中国高等级旅游景区资源多尺度时空差异及其影响因素

以中国337个地级以上城市内的5A和4A级景区为研究对象,通过构建总体分异指数（GDI）并采用探索性空间数据分析（ESDA）等方法,从省级、城市群、城市等不同空间尺度,对2001—2019年中国高等级旅游景区资源时空演化特征进行综合分析。结果表明：（1）4A级景区主导了中国高等级旅游景区数量与增长率的变化趋势,2015年之后增长速度进入相对平缓状态;（2）时序上,中部和东北地区的各省份高等级旅游景区数量较少,但增长率较高,而西部省份则均相对较高,东部省份则是数量最多但增长率最低,到2017年西部超过东部,数量最多;城市规模与高等级旅游景区数量呈正比,与增长率呈反比;（3）空间上,中国高等级旅游景区分布密集程度逐年升高,呈现出主要依托东部沿海地区与中部地区城市群集聚分布的特征,且空间热点、次热点地区占主导地位,形成了“两点集聚—多极多区—集群集聚”的空间演化格局;（4）GDI指数随着研究尺度的缩小而增大,空间尺度越小,高等级旅游景区资源的差异越大;（5）中国高等级旅游景区空间分布主要受社会、经济、资源等因素相互作用的影响,其中人口规模、旅游经济、人文资源是共同的最为关键的因子。研究结果可为优化中国景区资源空间布局以及旅游业高质量及区域协调发展提供一定参考。

Multiscale analysis of the spatiotemporal differences and the influencing factors of the high-level tourist attractions in China

Taking 4A and 5A scenic spots of high-level tourist attractions in 337 cities in China as the research object, the spatial and temporal evolution characteristics of high-level tourist attraction resources in China from 2001 to 2019 were comprehensively analyzed by constructing the overall differentiation index (GDI) and using exploratory spatial data analysis (ESDA). The results show that: (1) In China, 4A scenic spots dominate the developing trend of high-level tourist attractions in number and growth, and the growth rate enters a period of being relatively stable after 2015. (2) According to the time sequence analysis, the number of high-level tourist attractions in the central and northeast regions is small, but the growth rate is higher than that of other regions. In the western region, there is a large number of high-level tourist attractions and they are increasing fast, while the eastern region has the most high-level tourist attractions, they are experiencing the lowest growth rate, exceeded by the western region in number in 2017. The size of the city is proportional to the number of high-level tourist attractions, inversely proportional to the growth rate. (3) From the spatial perspective, the distribution density of high-level tourist attractions in China is increasing year by year. The density of high-level tourist attractions is related to the agglomeration of urban agglomerations in coastal areas and the central region. The spatial hot spots and sub-hot spots occupy the dominant position, forming a pattern of "two points agglomeration, multi-pole and multi-region agglomeration, clusters agglomeration". (4) The GDI index increases with the reduction of the research scale, and the smaller the spatial scale is, the greater the differences between high-level tourist attraction resources are. (5) The spatial distribution characteristics of high-level tourist attractions in China are influenced by the interaction of social factors, economic factors, resource factors and other factors, among which, the population scale, tourism economy as well as human and culture resources are critical. The research results can provide some reference for optimizing the spatial layout of scenic spots resources and coordinating high-quality regional development of tourism in China.