Evolution of Spatio-temporal Patterns and Influencing Factors of Tourism Economic Resilience in the Wuling Mountains Area
It is of great benefit to promote the high-quality development of tourism economy in concentrated and underdeveloped areas by scientifically studying the spatial and temporal differentiation characteristics of tourism economic resilience in the Wuling Mountains area and deeply revealing its intrinsic influencing factors.Taking 71 counties in the Wuling Mountains area as the research object,this paper comprehensively constructs the evaluation index system of tourism economic resilience,and uses entropy weight method,spatial autocorrelation,cold and hot spot analysis to explore the spatial and temporal evolution pattern of tourism economic resilience in the Wuling Mountains area from 2008 to 2020,and uses spatial Durbin model to analyze its influencing factors.It is found as follows.① From 2008 to 2020,the tourism economic resilience of the Wuling Mountains area showed a fluctuating upward trend,rising from 0.134 1 to 0.159 6.The overall level was low and the growth rate was moderate.There was a significant spatial non-equilibrium,and the transition direction was quite different.② The sub-dimension resilience index of average resistance ability,recovery ability and renewal ability of tourism economy in the Wuling Mountains area was 0.061 2,0.035 1 and 0.027 8 respectively.The distribution of cold and hot spots has gone through two stages of cluster-aggregation distribution and scattered-block distribution.③ There is a significant spatial spillover effect on the tourism economic resilience in the Wuling Mountains area.Tourism specialization and market scale have a significant positive effect on the improvement of tourism economic resilience in the Wuling Mountains area,while market scale and government management have a negative effect on the improvement of tourism economic resilience in the surrounding areas of the Wuling Mountains.
tourism economic resiliencespatial and temporal evolutioninfluencing factorsthe Wuling Mountains area
万方数据
维普
