城市景观空间自相关与自相似的尺度特征研究

在RS与GIS技术支持下,以上海市外环以内城市中心区为区域背景,研究城市景观格局空间自相关与分形结构在不同粒度下的变化特征,揭示其对粒度的响应特征和敏感程度。结果表明,城市景观格局的空间自相关性和自相似性依赖于粒度的选择,MoranⅠ与各种景观类型的分维数均存在尺度效应;城市景观格局空间自相关性与自相似性在一定尺度范围内具有尺度依赖性,但二者对尺度的依赖程度不同。空间自相关性具有较高的尺度依赖性;而不同景观类型的自相似性随尺度的变化没有统一规律;城市景观格局的空间自相关性和自相似性对尺度变化的敏感点不同。居住景观的MoranⅠ对尺度变化的敏感点为50m,其他景观类型无明显的敏感点;分维数对尺度的敏感点因景观类型不同而异。     

植物居群遗传变异的空间自相关分析

本文介绍了植物遗传变异空间自相关分析的理论、方法与应用,包括将基因型作为绝对型数据与等位基因频率作为连续型数据进行自相关分析的基本方法等。并对影响植物居群遗传变异空间结构的因素以及研究居群内遗传结构的重要意义作了评述。     

植物居群遗传变异的空间自相关分析

本文介绍了植物遗传变异空间自相关分析的理论、方法与应用 ,包括将基因型作为绝对型数据与等位基因频率作为连续型数据进行自相关分析的基本方法等。并对影响植物居群遗传变异空间结构的因素以及研究居群内遗传结构的重要意义作了评述     

郑州市景观多样性的空间自相关格局分析

景观多样性的研究在土地管理与规划、生态景观评价和自然保护区建设等方面起着重要的指导作用。随着城市经济的快速发展, 城市景观多样性也同样发生着剧烈的变化。利用四期Landsat TM 影像, 分析郑州市2000—2015年土地类型的时空变化, 并结合景观多样性指数和空间自相关构建方法, 在对景观多样性时空分布变化分析的基础上,进一步对地理空间单元上景观多样性的空间自相关关系进行了深入探讨。研究结果表明: 研究区整体景观多样性增加,景观破碎化加重; 城市边缘地带的变化符合初期地类转化强烈且景观多样性高值聚集, 随着时间推移最终下降的统一规律, 反映出人类活动与景观多样性分布密切相关; 研究区内区域经济带动性强, 导致研究区整体景观多样性单元之间关联性高, 尤其是郑州东部地区, 经济发展比较突出, 其高值聚集区显著性明显较高。该研究结果表明经济发展是引起城市景观多样性空间分布变化以及空间地理单元自相关强弱的主导因素。     

基于空间自相关的兰州市热环境

基于Landsat-8 OLI/TIRS数据,计算得到亮度温度热场,采用空间自相关方法分析了河谷型城市兰州市热环境的空间分布与内部结构。结果表明:兰州城市热环境的高温中心主要集中在4个主城区的工商业集中和人口密集区域,黄河及两岸的绿化带形成了一条贯穿城区的相对低温通道;波段10的热场能更好地突出高温中心和低温区域,同一土地利用类型的亮度温度分布更为集中,有利于城市热环境的分析;城市亮度温度热场具有高度的空间自相关性;相比较而言,波段10的亮度温度热场空间依赖性更好;在同等分辨率尺度下,城市热环境研究的采样间隔尺度可以确定为1 km;局部空间自相关能很好描述城市热环境内部关联模式,同时有效监测城市热环境的空间异常值区域,为城市生态规划提供依据。     

浙江天台山甜槠种群遗传结构的空间自相关分析

采用空间自相关分析方法对浙江天台山亚热带常绿阔叶林优势种甜槠种群全部个体及不同年龄级个体的小尺度空间遗传结构进行了分析,以探讨甜槠种群内遗传变异的分布特征及其形成机制。根据11个ISSR引物所提供的多态位点,经GenAlEx 6软件计算地理坐标和遗传距离矩阵在10个距离等级下的空间自相关系数。在样地内,甜槠种群内个体在空间距离小于10 m时存在显著的正空间遗传结构,其X-轴截矩为9.945。甜槠种群的空间遗传结构与其种子短距离传播和广泛的花粉传播有关。Ⅰ年龄级、Ⅱ年龄级和III年龄级个体在空间距离小于10 m时存在显著的正空间遗传结构,其X-轴截矩分别为11.820、9.746和9.792。当距离等级为5 m时,其空间自相关系数r分别为0.068、0.054和0.070。Ⅳ年龄级个体在所有空间距离等级中均不存在显著的空间遗传结构。甜槠是多年生、长寿命植物,自疏作用是导致IV年龄级个体空间遗传结构消失的主要原因。     

自激励门限自回归模型下我国人口增长率的时间序列分析

应用自激励门限自回归模型对我国1949年以来人口增长率的动态路径进行了模拟分析.通过估计和检验发现我国人口增长率具有明显的非线性特征,模型拟合的效果优于线性自回归模型,可以为政府决策提供更精确的数量依据.     

