旅游与环境

盐城麋鹿生态旅游区游客变化特征及预测

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  • 南京师范大学地理科学学院,南京210046
黄震方, 系主任、教授、博士生导师, 中国地理学会会员。主要从事生态旅游及旅游规划研究, 发表论文50 余篇。E-mail: zhfh@263.net

收稿日期: 2007-01-30

  修回日期: 2007-09-24

  网络出版日期: 2007-12-25

基金资助

国家自然科学基金项目(40471050)

Forecasts of Tourist Flow Features in Eco-tourism Area: A Case Study of Yancheng David's Deer Eco-tourism Area

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  • College of Geographic Science, Nanjing Normal University, Nanjing 210046, China

Received date: 2007-01-30

  Revised date: 2007-09-24

  Online published: 2007-12-25

Supported by

National Natural Science Foundation of China, No.40471050

摘要

生态旅游区的发展受到诸多确定性因素和随机性因素的共同作用,并反映于旅游区游客观测序列。通过构建游客序列分析框架,提出了游客序列分析的理论模型。并以江苏盐城麋鹿生态旅游区为例,集成多种数学方法,进行了游客序列的分解、调整和综合预测的实证 分析。运用Tramo/Seats 方法进行季节调整分析可有效提取时间序列各组分,从而获得了不同尺度的作用模式,所获得的去噪序列为预测研究提供了基础资料。基于ARIMA 模型、Winter 加法模型、Winter 乘法模型和Tramo/Seats 模型的综合预测表明,麋鹿生态旅游区未来两年年平均新增游客数量在1.95-3.14 万人次之间。各方法预测结果间的差异与其建模思路及实现方法有关,因此集成多种方法进行预测,并进行对比分析,有助于获得对游客波动变化更为全面的认识,同时所提供的多种可能预测方案,可为生态旅游区容量控制、旅游环境资源的保护与开发及旅游区的管理决策提供参考依据。

本文引用格式

黄震方, 袁林旺, 俞肇元 . 盐城麋鹿生态旅游区游客变化特征及预测[J]. 地理学报, 2007 , 62(12) : 1277 -1286 . DOI: 10.11821/xb200712004

Abstract

The number of touris ts is one of the key indexes in measuring the development of eco-tourism area . By s tudying touris t flow and its variation law and by analyzing the flow s eries to predict its trend, s cientific references could be provided for the area's planning, decis ion-making, protection, exploration and operational management. The development of eco-tourism area is subject to the interactions of a number of certainties and uncertainties , which is reflected via obs ervational touris t flow s eries . This paper es tablishes a touris t s erial analys is framework and puts forward its theoretic model. Taking Yancheng eco-tourism area for David's deer in J iangsu Province as a s tudy cas e , the paper applies various mathematic methods to dis compose , readjus t and synthetically forecas t touris t s eries . The Tramo/Seats method is us ed to undertake seasonal regulation analys is for extracting each component of time s eries , hence obtaining functional models at various s cales of long-term trend with s easonal, periodic and irregular variations . Moreover, the internal law of touris t flow fluctuation in eco-tourism area and its correlation between its influenting factors are analyzed, and thus we get the nois e elimination s eries which provides foundation for a forecas t s tudy. The synthetic forecas t results obtained by applying ARIMA model, Winter addition model, Winter multiplication model and Tramo/Seats model indicate an annual average increase of 19.5-3.14 thous and touris ts in the coming two years , coinciding with the former s eries . The differences in forecas t results between each method are relevant to the modeling thoughts and means of realization, this paper collects various methods to render forecas ts and analys is in order to gain more comprehens ive knowledge about touris t flow fluctuation. Furthermore , the various pos s ible forecas ts could offer references for capacity control, protection and exploration of tourism environmental resources and decis ion-making of tourism management.

参考文献


[1] Yang Guihua. The Exploitation for Eco-Tourism Area. Beijing: Science Pres s, 2004. 41-47.
[杨桂华. 生态旅游景区开发. 北 京: 科学出版社, 2004. 41-47.]

[2] Crampon L J . Gravitational model approach to travel market analys is . Journal of Marketing, 1966, 30(2): 27-31.

[3] Wolf R I. The inertia model. Journal of Leisure Research, 1972, (4): 73-76.

[4] Edwards S L, Dennis S J. Long dis tance day tripping in Great Britain. JournalofTransport Economics and Policy, 1976, 10: 237-256.

[5] Cesario F J,Knetsch J L.Arecreation s ite demand and benefit es timationmodel.RegionalStudies, 1976, 10: 97-104.

[6] Wilson A G. A s tatical theory of spatial dis tribution models . Transportation Research, 1967, (1): 253-267.

[7] Quan Hua.An overviewofeco-tourism research methods.Acta Ecologica Sinica, 2004, 24(6): 1267-1278.
[全华. 生态旅游 研究方法综述. 生态学报, 2004, 24(6): 1267-1278.]

[8] Bao J igang, Zheng Haiyan,DaiGuangquan.The evolvementofspatials tructure andthe s ignificance ofGuilin's domes tic touris torigins . Acta Geographica Sinica , 2002, 57(1): 96-106.
[保继刚, 郑海燕, 戴光全. 桂林国内客源市场的空间 结构演变. 地理学报, 2002, 57(1): 96-106.]

