Complexity
Volume 2020 (2020), Article ID 2807817, 13 pages
https://doi.org/10.1155/2020/2807817

Research Article

Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations

Jinlian Hao,¹ Haolong Liu,² Jie Chen,³ and Fankai Sun⁴

¹College of History and Tourism Culture, Shanxi Datong University, Datong, Shanxi 037009, China
²Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
³College of Architecture and Surveying Engineering, Shanxi Datong University, Datong, Shanxi 037009, China
⁴Department of Geomatics, Taiyuan University of Technology, Taiyuan, Shanxi 030024, China

Correspondence should be addressed to Haolong Liu; liuhl@igsnrr.ac.cn

Received 28 June 2020; Revised 17 July 2020; Accepted 20 July 2020; Published 17 August 2020

Guest Editor: Jun Yang

Copyright © 2020 Jinlian Hao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Reasonable spatial organization of the tourism industry can improve the utilization efficiency of regional tourism industry elements. Taking Dalian City in China as an example, this paper collects various types of tourism industry data and introduces GIS network analysis technology into tourism studies to determine the location, scale, and number of tourism nodes in Dalian and optimize the spatial organization nodes and organization models of the tourism industry. This will help ease the pressure on tourism reception in the southern area of Dalian and promote better development and utilization of tourism resources and tourism facilities in the central and northern regions. The results show that (1) when using the “minimizing facility points” model, a total of 17 second-level tourism nodes and 5 first-level tourism nodes are obtained after optimization. The location of these nodes is highly correlated with the level of tourist scenic spots, while tourist scenic spots play a significant role in leading and driving tourism nodes. (2) Using the “maximum coverage” model for optimization, 3138 tourism enterprises are connected with tourism nodes, thus realizing the shortest traffic path between tourism enterprises and tourism nodes, which minimizes the total cost of network services. Compared with suburban areas, enterprises in urban tourism areas are densely distributed, meaning that a smaller service radius of tourism nodes can cover more enterprises. (3) A total of 10 first-level tourism channels and 12 second-level tourism channels are optimized using the “nearest facility” model. The first-level tourism channels are mainly distributed in the central and southern areas of Dalian. These channels connect nodes mainly through national and provincial roads. The second-level tourism channels are mainly distributed in the central and northern areas of Dalian. (4) This study aims to analyze the evolution process of the spatial organization mode of Dalian’s tourism industry and construct a hub-spoke network tourism industry spatial organization mode composed of 17 hubs, 22 spokes, and 22 tourism domains. The analysis and construction are designed according to the optimization results of tourism nodes and tourism channels. The research results enrich the theories and technical means of tourism industry spatial organization and provide references and suggestions for local governments or tourism planning decision-makers; they also provide a scientific basis for the rational allocation of tourism industry elements and promote the rational distribution of tourism industry.

1. Introduction

The spatial layout and optimal design of industries are the core of industrial spatial organization research [1]. The spatial organization of the tourism industry is a process of organizing elements of the tourism industry rationally as well as forming tourism products and effective services in a certain geographical space. The aim of this organization is to identify the optimal allocation scheme of industrial elements and realize the balanced development of regional tourism industries. Based on the spatial interactions among tourism industry elements, a number of tourist cities and towns that are economically developed and have strong radiation and agglomeration effects on the tourism industry, as well as dense traffic networks and high-level tourist attractions, are determined to be the organization centers within the tourism destination. The tourism industry spatial organization nodes (i.e., tourism nodes) are therefore formed with these as the strongholds. Tourists are attracted to and gather in high-level tourism nodes and are then transported from these high-level tourism nodes to lower-level tourism nodes, experiencing tourism enterprises through tourism channels. In order to build a reasonable spatial organization mode for the tourism industry and improve the efficiency of tourism resource and facility utilization by connecting series of tourism enterprises through tourism nodes and tourism channels, tourism nodes, channels, and areas must be optimized in order to classify them according to their scales and functions and then determine the organization levels and development orders of tourism industry elements.

The provincial tourism industry presents a layout with cities as tourist distribution centers. At the city scale, three problems need to be solved: determining the locations and number of tourism nodes; determining their service scopes and scales; and determining the technical methods and steps for optimizing these nodes and channels. In this regard, the spatial distribution characteristics and optimization of tourism resources [2–7], spatial structure of tourist destinations [8–14], planning of tourist distribution centers, index system, and influencing factors [13–19], design of tourism channels [20–22], planning of tourism centers [23], and spatial organization mode of tourism [24–27] are investigated. Quantitative research methods such as the analytic hierarchy process [16], the GIS spatial analysis method [2–8, 14, 18, 28], the structuralism method [29], and cluster analysis [30] are introduced.

According to current research, the existing studies lack quantitative research on spatial organization nodes and organization mode of the tourism industry based on the collection of various types of tourism industry data and the comprehensive consideration of the interactions among tourism resources, service facilities, transportation, tourist towns, and other tourism industry elements. The existing studies also lack quantitative research on service scopes and scales of tourism nodes. GIS network analysis technology allows for the determination of reasonable number, locations, and service scope of facilities as well as optimization of the facility layout [31–34]. This method is widely used for optimizing site selection in public service facilities research on wholesale and retail trade, medical facilities, educational facilities, fire stations, logistics distribution center location, sports facilities, and other fields [35–46] but is almost never involved in the research of tourism nodes location. The city level undertakes tourism industry development at the provincial and county levels. Studying the problem of utilization efficiency of tourism industry elements in underdeveloped regions of tourist destinations in cities is therefore conducive to forming a realistic basis for optimizing the layout strategy of the tourism industry. This paper defines city-level tourist destinations as the background of the study and optimizes the spatial organization nodes and organization mode of Dalian’s tourism industry using GIS network analysis technology, which enriches the theories and technical means of spatial organization of the tourism industry and provides a reference for solving similar problems in comparable tourist cities. This paper focuses on (1) calculating the centrality index of tourism nodes and determining the two-level tourism nodes system with reference to the traffic principles in the central place theory; (2) using the models of minimizing facility points and maximizing coverage in the network analysis method to simulate and optimize tourism nodes and tourism channels so as to determine the locations, scales, and number of tourism nodes; and (3) optimizing the spatial organization mode of the tourism industry using the optimization results of tourism nodes and channels.

