Research on City Inbound Tourism Network System Characteristics of Henan Province Based on Space of Flows Jun-yuan Zhao

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1 2017 2nd International Conference on Humanities Science, Management and Education Technology (HSMET 2017) ISBN: Research on City Inbound Tourism Network System Characteristics of Henan Province Based on Space of Flows Jun-yuan Zhao (Xin Yang university school of business, Xin Yang, He Nan , China) Keywords: inbound tourism; network system characteristics; space of flows; Henan Province Abstract: This paper in the perspective of tourist flow, using spatial distribution of high grade scenic spot as the basic data, analysis inbound tourism network s structure and node s hierarchical system of key tourist cities in Henan with Arc GIS. It shows that: Firstly, due to the influence of inbound tourism flow which strength can be described as high, mid, low and network s structure of the inbound tourism flow in 9 key tourist cities have the trend to evolve diamond jumps T-shaped straight triangle table grid. Initiated by some central spots and then, centered on these spots to external radiation and later spread to all regions. The strength of inbound tourist flow forms a One Belt and One Spot pattern in space: The Zheng-Bian-Luo tourism belt as the core, periphery formed only one pole Nanyang. The distribution of the intensity of touristy of tourist flow is right skewed distribution and most tourist flow gather around mode with far less than the average number. Therefore, the tourist flow s link strength are partial weak as a whole in Henan province. Secondly, the inbound tourism network node of key tourist cities shows significant gradability. The node of key inbound tourist cities has 4 levels which as the center, sub-center, local tourist network associate center and general node of regional inbound tourist network association respectively. Those 4 hierarchical spots show the uniform distribution and each cities are basically same in number. 1. Introduction With the rapid development of information technology and the continuous improvement of the modern transportation system, the inherent boundary between regional cities has broken gradually. Thus, an open and intricate network system of the region is formed. The theory focuses on the role and value of the city in network modeling, and finds new points and breakthrough points for urban development in the background of information technology. The theory has been widely used since it was proposed. Ga WC provides an empirical study based on the layout of high-end production services. Since then, the scholars have studied the urban network with some entity flow such as traffic flow, information flow and logistics. Tourist flow belongs to the spatial category of flow and the core of it is flow. There is a certain mismatch between the increasing demand of tourism and the distribution of tourist resources in space and the travel flow is the fundamental method to solve this incongruity. Because the immovability of tourism resources, the movement of visitors in space manifests a movement of human beings and the logistics, capital flows, information flows, cultural flows and energy flows that are generated by it: At the same time, the system of origin system, destination system and the interacted O-D system of the tourist flow system are formed. As the support~~and carrier of O-D network, the cities function is carry out macro-control of the elements in the travel flow, such as mutual location, interaction, concentration and relative balance between regions, etc. In this way, we make travel flow from disorder to order, from inefficiency to efficiency. Eventually, the travel network will be deployed to the best state. For inbound tourism flow research, both at home and abroad mainly gather in several major aspects: the distribution of time of inbound tourism flow characteristics, space-time evolution, spatial structure, scale forecast, influence factors and dynamic mechanism. From the research scope, including the large scale of national, mesoscale of the pearl river delta, Yangtze river delta region and northeast region, and the study of small scale is les, the study of Henan tourism flow almost none. 262

