Spatial-temporal characteristics and influencing factors of network attention on virtual tourism
Virtual tourism network attention is an important reflection of the influence of virtu-al tourism.Based on the Baidu index of virtual tourism in 31 provinces(cities and districts)in China,this study uses the seasonal concentration index,geographic concentration index,varia-tion coefficient,primacy degree and geographic detector to analyze the spatial-temporal charac-teristics and influencing factors of network attention of virtual tourism.The results show that:(1)on the whole,the attention of virtual tourism networks shows a steady development trend,reaching a peak in 2016 and the second small peak in 2020.The seasonal variation of virtual tourism network attention is large.March,April,May,June and October are the peak seasons of virtual tourism network search.The public tends to pay more attention to virtual tourism in spring and autumn,and the seasonal and inter-annual differences are further expanding.(2)From the perspective of overall spatial evolution,the spatial evolution pattern of attention in vir-tual tourism networks is relatively stable,presenting an obvious distribution characteristic of"Hu Line".From the regional point of view,the attention of virtual tourism networks shows a trend of decreasing from east to middle to west.The spatial distribution difference of attention in vir-tual tourism networks is the largest in the western region,and the eastern and central regions are relatively balanced.The difference has a narrowing trend in the eastern region,and a further widening trend in the central and western regions.(3)The level of virtual tourism network atten-tion is influenced by the level of economic development,population size,education develop-ment,tourism development and network development.Among them,the level of tourism devel-opment has a certain explanatory power to the difference of attention,but it is not a strong fac-tor.In addition,policy support,typical virtual tourism projects and hot events will also affect the attention of the virtual tourism network.
virtual tourism network attentionspatial and temporal characteristicsinfluencing factorsBaidu Indexgeographic detector
NETL
NSTL
万方数据
