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RESEARCH ON THE TOURIST FLOW FEATURE OF SCENIC AREA BASED ON FRACTAL STATISTICAL MODEL — A CASE OF ZHANGJIAJIE

Economics School of Southwestern, University of Finance and Economics, Chengdu 610064, P. R. China

Research Center for Smart Tourism, Technology Application and Innovation of Sichuan Tourism University, Chengdu 610100, P. R. China

E-mail Address: dusiyuan@swufe.edu.cn

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ADEL A. BAHADDAD

Information System Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

E-mail Address: dbabahaddad10@kau.edu.sa

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MAOZHU JIN

Business School, Sichuan University, Chengdu 610065, P. R. China

E-mail Address: jinmaozhu@scu.edu.cn

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, and

QIAN ZHANG

Economic and Management School, Chengdu Agricultural College, Chengdu 610000, P. R. China

E-mail Address: 46265553@qq.com

Corresponding author.

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https://doi.org/10.1142/S0218348X2240103XCited by:4 (Source: Crossref)

This article is part of the issue:

Special Issue on Dynamical Systems and Their Applications to Engineering, Economy and Health Sciences
Guest Editors: Juan L. G. Guirao, Elbaz I. Abouelmagd and Bruce A. Wade

Abstract

The purpose is to apply the fractal statistical model to analyze tourist flow feature of scenic area, and promote the effective performance of scenic area. Zhangjiajie scenic area is taken as an example. First, its natural resources characteristics, ecological environment and problems in the development of scenic area are analyzed in detail; moreover, fractal theory and fractal dimension calculation method are elaborated; finally, wavelet transform is proposed to analyze the nonlinear transformation characteristics of tourist flow. The results show that the changes of the high-frequency layer $H_{1}$ $H_{1}$ , $H_{2}$ $H_{2}$ , $H_{3}$ $H_{3}$ and $H_{4}$ $H_{4}$ all have cyclical fluctuations in the change of tourist flow. The change range of tourist flow in spring and autumn is relatively large, and the tourist flow in winter is low. Inbound tourism tourist flow has a certain seasonal regularity, with obvious characteristics of low and peak seasons. Wulingyuan scenic spot and Tianmen Mountain scenic spot of Zhangjiajie have three non-scale ranges, which are related to the scale and change of tourist flow. The concentration of inbound tourist flow in the peak season still exists in both scenic spots, and it is more and more serious. Tourist flow of inbound tourists from April to May and from September to October is large, accounting for more than half of the annual tourist flow, while most of the other periods are in the off-season.