2 The tourism resources of Henan are strong and its type and quality have certain status in the whole country. The number of 5A grade scenic spot is third in China, only second to the Jiangsu and Zhejiang. But the number of inbound tourists and the tourism foreign exchange income are located in about 20 th, which shapes an obvious mismatch with the tourist resources. As an important hub contact in eastern and western region, and as a large part of the central plains city cluster, Henan plays an important role in the construction of the One Belt and One Road economic belt. Its development creates new opportunities for economic and tourism cooperation and tourism integration in the central region. Based on the above analysis, this paper in the perspective of tourist flow, studies the spatial structure of inbound tourism flow of key cities in Henan in order to provide a unique perspective for the research of tourism spatial structure and provide a relevant reference for regional planning and development, tourism multilateral cooperation and all-domain tourism. 2. The construction of inbound tourism network in key cities 2.1 Acquisition the basic data of network The basic unit of this research including 9 cities, they are Zhengzhou, Luoyang, Shangqiu, Anyang, Jiaozuo, Pingdingshan, Nanyang and Zhumadian. Because the main inbound tourists are mainly composed of high grade scenic spot, so we choose three world heritage sites and 12 5A grade scenic spot are located in Henan province as key tourist cities. Besides, the prefecture also was divided according to the original city. Duo to the effect of the spatial behavior rules of large scale tourists, inbound tourists generally choose to travel in high grade tourist destinations. So we choose the national 5A, 4A tourist area of those 9 key cities to conduct relevant research. By the end of 2016, there are more than 57 4A grade scenic spots. The data comes from the 2016 Tourism Statistics Yearbook of China and the official website of the local tourism bureau; The GDP, the third industrial value and the population data come from the Tourism Statistics Yearbook of Henan Province. The statistics of the prefecture are incorporated into the local cities in order to keep statistics consistent over time series. Basic data construction of urban tourism network uses parametric substitution method and we have counted the distribution of 57tourist scenic spots in 9 key cities. We considered that 5A grade scenic spot and 4A grade scenic spot are different in inbound tourism attraction, according to level of traveling scenic spots, endowed different scores: decided to give 5A grade tourist scenic spot 5 points, 4A grade 3 points. Then we got the final score of the distribution of scenic spots of key tourist cities. As show in table 1, the final score can be described as a value of the service energy of a tourist attraction in a particular. The value of service energy is an important reflection of the importance of city points in the regional travel network. But there is no way to say whether there is a link between the city points and the degree of its connections. So the correlation matrix model is built between cities. Ranking Table 1. The distribution and service energy of scenic spots of key cities in Henan province. City Name The number of 5A scenic spot The number of 4A scenic spot The value of the service energy 1 Nanyang Zhengzhou Luoyang Kaifeng Jiaozuo Anyang Shangqiu Pingdingshan Zhumadian Total

3 2.2 The correlation matrix of urban tourist network Network connections between two different cities are based on the overlay each other of the association between the two cities in the same level of tourism. Let s say that the value of J in the tourist scenic area of the tourist cities I is the important of the tourist area in the whole network, described as P ij. The network connection values of tourist attraction j between city m and city n can be represented as: C mn j =P mj P nj ; While the total network connection values between this two cities can be expressed as: Cmn C j mnj. In the end, the correlation matrix of 9 key cities and 97 tourist scenic spots in Henan province were constructed. The network connection values of each city and all other cities can be expressed as: Tm C i i. The total connection value of the inbound tourist flow network in Henan province can be expressed as: L T i i. The network connection degree of each city can be described as: N=Tm/L. The relative connectivity of individual cities is based on the city which network connectivity is the highest: Rm=Tm/Th. Through the above formula, the network connection value, connection degree, relative connectivity degree of the cities in Henan province are calculated, and the results are shown in table 2. Table 2. The network connection value, connection degree, relative connectivity degree of the cities in Henan province. The relative Rankin The network connection The network City Name connection g value connection degree degree 1 Nanyang Zhengzhou Luoyang Kaifeng Jiaozuo Anyang Shangqiu Zhumadian Pingdingshan Combined with the law at the same level and the calculation formula of tourist cities in Henan, the results and strength are shown in table 3. Table 3. The inbound tourist flow of high, middle, low strength cities. Intensity Number Grade Value of The major flow of tourism and value of Number flow intensity Nanyang Zhengzhou(1285);Nanyang Ⅰ Luoyang(730);Nanyang Kaifeng(655); Zhengzhou Luoyang(550);Nanyang 0 Jiaozuo(529);Zhengzhou Kaifeng(475); Nanyang Anyang(403) High tensile Middle tensile Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Zhengzhou Anyang(295);Luoyang Kaifeng(325);Nanyang Shangqiu(378);Z hengzhou Jiaozuo(385) Zhengzhou Shangqiu(270); 洛 Luoyang Jiaozuo(280) Luoyang Anyang(235);Kaifeng Jiao zuo(205) Kaifeng Anyang(160);Nanyang Zhu madian(151);nanyang Pingdingshan(151 );Luoyang Zhumadian(145);Luoyang Pingdingshan(145) Luoyang Shangqiu(135);Kaifeng Sh angqiu(135);jiaozuo Anyang(133);Zhen gzhou Zhumadian(115);Zhengzhou Pingdingshan(115);Jiaozuo Shangqiu(10 8) 264

