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基于GIS和人工神经网络模型的区域生态旅游适宜度评价——以浙江省为例

郑晓兴;孙铭;陈鹰;王祥荣   

  1. 复旦大学环境科学与工程系, 上海 200433
  • 收稿日期:2006-01-16 修回日期:2006-07-26 出版日期:2006-11-10 发布日期:2006-11-10

Evaluation of regional ecotourism suitability based on GIS and artificial neural network model: A case study of Zhejiang Province, China

ZHENG Xiaoxing;SUN Ming;CHEN Ying;WANG Xiangrong   

  1. Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
  • Received:2006-01-16 Revised:2006-07-26 Online:2006-11-10 Published:2006-11-10

摘要: 区域生态旅游适宜度评价作为区域资源理论与旅游开发活动的结合点,对于区域产业布局和宏观调控具有重大意义。从区域生态旅游适宜度概念出发,以旅游资源条件、生态环境质量和社会经济条件构建评价指标体系,并借助于GIS空间分析功能和人工神经网络模型中自组织特征映射网络方法,对浙江省72个县级行政单位的区域生态旅游适宜度进行聚类分析。根据聚类结果分异情况,把研究区域分为生态旅游最适宜区、高度适宜区、中度适宜区、一般适宜区和重点建设区,对各区的生态旅游适宜度进行了评价,并从区域可持续发展角度提出了相应的开发保护对策,为各区旅游产业定位和发展方向提供科学依据。

Abstract: As the link point of regional resource theory and tourism exploitation activity,the evaluation of regional ecotourism suitability plays a significant role in the regional industrial allocation and macroscopic control, but few researches have been done in this field. Based on the concept of regional ecotourism suitability, the evaluation indices system of regional ecotourism suitability was compos ed from the aspects of tourism resources, eco-environmental quality, and social-economical conditions, and by means of the spatial analysis of GIS and the self organization feature map in artificial neural network model, a cluster analysis was performed to assess the regional differentiation of ecotourism suitability in the 72 counties of Zhejiang Province. According to the results of cluster analysis, the study areas were divided into five types of regions,i.e, most suitable, highly suitable, medially suitable, generally suitable, and key construction regions. The ecotourism suitability of these regions was evaluated, and the countermeasures for regional sustainable development were put forward, which had consultation meaning to probe into the orientation and development strategy for regional tourism industry.

Key words: East China Sea, Red tide, Remote sensing