes and four groups’ (Figure 7). The three growing poles are Labrang Temple-Sanke Grassland Tourist Attraction, Langmu Temple Large-Scale Tourist Attraction, Lintan-Zhuoni Folk Customs Tourist Attraction. Three axes refer to Religious Folk Sightseeing Belt in the Western Grassland, the Landscape Tour Belt in the Eastern Region, Adventure-Experience Belt of Canyon and Forest in Sustainability 2020, 12, x FOR PEER REVIEW 13 of 17

Sustainability 2020, 12, 1015 13 of 17 Large-Scale Tourist Attraction, Langmu Temple Large-Scale Tourist Attraction, Lintan-Zhuoni Folk Customs Tourist Attraction. Three axes refer to Religious Folk Sightseeing Belt in the Western Grassland,the Southern the Region. Landscape Four Tour groups Belt in refer the toEastern tourism Region, groups Adventure-Experience of Hezuo-Xiahe Tibetan Belt Buddhism,of Canyon and the ForestLuqu-Daibu in the Grassland Southern Ecology Region. Canyon Four groups Forest, Lintan-Zhuonirefer to tourism Folk groups Custom of Ecology, Hezuo-Xiahe Diebu-Zhouqu Tibetan Buddhism,Folk Red Custom. the Luqu-Daibu Grassland Ecology Canyon Forest, Lintan-Zhuoni Folk Custom Ecology, Diebu-Zhouqu Folk Red Custom.

FigureFigure 7. 7. TheThe structure structure of of high-quality high-quality tourism tourism spatial spatial network. network. 5. Discussion and Conclusions 5. Discussion and Conclusions 5.1. Discussion 5.1. Discussion As a social and economic phenomenon, tourism activity occurs and develops within the certain space.As The a social tourism and spatialeconomic structure phenomenon, is an important tourism factor activity in tourismoccurs and research, develops and within reflects the the certain spatial space.attributes The andtourism interrelationships spatial structure of tourismis an important activities factor [51,52 in]. tourism It is a combination research, and of reflects the node, the line spatial and attributessurface. The and ‘node’ interrelationships refers to the of tourism tourism destination activities [51,52]. or the tourismIt is a combination resource,the of the ‘line’ node, is the line tra andffic surface.channel connectingThe ‘node’ tourismrefers to resources, the tourism and destinatio the ‘surface’n or is the touristtourism attraction resource, inlaid the ‘line’ with spaceis the patches traffic channelin the geographic connecting location tourism of tourismresource destinations, and the ‘surface’ [53,54]. In is the the traditional tourist attraction sense, tourism inlaid destinationswith space patchesmainly consistin the geographic of elements includinglocation of objective tourism touristdestination attractions, [53,54]. ruins In the and traditional remains, cultural sense, heritage,tourism destinationsetc. Based on mainly their diconsistfferences of elements in tourism including resources, objective geographic tourist location, attractions, traffi ruinsc conditions, and remains, social culturalculture, heritage, etc., these etc elements. Based on play their diff differenceserent roles in thetourism tourism resources, system, geographic forming a location, relatively traffic fixed conditions,tourism network social culture, [55]. However, etc., these in elements the process play of different tourism inroles rural in orthe urban tourism regions system, with forming a single a relativelytourist attraction fixed tourism as the core network tourism [55]. destination, However, thein the network process of tourismof tourism destination in rural followsor urban the regions law of withdistance a single attenuation tourist [attraction56]. With as the the advancement core tourism of destination, information the and network the continuous of tourism improvement destination of followspeople’s the living law standards,of distance the attenuation traditional [56]. tourism With network the advancement can no longer of information meet the needs and ofthe some continuous tourists. improvementMore and more of touristspeople's acquire, living standards, publish and the exchange traditional travel tourism information network through can no the longer Internet. meetAs the a needsresult, of some some new tourists. tourism More destinations, and more namely tourists subjective acquire, tourismpublish destinations,and exchange have travel become information popular throughwith potential the Internet. tourists. InAs this a paper,result, basedsome onnew the studytourism of thedestinations, spatial pattern, namely association subjective and structuretourism destinations,of objective tourism have become destinations, popular we with analyzed potential the spatialtourists. network In this ofpaper, subjective based tourismon the study destinations of the spatialfrom the pattern, perspective association of tourists, and structure and constructed of objective a spatial tourism structure destinations, based on we subjective analyzed and the objective spatial networktourism. of Compared subjective with tourism the case destinations of traditional from tourism the perspective spatial network, of tourists, the and movement constructed trajectory a spatial and structureconsumption based behavior on subjective of tourists and objective in the actual tourism. geographical Compared location with the changed, case of as traditional well as some tourism new spatialtourism network, ‘nodes’ andthe ‘surfaces’movement have trajectory been created. and co Thisnsumption makes upbehavior for the lackof tourists of traditional in the tourism actual geographicalspatial structure, location and playschanged, an important as well as rolesome in new further tourism concisely ‘nodes’ highlighting and ‘surfaces’ the characteristics have been created. of the Thistourism makes destination up for the as welllack asof thetraditional promotion tourism of the spatial theme structure, and quality and of theplays tourism an important destination role [57 in]. furtherIn addition, concisely according highlighting to the the subjective characteristics and objective of the tourism tourism destination spatial structures, as well as Gannan the promotion tourism ofshould the theme further and strengthen quality of the the cooperation tourism destination among tourist [57]. In attractions addition, according with high to tourism the subjective connectivity. and objectiveBy creating tourism features spatial of tourist structures, attractions, Gannan optimizing tourism public should transportation, further strengthen increasing the touristcooperation routes Sustainability 2020, 12, 1015 14 of 17 and improving resource sharing policies, groups consisting of special tourist attractions are formed for tourism marketing. Then, the integrated marketing of tourist attractions is developed to promote the tourism development in Gannan. Compared with the traditional methods, this paper has some innovations in constructing the tourism spatial network structure from the perspective of tourists. However, as an exploratory study limited by the actual conditions, most data in this paper is from travel notes. In the future research, we should focus on how to effectively integrate multi-source data of image, video and voice, as well as even real-time travel digital footprints, such as mobile phone signals, real-time microblogging interaction, etc. Then, based on the in-depth combination of GIS spatial-temporal data expression and analysis methods, the basic rules of tourist behavior should be analyzed and summarized to guide the regional tourism development and enrich the tourism spatial network [58]. In addition, the scope of the sample subject, the normative and scientific aspects of subjective behavior data extraction should be further discussed. The characteristics of this paper are to study the spatial network organization structure of tourism destinations from objective and subjective levels, and construct a new complex network system structure of tourism destinations. This theoretical framework is beneficial to guiding the construction of whole tourism destinations [59]. It should be noted that in reality, the tourism destination network is a complex adaptive system. This paper only discussed its static structural characteristics, ignoring the dynamic evolution and simulation prediction research. The optimization of the network structure of tourism destinations should be significantly improved. Especially the understanding of the complexity of the network of tourism destinations, such as deepening the research and application of the network organization and effects of tourism destinations, paying attention to the interaction between the association of spatial elements and the network structure of tourism destinations, as well as the comprehensive effect and guide of network structure of tourism destinations to the wide region, are all key issues for future research [5].