2. Methods

2.1. The Model of Minimizing Facility Points

This model is applied to determine the number and locations of tourism nodes (i.e., facility points). The unbalanced space of tourism industry elements makes it difficult to determine the number and location of tourism nodes, and the service radius of different levels of tourism nodes is different. Using this model to set the impedance cutoff value of tourism nodes, the tourism nodes that meet the requirements according to the distribution of tourism enterprises (i.e., request points) will be selected, and the tourism enterprises located in the tourism nodes impedance cutoff will allocate all of their request weights to the tourism nodes, so that as many tourism enterprises as possible are located within the impedance radius of tourism nodes, thus minimizing the number of tourism nodes covering tourism enterprises. The formulas [47] are

In formulas (1) to (3), represents a set of demand points, and represents a set of candidate facility points; if the candidate facility point is selected, then , and if it is not selected, then . Formula (1) minimizes the total number of facilities; formula (2) ensures that the demand points are covered at least once by service facilities; and formula (3) ensures that the decision variable is 0-1. If is used to represent the distance from the demand point to the facility point and is used to represent the coverage distance, then represents the set of facilities that can cover the demand point .

2.2. The Model of Maximizing Coverage

This model is applied to determine the service scope and scales of tourism nodes. The central guidance and radiation effect of tourism nodes encourage tourism enterprises to seek services from the nearest nodes. Using this model to obtain services from the nearest nodes therefore serves to minimize the total cost of network services [48]. The number of tourism enterprises served by each node is the scale of the tourism node, and the spatial distribution scope of the tourism enterprises served is the service scope of the tourism node. The service scope is calculated based on the average distance from the tourism node to the tourism enterprise. To prevent individual demand points from falling outside the coverage of facilities, the improved model is adopted, and the formulas [49] are

In formulas (4) and (5), and are decision variables. If  = 1, then the facility is configured at request point a; otherwise,  = 0; if  = 1, then the request point b is covered within the effective service radius of the facility; otherwise,  = 0; represents the comprehensive layout index from the request point to the facility point; is a binary value coefficient. When the distance from the request point a to the facility point b is within the effective service radius R, then  = 1; otherwise,  = 0; D is the specified number of facilities to be deployed.

3. Data

3.1. Research Area

Dalian City consists of 10 administrative units: Zhongshan District, Xigang District, Shahekou District, Ganjingzi District, Lüshunkou District, Jinzhou District, Pulandian District, Zhuanghe City, Wafangdian City, and Changhai County (Figure 1). Its jurisdiction covers 162 townships and subdistricts. Dalian was one of the first locations to be awarded the designation of best tourist city inching and is also a famous tourist destination. The development of its tourism industry has achieved remarkable results but has highlighted the contradiction between supply and demand within the leisure industry as well as the backward organization mode of the tourism industry, both of which affect the industry’s sustainable development. Therefore, using the GIS network analysis method, this paper puts forward the calculation steps for optimizing the spatial organization nodes and channels of the tourism industry. The spatial organization mode of the tourism industry is constructed on this basis, and its essential elements are scientifically organized to provide reference for development planning and policy formulation for Dalian’s tourism industry.

Figure 1: Study area.

3.2. Data Sources

3.2.1. Establishment of the Traffic Network Dataset

It is important to ensure that the travel time between the tourism nodes (or tourism attractions) is short; at the same time, tourist activities require minimization of transportation costs, selection of other types of transportation instead of expressways, consideration of the appropriate advancement of tourism node optimization, and establishment of a traffic network dataset including national roads, provincial roads, planned new roads, county roads, and township roads. Two types of transportation, highway and waterway, are also involved. With reference to the relevant provisions of traffic laws and considering the actual traffic operation in Dalian City, the driving speed on national and provincial roads is set at 60 km/h, while it is set at 50 km/h on country roads, 40 km/h on township roads, and 30 km/h for ferries.

3.2.2. Establishment of the Tourism Node Model

The development of the tourism industry depends on traffic conditions, and the number of tourism nodes can follow the traffic principle (K = 4) of the central place theory [50]. “Tourism node” is an expression of the tourism center. The centrality of tourism nodes indicates the scale of their external service functions. Generally, the greater the centrality, the greater the service scope. The hierarchy of tourism nodes matches their individual functional systems. The supply service systems of high-level tourism nodes are relatively sound, and they also cover the functions of tourism nodes at lower levels.

For tourism notes, the research scale and content are different, as are the meaning, scale, and level. On a larger scale, tourism nodes can be defined as regional central cities, which serve to gather and diffuse tourists [10]. At the county scale, the first-level tourism nodes can be defined as county cities, and the second-level tourism nodes can be defined as transit places from county cities to tourist attractions, which mainly manifest in some towns [13]. From the city-scale perspective, tourism nodes can be defined as townships and subdistricts. Dalian has 162 townships and subdistricts, that is, 162 tourist nodes. High-level tourism nodes within the city are economically developed, with dense traffic networks and high-level tourist attractions as well as planned key towns. According to the coherent distribution of tourism enterprises in the city, the high-level tourism nodes can be divided into two levels (Figure 2). The first-level tourism nodes gather tourists and transport them to second-level tourism nodes and the second-level tourism nodes serve the surrounding tourism enterprises. The first- and second-level tourism nodes play the role of gathering, diffusing, and transferring tourists. Among them, the first-level tourism nodes are the central nodes, the second-level tourism nodes are the secondary central nodes, and the remaining tourism nodes are noncentral nodes.

Figure 2: Tourism nodes grade and quantity relationship (K = 4).

