[1]GUI Yang,WANG Hongwei,CHAI Chunmei,et al.Analysis of County Economic Disparities in South of Xinjiang Based on Entropy Weight Method and ESDA[J].Research of Soil and Water Conservation,2017,24(02):223-228,233.
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Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
24
Number of periods:
2017 02
Page number:
223-228,233
Column:
Public date:
2017-04-28
- Title:
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Analysis of County Economic Disparities in South of Xinjiang Based on Entropy Weight Method and ESDA
- Author(s):
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GUI Yang1,2, WANG Hongwei1,2, CHAI Chunmei1,2, WEI Min1,2, ZHAO Zhe1,2
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1. College of Resource and Environment Science, Xinjiang University, Urumqi 830046, China;
2. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
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- Keywords:
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regional economic disparities; entropy weight; ESDA; south of Xinjiang
- CLC:
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F127
- DOI:
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- Abstract:
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In order to narrow the regional economic differences and promote the coordinated economy development of southern Xinjiang and whole Xinjiang, 42 counties in southern Xinjiang were chosen as the study samples. We used the entropy weight method to evaluate the counties’ comprehensive economy development levels; in addition,we analyzed the domain economy temporal evolution pattern based on ESDA and GIS spatial analysis method. The results are as followings. In terms of the global spatial differences, the autocorrelation levels were increasing from 0.149 2 in 2000 to 0.264 0 in 2013, since the index was at a lower level, the overall upward trend indicated that the level of economy development of 42 counties in southern Xinjiang existed a positive correlation but the relationship was not significant, and the economic ties among the counties were weak, though the economic differences among the counties decreased. In terms of local spatial disparity, the zone with higher levels of economic development had always focused in Korla City, meanwhile, the area with low level of economic development widely distributed, which mainly concentrated in Kashgar and Hotan area, and counties’ differences of the level economic development remained significant. According to the results, driving factors to the county economic differences were analyzed, which discuss the measures to resolve the status of county economic difference from four main aspects including basic economic development, industrial structure, social security and policy support and population quality and education level. Therefore, the results would provide a certain theoretical basis for local economic layout of the overall planning and policy implementation.