Resources Science ›› 2019, Vol. 41 ›› Issue (12): 2248-2261.doi: 10.18402/resci.2019.12.09

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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


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