Search

Internet search behavior is an intuitive expression of user needs and behavioral inertia in a virtual space, providing a new perspective for long-term sequenced and broad-scale spatial tourism demand analysis. Based on the Baidu search index of 9 keywords of rural tourism in China from 2011 to 2020, this paper tried to explore the spatial-temporal characteristics and influencing factors of rural tourism demand by means of spatial autocorrelation and geographic detector. The research found that: ① As for temporal characteristics of rural tourism demand, it shows an "inverted U-shaped" change pattern during 2011-2020, and exhibits non-linear phases characterized by periods of rapid development and fluctuating development. It shows "two peaks and two valleys" during the year, and the characteristics of off-season/peak season, and weekend effects are obvious. ② In terms of spatial distribution, it is basically in clustered distribution, and the clustered effect fluctuates slightly each year. The local spatial pattern shows the characteristics of declining gradients in which the east is high and the west is low, and it remains basically stable during 2011-2020. ③ There are five persistently significant influencing factors, and their explanatory power is as follows: GDP>number of star-rated hotels>total population> highway density>country-level rural tourist spots. Other factors have relatively low explanatory power or have great changes in ten years. This research reveals the spatio-temporal pattern of rural tourism demand and its influencing factors from the macro level with the help of network big data, which provides theoretical guidance and practical reference for optimizing industrial layout and promoting effective matching of supply and demand. [ABSTRACT FROM AUTHOR]