[9] Zhang J ie, Du J inkang, Zhou Yinkang et al. Spatial s tructure of touris t source areas for the naturally scenic s ightseeing places. Acta Geographica Sinica, 1999, 54(4): 357-364.
[张捷, 都金康, 周寅康等. 自然观光旅游地客源市场的空间 结构研究. 地理学报, 1999, 54(4): 357-364.]

[10] Lu Lin, Xuan Guofu, Zhang J inhe et al. An approach to seasonality of touris t flows between coas tland resorts and mountain resorts: Examples of Sanya, Beihai, Mt. Putuo, Mt. Huangshan and Mt. J iuhua . Acta Geographica Sinica , 2002, 57(6): 731-740.
[陆林, 宣国富, 章锦河等. 海滨型与山岳型旅游的客流季节性比较: 以三亚、北 海、普陀山、黄山、九华山为例. 地理学报, 2002, 57(6): 731-740.]

[11] Lu Song, Lu Lin, Wang Li et al. Temporalcharacteris tics of touris t flows to ancient villages:Acase s tudy of two world culturalheritages,XidiVillage and Hongcun Village . Scientia Geographica Sinica , 2004, 24(2): 250-256.
[卢松, 陆 林, 王莉等. 古村落旅游客流时间分布特征及其影响因素研究: 以世界文化遗产西递、宏村为例. 地理科学, 2004, 24(2): 250-256.]

[12] Yan Fen, Meng J ijun. The application of Logis tic growth model in forecas t of touris ts amount: A case s tudy of Suiyang, Guizhou Province . Human Geography, 2005, 20(4): 87-91.
[严汾, 蒙吉军. Logis tic 增长模型在游客流 量预测中的应用: 以贵州省绥阳县为例. 人文地理, 2005, 20(4): 87-91.]

[13] Zhu Xiaohua , Yang Xiuchun, CaiYunlong.Forecas tingmodelsoftourismpas sengerbasedonthegreytheory:Acases tudy oftheinternationaltourismpas sengers source of China . Economic Geography, 2005, 25(2): 232-235.
[朱晓华, 杨 秀春, 蔡运龙. 基于灰色系统理论的旅游客源预测模型: 以中国入境旅游客源为例. 经济地理, 2005, 25 (2): 232-235.]

[14] Sun Yanping, Zhang Lin, Lu Renyi. Touris t quantity forecas t by us ing neuralnetwork. Human Geography, 2002, 17(6): 50-52.
[孙燕平, 张琳, 吕仁义. 旅游客源预测的神经网络方法. 人文地理, 2002, 17(6): 50-52.]

[15] Bell WR, Hillmer S C. Is sues involved with the seasonal adjus tment of economic time series. JournalofBus ines s and Economic Statis tics, 1984, 2: 291-320.

[16] Cleveland W P, Tiao G C. Decompos ition of seasonal time series: A model for the X-11 Program. Journal of the American Statis ticalAs sociation, 1976, 71: 581-587.

[17] Fisc her B. Decompos ition of Time Series: Comparing Different Methods in Theory and Practice. Euros tat Working Group Document, 1995.

[18] Hillmer S C, Tiao G. C. An ARIMA2 Model based approach to seasonal adjus tment. Journal of the American Statis tical As sociation, 1982, 77: 63-70.

[19] Maravall Agus tín . Brief Description of the Programs . TRAMOP/SEATS, 2001.

[20] Cao Xin, Xu Nanrong, Sheng Zhaohan. An ARIMAmultiplicative load model of the city water supply sys tem and the innovation forecas ting method. Journal of Southeas t Univers ity (Natural Science Edition), 1986, (2).
[曹忻, 徐南 荣, 盛昭瀚. 城市供水系统负荷量的ARIMA 乘积模型与新息预报方法. 东南大学学报(自然科学版), 1986, (2).]

[21] Winters P R. Forecas ting sales byexponentiallyweightedmovingaverages.ManagementScience, 1960, 6: 324-342.

[22] McKenzie E. Comments on 'Exponential Smoothing: The State of the Art' by E S Gardner J r. Journal of Forecas ting, 1985, 4: 32-36.

[23] Ord J K,KoehlerAB, SnyderRD. Es timation and forecas ting for a clas s ofdynamic nonlinear s tatis ticalmodels, Journal ofthe American Statis ticalAs sociation, 1997, 92: 1621-1629.

[24] Koehler A B. An inappropriate forecas ting interval. International Journal of Forecas ting, 1990, 6: 557-558.

[25] S nyderRD.Progres s ivetuningofs impleexponentialsmoothingforecas ts.JournaloftheOperationalRes earchSociety,1988, 39:393-399.

[26] Archibald B C, Koehler A B. Normalization of s easonal factors in Winters ' methods . International Journal of Forecas ting, 2003, 19: 143-148.

[27] Huang N E, Shen Z, Long S R et al. The empiricalmode decompos ition and the Hilbert spectrum for nonlinear and non-s tationary s eries analys is . Proc R Soc Lond A, 1998, 454(1971): 903-995.

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