4 Low tensile 10 Ⅶ Anyang Shangqiu(81) Ⅷ Kaifeng Zhumadian(70);Kaifeng Pin gdingshan(70);jiaozuo Zhumadian(63);Ji aozuo Pingdingdhan(63);Anyang Zh umadian(52);anyang Pingdingshan(52) Ⅸ Pingdingshan Zhumadian(34);Shangqiu Zhumadian(27); Shangqiu Pingdingshan(27) 3. The analysis of the spatial structure of the tourist flow network of key tourist cities in Henan 3.1 The inbound tourist network rating of key tourist cities. Combined with Taylor PJ s network approach to urban research, the value of the tourist flow strength of this research will be expressed by the value of urban network connections is divided into three levels: high strength, middle strength and low strength. And then, continue refining within different levels and we are able to obtain a special map of inbound tourist flow network of key cities in Henan province based on high, middle and low strength tourist flows. The result is shown in figure 1. a. High strength inbound tourist network b. Middle strength inbound c. Low strength inbound tourist network tourist network Figure 1. High, middle and low strength inbound tourist network of Henan cities. In the chart of the inbound tourist flow network of the high strength key cities in figure 1a, the value of connections in different cities is biggest and there has a clear network structure which forms the overall skeleton of the network spatial structure of inbound tourist flow. The Ⅰ level s average is more 400 and is shaped a diamond network by 7 group cities which network connection value is the highest: Nanyang Zhengzhou, Nanyang Luoyang, Nanyang Kaifeng, Zhengzhou Luoyang, Nanyang Jiaozuo, Zhengzhou Kaifeng, Nanyang Anayang. Nanyang is at the top of the diamond structure, while Zhengzhou is an important node at the bottom of the diamond structure and forming a network surface with other cities. In Ⅱ and Ⅲ levels, we made Zhengzhou, Nanyang, Luoyang and Kaifeng as the center, the radiation was made toward the three cities of Jiaozuo, Shangqiu and Anyang in order to establish a relative network. But most of these cities are linear, and they have a strong jump: Zhengzhou and Luoyang are two important jumps; although there has formed 13 pairs tourist flow in the inbound tourist network structure of middle strength key cities in figure 1b, and the amount is as much as the high intensity. It is a significant change in the spatial patter. There are two pairs tourist flow in Ⅳ level and show a T on the distribution. Jiaozuo as the central point of T and then radiate to Luoyang, Anyang and Kaifeng. This is presented in general. There are 5 pairs tourist flow in Ⅴ level which presents 2 opposing triangles in space and middle tourist flow forms a broken flow, which is not fully access at this level. The Ⅵ level has the largest number of moderate strength inbound tourist flow, reaching 6 pairs and they have formed 2 radial centers Zhengzhou and Shangqiu. Through the structure of the inbound tourist network of low strength key cities in figure 1c, we can 265

5 see that the differentiation of three levels are obvious: there is only 1 pair tourist flow in Ⅶ level, and a streamline is formed between them. While Ⅷ has the largest number of tourist flow 6 pairs, which also is the largest number of low strength inbound tourist flow. At the same time, this level presents a complex grid in space. There are 3 pairs tourist flow in the last level which is distributed in a triangle, Shanghai, Pingdingshan and Zhumadian are just the three peak of this triangle Frequency Other Intensity 0 Figure 2. The histogram of intensities about the connection of Inbound tourism in Henan key tourism cities. In order to express the general distribution characteristics of high, mid and low level inbound tourist flow strength of key tourist cities more clear,we built the histogram of inbound tourism flow s connection strength between different key tourist cities. As shown in figure 2. The horizontal axis is the group condition of the contact strength of inbound tourist flow while the vertical axis is the number or frequency of inbound tourist flow indifferent groups. Using excel graph software and analysis, we can find that the partial state coefficient of histogram is , which is greater than 1. Therefore, it can be defined as a highly skewed distribution and is trend to the right. It shows that more strength of tourist flow is located on the left side of the symmetric axis of the normal distribution. The average number of inbound tourist flow s contact strength between key tourist cities is , below the average is 65.36% and most inbound tourist flow s contact strength are distributed around 108, which shows that the contact strength of inbound tourist flow of key tourist cities in Henan province is at a low level in general. Relative connectity NanyangZhengzhouLuoyang Kaifeng Jiaozuo Anyang ShangqiuZhumadian Pingdingshan City Figure 3. The scatter diagram of relative connectivity about flow of Inbound tourism in Henan key tourism cities. 266

