苏州市旅游者目的地消费支出空间分布特征研究

基于2003年江苏省国内游客抽样调查资料,选择以苏州为目的地的来自188个地级市的3337份有效游客样本,在综合考虑社会经济要素的基础上,采用相关分析等统计学方法进行研究,结果显示:旅游者在苏州市的目的地消费支出空间分布特征曲线分为两个阶段:递增阶段和停滞阶段。采用同样的方法对江苏省内游客样本进行了研究,结果验证了这种空间分布特征的可靠性。从不同细分消费项目角度的进一步研究发现,住宿、餐饮消费项目对总体消费的空间分布特征贡献最大,娱乐消费项目对总体消费的空间分布特征贡献较大,而其它消费项目贡献不显著。

RESEARCH ON THE FEATURES OF SPATIAL DISTRIBUTION OF TOURISTS' EXPENDITURE IN THE DESTINATION OF SUZHOU

So far, there have been many results about the model of the tourist's spatial distribution at home and abroad. At the same time, several models of the distribution of the resident's small-scale open space have been researched during the past study. However, most of the researches still focus on the macroscopic geographic models, such as gravity model and model of distance decay. Few researches are conducted on tourists' expenditure. Thus, we try to find an inner mechanism and establish a spatial model depending on tourists' expenditure. In general, the following 6 factors which impact on the tourist's expenditure are:tourists' income, price levels of the destination, the fluctuations of currency exchange rate, tourist's demographic features, special events and other factors. We find that no research has been made on the impact of tourists' expenditure in terms of the factor of pure distance. Tourists' expenditure covers two parts:the cost in destination and the cost on the way. To avoid the impact of the correlation between the total expenditure and the distance, we study tourists' expenditure in destination. Choosing 3337 available samples in sampling survey offered by Jiangsu Travel Bureau, using a series of statistical methods, such as the correlation analysis, etc., based on the factors of social economy, we educe and explain the two stages of the curve of spatial distribution of tourists' expenditure in the destination of Suzhou:the increasing stage and the stagnant stage. The result of our using the same method to analyse the samples of the intraprovincial tourists' cost in destination has verified the reliability of the spatial distribution. Correlation coefficient R between tourists' cost in destination and the distance is 0.712. And the correlation is significant at 0.01 level. Within 250 kilometers, the correlation is more intense. After that, the curve is becoming more and more gentle. This is consistent to previous research. Finally, in the view of segment cost, a further study shows that accommodation and restaurant make largest contribution to the whole spatial distribution, and the less important part is recreation. Other factors' impact is not so obvious.