5.2. Conclusions (1) There is significant regional difference between spatial distributions of objective and subjective tourist attractions in Gannan Prefecture. Both objective and subjective tourist attractions in Gannan Prefecture exhibit aggregated distribution. Among them, the spatial distribution of objective tourist attractions has a significant trend of contiguous aggregation, showing a relatively higher density in the northeastern and southeastern regions, and a lower density in the central and southwestern regions. This is opposite to that of the subjective tourist attractions. They present a relatively higher density in the central region, and a lower density in the periphery region. In addition, the regions with high density exhibit point distribution, except for the central part, which exhibits the surface distribution. (2) The connectivity and accessibility between objective and subjective tourist attractions in Gannan Prefecture are poor, and only a few tourist attractions form a traffic connection with neighboring ones. The spatial distribution of subjective accessibility is consistent with that of the objective one. The tourist attractions with the accessibility smaller than the average value are mainly distributed in the northeastern and central regions. The Maqu County and Zhouqu County in the southern region have the worst accessibility. (3) The objective tourism spatial network of Gannan Prefecture is layered with aggregation, and presents a significant cohesive development trend. This is opposite to the subjective one. The number of objective and subjective network nodes for Top3 and Top5 are more than that for Top1, respectively. This indicates a relatively stronger aggregation. (4) According to the identification results of objective and subjective tourism spatial structure, a ‘group tour’ spatial layout pattern combining ‘point’, ‘axis’ and ‘surface’ is formed. Based on this, the objective and subjective core tourism resources as well as tourist attractions should be integrated. The road transportation system should be constructed and improved. Then, a high-quality tourism spatial network with ‘three poles, three axes and four groups’ is constructed. Sustainability 2020, 12, 1015 15 of 17

Author Contributions: Conceptualization, L.M. and X.L.; methodology, X.L. and J.B.; software, X.L. and J.B.; resources, F.F.; writing—original draft preparation, L.M. and X.L.; writing—review and editing, L.M. and X.L. All authors have read and agree to the published version of the manuscript. Funding: This research was funded by National Natural Science Foundation of China, grant number 41661105. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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