Spatial data analysis in ecology and agriculture using R
著者
書誌事項
Spatial data analysis in ecology and agriculture using R
CRC Press, c2012
- : hardcover
大学図書館所蔵 全8件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 599-617) and index
内容説明・目次
内容説明
Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. Written in terms of four data sets easily accessible online, this book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions.
Based on the author's spatial data analysis course at the University of California, Davis, the book is intended for classroom use or self-study by graduate students and researchers in ecology, geography, and agricultural science with an interest in the analysis of spatial data.
目次
Working with Spatial Data
Analysis of Spatial Data
Data Sets Analyzed in This Book
R Programming Environment
R Basics
Programming Concepts
Handling Data in R
Writing Functions in R
Graphics in R
Other Software Packages
Statistical Properties of Spatially Autocorrelated Data
Components of a Spatial Random Process
Monte Carlo Simulation
Review of Hypothesis and Significance Testing
Modeling Spatial Autocorrelation
Application to Field Data
Measures of Spatial Autocorrelation
Preliminary Considerations
Join-Count Statistics
Moran's I and Geary's c
Measures of Autocorrelation Structure
Measuring Autocorrelation of Spatially Continuous Data
Sampling and Data Collection
Preliminary Considerations
Developing the Sampling Patterns
Methods for Variogram Estimation
Estimating the Sample Size
Sampling for Thematic Mapping
Design-Based and Model-Based Sampling
Preparing Spatial Data for Analysis
Quality of Attribute Data
Spatial Interpolation Procedures
Spatial Rectification and Alignment of Data
Preliminary Exploration of Spatial Data
Data Set 1
Data Set 2
Data Set 3
Data Set 4
Multivariate Methods for Spatial Data Exploration
Principal Components Analysis
Classification and Regression Trees (aka Recursive Partitioning)
Random Forest
Spatial Data Exploration via Multiple Regression
Multiple Linear Regression
Building a Multiple Regression Model for Field 4.1
Generalized Linear Models
Variance Estimation, the Effective Sample Size, and the Bootstrap
Bootstrap Estimation of the Standard Error
Bootstrapping Time Series Data
Bootstrapping Spatial Data
Application to the EM38 Data
Measures of Bivariate Association between Two Spatial Variables
Estimating and Testing the Correlation Coefficient
Contingency Tables
Mantel and Partial Mantel Statistics
Modifiable Areal Unit Problem and Ecological Fallacy
Mixed Model
Basic Properties of the Mixed Model
Application to Data Set 3
Incorporating Spatial Autocorrelation
Generalized Least Squares
Spatial Logistic Regression
Regression Models for Spatially Autocorrelated Data
Detecting Spatial Autocorrelation in a Regression Model
Models for Spatial Processes
Determining the Appropriate Regression Model
Fitting the Spatial Lag and Spatial Error Models
Conditional Autoregressive Model
Application of SAR and CAR Models to Field Data
Autologistic Model for Binary Data
Bayesian Analysis of Spatially Autocorrelated Data
Markov Chain Monte Carlo Methods
Introduction to WinBUGS
Hierarchical Models
Incorporation of Spatial Effects
Analysis of Spatiotemporal Data
Spatiotemporal Cluster Analysis
Factors Underlying Spatiotemporal Yield Clusters
Bayesian Spatiotemporal Analysis
Other Approaches to Spatiotemporal Modeling
Analysis of Data from Controlled Experiments
Classical Analysis of Variance
Comparison of Methods
Pseudoreplicated Data and the Effective Sample Size
Assembling Conclusions
Data Set 1
Data Set 2
Data Set 3
Data Set 4
Conclusions
Appendices
Review of Mathematical Concepts
The Data Sets
An R Thesaurus
References
Index
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