3.2.3. Tourism Node Centrality Index

Referring to the research of Huang et al., Deng et al., and Du [10, 15, 18], a total of 22 types of tourism resources, such as nature reserves, forest parks, geological parks, museums, scenic spots, and religious buildings, as well as 3138 tourism enterprises, including hotels, travel agencies, special restaurants, special shopping, and performing arts, are selected. Transportation accessibility is expressed by weighted average travel time, and a tourism node centrality index model is constructed. The formulas are

In formulas (6) to (8), is the candidate tourism node, is the degree of attraction of the node, , , and, respectively, represent the number of tourism resources, reception facilities, and performing arts and shopping enterprises, and , , and , respectively, represent the weights of factors affecting the attraction of tourism resources, reception facilities, and entertainment and shopping facilities; is the weighted average travel time of node i, which indicates the traffic accessibility of node i. The smaller the value of , the higher the accessibility. represents the shortest travel time between two nodes; represents the weight of node ; and the higher the value, the stronger the attraction to the surrounding area and the greater the radiation scope. In view of the agglomerating and driving effects of high-level scenic areas (spots) on regional tourism, is calculated by multiplying the number of scenic areas (spots) at all levels in the subdistrict where the node is located. The weights of 5A, 4A, 3A, 2A, and A scenic areas (spots) are set at 5, 4, 3, 2, and 1.5, respectively, and the weights of all undetermined scenic areas (spots) are set at 1. is the tourism node centrality index. The higher its value, the stronger the node’s ability to service the surrounding tourism enterprises and the greater its service scope.

According to formula (6), is calculated by the entropy weight method. According to formula (7), based on the traffic network dataset, GIS network analysis function is used to generate the time-distance matrix between the two nodes (Table 1), and the weighted average travel time of each node is further obtained, thus calculating the tourism node centrality index (Table 2).

Table 1: Tourism nodes time-distance matrix (partial) (unit: km).

Anbo Town
Anzixiang Subdistrict
Bayilu Subdistrict
Baishanlu Subdistrict
Baiyun Subdistrict
Beihai Subdistrict

Anbo Town
0
126.1
171.6
165.4
167.7
189.8

Anzixiang Town
126.1
0
220.8
214.6
216.8
239

Bayilu Subdistrict
171.6
220.8
0
9.451
8.719
55.75

Baishanlu Subdistrict
165.4
214.6
9.451
0
6.189
48.3

Baiyun Subdistrict
167.7
216.8
8.719
6.189
0
51.8

Beihai Subdistrict
189.8
239
55.75
48.3
51.8
0

Table 2: Tourism nodes centrality index (partial).

Node name
Li
Di
Qi

Anbo Town
0.491
127.235
0.004

Anzixiang Town
0.070
169.876
0.000

Bayilu Subdistrict
0.142
94.535
0.001

Baishanlu Subdistrict
0.164
88.960
0.002

Baiyun Subdistrict
0.240
91.512
0.003

Beihai Subdistrict
0.138
111.439
0.001

Beijing Subdistrict
0.213
87.360
0.002

Paoya Town
0.036
108.762
0.000

Paotai Town
0.403
89.670
0.004

Pikou Town
0.172
90.196
0.002

Qidingshan Subdistrict
0.257
86.443
0.003

Qingdui Town
0.104
160.482
0.001

Qingniwaqiao Subdistrict
0.469
89.675
0.005

Quanshui Subdistrict
0.304
83.561
0.004

4. Results

First, the low-level tourism nodes are optimized. The high-level tourism nodes are then optimized according to the locations and number of low-level tourism nodes, that is, the “bottom-up” idea. The two-step optimization methods of “minimizing facility points” and “maximizing coverage” are used to determine second-level tourism nodes, which serve neighboring tourism enterprises. A number of nodes with large service scale and leading roles for surrounding tourism enterprises are then selected from the second-level tourism nodes as first-level tourism nodes to achieve the optimization purpose of maximum number of enterprises served by minimum number of facilities.

4.1. Optimization of Second-Level Tourism Nodes

4.1.1. Selection of Required Tourism Nodes

The required tourism nodes must be selected during the optimization of the tourism nodes system, while the candidate tourism nodes may be selected during the system optimization. The required tourism nodes and candidate tourism nodes should be determined first; then, the first- and second-level nodes can be optimized. The second-level required tourism nodes come from two parts. First, the centrality index of tourism nodes is clustered into three levels by the natural fracture method; Laohutan and Daweijia Subdistricts, which are the first type of tourism nodes, are listed as second-level required tourism nodes. Second, key towns that have begun to take shape and whose plans and documents have been published can be included as a part of the first- and second-level required tourism nodes; Wangjia Town, Anbo Town, Xietun Town, Jinshitan Subdistrict, Guanglu Township, and Shuishiying Subdistrict are defined as second-level required tourism nodes; the other towns, townships, and subdistricts are the candidate nodes for the second-level tourism nodes.

According to central place theory, high-level central places function as low-level central places, and the first-level required tourism nodes must be the second-level required tourism nodes. As a classification system of tourist attractions with Chinese characteristics, A-level scenic spots are one of the most important bases to classify the importance levels of tourist destinations. High-level tourism scenic spots play a positive role in promoting the development of regional tourism industry, directly affect the spatial behavior of tourists, and have a profound impact on the speed and scale of development as well as the utilization efficiency of regional tourism resources. Jinshitan Subdistrict (with 5A-level tourist scenic area, Jinshitan National Tourist Resort) and Laohutan Subdistrict (with 5A-level tourist scenic area, Tiger Beach Ocean Park Tourist Area) are set as first-level required tourism nodes, and the remaining second-level required tourism nodes and newly added second-level tourism nodes are set as first-level candidate tourism nodes.

