摘要
社交媒体签到数据为旅游流时空特征研究提供了新的途径。基于2017年3月至2018年2月锦州市旅游流微博签到数据,运用统计分析、小波分析、自然间断点分类法、权重分析等方法,结合MATLAB、ArcGIS等软件,对锦州市一整年的旅游流时空分布特征进行研究,结果表明:旅游流出游呈现“峰林结构”特征,4—5月是锦州市全年旅游高峰期;省内游客集中于4—5月,省外游客集中于10月;旅游流呈现多时间尺度波动特征,其中8~12d和31~38d两个时间尺度的周期表现较为显著;旅游流客源属性不同,其出游时间集聚性、时间分布特征、时间尺度特征存在差异;旅游流在客源地和目的地空间上呈现集聚的特征,但其集聚特征受客源属性影响。
Social media sign-in data provides a new approach for the study of the temporal and spatial characteristics of tourism flows. Based on the check-in data of the micro-blog of Jinzhou City from March 2017 to February 2018,statistical analysis,wavelet analysis,natural breakpoint classification,weight analysis and other methods,were used to study the characteristics of temporal and spatial distribution of tourism flow of a full year in Jinzhou with MATLAB,ArcGIS and other software. The results show that tourist outflow shows the characteristics of “peak forest structure”. April and May are the peak periods of the whole year in Jinzhou City;tourists in the province concentrate in April and May and tourists outside the province concentrate in October;tourist flow shows the characteristics of multi-time scale fluctuation,of which the periods of 8~12 days and 31~38 days are more significant;tourist source attributes are different,and the characteristics of travel time agglomeration,time distribution and time scales are different. There are differences in scale characteristics.
作者
赵明成
周凤杰
鲁小波
王万山
鲁浚
ZHAO Mingcheng;ZHOU Fengjie;LU Xiaobo;WANG Wanshan;LU Jun(College of Management,Bohai University,Jinzhou 121013,China)
出处
《地域研究与开发》
CSSCI
CSCD
北大核心
2019年第3期84-88,共5页
Areal Research and Development
基金
2016年国家旅游局“万名旅游英才计划”项目(WMYC20165-1011)
辽宁省教育厅人文社会科学研究项目(WY2016001)
关键词
旅游流
时空特征
签到数据
小波分析
锦州市
tourist flow
spatio-temporal characteristics
check-in data
wavelet analysis
Jinzhou City
