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An Evaluation of Spatial Autocorrelation and Heterogeneity in the Residuals of Six Regression Models

机译:六个回归模型残差中空间自相关和异质性的评估

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摘要

Spatial effects include spatial autocorrelation and heterogeneity. Ignoring spatial effects in a modeling process causes misleading significance tests and suboptimal model prediction. In this study, we used three forest plots with different spatial patterns of tree locations (i.e., clustered, random, and regular patterns) to investigate the spatial distributions and heterogeneity in the model residuals from six regression models with the ordinary least squares (OLS) as the benchmark. Our results revealed that when significant spatial autocorrelations and variations existed in the relationship between tree height and diameter, as in the softwood plot (clustered) and hardwood plot (random), OLS was not appropriate for modeling the relationship between tree variables. Spatial regression models (i.e., spatial lag and spatial error models) were effective for accounting for spatial autocorrelation in the model residuals, but they were insufficient to deal with the problem of spatial heterogeneity. It was evident that the model residuals in both spatial lag and spatial error models had a similar pattern and magnitudes of spatial heterogeneity at spatial scales different from those of the OLS model. In contrast, the linear mixed model and geographically weighted regression incorporated the spatial dependence and variation into modeling processes, and consequently, fitted the data better and predicted the response variable more accurately. The model residuals from both the linear mixed model and geographically weighted regression had desirable spatial distributions, meaning fewer clusters of similar or dissimilar model residuals over space. [PUBLICATION ABSTRACT]
机译:空间效应包括空间自相关和异质性。忽略建模过程中的空间影响会导致误导性的重要性测试和次优模型预测。在这项研究中,我们使用三个具有不同树位置空间模式(即群集,随机和规则模式)的森林地块,研究了六个具有普通最小二乘(OLS)回归模型的模型残差中的空间分布和异质性作为基准。我们的结果表明,当树高与直径之间的关系存在显着的空间自相关和变化时,例如在软木图(群集)和硬木图(随机)中,OLS不适合建模树变量之间的关系。空间回归模型(即空间滞后模型和空间误差模型)可有效解决模型残差中的空间自相关问题,但不足以解决空间异质性问题。显然,在空间滞后模型和空间误差模型中,模型残差在不同于OLS模型的空间尺度上具有相似的模式和大小。相比之下,线性混合模型和地理加权回归将空间依赖性和变化纳入建模过程,因此,可以更好地拟合数据并更准确地预测响应变量。来自线性混合模型和地理加权回归的模型残差都具有理想的空间分布,这意味着在空间上更少的相似或不相似的模型残差簇。 [出版物摘要]

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  • 来源
    《Forest Science》 |2009年第6期|p.533-548|共16页
  • 作者单位

    Lianjun Zhang, Department of Forest and Natural Resources Management, State University of New York, College of Environmental Science and Forestry, One Forestry Drive, Syracuse, NY 13210- Phone: 315-470-6558, Fax: 315-470-6535, lizhang@esf.edu. Zhihai Ma, Department of Forest and Natural Resources Management, State University of New York, College of Environmental Science and Forestry, One Forestry Drive, Syracuse, NY 13210 - lizhang@esf.edu. Luo Guo, College of Life and Environmental Sciences, MinZu University of China, 27 Zhong-Guan-Cun South Ave., Beijing 100081, P.R. China-lizhang@esf.edu.Acknowledgments: We acknowledge the financial support by US Forest Service NE Agenda 2020 Project 05-JV- 1 1 242328-009 and by MinZu University of China, 111 Project of MUC (B08044).Manuscript received November 18, 2008, accepted August 7, 2009 Copyright © 2009 by the Society of American Foresters,;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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  • 入库时间 2022-08-17 13:45:58

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