Based On Spatial Analysis, Beijing Tourism Revenue Balance

With the development of the economy, the tourism industry has become the mainstay industry of Beijing with tourist-related income increasing with the Olympic Games providing a large opportunity for the development of this industry. The harmonious and quick development of the tourism-related economy is the main aim now. In the process of developing however, the disequilibrium among different districts is getting bigger. Coordinated development of 18 districts and counties of Beijing is an important way to enhance the overall competitiveness of the city. In this paper, the development of Beijing's recent tourism income targets, combined with spatial analysis technology, will reveal the spatial development patterns and the phenomenon of uneven development through exploration of regional differences. To solve these problems, analysis of the causes and recommendations for the economic development of tourism are made. The main contents include the following:Firstly, based on the data of years 2004-2007, calculations into the tourist income center of gravity are made. Also, it is proven that there is a Clustering effect in Beijing that hasn't been improved since 2004.Secondly, 18 districts and counties are divided into 3 classes by Spatial Clustering based on 4 indicators: Tourist numbers, tourism revenue, the number of business units in tourism and tourism practitioners. The results show that the first class includes Mentougou District, Tongzhou District, Pinggu District, Daxing District, Miyun District, Fangshan District, Shunyi District, Shijingshan District,Huairou District, Yanqing District, Changping District, Fengtai District,Chongwen District and Xuanwu District. The second class includes Haidian District, Dongcheng District and Xicheng District. The third class includes Chaoyang District. And all of these 3classes are in a Spatial pattern of'Pyramid''Thirdly, analysis of the form factors of'Pyramid'is undertaken. The reasons are factors of tourism resources, tourists per capita consumption, the number of hotel units and traffic density. Class one has a small number of regional tourism resources, low per capita consumption of tourists, small number of tourist hotels and small traffic density. This is the explanation for its low tourism revenues.Fourthly, based on the results of the analysis, it is recommended that changes should begin with the following four points: developing new tourism resources, developing transport, increasing tourism hotel revenues and Per capita consumption, increasing the tourism revenues and narrowing the gap with class 2 and class 3.