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Modeling and Analyzing the Dynamic Factors of Economic Growth Evolution in Coastal Tourism Cities

Author(s): Zhenli Jia

Source: Journal of Coastal Research , SUMMER 2020, SPECIAL ISSUE NO. 103. Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering (SUMMER 2020), pp. 1079-1083

Published by: Coastal Education & Research Foundation, Inc.

Stable URL: https://www.jstor.org/stable/10.2307/48639917

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Modeling and Analyzing the Dynamic Factors of Economic Growth Evolution in Coastal Tourism Cities Zhenli Jia*

School of International Culture and Study Yuxi Normal University Yuxi 653100, China

ABSTRACT

Jia, Z., 2020. Modeling and analyzing the dynamic factors of economic growth evolution in coastal tourism cities. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 1079–1083. Coconut Creek (Florida), ISSN 0749-0208.

As an important part of tourism economy, inbound tourism economy has an important impact on the development of regional economy. In order to improve the economic growth rate of coastal tourism cities, a modeling analysis method of dynamic factors of economic growth evolution of coastal tourism cities is proposed. On the basis of the regional economic theory, combined with the spatial analysis tools such as Geo Da and Arc GIS, through the analysis of the temporal evolution and spatial pattern evolution of inbound tourism foreign exchange income in coastal city clusters, furthermore, this paper uses the geographical weighted model (GWR) to analyze the factors that affect the foreign exchange income of inbound tourism of coastal city groups, and obtains the influencing coefficient. In view of the current situation and influencing factors of foreign exchange income of inbound tourism in coastal city groups, the corresponding countermeasures are put forward for the balanced and sustainable development of inbound tourism in coastal city groups. The comparative experimental results show that the proposed method of dynamic factor modeling and analysis for economic growth evolution of coastal tourism cities is more efficient and effective than the traditional method of dynamic factor modeling and analysis for economic growth evolution of coastal tourism cities.

ADDITIONAL INDEX WORDS: Coastal tourism cities, urban economic growth, evolution of economic growth, dynamic factor modeling.

Journal of Coastal Research SI 103 1079–1083 Coconut Creek, Florida 2020

DOI: 10.2112/SI103-225.1 received 20 August 2019; accepted in revision 17 January 2020. *Corresponding author: [email protected] ©Coastal Education and Research Foundation, Inc. 2020

INTRODUCTION In recent years, driven by the national policy of stimulating

domestic demand and increasing investment, China’s coastal urban agglomerations, as the country’s exporters, have made rapid development of their national economy with the help of the spring breeze of reform and opening up. By virtue of its superior geographical location, convenient transportation location, rich natural and cultural tourism resources, developed economic development level provides superior economic support for coastal tourism development. Under the favorable policy environment of the national strategy of vigorously developing the ocean, the coastal tourism industry has maintained steady and rapid development in general, and the income of inbound tourism has developed rapidly (Chen, 2018).

This paper designs a dynamic factor modeling analysis of the economic growth and evolution of new coastal tourism cities. Starting from the current situation of the inbound tourism development of coastal cities, it uses the quantitative analysis method to analyze the dynamic factors of the economic growth and evolution of coastal tourism cities, finds out the existing

problems through the analysis of the current situation, and then studies the influencing factors, in order to realize the sharing of tourism resources and tourism information in coastal areas, realize the brand effect of urban tourism, better enrich the types of tourism products, and promote the steady economic growth of coastal tourism cities (Ma and Liu, 2019).

MATERIALS AND METHODS Data Analysis of Economic Growth Conditions of Coastal Tourist Cities

The level of regional economic development is an important basis for the development of regional tourism industry, which determines the development of local tourism economy to a certain extent and affects the spatial pattern of regional tourism economy. The high level of regional economic development can provide certain financial and technical support for the development of regional tourism economy, promote the improvement of tourism infrastructure, and then affect the attraction of the region to tourists (Cheng, Xu, and Guo, 2019). Since the Reform and Opening up, the coastal areas as the mouth of foreign economic development, with its superior location conditions, perfect transportation network, suitable climate, the government in policy preferences and support, so that the coastal areas have a rapid economic development. Coastal city clusters have perfect infrastructure, relatively abundant capital flow, convenient conditions to facilitate the development of foreign trade and maritime transport, rapid

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economic development has also brought good economies of scale (Wen, Wu, and Gong, 2019; Xie et al., 2019).