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References

1. M.-D. Sarah and E. G. E. Zuelow , A history of modern tourism, J. Contem. His. 52 (2017) 165–167. Crossref, Web of Science, Google Scholar
2. J. C. Henderson , Local and traditional or global and modern? Food and tourism in Singapore, J. Gastron. Tour. 2 (2016) 55–68. Crossref, Google Scholar
3. L. Zhou, E. Chan and H. Song , Social capital and entrepreneurial mobility in early-stage tourism development: A case from rural China, Tour. Manag. 63 (2017) 338–350. Crossref, Web of Science, Google Scholar
4. Z. X. Wang, H. H. Zheng, L. L. Pei and T. Jin , Decomposition of the factors influencing export fluctuation in China’s new energy industry based on a constant market share model, Energy Policy 109 (2017) 22–35. Crossref, Web of Science, Google Scholar
5. X. Chen and B. Lin , Assessment of eco-efficiency change considering energy and environment: A study of China’s non-ferrous metals industry, J. Clean. Prod. 277 (2020) 123388. Crossref, Web of Science, Google Scholar
6. M. Zhou, X. Liu, B. Pan, X. Yang, F. Wen and X. Xia , Effect of tourism building investments on tourist revenues in China: A spatial panel econometric analysis, Emerg. Markets Finance Trade 53 (2017) 1973–1987. Crossref, Web of Science, Google Scholar
7. X. Chi and H. Han , Emerging rural tourism in China’s current tourism industry and tourist behaviors: The case of Anji County, J. Travel Tour. Market. 38 (2021) 58–74. Crossref, Web of Science, Google Scholar
8. M. Romero Orue, D. Gaiero and A. Kirschbaum , Seasonal characteristics of the chemical composition of rainwaters from Salta city, NW Argentina, Environ. Earth Sci. 78 (2019) 1–9. Web of Science, Google Scholar
9. T. J. Liang and J. W. Ward , Hepatitis C in injection-drug users — A hidden danger of the opioid epidemic, New England J. Med. 378 (2018) 1169. Crossref, Web of Science, Google Scholar
10. X. Li, B. Ma, C. Lu, H. Yang and M. Sun , Spatial pattern and development of protected areas in the north-south transitional zone of China, Chin. Geograph. Sci. 31 (2021) 149–166. Crossref, Web of Science, Google Scholar
11. D. Sornette, A. Johansen, A. Arneodo, J. F. Muzy and H. Saleur , Complex fractal dimensions describe the hierarchical structure of diffusion-limited-aggregate clusters, Phys. Rev. Lett. 76 (2017) 251. Crossref, Web of Science, Google Scholar
12. Y. Yuan, L. Gan, K. Liu and X. Yang , Elastoplastic contact mechanics model of rough surface based on fractal theory, Chin. J. Mech. Eng. 30 (2017) 1–9. Crossref, Web of Science, Google Scholar
13. Wu. Shaofei , An user intention mining model based on fractal time series pattern, Fract.-Complex Geometry Patterns Scal. Nat. Soc. 28 (2020) Article Number 2040017. Google Scholar
14. X. Li , Space-time distribution model of visitor flow in tourism culture construction via back propagation neural network model, Personal Ubiquitous Comput. 24 (2020) 223–235. Crossref, Web of Science, Google Scholar
15. B. Zuo and S. S. Huang , A structural change and productivity perspective of tourism’s contribution to economic growth: The case of Zhangjiajie in China, J. Travel Res. 59 (2020) 465–476. Crossref, Web of Science, Google Scholar
16. M. Fernández-Martínez , A survey on fractal dimension for fractal structures, Appl. Math. Nonlinear Sci. 1 (2016) 437–472. Crossref, Google Scholar
17. V. M. Pérez-García, S. Fitzpatrick, L. A. Pérez-Romasanta, M. Pesic and P. Sánchez-Gómez , Applied mathematics and nonlinear sciences in the war on cancer, Appl. Math. Nonlinear Sci. 1 (2016) 423–436. Crossref, Google Scholar
18. S. Zhou, L. Xu and Y. Xu , A sufficient condition for the existence of a $k$ $k$ -factor excluding a given $r$ $r$ -factor, Appl. Math. Nonlinear Sci. 2 (2017) 13–20. Crossref, Google Scholar
19. F. M. Smits, C. Porcaro, C. Cottone, A. Cancelli and F. Tecchio , Electroencephalographic fractal dimension in healthy ageing and Alzheimer’s disease, Plos One 11 (2016) 1–16. Crossref, Web of Science, Google Scholar
20. A. C. Potter, T. Morimoto and A. Vishwanath , Classification of interacting topological Floquet phases in one dimension, Phys. Rev. X 6 (2017) 041001. Web of Science, Google Scholar
21. M. N. Akhtar, M. G. P. Prasad and M. A. Navascués , Box dimension of $α$ $α$ -fractal function with variable scaling factors in subintervals, Chaos Solitons Fract. 103 (2017) 440–449. Crossref, Web of Science, Google Scholar
22. M. Jacob, D. Chappell and B. F. Becker , Regulation of blood flow and volume exchange across the microcirculation, Crit. Care 20 (2016) 319. Crossref, Web of Science, Google Scholar
23. P. Pathak, A. Kalra, S. Ahmad and M. Bernardez , Wavelet-aided analysis to estimate seasonal variability and dominant periodicities in temperature, precipitation, and streamflow in the midwestern United States, Water Resour. Manag. 30 (2016) 4649–4665. Crossref, Web of Science, Google Scholar
24. Wu. Shaofei , Research on the application of spatial partial differential equation in user oriented information mining, Alex. Eng. J. 59 (2020) 2193–2199. Crossref, Web of Science, Google Scholar
25. S. Adarsh, S. Karthik, M. Shyma, G. D. Prem and N. Sruthi , Developing short term drought severity-duration-frequency curves for Kerala meteorological subdivision, India using bivariate copulas, KSCE J. Civil Eng. 22 (2018) 962–973. Crossref, Web of Science, Google Scholar
26. V. Klausner, M. O. Domingues, O. M. Jr, A. M. D. Costa, A. R. R. Papa and A. O. Gonzalez , Latitudinal and longitudinal behavior of the geomagnetic field during a disturbed period: A case study using wavelet techniques, Adv. Space Res. 58 (2016) 2148–2163. Crossref, Web of Science, Google Scholar
27. Wu. Shaofei , Internet public informatioan text data mining and intelligence influence analysis for user intent understanding, J. Intell. Fuzzy Syst. 38 (2020) 487–494. Crossref, Web of Science, Google Scholar
28. V. Joevivek, N. Chandrasekar and R. Jayangondaperumal , Evaluation of optimal wavelet filters for seismic wave analysis, Himalayan Geol. 37 (2016) 176–189. Web of Science, Google Scholar

Vol. 30, No. 02

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History

Received 5 January 2021

Accepted 7 July 2021

Published: February 14, 2022

Information

This is an Open Access article in the “Special Issue on Dynamical Systems and Their Applications to Engineering, Economy and Health Sciences”, edited by Juan L.G. Guirao (Technical University of Cartagena, Spain), Elbaz I. Abouelmagd (National Research Institute of Astronomy and Geophysics (NRIAG), Cairo, Egypt) & Bruce A. Wade (University of Louisiana at Lafayette, USA) published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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RESEARCH ON THE TOURIST FLOW FEATURE OF SCENIC AREA BASED ON FRACTAL STATISTICAL MODEL — A CASE OF ZHANGJIAJIE

Abstract

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