摘要
以2005年、2010年和2015年甘肃省87个县区、县级市及自治县的工业化、城镇化、信息化、农业现代化与绿色化(简称"五化")为研究对象,利用空间自相关分析、地理加权回归分析等空间分析方法,结合构建的"五化"协同发展模型,对甘肃省"五化"协同发展的时空分异格局、时空演变趋势和影响因素进行研究分析,以期为甘肃省各市州科学的制定发展政策提供参考。结果表明:甘肃省"五化"协同发展格局具有明显的空间相关性且空间差异显著,协同水平呈现出明显的西北高、东南低分布格局;"五化"发展水平和综合水平较低,协同水平总体不高,以轻度失调、濒临失调和勉强协同为主,但总体上升幅度较大;"五化"协同发展的影响因素,按其影响力大小依次为:农村居民人均可支配收入>城乡居民可支配收入差>固定资产投资总额>财政支出>地形起伏度>城乡居民消费差>降水量。
This paper selected 87 counties, county-level cities and autonomous counties in Gansu Province, China as the research unit and measured these regions＇ level of industrialization, urbanization, informatization, agricultural modernization and greenization （abbreviation as ＂Five Modernizations＂） , and the coordination development degree of ＂Five Modernizations＂, as well as the spatio-temporal evolution pattern and influencing factors. Constructing the model of the ＂Five Modernizations＂ collaborative development, the weight of each index was calculated by entropy method. Using the spatial analysis method, Spatial Auto-correlation Analysis and Geographically Weighted Regres- sion analysis （GWR）, the spatial-temporal pattern, spatio-temporal evolution trend and influence factors of ＂Five Modernizations＂ collaborative development were analyzed. The results show that the coordination development level of ＂Five Modernizations＂ in Gansu has been increased gradually. The index of coordination development level of ＂Five Modernizations＂ was 0. 510 2 in 2015 which suggested a constrained synergy type ,meaning the coordination was mildly disordered, or on the verge of disorder or constrained. Considering social and economy factors, this paper selected 12 factors including GDP, total investment in fixed assets,fiscal expenditure, disposable income of urban residents, consumption level of urban residents, disposable income of rural residents, consumption level of rural resi- dents, disposable income of urban and rural residents, consumption level of urban and rural residents, total retail sales of social consumer goods, road length, topography relief and precipitation to explore the influence factors on co- ordination development of ＂Five Modernizations＂. Combining with the spatial autocorrelation analysis method, the global Moran＇s 1 value of the coordination development of ＂Five Modernizations＂ index in Gansu Province was 0. 496 1, which indicated a significant spatial correlation with the confidence level being more than 0.05. In addi- tion using space exploration analysis tool to eliminate the factors whose variance inflation factor variables （VIF） were greater than 10, the remaining factors included total investment in fixed assets, difference in disposable income of urban and rural residents, difference in consumption of urban and rural residents, disposable income of rural resi- dents, fiscal expenditure, precipitation and topographic relief. Input these factors to the GWR model, we had the fol- lowing results AICc value is - 118.63 ,the R2 value is 0. 728 0 and Cond values is less than 30. According to the value of AICc, R2 and Cond, GWR model has good rationality in analyzing the influencing factors. By comparing co- efficient of each variable, the influence degree of the seven variables were listed in a descending order as follows： per capita disposable income of rural residents 〉 discretionary disposable income of urban and rural residents 〉 total fixed asset investment 〉 expenditure on finance 〉 topographic relief 〉 consumption difference between urban and ru- ral areas 〉 precipitation. Study of the coordination development level of ＂Five Modernizations＂ in Gansu Province can provide reference for the scientific development policy of the county level units in Gansu Province, and put for- ward some suggestions for the coordination development.
作者
郑海松
石培基
康靖
ZHENG Hai-song;SHI Pei-ji;KANG Jing(College of Geographic and Environmental Science,Northwest Normal University,Lanzhou 730070,Gansu,Chin)
出处
《干旱区地理》
CSCD
北大核心
2018年第4期874-884,共11页
Arid Land Geography
基金
河湟地区多尺度地理格局与兰西城市群相互作用的空间效应(41771130)
关键词
“五化”协同
熵权法
地理加权回归分析
时空演变格局
collaborative development of ＂Five Modernizations＂
entropy method
geographically weighted regression analysis
spatio-temporal evolution trend