4.1.2. Number and Locations of Second-Level Tourism Nodes

The number and locations of second-level tourism nodes are determined by the minimizing facility points model. A total of 3138 tourism demand enterprises, 8 second-level required tourism nodes, and 154 second-level candidate tourism nodes are loaded. Tourism nodes are “facility points,” and tourism enterprises are “request points.” According to the traffic principle of the central place theory, the number of tourism enterprises, and the urban-rural differences in the distribution of tourism enterprises, it is estimated that there are about 5 first-level tourism nodes and 18 second-level tourism nodes; of these, 3 are urban second-level tourism nodes and 15 are suburban second-level tourism nodes. The theoretical service radiuses of urban and suburban tourism nodes are calculated, and the maximum impedance interruption distances to receive services are set for urban and suburban tourism enterprises. After repeated experiments, the second-level tourism nodes cover all tourism enterprises, and 17 second-level tourism nodes are optimized; these include 8 second-level required tourism nodes and 9 newly added second-level tourism nodes.

4.1.3. Scales and Service Scopes of Second-Level Tourism Nodes

Using the maximum coverage model, under the condition of no limitation on service radius, each tourism enterprise can obtain services from second-level tourism nodes nearby (Figure 3), and the service scale of each tourism node is determined according to the number of tourism enterprises served by the tourism node (Table 3).

Figure 3: Distribution of second-level tourism nodes and enterprises.

Table 3: The parameters table of second-level tourism nodes.

Second-level tourism nodes
Location attribute
Nodes attribute
Number of services under impedance (a)
Number of services under maximum coverage (a)
Average distance under impedance (km)
Average distance under maximum coverage (km)

Shuishiying Subdistrict
Suburbs
Required
147
147
9.420
9.420

Jinshitan Subdistrict
Suburbs
Required
75
75
7.806
7.806

Daweijia Subdistrict
Suburbs
Required
41
41
12.210
12.210

Guanglu Township
Suburbs
Required
26
54
18.221
33.747

Xietun Town
Suburbs
Required
56
56
17.137
17.137

Anbo Town
Suburbs
Required
43
59
15.043
24.051

Wangjia Town
Suburbs
Required
13
13
6.432
6.432

Laohutan Subdistrict
Urban
Required
621
621
5.663
5.663

Guangming Subdistrict
Suburbs
Newly
338
338
7.847
7.847

Gongji Subdistrict
Suburbs
Newly
135
141
6.936
8.528

Fuzhoucheng Town
Suburbs
Newly
43
48
15.206
18.572

Fengrong Subdistrict
Suburbs
Newly
121
124
16.276
17.023

Xingda Subdistrict
Suburbs
Newly
134
140
8.840
10.266

Xianrendong Town
Suburbs
Newly
19
25
16.114
24.144

Baishanlu Subdistrict
Suburbs
Newly
855
855
4.574
4.574

Airport Subdistrict
Urban
Newly
371
371
6.716
6.716

Yingchengzi Subdistrict
Urban
Newly
30
30
5.948
5.948

Note: the average distance is the average distance between second-level tourism nodes and enterprise.

According to Table 3, in terms of the service scale of tourism nodes, under the “maximum coverage” model, 3138 tourism enterprises are all served by tourism nodes. Guanglu Township, Anbo Town, and Xianrendong Town are located at the edge of the city. Due to their locations, their average service distances to the surrounding tourism enterprises are much farther than those of other subdistricts. The average service scale of second-level tourism nodes in urban areas is 469, while that of second-level tourism nodes in suburban areas is 92. Urban tourism enterprises are densely distributed, and a smaller service radius can cover more tourism enterprises.

The levels, quantities, and spatial combinations of tourist attractions affect the development speed, scale, and time-space arrangement of the tourism industry. At the same time, tourist attractions are also an important basis for the construction of tourism nodes. Among the 17 second-level tourism nodes, 10 nodes belong to regions with high-level and newly built government-planned tourist attractions and leisure resorts. Tourist attractions play a significant role in leading and driving tourism nodes. Such nodes rely on the advantages of high-level tourism resources to develop and prosper, expand their scales, and increase investment in construction.

4.2. Optimization of First-Level Tourism Nodes

The number, locations, scales, and service scopes of the first-level tourism nodes are determined by adopting the minimizing facility points model. Jinshitan Subdistrict and Laohutan Subdistrict, with 2 first-level required tourism nodes and 17 second-level tourism nodes, are loaded. According to the traffic principle of the central place theory, it is initially estimated that the optimization result of the first-level tourism nodes is about four. According to the theoretical service radius of the first-level tourism nodes, the impedance interruption distance value of the tourism enterprise request points is set. After repeated tests, when the maximum impedance radius of the tourism enterprises’ request to receive service is set at 80 km, a relatively ideal layout scheme can be obtained, that is, two first-level required tourism nodes and three newly added first-level tourism nodes, which add up to a total of five first-level tourism nodes (Figure 4). Considering the traffic locations and the relevance of tourism enterprises, the administrative division limit of tourism development has been broken, and 5 first-level tourism nodes and 12 second-level tourism nodes have been formed.

Figure 4: The optimization results of tourism nodes.

4.3. Extraction of Tourism Channels

According to the principle of shortest path, the nearest facility point model [51] is used to extract tourism channels. The first-level tourism nodes are set as “facility points” and the second-level tourism nodes are set as “event points.” The number of searched facility points for event points is set to 1, and the simulation is run. This uncovers the interconnection among all the first-level tourism nodes and eliminates duplicate routes, thus obtaining a total of 10 first-level tourism channels and 12 second-level tourism channels (Figure 5). The first-level tourism channels are mainly distributed in the central and southern areas of Dalian. These channels establish connections between nodes primarily through national and provincial roads and have high tourist distribution capacity. The second-level tourism channels are mainly distributed in the central and northern areas of Dalian City, where tourism enterprises are relatively sparse; it is therefore not reasonable to build first-level tourism channels in the near future. In addition to relying on national and provincial roads, the second-level tourism channels also include county-level roads and ferries.

Figure 5: The extraction results of tourism channels.

