PROGRESS IN GEOGRAPHY ›› 2016, Vol. 35 ›› Issue (4): 515-525.doi: 10.18306/dlkxjz.2016.04.012

• Articles • Previous Articles    

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;
  • Supported by:
    West Light Foundation of the Chinese Academy of Sciences, No;Y435133001;National Natural Science Foundation of China, No.41301204


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