| 基于互联网搜索数据的甘肃省旅游客源地时空分析 |
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| 引用本文： | 马威,张耀南,敏玉芳,陈玥.基于互联网搜索数据的甘肃省旅游客源地时空分析[J].中国沙漠,2016,36(3):857-864. |
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| 作者姓名： | 马威 张耀南 敏玉芳 陈玥 |
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| 作者单位： | 1. 中国科学院寒区旱区环境与工程研究所 甘肃省资源环境科学数据工程技术研究中心, 甘肃 兰州 730000;2. 中国科学院大学, 北京 100049;3. 中国科学院地理科学与资源研究所, 北京 100101 |
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| 基金项目： | 国家自然科学基金项目(91125005);国家基础科学人才培养基金冰川冻土学科点人才培养基金项目(J1210003/J0109) |
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| 摘 要： | 传统的统计手段可以获得旅游城市或景点的游客量,而无法获得旅游客源地游客量,本文首先把互联网搜索数据与现实游客行为之间进行关联和映射,然后将搜索量最高关键词通过自由组合和非线性多项式拟合,发现3个词组合时与现实游客行为之间R2高达0.999,最后反演出2011-2014年中国(港澳台除外)各省、直辖市和自治区至甘肃省旅游的人数,进行甘肃省旅游客源地时空数据可视化、时空数据异常探测、时空过程分析等,帮助旅游部门了解游客的来源及去处、游客的出行规律和爱好偏向,做出有针对性的决策。
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| 关 键 词： | 互联网搜索数据 客源地 时空分析 |
| 收稿时间： | 2015-01-12 |
| 修稿时间： | 2015-04-17 |
Spatiotemporal Analysis on Tourist Source of Gansu Province Based on Internet Search Data |
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| Ma Wei,Zhang Yaonan,Min Yufang,Chen Yue.Spatiotemporal Analysis on Tourist Source of Gansu Province Based on Internet Search Data[J].Journal of Desert Research,2016,36(3):857-864. |
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| Authors: | Ma Wei Zhang Yaonan Min Yufang Chen Yue |
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| Institution: | 1. Gansu Engineering and Technology Research Center for Resources and Environment Data, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | The tourist number of a city or a scenic spot can be obtained by traditional statistical methods, while those methods could not gain the visitor number of the tourist source. In this paper, we related and mapped the data searched from Internet and the real tourists behavior, then fitted the keywords topped the list with real behaviors by free combination and non-linear polynomial. The results showed that the R2 between the three phrases combination and the real tourists behavior was up to 0.999. Based on the results, we can deduce the number of tourists from 2011 to 2014 from 31 regions to Gansu province in china, except for Hong Kong, Macao and Taiwan. Besides, spatiotemporal data visualization of the tourist source in Gansu, anomaly detection of time and space data, and analysis of the spatiotemporal process were conducted. On the basis of the work above, the tourism department could better understand the source and destination of tourists, their travel patterns and their tendencies, so that the targeted and personalized decisions could be made. |
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| Keywords: | Internet search data tourist source spatiotemporal analysis |
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