【摘要】 大数据背景下，网络上产生了大量带有游客时空信息的“数字足迹”。本文以北京市为例，利用游记数据探究北京市旅游流的时空及网络结构特征。结果表明：（1）2月、7至9月为北京市的客流高峰期；（2）北京市旅游流的空间分布具有明显的等级分异特征且密度分布不均衡，总体呈“小聚集、大分散”的特征；（3）北京市旅游流网络中，平均每个节点与7个其他节点存在旅游流集聚与辐射联系；平均每个节点充当49.205次旅游中介者；（4）网络中旅游节点的分布十分不均衡，南锣鼓巷、故宫、八达岭长城、天安门广场、天坛等处于核心点，在旅游网络中集聚和辐射功能都很强；（5）北京市网络密度较低（0.162 8），结构非常松散，对核心景点依赖性较强。更多还原

【Abstract】 In the context of big data,a large number of " digital footprints" with spatio-temporal information of tourists have been generated on the Internet. Taking Beijing as an example,this paper used travel notes data to explore the spatio-temporal and network structure characteristics of tourism flow in Beijing. The results showed that:(1)February and July to September are the peak periods of passenger flow in Beijing.(2)The spatial distribution of tourism flows in Beijing has obvious hierarchical differentiation characteristics and uneven density distribution. The overall distribution is characterized by " part aggregation and whole dispersion".(3)In the tourism flow network of Beijing,each node has agglomeration and radiation relationship with7other nodes averagely. On average,each node acted as an intermediary for49.205trips.( 4)The distribution of tourism nodes in the network is very uneven. Nanluoguxiang,the Forbidden City,the Badaling Great Wall,Tiananmen Square,Temple of Heaven,etc. are at the core,which have strong agglomeration and radiation functions in the tourism network.(5)The network density of Beijing is low(0.1628),the structure is very loose,and have a strong dependence on core nodes.更多还原