刘海军, 闫俊杰, 崔东, 孙国军, 图热妮萨·图尔贡. 伊宁市土地利用结构变化及人文驱动因子分析[J]. 云南大学学报(自然科学版), 2019, 41(2): 309-316. doi: 10.7540/j.ynu.20180108
引用本文: 刘海军, 闫俊杰, 崔东, 孙国军, 图热妮萨·图尔贡. 伊宁市土地利用结构变化及人文驱动因子分析[J]. 云南大学学报(自然科学版), 2019, 41(2): 309-316. doi: 10.7540/j.ynu.20180108
LIU Hai-jun, YAN Jun-jie, CUI Dong, SUN Guo-jun, Turansa·turgon. Analysis of land use structure change and human driving factors in Yining[J]. Journal of Yunnan University: Natural Sciences Edition, 2019, 41(2): 309-316. DOI: 10.7540/j.ynu.20180108
Citation: LIU Hai-jun, YAN Jun-jie, CUI Dong, SUN Guo-jun, Turansa·turgon. Analysis of land use structure change and human driving factors in Yining[J]. Journal of Yunnan University: Natural Sciences Edition, 2019, 41(2): 309-316. DOI: 10.7540/j.ynu.20180108

伊宁市土地利用结构变化及人文驱动因子分析

Analysis of land use structure change and human driving factors in Yining

  • 摘要: 以伊宁市土地利用现状数据、土地利用变更数据及年鉴等数据为基础,探究研究区的土地利用结构变化及其人文驱动因素. 运用信息熵、均衡度、优势度等方法分析土地利用结构的时序变化和空间分异规律. 2009—2015年城镇村及工矿用地面积增加8 198.50 hm2,草地减少7 777.74 hm2,变化率和动态度较为明显;土地利用结构信息熵时间维分为:上升区、稳定区、下降区;区域差异在空间维为:高熵值、中熵值、低熵值. 利用主成分分析法研究土地利用结构变化的动力因素,提取的3个主成分因子的累积方差贡献率达到93.415%. 结果表明:在研究期城镇村及工矿用地、草地变化趋势较明显;经济规模、中心城区的辐射效应、政策导向性是土地利用结构信息熵时空分异的主要控制因素;人口密度、第三产业占GDP的比重、社会消费品零售总额、国有固定资产投资、农村居民人均生活消费支出等在其主成分中最为显著.

     

    Abstract: Based on the data of land use status, land use change and yearbook of Yining City, the author explored the change of land use structure and its humanistic driving factors in the research area. The sequential variation and spatial differentiation of land use structure were analyzed by means of information entropy, equilibrium degree and dominance degree. From 2009 to 2015, the area of urban villages and industrial and mining land increased by 8 198.50 hm2, while the grassland decreased by 7 777.74 hm2. The change rate and dynamic degree were obvious. The time dimension of information entropy of land use structure is divided into ascending area, stable area and descending area, and the spatial dimension of regional difference is high entropy value, medium entropy value and low entropy value. The dynamic factors of land use structure change were studied by principal component analysis. The cumulative variance contribution rate of the three principal component factors was 93.415%. The results show that the change trend of urban villages, industrial and mining land and grassland is obvious during the study period, and the main controlling factors of spatial and sequential differentiation of land use structure information entropy are economic scale, radiation effect of central urban area and policy orientation, and population density, the proportion of tertiary industry to GDP, total retail sales of consumer goods, investment in state-owned fixed assets, and per capita consumption expenditure of rural residents are the most significant components.

     

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