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基于UGC数据的乌兹别克斯坦国际游客行为时空特征研究
引用本文:苏红霞,张雪,艾欣,郝国华.基于UGC数据的乌兹别克斯坦国际游客行为时空特征研究[J].世界地理研究,2019,28(3):213-222.
作者姓名:苏红霞  张雪  艾欣  郝国华
作者单位:西安外国语大学旅游学院/人文地理研究所,西安 710128
基金项目:张雪(1991-),女,硕士研究生,主要从事游客消费行为研究,E-mail:870646854@qq.com。
摘    要:选取乌兹别克斯坦作为案例地,以Flickr网站上的地理标记照片为研究数据,采用时间变化分析、核密度分析以及追踪分析对数据信息进行时间和空间上的处理与表达。研究发现,在时间变化上,访乌国际游客数量及拍摄照片数量逐年上升,全年呈“M”型变化趋势;在空间变化上,访乌国际游客发布的照片沿主要交通线路分布,热点区向交通、旅游资源富集的中心城市聚集并向外辐射。游客流动主要集中在塔什干、撒马尔罕、布哈拉和希瓦四个城市之间,并向其他城市辐射延伸形成次一级流向目的地。随着停留天数的延长,访乌国际游客在乌兹别克斯坦境内形成的热点集聚区增加,其移动轨迹也逐渐复杂化。

收稿时间:2018-01-25
修稿时间:2018-05-07

A study on spatio-temporal behavior of international tourists in Uzbekistan based on User Generated Content
SU Hongxia,ZHANG Xue,AI Xin,HAO Guohua.A study on spatio-temporal behavior of international tourists in Uzbekistan based on User Generated Content[J].World Regional Studies,2019,28(3):213-222.
Authors:SU Hongxia  ZHANG Xue  AI Xin  HAO Guohua
Institution:School of Tourism and Research, Institute of Human Geography, Xi’an International Studies University, Xi’an 710128, China
Abstract:This paper studies Uzbekistan international tourists and it collects Geo-referenced photos on the Flickr website as the research data, using the method of statistical analysis of time variation, kernel density analysis, tracking analysis to process the research data and represent it spatially and temporally. In conclusion, firstly, the number of international tourists visiting to Uzbekistan and the number of photos taken by tourists increase year by year, showing a trend of "M" type within twelve months of a year, and the photos taken change within a day in accordance with the time pattern of human activity; secondly, the photos are distributed along the main traffic line, hot spots tend to concentrate in central cities with rich tourism resources where traffic radiates outwards to other cities. International tourists mainly move among Tashkent, Samarkand, Bukhara, and Khiva, radiating from the four hub cities to other cities. Meanwhile, the hot spots area and trajectory also change along with tourists’ length of stay.
Keywords:
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