节点文献
上海市入境旅游市场统计分析及预测模型研究
【作者】 熊靓;
【导师】 孙厚琴;
【作者基本信息】 华东师范大学 , 旅游管理, 2004, 硕士
【摘要】 20世纪90年代以来,上海入境旅游业获得了飞速发展,2001年上海入境旅游者人数首次突破200万人次,上海入境旅游业步入了新的发展阶段。随着中国加入WTO和上海市申办世界博览会的成功,上海的对外丌放程度必将日益扩大,上海面向全世界展示、宣传自我的机会也将不断增多,上海的入境旅游业必将有越来越广阔的发展空间。入境旅游业具有赚取外汇、解决就业等多种功能,在整个旅游业发展中占有举足轻重的地位,研究上海市入境旅游市场的动态发展趋势、建立动态预测模型对上海市入境旅游市场的健康发展具有重要的指导意义。 本文以上海市入境旅游市场为研究对象,重点对上海市入境旅游市场发展现状进行统计分析,并以此为基础利用多种量化分析方法对上海市入境旅游人数建立预测模型,同时进行模型的检验和比较。之所以选取入境旅游人数为预测对象,是因为入境旅游人数是衡量入境旅游发展水平的最基本的指标。在本文所建立的上海入境旅游人数模型中,时间序列模型的精度最高,预测效果最为理想,时间序列的方法广泛适用于随时间流变动的随机序列,如外汇收入等;四次曲线模型是一元模型中效果较为理想的模型,适合于入境旅游人数的中短期预测:在入境旅游指标体系不完备的现实条件下,多元回归模型更适合于做主因素排序分析。 本文共分五个部分展开对上海市入境旅游市场的分析和研究: 第一部分,绪论:分析了本文研究背景、研究意义和研究目的、研究综述、研究内容和研究方法以及相关关键词和相关概念的界定等。 第二部分,预测方法概述:对定量预测方法进行了简单介绍。 第三部分,上海入境旅游市场发展现状统计分析:从不同角度对上海市入境旅游市场的结构进行了描述性统计分析,对上海市入境旅游市场进行了数学分类研究,并用模糊数学的方法对上海市入境旅游市场进行了模糊综合评价。 第四部分,上海入境旅游人数的预测模型:具体应用第二部分介绍的部分预测方法对上海入境旅游人数建立数学模型,包括时间序列模型和回归分析模型。 第五部分,结论和建议。 本论文的创新之处在于:在选题上,选取具有创汇能力的入境旅游为研究对象,充分利用统计学工具,将统计方法具体运用到旅游市场的研究中,以定量分析为主,定性分析为辅,全文突出了强烈的数学逻辑,视角独特。在写作过程中,采取由浅入深、层层推进的方法,从基本的统计分析上升到统计模型的建立,从更深层面上对上海入境旅游市场进行把握和理解。本文尚待解决的问题有:衡量上海入境旅游业发展的具体指标如何建立、定性的指标如何量化、模型的选择如何更加合理等等,这些问题还有待于今后进一步的学习和研究探讨。
【Abstract】 Since 1990’s, the inbound tourism has been developed rapidly in Shanghai. In 2001, the number of tourists to Shanghai from abroad topped about 2 million for the first time, which means the inbound tourism in Shanghai has stepped into a new stage. With China entering WTO and Shanghai biding for the International Fair successfully, the city will be opened to the outside world further and will get more chances to propagate tourism and show itself. It’s certain that the international tourism will continue to develop in Shanghai day by day, for government has begun to pay more and more attention to the development of tourism and has held great promise to it. Practical plans have been made, including a 3-year tourism plan, and a plan increasing the tourism plan, and a plan increasing the tourism multiply. Their purpose is to make Shanghai a well-known international city.The article studies the inbound tourism, mainly analyzing the current situation of it statistically. Based on it, various means of maths and analysis have been used to set up maths analytical models. Meanwhile, the article has tested and compared the models as well.There are five chapters here analyzing and researching on the inbound tourism market in Shanghai.Chapter I : IntroductionIt analyses the background, meanings, purposes, summaries, contents, methods and the limitation of the related key words and concepts.Chapter II: the statistical analysis about the current situation of the inbound tourism market in ShanghaiIt is the most important part of all, analyzing the structure of the tourism market from all aspects. In this part, some classified research has been done on maths and the indistinct maths has also been used to analyze the inbound tourism market in Shanghai.Chapter III: A summary of forecasting methodsThis part is an introduction of this method and the theoretical basis of Chapter IV.Chapter IV: Forecasting models for the number of the tourists from abroad in ShanghaiIt’s one of the most important points. Statistics and the time series models are set up to number the tourists to Shanghai using the forecasting models in Chapter III.Chapter V: Conclusion and suggestionsThe new ideas the article brings forth are:Choose the inbound tourism which earns foreign exchange as its target.Make use of statistics in the research. It mainly uses the way of quantitative analysis and the supplementary way of qualitative analysis.The whole article emphasizes on maths logic with special angles of view. During the course of writing it, we can find changes step by step. By setting up statistical models, we will get to know more about the inbound tourism market and understand it better. But there are still some problems to be solved, such as how to measure the target , how to quantificate the qualitative target, how to modify the models and so on.These opinions need further discussion.
- 【网络出版投稿人】 华东师范大学 【网络出版年期】2004年 04期
- 【分类号】F592.7
- 【被引频次】30
- 【下载频次】3752