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基于GWR对中国乡村居民生活富裕影响因素研究
引用本文:张俊,边振兴,林琳,杨祎博. 基于GWR对中国乡村居民生活富裕影响因素研究[J]. 中国农业资源与区划, 2021, 42(4): 10-17
作者姓名:张俊  边振兴  林琳  杨祎博
作者单位:沈阳农业大学土地与环境学院,辽宁沈阳 110866
基金项目:辽宁省科协科技创新智库项目“辽宁省乡村振兴主要途径研究”(lnkx2017B09)
摘    要:[目的]实现乡村"生活富裕",是以乡村居民为中心,让乡村居民过上稳定美好的日子。也是美丽乡村建设,实现乡村振兴的最终目的。文章通过对模型的模拟分析,找出不同地区自变量与因变量之间的相关性,对精准施策提供参考依据。[方法]该文依托2017年中国农村统计数据,选取农村居民可支配收入作为生活富裕的指示因子,在我国农村居民可支配收入空间分布的基础上,从经济条件、基础设施建设、生态环境等方面选取8个影响因子。运用GWR (地理加权回归模型),对影响农村居民可支配收入的因子要素数据进行驱动力分析,运用ArcGIS对模型分析结果可视化,得到更加直观的影响效果图。[结果]研究模型结果拟合度较高,指示因子与各变量间存在相关性,符合客观规律,研究结果具有一定价值。[结论](1)农村居民消费水平,农业机械总动力,自然保护区个数,水利设施(水库数),农村住户固定资产投资完成额与农村居民可支配收入呈正相关。改水改厕,各地区农村卫生室,农作物播种总面积与农村居民可支配收入呈负相关。(2)各要素对农村居民的可支配收入的影响程度不同。且各影响要素对不同行政区驱动力的影响大小不同,东南地区驱动力大于西北地区。(3)对农村可支配收入的提高,使农村达到人民富裕的目标要根据地域不同,制定不同有特色的战略决策。该研究创新点在于将各要素进行局部空间性加权分析,避免空间非均质导致变量产生的不稳定性,能更加科学地反应各地区因子影响的空间形态。

关 键 词:乡村振兴  乡村居民  GWR  生活富裕  要素分析
收稿时间:2019-07-03

THE RESEARCH ON THE INFLUENTIAL FACTORS OF GWR ON THE LIFE WEALTH OF RURAL RESIDENTS IN CHINA
Zhang Jun,Bian Zhenxing,Lin Lin,Yang Yibo. THE RESEARCH ON THE INFLUENTIAL FACTORS OF GWR ON THE LIFE WEALTH OF RURAL RESIDENTS IN CHINA[J]. Journal of China Agricultural Resources and Regional Planning, 2021, 42(4): 10-17
Authors:Zhang Jun  Bian Zhenxing  Lin Lin  Yang Yibo
Affiliation:School of Land and Environment, Shenyang Agricultural University, Shenyang 110866, Liaoning, China
Abstract:The actualization of rural "rich life" focuses on rural residents, so that they live a stable and beautiful life. It is also the ultimate goal for building the beautiful countryside and actualizing rural revitalization. The purpose of this study is to investigate the correlation between independent variables and dependent variables in different regions through the simulation analysis of the model, and to provide reference for accurate policy implementation. Based on the statistical data of rural areas in China in 2017, this paper selected the disposable income of rural residents as an indicator of wealth. Based on the spatial distribution of disposable income of rural residents in China, 8 impact factors including aspects of economic conditions, infrastructure construction and ecological environment were selected. And GWR (geographic weighted regression model) was used to analyze the driving force of factor data affecting the disposable income of rural residents. Then, by using ArcGIS to visualize the model analysis results, a more intuitive effect diagram was obtained. The degree of fit of the model is relatively high. The correlation between the indicator and the variables is in accordance with the objective law, and the result of the study is of certain value. The research draws the following conclusions. (1) The consumption level of rural residents, the total power of agricultural machinery, the number of nature reserves, water conservancy facilities (the number of reservoirs), and the amount of fixed asset investment of rural households are positively correlated with the disposable income of rural residents. The change of water supply and toilets, the number of rural clinics in each areas, and the total planting area of crops are negatively correlated with the disposable income of rural residents. (2) Different factors have different influences on the disposable income of rural residents, and the degree of influence differs between different administrative divisions. The driving force of the southeast region is greater than that of the northwest region. (3) To increase the disposable income in rural areas and to reach the goal of prosperity of rural residents, different strategic decisions with different characteristics should be made according to the difference in regions. The prosperity of life is not only the foundation of rural revitalization, but also the inevitable requirement of actualization of the common prosperity of all people. It is the ultimate goal of building beautiful villages and villages to actualize the "prosperity of life" in rural areas. The innovation of this research lies in the local spatial weighting analysis of each factor, which scientifically reflects the spatial forms affected by each regional factor. The geographical weighted regression model highlights the local regression results in the study area, avoid the instability of variables caused by spatial heterogeneity, and selects the appropriate regression weighted model according to the surrounding environmental variables for the disposable income of rural residents in the region.
Keywords:rural revitalization  rural residents  GWR  life rich  factor analysis
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