Spatiotemporal Analysis on Tourist Source of Gansu Province Based on Internet Search Data

Ma Wei; Zhang Yaonan; Min Yufang; Chen Yue

doi:10.7522/j.issn.1000-694X.2015.00114

Journal of Desert Research >

2016 , Vol. 36 >Issue 3: 857 - 864

DOI: https://doi.org/10.7522/j.issn.1000-694X.2015.00114

Spatiotemporal Analysis on Tourist Source of Gansu Province Based on Internet Search Data

Ma Wei ,
Zhang Yaonan ,
Min Yufang ,
Chen Yue

Expand

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

Received date: 2015-01-12

Revised date: 2015-04-17

Online published: 2016-05-20

Fold

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.

Key words： Internet search data; tourist source; spatiotemporal analysis

Cite this article

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 . DOI: 10.7522/j.issn.1000-694X.2015.00114

References

[1] Lambert F.Online community information seeking:the queries of three communities in Southwestern Ontario[J].Information Processing & Management,2010,46(3):343-361.
[2] Ortiz-Cordova A,Jansen B.Classifying web search queries in order to identify high revenue generating customers[J].Journal of the American Society for Information Sciences and Technology,2012,63(7):1426-1441.
[3] McLaren N,Shanbhoge R.Using Internet Search Data as Economic Indicators[R].London,UK:Bank of England,2011:134-140.
[4] Choi H,Varian H.Predicting Initial Claims for Unemployment Benefits[R].Google Inc.,2009.
[5] Vosen S,Schmidt T.Forecasting private consumption:survey-based indicators vs.Google trends[J].Journal of Forecasting,2011,30(6):565-578.
[6] Kholodilin K A,Podstawski M,Siliverstovs B.Do Google Searches Help in Now Casting Private Consumption?A Real-Time Evidence for the US[R].Berlin,Germany:German Institute of Economic Research,2010.
[7] Wu L,Brynjolfsson E.The Future of Prediction:How Google Searches Foreshadow Housing Prices and Sales[R].Cambridge,USA:MIT,2010.
[8] Webb G K.Internet search statistics as a source of business intelligence:Searches on foreclosure as an estimate of actual home foreclosures[J].Issues in Information Systems,2009,10(2):82-87.
[9] Askitas N,Zimmermann K F.Google econometrics and unemployment forecasting[J].Applied Economics Quarterly,2009,55(2):107-120.
[10] Baker S R,Fradkin A.What drives job search? evidence from Google search data[Z].SIEPR Discussion Paper No.10-020.Stanford University,2011.
[11] Preis T,Reith D,Stanley H E.Complex dynamics of our economic life on different scales:insights from search engine query data[J].Philosophical Transactions of the Royal Society of London,2010,368:5707-5719.
[12] Da Z,Engelberg J,Gao P.In search of attention[J].Journal of Finance,2011,66(5):1461-1499.
[13] Guzman G.Internet search behavior as an economic forecasting tool:the case of inflation expectations[J].Journal of Economic and Social Measurement,2011,36:119-167.
[14] Rech J.Discovering trends in software engineering with Google Trends[J].ACM SIGSOFT Software Engineering Notes,2007,32(2):2-3.
[15] Goel S,Hofman J M,Lahaie S,et al.Predicting consumer behavior with web search[J].Proceedings of the National Academy of Sciences of the United States of America,2010,107(41):17486-17490.
