长株潭衡城市群试点贫困空间分布与发展变化分析
首发时间:2019-06-17
摘要:农村贫困问题是中国全面建成小康社会的主要障碍之一。面向当前国家瞄准贫困村和贫困人口的精准脱贫战略需求,以分析长株潭衡城市群试点贫困空间分布特征和经济发展变化为目标,利用GWR模型、区域平均夜间灯光强度和年均增长率等方法对研究区的贫困空间分布特征进行分析,并从市、县、村三个角度来研究2015-2018年灯光强度的变化情况,结果表明长株潭衡城市群试点贫困村分布存在较强的全局空间依赖性,呈现出西南部聚集,东北部稀疏,即衡阳市和株洲市是贫困村分布的聚集区,且同时存在多个不同量级的块状密集区。自然增长率和规模以上服务业企业单位数是贫困村数量的正向指标,全体人均可支配收入为负向指标。总体上,2015-2018年期间所有地级市或区县的灯亮度均值都有一定程度提高。740个贫困村中有93.7%的贫困村年均增长率超过20%,有259个贫困村年均增长率超过了50%,大部分贫困村在这3年里经济有了较大的提高,国家精准扶贫政策取得了明显的成效。
关键词: GIS 贫困村分布 空间自相关 GWR模型 灯光强度
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Analysis on Spatial Distribution and Development Change of Poverty in Chang-Zhu-Tan-Heng Urban Agglomeration
Abstract:Rural poverty is one of the main obstacles to building a well-off society in China. Aiming at the demand of precise poverty alleviation strategy aiming at the poor villages and the poor population, aiming at analyzing the spatial distribution characteristics of poverty and the changes of economic development in the pilot project of Changsha-Zhuzhou-Tan-Heng urban agglomeration, this paper analyzed the spatial distribution characteristics of poverty in the study area by using GWR model, average night light intensity and average annual growth rate, and studied 2015-20 from three perspectives of city, county and village. The changes of light intensity in the past 18 years show that the distribution of poverty-stricken villages in Changsha-Zhuzhou-Tan-Heng urban agglomeration has a strong global spatial dependence, showing a clustering in the southwest and a sparse in the northeast. Hengyang City and Zhuzhou City are the agglomeration areas of poverty-stricken villages, and there are several massive dense areas of different scales at the same time. The natural growth rate and the number of enterprises in service industry above the scale are positive indicators of the number of poor villages, while the per capita disposable income is negative indicators. Overall, the average luminance of all prefecture-level cities or districts and counties increased to a certain extent during the period of 2015-2018. In 740 poor villages, 93.7% of the poor villages have an average annual growth rate of more than 20%, and 259 poor villages have an average annual growth rate of more than 50%. The economy of most poor villages has improved greatly in the past three years, and the state\'s precise poverty alleviation policy has achieved remarkable results.
Keywords: GIS Poor Village Distribution Spatial Autocorrelation GWR Model Lighting Intensity
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