6 3.2 The gradability of inbound tourist network nodes of key tourist cities We further explore the node s hierarchical system of key tourist cities on the basis of the overall structure analysis of key tourist cities inbound tourist network. Based on the relative connection degree of key cities network, we set up the map of key cities inbound tourist network, as shown in figure 3: the relative connectivity of inbound tourist network of that 9 key cities nodes is arranged in a downward trend line, the nodes are scattered across the nodes and the gradient is consistent. At the divergence of scatter curve, the nodes of key cities can be divided into several different groups. Therefore, we adopt the level fracture method of Arc GIS software to divided the relative connection degree of inbound tourist flow network into 4 grades and we got a hierarchy system s distribution pattern of inbound tourist flow network of different cities nodes, as shown in figure 4. Figure 4. The spatial pattern of relative connectivity about flow of Inbound tourism in Henan key tourism cities. The relative connectivity of inbound tourist flow network of Nanyang and Zhengzhou is much higher than other cities and regions. Nanyang as the city which owns the most populous and the largest area, including 1 5A grade scenic spot and 14 4 A grade scenic spot within its jurisdiction. So its relative connectivity of inbound tourist flow network is the highest in the calculation. Zhengzhou at the same level has 1 5A grade scenic spot and 14 4A grade scenic spots. Meanwhile, it is the center for tourism, destination and the collection and distribution area of inbound tourist, as well as the land of the portal when visitors enter Henan province. The advantage of various resources make Zhengzhou become the hub city of tourist network connection in the area of Henan. Luoyang and Kaifeng belong to the second ranking of the relative connectivity of inbound tourist network in Henan province, between and Both of them are historical and cultural cities, ancient capitals and two endpoints in the tourist belt of Zheng-Bian-Luo. Their tourist attractions and relative abilities are lower than the core cities. So they are secondary centers of inbound tourism; Anyang, Jiaozuo and Shangqiu are in the third grade. The relative connectivity of the inbound tourist network is between 0.11 and Among them, there are 2 historical and cultural cities, another one is the first batch of 5A grade scenic spot as well as the travel card of Henan 267

7 province the location of mount Yuntai. However, because of the lower level, they can only be the center of the of inbound tourist network. The number of high grade, especially the 4A grade scenic spot, is limited in Pingdingshan and Zhumadian. What s more, the non-traffic centrality in geographical space determines their relative connectivity of inbound tourist flow network is lower. So this two cities are in the last grade of key tourist cities and become the common center of inbound tourist network connection in Henan province. The inbound tourist network nodes of key tourist cities basic present a condition of the equilibrium distribution, the number of nodes in each grade is basically 2 and there is a significant difference when compared with some provinces which concentration are more obvious. 4. Conclusion and Discussion The paper in the perspective of tourist flow, carried out a systematic study for inbound tourism network s spatial structure and node s hierarchical system of key tourist cities in Henan. Based on the traditional geographical and grade distribution, the inbound tourist network s spatial structure has formed a new pattern with the development context of inbound tourism. 4.1 Conclusion The results of this research prove that: Due to the influence of inbound tourism flow which strength can be described as high, mid, low and network s structure of the inbound tourism flow in 9 key tourist cities have the trend to evolve diamond jumps T-shaped straight triangle table grid. Initiated by some central spots and then, centered on these spots to external radiation and later spread to all regions. The strength of inbound tourist flow forms a One Belt and One Spot pattern in space: The Zheng-Bian-Luo tourism belt as the core, periphery formed only one pole Nanyang. The distribution of the intensity of touristy of tourist flow is right skewed distribution and most tourist flow gather around mode with far less than the average number. Therefore, the tourist flow s link strength are partial weak as a whole in Henan province. Secondly, the inbound tourism network node of key tourist cities shows significant gradability. The node of key inbound tourist cities has 4 levels which as the center, sub-center, local tourist network associate center and general node of regional inbound tourist network association respectively. Those 4 hierarchical spots show the uniform distribution and each cities are basically same in number. 4.2 Discussion Space of Flows provides a new thinking and new perspective for the research of urban tourist network. However, we only used the grade of scenic spots rather than much consideration for centrality, traffic factor, the popularity of city and the factors that have significant influence on large scale visitors to evaluate the service value, so there may be a certainly difference between the results and reality. In the calculation, the network connection degree of Nanyang is more than that of ZhengZhou to some extent, which also reflects the problem. Besides, due to the obvious homogenicity of tourist flow in space, but there is no way to rule out the possibility of tourist flow between different grade scenic spots in reality. Therefore, the result of calculation will be a weakening effect on the cities which levels are obviously different with each other. And then, the results will be small. Acknowledgement Supported by Key Scientific Research Project Of Colleges and Universities of Henan province (Project Number: 18A630048). This Work is supported by General Project of the Humanities and Social Science Department of Education Department of Henan province (Project Number: 2017-zzjh-493). 268

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