基于深度学习的旅游景区空间格局模拟与预测——以中国“一带一路”沿线18个省份为例

Spatial Pattern Simulation and Prediction of Scenic Spots Based on Deep Learning:A Case Study of 18 Provinces along“Belt and Road”in China

应用核密度、基尼系数、地理探测器及深度学习技术,分析了中国“一带一路”沿线高等级旅游景区空间分布特征、影响机制,模拟预测出适宜高等级景区发展的新区域。结果表明:(1)中国“一带一路”沿线高级别景区呈集聚分布,集中程度高,均衡性差异悬殊,景区密度东南高、西北低。(2)通过地理探测器识别与高等级旅游景区空间分异密切相关的因子,得出城市分布、交通状况、植被覆盖情况及地形起伏对高级别景区空间分异有较强的影响力。(3)采用深度学习技术对景区空间格局进行模拟和预测,发现西南地区极适宜发展高级别景区,且无论是从自然环境角度还是从社会发展角度,均具有地域的成熟性与发达性;沿海地区和东北三省次之;西北地区较不适宜。

Based on the methods of nuclear density,Gini coefficient,geographical detector and deep learning,this paper analyzes the spatial differentiation characteristics,the influence mechanism and the new area suitable for the development of high-level scenic spots in the provinces along the“Belt and Road”in China.The results show that:(1)The high-level scenic spots in 18 provinces and regions along the“Belt and Road”in China show an agglomeration distribution,the overall performance is characterized by“high cluster and poor balance”,and the density characteristic of scenic spots is“high density in the southeast,low density in the northwest”.(2)Through the identification of the factors closely related to the spatial differentiation of high-level tourist scenic spots by geographical detectors,it is concluded that the urban distribution,traffic conditions,vegetation coverage and topography have a strong influence on the spatial differentiation of high-level scenic spots.(3)Using deep learning technology to simulate and predict the spatial pattern of scenic spots,it is found that the southwestern region is extremely suitable for the development of high-level scenic spots,followed by coastal areas and northeastern three provinces,and the northwestern regions are less suitable.It is extremely suitable for the development of high-level scenic spots,both in terms of natural environment and social development,and it has regional maturity and development.