4.4. Optimization of the Organization Mode

4.4.1. Evolution of the Organization Mode

An analysis of the formation and evolution of Dalian’s tourism industry spatial organization mode can provide an understanding of relevant patterns for the construction of a more reasonable tourism industry spatial organization mode. Dalian’s tourism industry began to grow between 1978 and 1989. Tourists were mainly sporadic inbound tourists as well as a small number of tourists from surrounding regions. Along the coastline, which had better utilization of tourism resources and convenient transportation conditions, Binhai Road, Baiyun Mountain Scenic Area, and Xiuyue Peak Scenic Area were the main attractions. These areas formed a singular agglomeration mode of a tourism center-hinterland system.

The period from 1990 to 1999 saw significant growth in Dalian’s tourism industry. High-quality tourism resources were further developed. Tourists took the downtown area as their base camp and spread to the surrounding areas, showing a one-way radiation pattern from the downtown area to the periphery. The downtown area is the organization center for Dalian’s tourism industry development. Other areas rely on high-quality tourism resources to form second-level tourism nodes, thus improving traffic accessibility and strengthening the links among tourism nodes.

Since 2000, Dalian’s tourism industry has matured. The downtown area has a strong tourist distribution function, leading and guiding the development scale and direction of the city’s tourism industry. The tourism has further expanded to the periphery. Second-level tourism areas of different natures have established contact with upper-level tourism centers through tourism channels, dispersing the pressure of tourist flows in Dalian’s downtown area, expanding the scope of tourist activities, enhancing the attraction of regional tourism, and forming an expansion model from central tourism areas of tourist destinations to second-level central tourism areas as well as to tourism hinterland channels.

The coexistence of multiple second-level tourism areas has made the competition among tourist destinations in Dalian increasingly intense, and the likelihood of regional tourism economy disorder has increased. The connection level among second-level tourism areas is not high, and the tourism industry’s development is uneven. Analyzing the comparative advantages and development trends of tourist destinations at all levels is necessary as it will strengthen regional tourism cooperation, optimize the spatial organization mode of the tourism industry, and reconstruct the scientific spatial structure of regional tourism.

4.4.2. Reconstruction of the Organization Mode

The hub-spoke service network is a type of efficient network that is conducive to reducing the cost of element flow, improving the efficiency of element utilization, and exerting the scale effect [52]. The hub-spoke network tourism spatial mode refers to the integrated organizational form and process of tourism flow presented by interactions among special nodes, between nodes and connection paths, and between nodes and influence domains in the network [24] (Figure 6). With reference to the hub-spoke network tourism space theory proposed by Liang et al., according to the optimization results of tourism nodes and tourism channels (Figures 4 and 5), and on the basis of the spatial interactions among tourism nodes and tourism channels at all levels, the hub-spoke network tourism industry spatial organization mode for Dalian (Figure 7) is constructed. Different roles are assigned according to the functions of tourism nodes at different levels (Table 4) so as to improve the operational efficiency of tourism channels in organizing tourism flow and the utilization efficiency of tourism industry elements, thus reducing the total cost of road network services.

Figure 6: The hub-spoke network tourism spatial structure [24].

Figure 7: The hub-spoke network spatial organization mode of Dalian’s tourism industry.

Table 4: The hub-spoke network spatial organization mode of Dalian’s tourism industry.

Element
Item
Level
Details

Axes
Central nodes
First-level tourism nodes
Laohutan Subdistrict, Shuishiying Subdistrict, Jinshitan Subdistrict, Gongji Subdistrict, and Xingda Subdistrict

Secondary central nodes
Second-level tourism nodes
Baishanlu Subdistrict, Airport Subdistrict, Yingchengzi Subdistrict, Guangming Subdistrict, Daweijia Subdistrict, Fuzhoucheng Town, Xietun Town, Fengrong Subdistrict, Guanglu Township, Anbo Town, Xianrendong Town, and Wangjia Town

Spokes
Main paths
First-level tourism channels
Laohutan Subdistrict-Shuishiying Subdistrict, Laohutan Subdistrict-Jinshitan Subdistrict, Laohutan Subdistrict-Gongji Subdistrict, Laohutan Subdistrict-Xingda Subdistrict, Shuishiying Subdistrict-Jinshitan Subdistrict, Shuishiying Subdistrict-Gongji Subdistrict, Shuishiying Subdistrict-Xingda Subdistrict, Jinshitan Subdistrict-Gongji Subdistrict, Jinshitan Subdistrict-Xingda Subdistrict, and Gongji Subdistrict-Xingda Subdistrict

Secondary paths
Second-level tourism channels
Yingchengzi Subdistrict-Shuishiying Subdistrict, Airport Subdistrict-Laohutan Subdistrict, Baishanlu Subdistrict-Laohutan Subdistrict, Guangming Subdistrict-Jinshitan Subdistrict, Daweijia Subdistrict-Jinshitan Subdistrict, Guanglu Township-Gongji Subdistrict, Fengrong Subdistrict-Gongji Subdistrict, Xietun Town-Gongji Subdistrict, Fuzhoucheng Town-Gongji Subdistrict, Anbo Town-Gongji Subdistrict, Xianrendong Town-Xingda Subdistrict, and Wangjia Town-Xingda Subdistrict

Domains
First-level tourism domains
First-level tourism areas
Central urban tourism area, Lüshunkou tourism area, Jinzhou tourism area, Wafangdian-Pulandian-Changhai tourism area, and Zhuanghe tourism area

Second-level tourism domains
Second-level tourism areas
Laohutan tourism area, Shuishiying tourism area, Jinshitan tourism area, Gongji tourism area, Xingda tourism area, Baishanlu tourism area, Airport tourism area, Yingchengzi tourism area, Guangming tourism area, Daweijia tourism area, Fuzhou tourism area, Xietun tourism area, Fengrong tourism area, Guanglu tourism area, Anbo tourism area, Xianrendong tourism area, and Wangjia tourism area