福建省毛竹林碳贮量的空间自相关分析

以1985年、1996年和2006年福建省毛竹〔Phyllostachys edulis(Carr.)J.Houz.〕林资源二类清查资料为基础数据估算福建省毛竹林碳贮量,在此基础上,采用空间自相关分析法对福建省毛竹林碳贮量的空间分布规律和空间自相关关系进行研究。结果表明:1985年、1996年和2006年福建省毛竹林碳贮量的空间分布特征相似,均呈现闽西和闽北区域较高、闽东和闽南区域较低的规律;并随时间的推移毛竹林碳贮量逐渐增加。1985年、1996年和2006年福建省毛竹林碳贮量的全局空间自相关指数Moran’s I分别为0.36、0.38和0.41,Z值分别为4.98、5.32和5.57,说明福建省毛竹林碳贮量呈现显著的正空间自相关性,具有明显的集聚现象,且随时间推移集聚现象越来越明显。局部空间自相关分析结果表明:闽西和闽北区域毛竹林碳贮量的空间分布呈现显著的"高-高"正相关集聚特点,而闽东和闽南区域则呈现显著的"低-低"正相关集聚特点,其他少数区域则呈现"高-低"或"低-高"负相关集聚特点。研究结果显示:福建省毛竹林碳贮量的分布格局与各地区毛竹林的发展状况有关。     

家畜试验中的一种相关扰动回归模型

本文根据数量遗传学原理,从家畜群体中同一家系内的个体之间存在的遗传相关性出发,建立了扰动项相关的回归模型,运用回归理论讨论一类相关样本的统计推断及其应用。     

长白山阔叶红松林稠李居群遗传结构的空间自相关分析

以长白山阔叶红松林25 hm²样地为平台,选取样地中胸径≥1 cm的稠李个体共计396株,将其分为1～3、3～10和>10 cm 3个径级,通过微卫星标记对这些个体进行了空间遗传结构的空间自相关分析.结果表明: 样地中的稠李种群总体上具有一定的空间特征,在70 m范围内呈显著正相关, 110 m外则呈负相关.不同年龄级的个体空间遗传结构相似, 且主要源于有限的花粉和种子扩散距离.该稠李居群未出现明显的自疏现象.     

Spatially correlated disturbances in a locally dispersing population model

The basic contact process in continuous time is studied, where instead of single occupied sites becoming empty independently, larger-scale disturbance events simultaneously remove the population from contiguous blocks of sites. Stochastic spatial simulations and pair approximations were used to investigate the model. Increasing the spatial scale of disturbance events increases spatial clustering of the population and variability in growth rates within localized regions, reduces the effective overall population density, and increases the critical reproductive rate necessary for the population to persist. Pair approximations yield a closed-form analytic expression for equilibrium population density and the critical value necessary for persistence.     

Regression Models for Clinical Trials with Correlated Categorical Responses

Regression models for correlated categorical data are presented in which the covariance is a function of measured effects. The regression and covariance parameters are estimated by extended least square methods. A numerical example of a clinical trial comparing two antiemetic treatment regimes for patients receiving chemotherapy is given to illustrate the approach.     

Quasi Likelihood/Moment Method for Generalized and Restricted Generalized Poisson Regression Models and Its Application

This paper reviews the generalized Poisson regression model, the restricted generalized Poisson regression model and the mixed Poisson regression (negative binomial regression and Poisson inverse Gaussian regression) models which can be used for regression analysis of counts. The aim of this study is to demonstrate the quasi likelihood/moment method, which is used for estimation of the parameters of mixed Poisson regression models, also applicable to obtain the estimates of the parameters of the generalized Poisson regression and the restricted generalized Poisson regression models. Besides, at the end of this study an application related to this method for zoological data is given.     

