Spatio-temporal differentiation of tourism safety events and its driving factors in the plateau area of China
Plateau tourism risk management is of great significance to the healthy development of plateau tourism.Methods of seasonal index,spatio-temporal differentiation measure and other methods are applied to reveal the spatio-temporal differentiation of tourism safety events in plateau areas from 2010 to 2019,and identify the driving factors and the effect intensity of spatio-temporal differentiation pattern.The results show that:(1)the number of tourism safety events in plateau areas is increasing year by year,and the temporal variation of them with the seasonal characteristics of"high occurrence in summer and low occurrence in winter",and the types of those events are complex,but the frequency of accidental injury is the highest;(2)the spatial and temporal distribution of travel-related safety events in the plateau region presents the pattern of agglomeration and differentiation,and the hotspot areas of tourism safety events show an expanding trend year by year,forming multiple high-density areas in the Yunnan-Guizhou Plateau,Yunnan and Sichuan,Qinghai-Tibet Plateau,Xinjiang and Loess Plateau,Henan;(3)tourism safety events in plateau areas show significant spatial positive correlation and spatial high value clustering phenomenon in general,and obvious spatial polarization phenomenon exists in the Yunnan-Guizhou Plateau,Qinghai-Tibet Plateau and Loess Plateau locally,and the clustering scale decreases and the clustering intensity increases;(4)the coupling effect of personnel,facility,environment and management factors leads to the spatio-temporal differentiation of tourism safety events in plateau areas,among which,the number of tourism practitioners,medical security facilities,average gradient,natural ecological environment and tourism resource endowment are the dominant factors,and the two-by-two interaction of those factors will enhance the spatio-temporal differentiation of tourism safety events in plateau areas.
万方数据
维普