Figure 7 and Table 4 show that the spatial organization mode of Dalian’s tourism industry presents a hub-spoke network tourism industry spatial organization mode with 5 central nodes and 12 secondary central nodes, adding up to a total of 17 hubs, combined with 10 main paths and 12 secondary paths, adding up to a total of 22 spokes, to form 5 first-level tourism domains and 17 second-level tourism domains, adding up to a total of 22 tourism domains. This mode integrates the advantages of agglomeration mode, radiation mode, and expansion mode. The central nodes are endowed with superior tourism resources and frequent tourist activities. They enjoy traffic locations with fast main roads and thus can easily gather tourists, forming a regional tourist source gathering place and center. They are the first-level growth poles of Dalian’s tourism industry and are responsible for driving tourism growth while serving as a transportation hub, a reception and information service, industry management, and diffusion and radiation. At the same time, the central nodes have the most complete tourism service functions, gathering tourists, increasing the traffic density on their corresponding spokes, and generating scale, agglomeration, and spatial benefits. In this mode, the hub-spoke network can be used to develop hub-spoke route tourism products. The central nodes gather tourists and create a “feeding” effect for secondary central nodes (or tourist attractions) through spokes. Tourists choose to stay in the tourist accommodation facilities in the central nodes and visit the scenic spots through the adjacent “spokes” of these hubs. The mode realizes the spatial tourism flow and balanced development of regional tourism by means of spatial connection between the hubs and spokes in the region.

5. Discussion

5.1. Construction of Centrality Index Model of Tourist Nodes

The construction of the tourism nodes centrality index model is based on the six elements of the tourism industry: food, housing, transportation, tourism, purchase, and entertainment. These factors have enriched the theoretical basis of tourism node optimization research, but the model needs to be improved. Due to availability, only the data of tourist attractions, hotels, travel agencies, traffic conditions, and so on that are closely related to the development of the regional tourism industry within the jurisdiction of townships and subdistricts are considered. The influencing factors of the tourism nodes centrality index, such as the number of tourists, regional economic development level, natural and human environment, residents’ cultural literacy, and hospitality, are not considered. At the same time, the continuous emergence of new tourism formats will cause changes in the type of tourism resources available. In the future, it will be necessary to update data regularly and adjust the selection of central index items for tourism nodes, thereby reinforcing the scientificity and reasonability of the research conclusions. In addition, the research perspective can try to expand from considering only the tourism industry and regional economic development to also considering the spiritual and psychological fields of residents’ life and culture. This will allow the layout of tourism nodes to be more fully understood.

5.2. Selection of Network Analysis Methods

GIS network analysis technology is introduced into tourism-themed research to optimize tourism nodes and channels. Based on this, the spatial organization mode of the tourism industry is constructed; this differs from most current research, which has typically used qualitative research methods to determine tourist distribution centers and organization modes [25, 27]. Based on the traffic network dataset and the simulation of the distribution of tourism enterprises, the layout scheme is obtained; this provides more scientific research results and is superior to the empirical layout method [3, 13, 20] in predicting the service effect, quantity, and service scale of tourism nodes. The research results based on the traffic network dataset perfect the research ideas proposed by Deng and Zheng [15, 21]. Using the network analysis method, all tourism enterprises are covered by tourism nodes, and the shortest traffic path between the two is realized, thus improving the efficiency of tourism industry elements utilization. This breaks through the disadvantages of the buffer analysis method [53] and proximity analysis method [54] in solving such problems. In the future, setting simulated traffic impedance should be more refined, so that the optimization scheme is more conducive to improving the scientific validity of tourism decision-making.

5.3. Restrictive Factors

Since tourism reception facilities, such as catering, shopping, and entertainment, serve both tourists and nontourists, there is no-good method to separate the data, so only some representative enterprises are selected to construct an index system. The number and types of index items affect the centrality index of tourism nodes, thus affecting the determination of the number and locations of tourism nodes at all levels, which, to some extent, limits a more scientific understanding of tourism nodes.

Calculations for the time distance between tourist nodes are currently completed according to the transportation speed laws in the relevant areas. The accuracy of this method of determining the tourism nodes centrality needs to be improved. Navigation data can be used to improve the calculation method of time distance in the future so that the tourism node centrality index can be more realistic.

Since the research scale is townships and subdistricts, the conclusions are based on the road network optimization without considering the railways. With the change of transportation mode, the road network system is gradually improved, the accessibility between nodes is improved, and the regional tourism network is further expanded. Dynamic regulation and long-term tracking should be strengthened in the future, and follow-up research should be carried out through multisource and multiscale data fusion. With multiple modes of transportation, grids and communities are used as research units to construct tourism nodes and tourism channels above level three and to improve the tourism industry spatial organization model. This study offers a reference point for the solution of similar problems. The analysis results are based on certain assumptions and simulation premises and need to be repeatedly verified in practice.

6. Conclusion

(1)In terms of the number of tourism nodes, 17 second-level tourism nodes and 5 first-level tourism nodes are optimized using the model of “minimizing facility points” on the basis of considering the traffic locations and the relevance of tourism enterprises. Tourism resources are the basic conditions for the development of the tourism industry. The level and quantity of tourist attractions play a significant role in leading and driving tourism nodes. Tourism nodes rely on tourist attractions to develop and grow.(2)In terms of the service scale of tourism nodes, under the “maximum coverage” model, 3138 tourism enterprises are served by tourism nodes. The average service size of second-level tourism nodes in urban areas is 469, while that of second-level tourism nodes in suburban areas is 92. Compared with suburban areas, tourism enterprises in urban areas are densely distributed, and a smaller service radius can cover more tourism enterprises.(3)A total of 10 first-level tourism channels and 12 second-level tourism channels are optimized using the nearest facility model. The first-level tourism channels are mainly distributed in the central and southern areas of Dalian. These channels establish connections between tourism nodes mainly through national and provincial roads and have high tourist distribution capacity. The second-level tourism channels are mainly distributed in the central and northern areas of Dalian City, where tourism enterprises are relatively sparse, and it is thus not reasonable to build first-level tourism channels in the near future.(4)The optimization scheme enables all tourism enterprises to connect with tourism nodes and tourism channels, thus making full use of tourism industry elements. Tourism enterprises can obtain services from the nearest tourism nodes, allowing them to realize the shortest traffic path between tourism enterprises and tourism nodes, thus minimizing the total cost of network services.(5)Dalian’s tourism industry spatial organization mode has gone through three stages of development, namely, condensation, radiation, and expansion. Based on the optimization results of tourism nodes and tourism channels, Dalian’s hub-spoke network tourism industry spatial organization mode is constructed, which presents 5 central nodes and 12 secondary central nodes, adding up to 17 hubs. These hubs combine with 10 main paths and 12 secondary paths, adding up to 22 spokes, to form 5 first-level tourism domains and 17 second-level tourism domains for a total of 22 domains. This model integrates the advantages of the condensation, radiation, and expansion modes.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant no. 41771132), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDA23000000), the Humanities and Social Sciences Key Research Base of China Ministry of Education (Grant no. 14JJD790044), and Shanxi Datong University Research Startup Fund (2019). The authors would like to express appreciation to colleagues in the laboratory for their constructive suggestions.