A Primer on Interpreting Regression Models

Abstract: We perceive a need for more complete interpretation of regression models published in the wildlife literature to minimize the appearance of poor models and to maximize the extraction of information from good models. Accordingly, we offer this primer on interpretation of parameters in single- and multi-variable regression models. Using examples from the wildlife literature, we illustrate how to interpret linear zero-intercept, simple linear, semi-log, log-log, and polynomial models based on intercepts, coefficients, and shapes of relationships. We show how intercepts and coefficients have biological and management interpretations. We examine multiple linear regression models and show how to use the signs (+, -) of coefficients to assess the merit and meaning of a derived model. We discuss 3 methods of viewing the output of 3-dimensional models (y, x₁, x₂) in 2-dimensional space (sheet of paper) and illustrate graphical model interpretation with a 4-dimensional logistic regression model. Statistical significance or Akaike best-ness does not prevent the appearance of implausible regression models. We recommend that members of the peer review process be sensitive to full interpretation of regression models to forestall bad models and maximize information retrieval from good models     

Bayesian Variable Selection for Multivariate Spatially Varying Coefficient Regression

Summary Physical activity has many well‐documented health benefits for cardiovascular fitness and weight control. For pregnant women, the American College of Obstetricians and Gynecologists currently recommends 30 minutes of moderate exercise on most, if not all, days; however, very few pregnant women achieve this level of activity. Traditionally, studies have focused on examining individual or interpersonal factors to identify predictors of physical activity. There is a renewed interest in whether characteristics of the physical environment in which we live and work may also influence physical activity levels. We consider one of the first studies of pregnant women that examines the impact of characteristics of the built environment on physical activity levels. Using a socioecologic framework, we study the associations between physical activity and several factors including personal characteristics, meteorological/air quality variables, and neighborhood characteristics for pregnant women in four counties of North Carolina. We simultaneously analyze six types of physical activity and investigate cross‐dependencies between these activity types. Exploratory analysis suggests that the associations are different in different regions. Therefore, we use a multivariate regression model with spatially varying regression coefficients. This model includes a regression parameter for each covariate at each spatial location. For our data with many predictors, some form of dimension reduction is clearly needed. We introduce a Bayesian variable selection procedure to identify subsets of important variables. Our stochastic search algorithm determines the probabilities that each covariate's effect is null, non‐null but constant across space, and spatially varying. We found that individual‐level covariates had a greater influence on women's activity levels than neighborhood environmental characteristics, and some individual‐level covariates had spatially varying associations with the activity levels of pregnant women.     

A Comparison of Regression Models for Small Counts

ABSTRACT Count data with means <2 are often assumed to follow a Poisson distribution. However, in many cases these kinds of data, such as number of young fledged, are more appropriately considered to be multinomial observations due to naturally occurring upper truncation of the distribution. We evaluated the performance of several versions of multinomial regression, plus Poisson and normal regression, for analysis of count data with means <2 through Monte Carlo simulations. Simulated data mimicked observed counts of number of young fledged (0, 1, 2, or 3) by California spotted owls (Strix occidentalis occidentalis). We considered size and power of tests to detect differences among 10 levels of a categorical predictor, as well as tests for trends across 10-year periods. We found regular regression and analysis of variance procedures based on a normal distribution to perform satisfactorily in all cases we considered, whereas failure rate of multinomial procedures was often excessively high, and the Poisson model demonstrated inappropriate test size for data where the variance/mean ratio was <1 or >1.2. Thus, managers can use simple statistical methods with which they are likely already familiar to analyze the kinds of count data we described here.     

Heteroscedastic Nonlinear Regression Models with Random Effects and Their Application to Enzyme Kinetic Data

We present a new modification of nonlinear regression models for repeated measures data with heteroscedastic error structures by combining the transform-both-sides and weighting model from Caroll and Ruppert (1988) with the nonlinear random effects model from Lindstrom and Bates (1990). The proposed parameter estimators are a combination of pseudo maximum likelihood estimators for the transform-both-sides and weighting model and maximum likelihood (ML) or restricted maximum likelihood (REML) estimators for linear mixed effects models. The new method is investigated by analyzing simulated enzyme kinetic data published by Jones (1993).     

Improved Estimation of Coefficients in Regression Models with Incomplete Prior Information

The method of mixed regression is considered for the estimation of coefficients in a linear regression model when incomplete prior information is available, and two families of improved estimators stemming from Stein-rule are proposed. Their properties are studied when disturbances are normal but small.     

Nomograms Aid Interpretation of Complex Regression Models

ABSTRACT Ecologists often develop complex regression models that include multiple categorical and continuous variables, interactions among predictors, and nonlinear relationships between the response and predictor variables. Nomograms, which are graphical devices for presenting mathematical functions and calculating output values, can aid biologists in interpreting and presenting these complex models. To illustrate benefits of nomograms, we developed a logistic regression model of elk (Cervus elaphus) resource selection. With this model, we demonstrated how a nomogram helps scientists and managers interpret interactions among variables, compare the relative biological importance of variables, and examine predicted shapes of relationships (e.g., linear vs. nonlinear) between response and predictor variables. Although our example focused on logistic regression, nomograms are equally useful for other linear and nonlinear models. Regardless of the approach used for model development, nomograms and other graphical summaries can help scientists and managers develop, interpret, and apply statistical models.