A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares (PLS) regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and in-fluencing factors demonstrate the land use character of rural industrialization and urbaniza-tion in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.     

Spatially filtered ridge regression (SFRR): A regression framework to understanding impacts of land cover patterns on urban climate

Understanding the impacts of land cover pattern on the heat island effect is essential for sustainable urban development. Conventional model fitting methods have restricted ability to produce accurate estimates of the land cover‐temperature association due to the lack of procedures to address two important issues: spatial dependence in proximal spatial units and high correlations among predictor variables. In this study, we seek to develop an effective framework called spatially filtered ridge regression (SFRR) to estimate the variations in the quantity and distribution of land surface temperature (LST) in response to various land cover patterns. The SFRR effectively integrates spatial autoregressive models and ridge regression, and it achieves reliable parameter estimates with substantially reduced mean square errors. We show this by comparing the performance of the SFRR to other widely adopted models using Monte Carlo simulation followed by an empirical study over central Phoenix. Results highlight the great potential of the SFRR in producing accurate statistical estimates, providing a positive step toward informed and unbiased decision‐making across a wide variety of disciplines. (Code and data to reproduce the results in the case study are available at: https://github.com/cfan13/SFRRTGIS.git .)     

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北部湾墨西哥湾扇贝形态性状与闭壳肌质量的关系

为寻找墨西哥湾扇贝中对其闭壳肌重量影响最显著的形态学特征,在整个扇贝的生长过程中,每一个月测一次壳高(mm)、壳宽(mm)、铰合线长(mm)、体质量(g)和闭壳肌质量(g),每一轮的样本采集量为60,通过通径分析研究这些形态学特征对闭壳肌质量的影响。结果显示,根据各形态学特征对闭壳肌质量的直接的、间接的、总路径的贡献,发现体质量的影响是最显著的(p<0.01),其他的形态学特征如壳高、壳宽和铰合线长对于闭壳肌生长既不限制也不促进。     

病态加权总体最小二乘靶向病灶的正则化方法

病态EIV模型的病灶源于设计矩阵的部分数据列之间存在复共线性关系。针对病灶特点制定正则化策略,在克服病态性的同时尽量减小正则化过程所引起的副作用,提出靶向病灶的正则化方法。通过数值试验,与总体最小二乘方法、病态总体正则化方法等进行比较,结果表明靶向病灶的正则化方法最优。     

SPOT影像光束法平差复共线性消除

CCD卫星影像空间后方交会时,存在系数矩阵列向量间的强相关的问题,用光束法平差同样存在这个问题,将光束法平差与线角元素分求法、广义岭估计、附有限制条件的平差结合,证实三种方法都可以克服平差时外元素和变率改正数震荡大的缺点,并且取得了合理的空间后方交会精度和地面点定位精度。     

利用复共线性诊断确定偏差矫正项的截断型岭估计

为了避免有偏估计的偏差对可靠部分的影响,提出了偏差矫正的正则化方法,但是偏差矫正项的选取是个关键问题。首先采用复共线性诊断、度量和检验所获得的重要信息,对受复共线性危害严重的分量进行估计,且使得均方误差达到极小。然后基于偏差矫正的正则化解法的一般理论,得到偏差矫正的分析性条件,从而得到一种新的基于复共线性诊断确定偏差矫正项的截断型岭估计。最后通过算例分析验证了该方法在提高解的质量、参数估值的准确性和稳定性方面的优良性。     

On the estimation of the generalized covariance function

The estimation of the generalized covariance function, K, is a major problem in the use of intrinsic random functions of order k to obtain kriging estimates. The precise estimation by least-squares regression of the parameters in polynomial models for K is made difficult by the nature of the distribution of the dependent variable and the multicollinearity of the independent variables.     

基于Tweedie-GAM模型研究海州湾小黄鱼资源丰度与栖息环境的关系

本研究根据2011年及2013—2016年春季和秋季在海州湾及其邻近海域进行的底拖网调查数据,结合同步采集的底层海水温度、底层海水盐度、水深、底质类型,以及脊腹褐虾(Crangon affinis)、细螯虾(Leptochela gracilis)、鳀(Engraulis japonicus)、赤鼻棱鳀(Thrissa kammalensis)等小黄鱼(Larimichthys polyactis)主要饵料生物的资源丰度数据,采用条件数κ和方差膨胀因子(VIF)度量多重共线性的程度,选取关键环境因子,再应用基于Tweedie分布的广义可加模型(GAM)研究不同季节和不同生长阶段的小黄鱼资源丰度与环境因子的关系。多重共线性的检验表明,所有初始变量之间没有显著的多重共线性,均可作为解释变量代入模型。结果表明:不同季节和生长阶段,影响小黄鱼资源分布的主要因子及其偏差解释率各不相同,各变量所对应的适宜范围也不同。例如:影响春季小黄鱼幼体资源分布的主要因子有底层海水温度、底层海水盐度、水深和脊腹褐虾的分布,其中偏差解释率最大的因子为水深(16.09%);而影响春季成体资源分布的因子为底层海水温度、底层海水盐度、水深及脊腹褐虾和鳀的分布,其中偏差解释率最大的因子为底层海水盐度(13.56%)。本研究表明,海州湾及其邻近海域不同季节和不同生长阶段小黄鱼的资源分布与其自身的生态习性、海洋环境以及饵料生物的分布密切相关。     

信噪比检验设计与剖析

利用信噪比概念对测量平差质量提出了一种度量检验方式。分析了复共线性、数据列短型、待估参数数量级差异和模型误差等影响平差质量的一些关键因素,找到了这些因素在信噪比中的数值反映。通过对这些数值的进一步分析,给出了关键因素对平差质量综合影响的一种解释。对于研究测量平差特别是病态问题平差的质量具有一定的价值。