摘要
网络时代,游客不但可以通过网络在线获取旅游信息、购买旅游产品,还可以利用社交媒体分享自己的旅游经历。在游中和游后,游客所发布的旅游照片、游记、旅游攻略和旅游评论中都包含了大量的时空信息,这些信息共同组成了反映游客时空行为轨迹的"旅游数字足迹"。本文以成都市为例,通过从Flickr和Trip Advisor两个网站中所获取成都入境游客的"旅游数字足迹",深度挖掘入境游客的时空信息,从而发现和总结入境游客行为的时空规律。研究结果表明:成都入境游客在时间上主要集中于第三季度和第四季度;在空间上,入境游客偏好于成都大熊猫繁育研究基地、青城山-都江堰、锦里、宽窄巷子、人民公园和文殊院等景区;其中,成都大熊猫繁育研究基地和锦里等景区则是入境旅游流网络的重要节点。这些通过旅游数字足迹所得出的研究结论和笔者的实地调研结果基本一致。由此可见,利用旅游数字足迹研究游客的时空行为特征不仅具有可行性,且能够为旅游目的地管理和营销提供科学参考。
In the Internet era,tourists can not only have access to information through the network to buy products,but also have the chance of using social media to share their travel experiences. All of the photos,forums,strategies,as well as the comments visitor leave both on-trip and after trip on the network contain spatial and temporal information,which reflect the visitors' traces as footprints. These are the ' digital footprints' of tourists. We take Chengdu as a case in point to make an in-depth mining of the spatial-temporal behaviors of inbound tourists with their digital footprints obtained from Flickr and Trip Advisor in order to discover and sum up the spatial-temporal patterns of their behaviors. The findings show that most inbound visitors come to Chengdu during the second half of a year to mostly visit China Lanes,People's Park,Wenshu Monastery,Qingchengshan Mountain,Dujiang Weirs,Chengdu Giant Panda Breeding& Research Base,and Jin-li,with the last two as nodes in common of inbound tourist flows. These conclusions from the digital footprints study are substantially identical with those we get from field survey. From this,we conclude that using digital footprints to study the spatial-temporal behaviors of inbound tourists is not only feasible,but also be able to offer scientific references for tourism destination management and marketing.
出处
《旅游科学》
CSSCI
北大核心
2015年第3期59-68,共10页
Tourism Science
基金
国家自然科学基金项目"社交媒体下景区危机信息扩散及时空效应研究"(41401639)
教育部人文社会科学青年基金项目"Internet视角下的旅游利基市场发掘模式研究"(11YJC790234)
关键词
数字足迹
入境游客
时空行为
入境旅游流网络
digital footprint
inbound tourist
spatial-temporal behavior
inbound tourist flow network