References

H. Luo and Y. Yang, “Study on the location of urban hotels based on the theory of industrial spatial organization and the method of spatial econometrics,” Tourism Tribune, vol. 26, no. 12, pp. 71–77, 2011.

L. Li, G. L. Hou, S. Y. Xia et al., “Spatial distribution characteristics and influencing factors of leisure tourism resources in Chengdu,” Journal of Natural Resources, vol. 35, no. 03, pp. 683–697, 2020.

Q. Tian and B.Y. Li, “Study on spatial characteristics and optimization of rural tourism spots in great wuyi tourism circle,” Journal of Fujian Normal University (Philosophy and Social Sciences Edition), vol. 2, pp. 91–99, 2020.

C. Jin and Z. F. Huang, “Tourism regionalization in the yangtze river delta based on accessibility,” Geographical Research, vol. 31, no. 4, pp. 745–757, 2012.

C. C. Tang, M. Y. Sun, and A. W. Wan, “Spatial distribution characteristics of high-level scenic spots and its influencing factors in beijing-tianjin-hebei urban agglomeration,” Economic Geography, vol. 39, no. 10, pp. 204–213, 2019.

J. Liu, H. Cheng, D. Jiang, and L. Huang, “Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan,” Tourism Management, vol. 70, pp. 262–272, 2019.

X. Yang, C. Peng, X. L. Jiang et al., “Spatiotemporal changes and simulation of the architectural ethnicity at world heritage sites under tourism development,” Complexity, vol. 2020, Article ID 6715897, 15 pages, 2020.

D.-G. Wang, Y. Niu, and J. Qian, “Evolution and optimization of China’s urban tourism spatial structure: a high speed rail perspective,” Tourism Management, vol. 64, no. 2, pp. 218–232, 2018.

D. Park, G. Lee, and J. Kim, “Identifying the spatial structure of the tourist attraction system in South Korea using GIS and network analysis: an application of anchor-point theory,” Journal of Destination Marketing & Management, vol. 9, pp. 358–370, 2018.

T. Huang, J. Zhang, Z. Xie, and Z. X. Liu, “On spatial organization of tourism destination system with special reference to regional urban system: a case study of Jiangsu Province,” Human Geography, vol. 18, pp. 49–54, 2003.

A. E. Aleksandrova, “Modern transformation of tourism spatial organization in Russia,” Political Sciences Law Finance Economics & Tourism, vol. 3, pp. 333–341, 2014.

P. Yin, H. Y. Yang, and T. H. Zhang, “High-speed railway and tourism development in Beijing-Tianjin-Hebei Region: spatial interaction and restructuring,” Tourism Tribune, vol. 34, no. 3, pp. 102–112, 2019.

N. S. Guan, “Preliminary study on planning and construction of tourist distribution center: a case study of Heqing county of Yunnan Province,” Social Scientist, vol. 3, pp. 53–55, 1999.

S. Jin, J. Yang, E. Wang, and J. Liu, “The influence of high-speed rail on ice-snow tourism in northeastern China,” Tourism Management, vol. 78, Article ID 104070, 2020.

Y. Deng, Z. Wang, Y. Liu et al., “Several geocomputation problems on tourism distribution center planning,” Acta GeographicaSinica, vol. 5, pp. 781–788, 2003.

J. G. Ma, F. Li, B. X. Zhou et al., “The tourism hub: locational rationally and functional promotion: a case study of Kunming,” Economic Geography, vol. 34, no. 2, pp. 174–179, 2014.

Q. Q. Gao, “Study on spatial structure of tourism in gansu province,” Northwest Normal University, Lanzhou, China, 2017, M S. Thesis.

D. Du, “Study on spatial structure of tourism in sichuan province,” Shaanxi Normal University, Xi’an, China, 2015, M S. Thesis.

J. Yang, Y. Ge, Q. Ge, J. Xi, and X. Li, “Determinants of island tourism development: the example of Dachangshan Island,” Tourism Management, vol. 55, no. 55, pp. 261–271, 2016.

X. G. Wu, H. G. Li, and Y. J. Feng, “Researches on nodes configuration of domestic tourist routes in jiangsu province,” Areal Research and Development, vol. 30, no. 5, pp. 118–122, 2011.

W. Zheng, Z. Liao, and J. Qin, “Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction,” Tourism Management, vol. 62, pp. 335–349, 2017.

Z. Zhu and Q. Y. Huang, “Performance evaluation system for large-scale tour routes and its empirical test on the sightseeing tour products in Europe,” Tourism Tribune, vol. 34, no. 1, pp. 23–33, 2019.

Y. W. Chai, T. Liu, Z. L. Liu et al., “Theoretical construction of tourism central place and its application to Jilin tourism planning,” Scientia GeographicaSinica, vol. 5, pp. 547–553, 2003.

B. Liang and Y. Mao, “A study on hub and spoke network tourism spatial structure of Wuhan metropolitan area,” Economic Geography, vol. 29, no. 07, pp. 1214–1217, 2009.

