中文旅游网站的空间类型及发展战略研究

文章通过对网上调查统计和旅游网站特征指数(TWCI)的设计及其分析,描述了中国旅游网站的空间分布类型并分析了其影响因子。结果表明国内旅游网站的空间分布存在差异,而区域旅游网站发展规模数量与区域社会经济基础、网络技术基础背景以及区域旅游发展的区域分异具有较大的一致性。文章利用TWCI进行了分区并提出了中国旅游网站的若干发展策略。

On Types of the Spacial Distribution of Chinese Tourist Website

This is a case study of information geography or cybergeography with case of Chinese Tourism Websites (CTW).In this paper, with online statistics and analysis using the Tourism Websites Characteristic Indices (TWCI), the spatial distribution of CTW was described, and its zoning were analyzed. The regional TWCI is defined as the ratio of the total number of the regional tourism websites linked to the main engine website divided by total number of the regional domains as a whole with a multiplied coefficient of 100. With TWCI it is easy to revealing the characteristic informationized regions like Yunnan with well development tourism websites in China, which was difficult to revealed with normal indices like total number of domains or tourism websites. It is proposed that the distribution of CTW in China is spatially differentiated, and the total number of regional CTW is related to the levels of regional social economic background, internet techniques foundation and the status of regional tourism development. With investigation of some populated CTW, it is shown that the website covered all aspects of tourism operation sectors, and two types of CTW, i.e., hotel website and travel service website, become the main composition of CTW. Concerning with total number of CTW and the TWCI, all provinces can be classified into 5 groups, i.e., the synthesized type of region like Beijing, the imformationized type of region like Yunan and Hainan, the distributing type of region like Guangdong Province and Shanghai, the relatively developed type of region like Jiangsu, Zhejiang and the less developed but with superior attraction type of region like Tibet, Guizhou and Jiangxi. With regression analysis, the total number of CTW is related to the total number of regional domains with correlation coefficient R larger than 0.867 and an identified efficient model. And the number of CTW is also related to the total number of regional hotels and the number of regional travel services with R of 0.898 and a tested efficient linear regression model. Moreover, we also analyzed the relationship of CTW to integrated background of internet technique and the developing level of regional tourism, with result that the total number of CTW is related to numbers of domain and the total number of tourism units (Hotels and Travel Services), with R of 0.935 and a test-passed linear regression model.