[16] Reilly S,Richey S,Taylor J B.Using Google search data for state politics research:an empirical validity test using roll-off data[J].State Politics & Policy Quarterly,2012,12(2):146-159.
[17] Vaughan L.Web search volume as a predictor of academic fame:an exploration of Google trends[J].Journal of the Association for Information Science and Technology,2013,65(4):707-720.
[18] Xiang Z,Pan B.Travel queries on cities in the United States:implications for search engine marketing for tourist destinations[J].Tourism Management,2011,32(1):88-97.
[19] Gawlik E,Kabaria H,Kaur S.Predicting Tourism Rrends with Google Insights[R].2011.
[20] Choi H,Varian H.Predicting present with google trends[J].Economic Record,2012,88(S1):2-9.
[21] Yang Y,Pan B,Song H.Predicting hotel demand using destination marketing organization's WEB traffic data[J].Journal of Travel Research,2014,53(4):433-447.
[22] 王劲峰,葛咏,李连发,等.地理学时空数据分析方法[J].地理学报,2014,69:1326-1345.
[23] 张涛,曹广超,曹生奎.2000-2012年青海湖流域NPP时空分布特征[J].中国沙漠,2015,35(4):1072-1080.
[24] 王芳,张小雷,杨兆萍,等.4 000 a BP以来伊犁河谷文化遗址时空分布及变异性[J].中国沙漠,2015,35(4):1103-1112.
[25] 高婧,李晋昌,杨永刚.汾河源区生态系统服务价值的时空变化[J].中国沙漠,2015,35(3):814-820.
[26] 张慧.1990-2010年西北地区县域人口数量与空间集疏变化时空分析[J].干旱区资源与环境,2013,27:33-39.
[27] 单楠,杨晓晖,时忠杰,等.京津风沙源区1959-2011年潜在蒸散气候敏感性的时空变异[J].中国沙漠,2015,35(5):1330-1337.
[28] 曹永旺,延军平.1971-2013年环渤海地区风速的时空特征[J].中国沙漠,2015,35(5):1320-1329.
[29] 张伟,段学军,张维阳.长三角地区可持续发展测度与演化分析[J].长江流域资源与环境,2013,22(10):1243-1249.
[30] 张仁平,冯琦胜,郭靖,等.2000-2012年中国北方草地NDVI和气候因子时空变化[J].中国沙漠,2015,35(5):1403-1412.
[31] Loarie S R,Duffy P B,Hamilton H,et al.The velocity of climate change[J].Nature,2009,462:1052-1055.
[32] Li A,Bo Y,Zhu Y,et al.Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method[J].Remote Sensing of Environment,2013,135:52-63.
[33] Chalmond B.Spatiotemporal graphical modeling with innovations based on multi-scale diffusion kernel[J].Spatial Statistics,2014,7:40-61.
[34] Diaz-Avalos C,Mateu P J J.Significance tests for covariate-dependent trends in inhomogeneous spatiotemporal point processes[J].Stochastic Environmental Research and Risk Assessment,2014,28:593-609.
[35] Wang J F,Xu C D,Hu M G,et al.A new estimate of the China temperature anomaly series and uncertainty assessment in 1900-2006[J].Journal of Geophysical Research:Atmospheres,2014,119(1):1-9.
[36] 路紫,赵亚红,吴士锋,等.旅游网站访问者行为的时间分布及导引分析[J].地理学报,2007,62(6):621-630.
[37] 林志慧,马耀峰,刘宪锋,等.旅游景区网络关注度时空分布特征分析[J].资源科学,2012,34(12):2427-2433.
[38] 马丽君,孙根年,黄芸玛,等.城市国内客流量与游客网络关注度时空相关分析[J].经济地理,2011,31(4):680-685.
[39] 吴必虎.上海城市游憩者流动行为研究[J].地理学报,1994,49(2):117-127.
[40] Cressie N,Wikle C K.Statistics for Spatio-Temporal Data[M]. Hoboken,USA:Wiley Press,2011.
[41] Mac Eachren A.How Maps Work:Representation,Visualization,and Design[M].New York,USA:Guilford Press,1995.
[42] Ginsberg,Mohebbi,Patel,et al,Detecting influenza epidemics using search engine query data[J].Nature,2009,457:1012-1014.
[43] Pelat C,Turbelin C,Bar-Hen A,et al.More diseases tracked by using Google Trends[J].Emerging Infectious Diseases,2009,15(8):1327-1328.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References