L. T. Zhu, L. F. Wang, and M. F. Huang, “Spatial structure evolution and development mode of the central plains tourism circle of Henan Province,” Economic Geography, vol. 32, no. 07, pp. 147–151, 2012.

L. Yang, M. Wang, W. Wu, and L. Zhang, “Transcriptome analysis of cold syndrome using microarray,” The American Journal of Chinese Medicine, vol. 35, no. 4, pp. 609–620, 2007.

X. L. Cheng and W. H. Hu, “Pattern of integrated development of cultural tourism resources in international tourism and culture demonstration zone of southern Anhui Province,” Geographical Research, vol. 31, no. 1, pp. 169–177, 2012.

L. Wang, T. Li, X. Gao, and X. C. Gao, “Accessibility comparison and spatial differentiation of xi'an scenic spots with different modes based on baidu real-time travel,” Chinese Geographical Science, vol. 29, no. 5, pp. 848–860, 2019.

J. G. Ma, J. P. Song, and B. X. Zhou, “A pilot study on tourism hub:Integrity,transformation mechanism and selfregulation: From the perspective of structuralism,” Human Geography, vol. 26, no. 2, pp. 116–121, 2011.

J. Zhong, J. Zhang, D. Li, Y. Zhao, S. Lu, and C. Shi, “Nodes and field of tourist origins to ancient village—a case study of huangcheng village in shanxi province of China,” Chinese Geographical Science, vol. 17, no. 3, pp. 280–287, 2007.

L. Wang and Z. L. Han, “The theory and demonstration of different scale space development regionalization,” Science Press, Beijing, China, 2010.

Q. Niu and C. Peng, “A preliminary study on the optimal layout model of public and municipal public facilities based on real road network,” Transportation and Computer, vol. 5, pp. 49–53, 2004.

C. G. Zhu, C. L. Gu, R. H. Ma et al., “The influential factors and spatial distribution of floating population in China,” Acta GeographicaSinica, vol. 56, no. 5, pp. 549–560, 2001.

S. P. Chen, Urbanization and Urban Geographic Information System, Science Press, Beijing, China, 1999.

G. Giuliano, P. Gordon, Q. Qisheng Pan, and J. Park, “Accessibility and residential land values: some tests with new measures,” Urban Studies, vol. 47, no. 14, pp. 3103–3130, 2010.

Y. Lee, H. Kim, and H. Yoon, “Spatial representation of community shared spaces preferred by residents,” Indoor and Built Environment, vol. 19, no. 1, pp. 163–174, 2010.

N. Yoshida and U. Deichmann, “Measurement of accessibility and its applications,” Journal of Infrastructure Development, vol. 1, no. 1, pp. 1–16, 2009.

D. M. Smith, S. Cummins, M. Taylor et al., “Neighborhood food environment and area deprivation: spatial accessibility to grocery stores selling fresh fruit and vegetables in urban and rural settings,” International Journal of Epidemiology, vol. 39, pp. 277–284, 2010.

Z. L. Hang, P. Du, L. Wang et al., “Method for optimization allocation of regional public service infrastructure: a case study of Xinghua Street primary school,” Scientia GeographicaSinica, vol. 34, pp. 803–809, 2014.

P. Harper, A. K. Shahani, J. E. Gallagher et al., “Planning health services with explicit geographical considerations: a stochastic location?allocation approach,” Omega, vol. 33, no. 2, pp. 141–152, 2005.

R. C. Menezes and N. D. Pizzolato, “Locating public schools in fast expanding areas: application of the capacitated p-median and maximal covering location models,” Pesquisa Operacional, vol. 34, no. 2, pp. 301–317, 2014.

F. Caro, T. Shirabe, M. Guignard, and A. Weintraub, “School redistricting: embedding GIS tools with integer programming,” Journal of the Operational Research Society, vol. 55, no. 8, pp. 836–849, 2004.

A. T. Murray, “Optimising the spatial location of urban fire stations,” Fire Safety Journal, vol. 62, pp. 64–71, 2013.

Z. B. Xu, L. Zhou, T. Lan et al., “Spatial optimization of mega-city fire station distribution based on Point of Interest data: acase study within the 5th Ring Road in Beijing,” Progress in Geography, vol. 37, pp. 535–546, 2018.

Z. X. Yin, “Research on regional express delivery network service optimization based on gis technology—a case study of novice station in dalian,” Liaoning Normal University, Dalian, China, 2018, M Sc thesis.

J. Xia, “Study on the method of optimal allocation of sports facilities planning in community—a case study of tongling,” Anhui University of Architecture, Hefei, China, 2014, M Sc thesis.

X. H. Yang, Siting facilities in continuous space to maximize spatial demands based on GIS, Tsinghua University, Beijing, China, 2013.

C. S. R. RLChurch, “Theoretical and computational links between the median, location setcovering, and the maximal covering location problem,” Geographical Analysis, vol. 8, no. 4, pp. 406–415, 1976.

Q. L. Reng, The layout and optimization of logistics nodes and channel in Dalian, Liaoning Normal University, Dalian, China, 2017, Ph.D. thesis.

W. Y. Yang, Higher economic geography, Peking University Press, Beijing, China, 1997.

Q. Niu, Urban planning GIS technology application guide, China Building Industry Press, Beijing, China, 2012.

Y. Tian, J. Luo, J. W. Sun et al., “Urban spatial linkages and the hub-spoke network structure in the Wuhan metropolitan area,” Progress in Geography, vol. 38, no. 7, pp. 1093–1102, 2019.

N. X. Mou et al., ArcGIS10 geographic information system tutorial: From beginner to proficient, Surveying and Mapping Press, Beijing, China, 2012.

J. Chen, R. L. Zhao, and C. F. Qiao, “Research on GIS spatial analysis based on voronoi diagram,” Journal of Wuhan University (Information Science Edition), vol. 28, no. 1, pp. 32–37, 2003.