“交旅”融合下旅游效率与高速交通协调格局研究——以长三角41市为例

长三角是众多旅游要素的集聚地,高速交通的要素集聚与扩散效应对城市旅游效率增长至关重要。以长三角41市为研究对象,基于多源异构数据构建高速交通与旅游效率评价指标体系,在采用VAR模型验证旅游效率与高速交通关系,以及构建二者耦合机理基础上,综合采用TOPSIS方法、Bootstrap-DEA模型和耦合协调模型对2005—2016年长三角41市旅游效率与高速交通耦合协调度进行测度,并运用核密度曲线、ArcGIS空间分析模块,从时空异质性视角解析旅游效率与高速交通协调度的趋势特征、时空变化规律及空间关联模式,同时采用线性拟合回归方程对比不同高速交通方式对旅游效率影响的边际效应。结果表明：① 长三角旅游效率与高速交通协调度的核密度曲线右拖尾明显变长,曲线波形不断右移,表明二者协调度总体水平持续攀升。② 旅游效率与高速交通协调度空间分异明显,呈现由东至西随地理距离增加而梯度递减的级差化特征,是协调度空间分异的主要方向;南北方向呈倒“U”型分布,苏南、浙东北和上海地区协调度位于倒“U”型谷峰位置,是协调度的优势发展区域。③ 协调度差异呈现“上海>江苏>浙江>安徽”的空间异质特征,且高、低等级协调区分别呈现空间“扩张-收敛”相背离的变化态势。④ 旅游效率和高速交通协调度具有显著空间依赖和溢出效应;协调度热点、次热点稳定在沪宁、沪杭、杭甬等高速交通干线地带,呈空间拓展态势,冷点、次冷点区位指向于皖北、苏北等边缘地带,空间收缩明显。⑤ 不同高速交通方式优势度对旅游效率影响的边际效应呈现“高速铁路>民航运输>高速公路”的差异化特征。

The coordination pattern of tourism efficiency and high-speed transportation: A case study of 41 cities in the Yangtze River Delta

Taking 41 cities in the Yangtze River Delta (YRD) as the research object, high-speed transportation and tourism efficiency evaluation index system is constructed based on multi-source heterogeneous data. Using the VAR model to verify the interaction between tourism efficiency and high-speed transportation, and to construct the coupling mechanism between the two, we adopt the TOPSIS method, Bootstrap-DEA model, and coupling coordination model to measure the coupling degree of tourism efficiency and high-speed transportation in the 41 cities in the study area from 2005 to 2016. The kernel density curve and ArcGIS spatial analysis tools are used to examine the trend characteristics, spatio-temporal distribution, and spatial correlation patterns of the coordination between tourism efficiency and high-speed transportation from the perspective of temporal and spatial heterogeneity. The linear regression fitting equations are used to explore the effect of different high-speed transportation modes on tourism efficiency. The research results are as follows: (1) The right tail of the kernel density curve of the coordinated degree between high-speed transportation and tourism efficiency in the YRD is significantly longer, and the waveform of the curve continues to move to the right, indicating that the overall level of coordination continues to rise. (2) The spatial differentiation between tourism efficiency and high-speed transportation coordination is obvious, showing a stepwise gradient from east to west with increasing geographical distance, which is the main direction of the spatial differentiation of coordination; the north-south direction presents an inverted U-shaped distribution, and the coordination degree of southern Jiangsu, northeastern Zhejiang and Shanghai is found at the position of inverted U-shaped valley peaks, which is an advantageous area with the remarkable development of coordination degree. (3) The regional differences of coordination degree show the spatial heterogeneity of “Shanghai > Jiangsu > Zhejiang > Anhui”, and the high-level and low-level coordination areas show a spatial changing trend of “expansion-convergence”. (4) Tourism efficiency and high-speed transportation coordination have significant spatial dependence and spillover effects; the hot spots and sub-hot spots of coordination degree are stable along Shanghai-Nanjing, Shanghai-Hangzhou, Hangzhou-Ningbo and other high-speed rail trunk lines, showing a spatial expansion trend. However, the cold spots and sub-cold spots point to the edge areas of northern Anhui and northern Jiangsu, and space shrinks significantly. (5) The marginal effects of different high-speed transportation modes on tourism efficiency present a differentiated characteristic of“high-speed railway > air transportation > expressway”.