中国红色旅游经典景区网络关注度时空演变及影响因素

借助“百度指数”,获取2011—2019年中国31个省（市、自治区）对红色旅游经典景区的网络关注度,结合区域差异分析法、景区偏好系数分析红色旅游经典景区网络关注度的时空演变特征,利用面板数据回归模型和地理探测器揭示其影响机理。研究发现：（1）从时序上看,2011—2019年红色旅游经典景区的网络关注度持续上升,具有明显的季节性差异,呈“M”型变化形态。（2）从区域上看,东—中—西部具有明显的梯度递减特征,区域差异呈东—西—中部依次递减态势,空间结构比较均衡,东部地区多位于热点区,中部地区多处于一般区,西部地区则属于冷点区,各省（市、自治区）存在一定季节性差异。（3）景区角度方面,景区的月际变化分为单峰型、双峰型和多峰型三种,单峰型季节差异显著,双峰型和多峰型的季节差异较小,景区偏好类型呈“金字塔”结构,具有“多中心”分布特征,整体格局相对稳定。（4）影响因素方面,客源地网络发展水平的贡献>人口规模>教育水平>经济发展水平,而旅游恩格尔系数的影响不显著,目的地的星级酒店数量、旅游化水平、市外交通可达性是重要影响因素,信息化水平、空气质量、旅行社及公共管理能力是主要影响因素,其他因素影响力较小。

Spatio-temporal evolution and influencing factors of Chinese red tourism classic scenic spots network attention

The network attention of red tourism classic scenic spots in 31 provinces (autonomous regions and municipalities) of China from 2011 to 2019 was obtained by means of "Baidu Index". The spatio-temporal evolution characteristics and the preference degree of the network attention of the red tourism classic scenic spots were analyzed through coefficients of scenic spot preference. The influence mechanism of the changes in the network attention of the red tourism classic scenic spots was revealed by using panel data regression model and geographic detector model. The research found that: (1) In terms of time series, the network attention of red tourism classic scenic spots continuously increased from 2011 to 2019, with obvious seasonal differences, showing an "M"-shaped variation form. (2) From the regional perspective, network attention of red tourism classic scenic spots in the eastern-central-western regions showed obvious gradient-descending characteristics. The regional differences show a decreasing trend from east, west to center, with a relatively balanced spatial structure. The majority of eastern provinces are located in hot spots, central provinces are mostly at an average level, and western provinces are in cold spots. The eastern and western regions have relatively stable order series, the central region mostly showed an upward trend, and there are certain seasonal differences between provinces, but the time series do not change greatly. (3) From the perspective of scenic spots, the inter-monthly variations of scenic spots are divided into three types, namely, unimodal type, bimodal type and multimodal type. The seasonal difference of unimodal type is significant, but the seasonal difference between bimodal type and multimodal type is small. The preference of scenic spots with a "pyramid" structure is characterized by "multi-center" distribution with a relatively stable pattern on the whole. (4) Regarding influencing factors, the contribution is listed in the order of the development level of the tourist source network>population size>education level>economic development level. But the influence of the tourism Engel coefficient is not significant. The number of star-rated hotels in the destination and tourist level and the transportation accessibility outside the city are important influencing factors. The informatization level, air quality, travel agency and public management ability are the primary influencing factors.