| 基于Logistic回归的CA模型改进方法——以广州市为例 |
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| 引用本文： | 聂婷,肖荣波,王国恩,刘云亚.基于Logistic回归的CA模型改进方法——以广州市为例[J].地理研究,2010,29(10):1909-1919. |
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| 作者姓名： | 聂婷 肖荣波 王国恩 刘云亚 |
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| 作者单位： | 广州市城市规划勘测设计研究院,广州,510060 |
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| 基金项目： | "十一五"国家科技支撑计划(2007BAC28B01) |
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| 摘 要： | 基于Logistic回归的CA模型因其结构简单和数据要求相对较小的优势,被广泛应用于城市模拟领域,但数据的空间自相关性影响了模型机制挖掘与模拟精度。通过将影响城市发展演变的各种约束条件划分为强制和普通约束条件,以及运用主成分分析降低普通约束条件的数据相关性,构建了改进型Logistic回归CA模型,并在2000~2008年广州市城市增长模拟研究中进行应用。结果表明:与传统型Logistic回归CA模型相比,改进型Logistic回归CA模型在模型拟合度和精度上均有4%左右的提高。其中约束条件划分对非城市像元模拟精度约有6%的提高,对整体精度有3%的提高。更为重要的是,降低数据相关性后,Logistic回归CA模型对于城市扩展机制的解释更符合实际。本研究旨在寻求一种简单可行且易于构建的CA模型,探求城市发展机理,为城市规划管理提供更为准确的科学依据。
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| 关 键 词： | 逻辑回归 元胞自动机 空间自相关 城市增长模拟 |
| 收稿时间： | 2009-12-03 |
| 修稿时间： | 2010-04-18 |
An improvement on CA model of logistic regression: A case study of Guangzhou |
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| NIE Ting,XIAO Rong-bo,WANG Guo-en,LIU Yun-ya.An improvement on CA model of logistic regression: A case study of Guangzhou[J].Geographical Research,2010,29(10):1909-1919. |
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| Authors: | NIE Ting XIAO Rong-bo WANG Guo-en LIU Yun-ya |
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| Institution: | Guangzhou Urban Planning &|Design Survey Research Institute, Guangzhou 510060, China |
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| Abstract: | Due to its simple structure and less input data, CA model of logistic regression is widely applied in urban simulation. However, data dependency has some impact on the accuracy. Therefore, an in-depth research should be conducted to modify the traditional model. This paper established an improved CA model of logistic regression in two major aspects. First, the urbanization factors were divided into forbidden constraint and general constraint. The input data were sampled only in general constraint, while the urbanization probability in forbidden constraint was set to be 0. Second, we reduced the data dependency of general constraint using principal component analysis in SPSS. In the case study of Guangzhou, the improved CA model was applied to simulate the urban growth from 2000 to 2008. Compared to the traditional CA model, the improved CA model made a 4% improvement both on model fitness and simulation accuracy, in which constraints division contributed a 3% improvement on overall simulation accuracy and a 6% improvement on non-urban simulation accuracy, while data dependency reduction gave a more reasonable explanation for urbanization mechanism. The study aimed to establish an improved CA model, which can mine a more reasonable urbanization mechanism, and provide more scientific support for urban planning and land management. |
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| Keywords: | logistic regression cellular automata data dependency urban simulation |
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