地理科学进展 ›› 2016, Vol. 35 ›› Issue (4): 515-525.doi: 10.18306/dlkxjz.2016.04.012

• 研究论文 • 上一篇    

南疆三地州贫困与旅游资源优势空间关联研究

安强1,2(), 杨兆萍1,**(), 徐晓亮1, 时卉1, 张璐1,2   

  1. 1. 中国科学院新疆生态与地理研究所,乌鲁木齐 830011
    2. 中国科学院大学,北京 100049
  • 出版日期:2016-04-27 发布日期:2016-04-27
  • 通讯作者: 杨兆萍 E-mail:543014828@qq.com;yangzp@ms.xjb.ac.cn
  • 作者简介:

    作者简介:安强(1988-),男,山东日照人,硕士研究生,主要从事区域经济与旅游规划研究,E-mail: 543014828@qq.com

  • 基金资助:
    中国科学院西部博士资助项目(Y435133001);国家自然科学基金项目(41301204)

Spatial correlation of poverty and tourism resources in three prefactures in South Xinjiang

Qiang AN1,2(), Zhaoping YANG1,*(), Xiaoliang XU1, Hui SHI1, Lu ZHANG1,2   

  1. 1. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2016-04-27 Published:2016-04-27
  • Contact: Zhaoping YANG E-mail:543014828@qq.com;yangzp@ms.xjb.ac.cn
  • Supported by:
    West Light Foundation of the Chinese Academy of Sciences, No;Y435133001;National Natural Science Foundation of China, No.41301204

摘要:

2020年全面建成小康社会的目标对贫困治理更具现实意义。本文通过构建SOFM神经网络评估模型,聚类分析南疆三地州24个县市的经济--社会--自然多维度贫困。在评价县域旅游资源优势度的基础上,运用空间数据分析方法(ESDA)分析研究区县域贫困度和旅游资源优势度的空间分布格局,将两者在空间匹配后发现:①致贫因子集合向量的Matlab分类显示,南疆三地州各县市贫困类型可划分为经济型贫困、社会型贫困和生态型贫困,并且贫困空间聚合分布态势显著。②旅游资源优势区集中在地理环境结构复杂和多民族聚居的地区,如喀什市、塔县、阿克陶县、和田市;旅游资源优势区局域空间自相关呈现集群分布特征。③贫困类型与旅游资源优势在县域存在显著的空间关联,出现3类拟合性较好的区域。建议采取“旅游+旧城改造”和“旅游+商品基地”、“领头雁”旅游扶贫等旅游扶贫模式。

关键词: 经济--社会--自然多维度贫困, 空间数据分析, 旅游扶贫, SOFM网络聚类, 南疆三地州

Abstract:

This study constructed a self-organizing feature map (SOFM) clustering neural network model to evaluate economic-social-ecological poverty for 24 counties in three southern prefectures of Xinjiang Autonomous Region, constructed the evaluation model of regional tourism resources development, and analyzed the spatial distribution patterns of poverty and tourism resources development advantages in the study area with spatial data analysis method (ESDA). By matching the spatial patterns: (1) Factors of poverty-inducing set Matlab classification vector show that the type of southern counties under poverty can be divided into economic, social, and ecological poverty and there is a clear trend of spatial aggregation of poor counties. (2) Places with advanced tourism resources development are concentrated in areas where the geographical environment structure is complex and multiple ethnic communities reside, such as Kashi, Taxian County, Akto, and Hetian. Advanced tourism resources development areas show clear spatial autocorrelation and are distributed in clusters. (3) There is a significant spatial correlation between poverty and tourism resources development.

Key words: economic-social-ecological poverty, spatial data analysis, poverty elimination by tourism, SOFM clustering, three prefectures in South Xinjiang