Regional accessibility plays an important role in improving the economic level of inbound tourism in a region. Regional traffic conditions will directly affect the number and frequency of inbound tourists, and further affect the development of regional tourism economy. It is found that the level of tourism economic development is positively related to the convenience of traffic location conditions. The coastal areas are relatively flat and economically developed. The roads, railways, waterways and air traffic networks are perfect. The perfect traffic network can accelerate the connection between the coastal areas and between the coastal areas and the mainland (Wang et al., 2017). The improvement of traffic conditions and the improvement of traffic network promote the improvement of regional economy. Efficient and convenient traffic conditions can improve the traffic conditions of external regions relative to other regions to a certain extent, and enhance the accessibility and attraction of tourist destinations in interrelated or unrelated regions. The increase in the number of tourists will correspondingly expand the market model of tourists, and then make relevant departments take measures to optimize the market structure of tourists. With the gradual improvement of high-speed rail and EMU transportation network, the transportation in coastal areas is more convenient and efficient. The improvement of intercity high-speed rail shortens the distance between cities, and the resources between regions can be replaced with each other, which shortens the travel time of tourists, increases their stay time in the destination, and thus increases their consumption in the tourist destination. To a certain extent, the tourism carrying capacity of a region affects the traffic location conditions of the region. If the tourism carrying capacity of a region is small, the increase of its tourist turnover may damage the local environment. Therefore, the carrying capacity of tourism environment should be considered while improving the regional traffic network (Dong, Sun, and Li, 2018).

Tourism infrastructure is the basic guarantee for the development of regional tourism economy, which can provide strong support for the development of regional tourism economy. The perfect tourism infrastructure itself can also be used as a tourist attraction to attract tourists. The continuous improvement of tourism infrastructure also plays an important role in enhancing tourists’ tourism perception and improving tourism quality. The coastal economy is relatively developed, and the tourism infrastructure construction is relatively perfect (Wang et al., 2020). Star hotels have complete supporting service facilities, complete entertainment facilities, high quality service personnel, high level of service, and strong professionalism, so that inbound tourists can experience intimate services in the process of tourism, as well as various leisure and entertainment facilities to relieve the pressure. Multilingual tour guides can better provide good services for inbound tourists, and the overall business level of travel agencies is constantly improving, so that inbound tourists can get better tourism experience in China. The continuous improvement of tourism infrastructure has provided strong support for the development of inbound tourism in coastal city clusters (Wang and Chen, 2019).

Absolute difference is a single index quantization method. It can only show the difference in quantity of indicators, and can’t take into account the influence of other indicator factors. It has poor comparability for different regions and different times.

Relative difference is also a single index difference measure. The data of relative difference is a ratio, which is not affected by time factor, space factor, economic factor and other factors, so it is comparable. However, when the selected index is larger and the dimension is larger, the result obtained by the relative difference is relatively small, but the internal difference may be larger (Xiong et al., 2019). Therefore, in the case of measuring relative difference and absolute difference, comprehensive consideration should be carried out, and a more appropriate method should be selected. Considering both absolute difference and relative difference, the error of results can be relatively reduced Equation (1):

( )

,i i x x

S x x NN v S x

− = =

=

(1)

In the equation, S is the standard deviation, ix is the comprehensive index of the economic development of inbound tourism in cities. It is the average value of the city group of the comprehensive index of tourism economic development level; n is the number of regional samples, V is the coefficient of variation.

The data motion guidance diagram is set accordingly in Figure 1:

It further explains the variation trend of the overall development speed of inbound tourism in coastal areas, and introduces the relative development rate here. The relative development rate index is the ratio of the change of tourism income in one period to

Figure 2. Internal economic data processing diagram.

Figure 1. Data motion guidance diagram.

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Modeling and Analyzing the Dynamic Factors of Economic Growth Evolution in Coastal Tourism Cities 1081

Journal of Coastal Research, Special Issue No. 103, 2020

the change of tourism income in another period. The equation is as follows Equation (2):

2 1 2 1

i iY YNICH Y Y

− =

− (2)

In this equation, 1iY and 2iY are used to represent the inbound tourism revenue at the end of the year and the beginning of the year of the i city, 2Y , 1Y represents the inbound tourism revenue of the whole region at the end of the study period and the beginning of the study period. When the value of NICH is greater than 1, it indicates that the economic development speed of inbound tourism in a city is greater than that of the whole region. And set the internal economic data processing chart in Figure 2:

Dynamic Factor Modeling of Economic Growth Evolution of Coastal Tourism Cities

Exploratory spatial data analysis (ESDA) is a spatial analysis method, and spatial measurement is its core. Through the research and analysis of the spatial distribution of economic phenomena, it is concluded that the global statistics and the local statistics of their spatial interaction are two different aspects of ESDA. The focus of global statistics research is to analyze the spatial layout of specific characteristics of a certain index in an area. Local statistics is to compare and study the data contained in a small area in the global area, so as to study whether the regional information changes are homogeneous or heterogeneous (Tian et al., 2020). The focus of global statistical research is to analyze the spatial distribution of a specific index in a region. Local statistics is to study whether there is homogeneity or heterogeneity in regional information change by comparing the data contained in a small region in the global. The local Moran’s I value is positive, which means that regions

with higher economic level are surrounded by regions with higher economic level Surrounding or low-level areas are surrounded by surrounding low-level areas. In LISA’s agglomeration diagram, HH (high agglomeration) indicates that the value of this city and its surrounding cities is relatively high, indicating high value agglomeration; LH (low wealth cluster), which means that the value of the city is relatively low but the observed value of the surrounding area is relatively rich; LL (low concentration) means that the observed values of cities and surrounding cities are relatively low, and represents low concentration. HL (from Low Agglomeration) means that the observed value of the city is high and that of the surrounding city is low. LH and HL are spatial outliers without obvious clustering phenomenon, so they are called atypical regions. The significance level is judged by testing the Z value of the normal statistics of the index. And set the data filtering diagram as follows:

