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土地利用变化驱动力多尺度因素的定量影响分析 被引量:27

An Analysis of Quantitative Impacts of Multi-scale Factors on Driving Forces in Land Use Change
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摘要 研究目的:揭示多尺度土地利用变化研究中尺度因素对驱动力的定量影响。研究方法:构建Logistic回归模型和二分类反应变量两水平Logistic回归模型,分别基于面向尺度对比的多尺度回归分析和基于系统观点的多水平模型分析两个角度,探讨多尺度土地利用变化核心驱动力及其定量影响关系,并对两类模型的分析结果进行对比分析。研究结果:面向尺度对比的多尺度回归分析法能够从更为微观的角度揭示各个尺度层级上地类变化的核心驱动因子,而基于系统观点的多水平模型分析方法则能够更好地顾及尺度因素的定量影响。研究结论:多水平模型分析方法揭示的各个地类的核心驱动因子集与多尺度回归分析法推导的对应地类的核心驱动因子集之间呈现子集关系,两者在多尺度土地利用变化驱动力的研究方法上能够形成互补。 The purpose of this study is to reveal quantitative impacts of multi-scale factors on driving forces in multiscale land use change. Firstly, Logistic Regression Model and Two Level Logistic Regression Model of two category response variable are constructed. Secondly, the core driving forces of multi-scale land use change and relationship among them and their quantitative effects are discussed and compared from the two perspectives i.e., one is comparison of multi-scale, the other is systematic viewpoint. The results of the study show that the method of Logistic Regression Model can be used to reveal quantitative relationship among driving factors and land use change in different scale levels, and the method of Two Level Logistic Regression Model of two category response variable has taken quantitative effects of scale factors into consideration. It is concluded that the sub-assembly of core driving factors revealed by the multi-level model is included in the sub-assembly explored by multi-scale model, and the two methods can also form complementary research on core driving forces of multi-scale land use change.
作者 袁磊 杨昆 YUAN Lei YANG Kun(School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China Engineering Research Center of Ministry of Education on Geography Information Technology of Western Resource Environment, Kunming 650500, China)
出处 《中国土地科学》 CSSCI 北大核心 2016年第12期63-70,共8页 China Land Science
基金 云南师范大学博士科研启动项目(01000205020503063) 国家科技支撑计划项目(2013BAJ07B00)
关键词 土地利用 多尺度 驱动力 多水平模型 LOGISTIC回归模型 land use multi-scale driving forces multilevel model Logistic regression model
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