资源科学 ›› 2019, Vol. 41 ›› Issue (12): 2248-2261.doi: 10.18402/resci.2019.12.09

• 旅游资源 • 上一篇    下一篇

基于BP神经网络的旅游经济系统脆弱性省际空间分异

马慧强1,2, 廉倩文1, 论宇超3, 席建超2()   

  1. 1. 山西财经大学文化旅游学院,太原 030000
    2. 中国科学院地理科学与资源研究所/陆地表层格局与模拟重点实验室,北京 100101
    3. 辽宁师范大学地理科学学院,大连 041000
  • 收稿日期:2019-03-25 修回日期:2019-09-21 出版日期:2019-12-25 发布日期:2019-12-25
  • 通讯作者: 席建超
  • 作者简介:马慧强,男,山西大同人,博士,副教授,研究方向为经济地理,旅游开发与规划。E-mail: Mahuiqiang001@126.com
  • 基金资助:
    国家自然科学基金项目(41671151);山西省教育厅项目(2019W074);山西省体育局项目(18TY136)

Spatial differentiation of tourism economic system vulnerability based on BP neural network in different provinces of China

MA Huiqiang1,2, LIAN Qianwen1, LUN Yuchao3, XI Jianchao2()   

  1. 1. College of Culture and Tourism, Shanxi University of Finance and Economics, Taiyuan 030000, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS /Key Laboratory of Land Surface Pattern and Simulation, Beijing 100101, China
    3. College of Geographic Sciences, Liaoning Normal University, Dalian 041000, China
  • Received:2019-03-25 Revised:2019-09-21 Online:2019-12-25 Published:2019-12-25
  • Contact: XI Jianchao

摘要:

开展旅游经济系统脆弱性评价与影响因素研究,是制定旅游经济科学发展策略,提高区域旅游发展质量的客观要求。本文在探讨旅游经济系统脆弱性内涵的基础上,从旅游经济系统敏感性和应对能力两个方面来构建旅游经济系统脆弱性评价指标体系,并运用BP人工神经网络模型、脆弱性评价指数模型、地理探测器等研究方法,对中国30个省级行政单元(不包括西藏和港澳台地区)的旅游经济系统脆弱性空间分异特征及影响因素进行分析。结果表明:①中国旅游经济系统整体处于较高脆弱、中等敏感、较高应对能力状态,且旅游经济系统脆弱性、敏感性和应对能力省际空间分异明显;②中国旅游经济系统脆弱性、敏感性和应对能力均呈现“集群化”和“极差化”分布特征,东部地区旅游经济系统脆弱性较高,且内部差异显著,中部和西部地区脆弱性较低,内部差异较不明显;③中国旅游经济系统脆弱性空间分异是不同影响因素共同作用的结果,其中产业结构多样化指数对脆弱性空间分异的影响最大,旅游外汇收入占旅游总收入比重对脆弱性空间分异的影响最小。研究结果可丰富旅游可持续发展理论,并为解决旅游经济质量提升问题等提供科学依据。

关键词: 旅游经济系统, 脆弱性, BP神经网络, 地理探测器, 空间分异

Abstract:

Vulnerability evaluation of tourism economic systems and influencing factor identification are necessary for improving the quality of regional tourism development and formulating a scientific strategy for such development. This study constructed an index system of vulnerability assessment of tourism economic system from three aspects of vulnerability, sensitivity, and coping ability of the system. By using the back propagation (BP) artificial neural network model, vulnerability evaluation index, geographic detector, and other research methods, the spatial differentiation characteristics and influencing factors of tourism economic system vulnerability of 30 provincial-level administrative units in China (excluding Tibet, Hong Kong, Macao, and Taiwan) were evaluated and analyzed. The results show that: (1) The tourism economic system of China is in a state of high vulnerability, medium sensitivity, and high coping capacity as a whole, and the vulnerability, sensitivity, and coping ability of China’s tourism economic system are clearly different among the examined provinces; (2) The vulnerability, sensitivity, and coping capacity of China’s tourism economic system present the characteristics of clustering and polarized distribution. The vulnerability of the tourism economic system in the eastern region is relatively high, with significant internal differences, while the vulnerability of the central and western regions is relatively low, with less obvious internal differences; (3) The spatial differentiation of vulnerability in China’s tourism economic system is the result of the joint action of different influencing factors, among which the diversification of industrial structure has the greatest influence on the spatial differentiation of vulnerability, and the proportion of tourism foreign exchange income in total tourism income has the least influence on the spatial differentiation of vulnerability. The results of this study provide some scientific basis for enriching the theory of sustainable tourism development and solving the problems of improving the quality of tourism economy.

Key words: tourism economic system, vulnerability, BP neural network, geographical detector, spatial differentiation