Moran scatter diagram is a method of local spatial autocorrelation analysis, which can study the local spatial heterogeneity. It is represented by Cartesian rectangular coordinate system. Abscissa represents the research area. The research index is the value after standardization. The ordinate is the average value of the attribute value of the adjacent element determined by the spatial adjacency matrix after standardization. M the oran scatter diagram is divided into four quadrants: high-high (the first quadrant), low high (the second quadrant), low-low-low (the third quadrant), quotient low (the fourth quadrant). Compared with local Moran’s I statistics, Moran scatter-plot can further distinguish which spatial correlation mode a region belongs to and its adjacent regions. The results of exploratory spatial data analysis largely depend on the determination of spatial weight matrix. The commonly used spatial weight matrix mainly includes: The spatial weight matrix based on proximity concept, k-value nearest neighbor matrix, distance based spatial weight matrix and economic and social spatial weight matrix, in this paper, through the comparative analysis of several methods of establishing weight matrix, considering the objectivity, reliability and rationality of the analysis results, the

Figure 3. Data filtering diagram. Figure 4. Data filtering diagram.

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Journal of Coastal Research, Special Issue No. 103, 2020

k-value nearest neighbor matrix is finally selected. And build the data filtering diagram

RESULTS AND DISCUSSION According to the basis, characteristics and trend of China’s

inbound tourism development, this paper selects the tourism foreign exchange income index reflecting the level of inbound tourism development as the explanatory variable. The main factors influencing the development level of inbound tourism, such as regional economic development level (RIGDP), tourism traffic conditions (JT), tourism infrastructure (JD), tourism resource endowment (JQ), are selected as explanatory variables. Among them, the level of regional economic development (GDP) is used to measure the economic basis of the development of inbound tourism in a region. The higher the level of economic development in a region, the development of other industries will be driven. Therefore, the development of regional economy plays a leading role in the development of inbound tourism. The higher the level of regional economic development, the higher the per capita disposable income in the region, and then make the residents here use the per capita national economic income (RIGDP) to characterize; Tourism traffic condition (JT) is used to measure the impact of the accessibility of a region on the tourism attraction of a region. The more convenient the transportation of a region is, the more tourism development of the region will be driven to a certain extent, and more inbound tourists will be attracted. Here, traffic flow is used to represent the convenience, accessibility and accessibility of tourism traffic; Tourism infrastructure factor (JD), the perfection degree of tourism infrastructure in a region plays an important role in the development of inbound tourism. The perfection degree of tourism infrastructure will promote or restrict the development of inbound tourism in a region. Here, the number of star hotels is used to represent the perfection degree of tourism infrastructure; Tourism resource endowment factor (JQ), tourism resource endowment is an important factor affecting the development of inbound tourism in a region. The uniqueness of tourism resources can attract more inbound tourists, thus creating more inbound tourism income. The development degree of scenic spots also affects the consumption of inbound tourism, so we should pay attention to the appropriate development and uniqueness of scenic spots in the development process. Here, the number of scenic spots is used to represent the tourism resource endowment of a region. And set up the analysis

efficiency comparison diagram of this modeling analysis method and traditional modeling analysis method as follows:

Compared with the above figure, under the same parameter conditions, the analysis efficiency of the modeling and analysis method in this paper is higher and always on top of the traditional method, which can better serve the experimental research and promote the development of the modeling system.

The development level of tourism in urban agglomerations depends on the economic development of the region, which has an important impact on the improvement of the level of urban tourism and its spatial layout. The high level of regional economic development can provide certain financial and technical support for the development of regional tourism economy, promote the improvement of tourism infrastructure, and then affect the attraction of the region to tourists. Its influence on the development of inbound tourism economy is indirect to some extent and its direct influence is small.

CONCLUSION Based on the theory of regional economy, combined with the

spatial analysis tools such as Geo Da and Arc GIS, this paper analyzes the time evolution and spatial pattern evolution of the inbound tourism foreign exchange income of coastal city groups, and further draws the following conclusions: according to the development situation and influencing factors of the inbound tourism foreign exchange income of coastal city groups, the paper uses the geographical weighted model (GWR) to influence the inbound tourism foreign exchange income of coastal city groups. Based on the analysis of the factors of foreign exchange income, the influencing coefficient is obtained, and the Countermeasures for the balanced and sustainable development of inbound tourism of coastal city groups are put forward.

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