Yongwan Chun & Daniel A. Griffith 
Spatial Statistics and Geostatistics [EPUB ebook] 
Theory and Applications for Geographic Information Science and Technology

‘Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice’

– Professor Tao Cheng, University College London

Focusing specifically on spatial statistics and including components for Arc GIS, R, SAS and Win BUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and demonstrates techniques in:

• spatial sampling


• spatial autocorrelation


• local statistics


• spatial interpolation in two-dimensions


• advanced topics including Bayesian methods, Monte Carlo simulation, error and uncertainty.

It is a systematic overview of the fundamental spatial statistical methods used by applied researchers in geography, environmental science, health and epidemiology, population and demography, and planning.



A companion website includes digital R code for implementing the analyses in specific chapters and relevant data sets to run the R codes.

Jadual kandungan

About the Authors

Preface

Introduction

Spatial Statistics and Geostatistics

R Basics

Spatial Autocorrelation

Indices Measuring Spatial Dependency

Important Properties of MC

Relationships Between MC And GR, and MC and Join Count Statistics

Graphic Portrayals: The Moran Scatterplot and the Semi-variogram Plot

Impacts of Spatial Autocorrelation

Testing for Spatial Autocorrelation in Regression Residuals

R Code for Concept Implementations

Spatial Sampling

Selected Spatial Sampling Designs

Puerto Rico DEM Data

Properties of the Selected Sampling Designs: Simulation Experiment Results

Sampling Simulation Experiments On A Unit Square Landscape

Sampling Simulation Experiments On A Hexagonal Landscape Structure

Resampling Techniques: Reusing Sampled Data

The Bootstrap

The Jackknife

Spatial Autocorrelation and Effective Sample Size

R Code for Concept Implementations

Spatial Composition and Configuration

Spatial Heterogeneity: Mean and Variance

ANOVA

Testing for Heterogeneity Over a Plane: Regional Supra-Partitionings

Establishing a Relationship to the Superpopulation

A Null Hypothesis Rejection Case With Heterogeneity

Testing for Heterogeneity Over a Plane: Directional Supra-Partitionings

Covariates Across a Geographic Landscape

Spatial Weights Matrices

Weights Matrices for Geographic Distributions

Weights Matrices for Geographic Flows

Spatial Heterogeneity: Spatial Autocorrelation

Regional Differences

Directional Differences: Anisotropy

R Code for Concept Implementations

Spatially Adjusted Regression And Related Spatial Econometrics

Linear Regression

Nonlinear Regression

Binomial/Logistic Regression

Poisson/Negative Binomial Regression

Geographic Distributions

Geographic Flows: A Journey-To-Work Example

R Code for Concept Implementations

Local Statistics: Hot And Cold Spots

Multiple Testing with Positively Correlated Data

Local Indices of Spatial Association

Getis-Ord Statistics

Spatially Varying Coefficients

R Code For Concept Implementations

Analyzing Spatial Variance And Covariance With Geostatistics And Related Techniques

Semi-variogram Models

Co-kriging

DEM Elevation as a Covariate

Landsat 7 ETM+ Data as a Covariate

Spatial Linear Operators

Multivariate Geographic Data

Eigenvector Spatial Filtering: Correlation Coefficient Decomposition

R Code for Concept Implementations

Methods For Spatial Interpolation In Two Dimensions

Kriging: An Algebraic Basis

The EM Algorithm

Spatial Autoregression: A Spatial EM Algorithm

Eigenvector Spatial Filtering: Another Spatial EM Algorithm

R Code for Concept Implementations

More Advanced Topics In Spatial Statistics

Bayesian Methods for Spatial Data

Markov Chain Monte Carlo Techniques

Selected Puerto Rico Examples

Designing Monte Carlo Simulation Experiments

A Monte Carlo Experiment Investigating Eigenvector Selection when Constructing a Spatial Filter

A Monte Carlo Experiment Investigating Eigenvector Selection from a Restricted Candidate Set of Vectors

Spatial Error: A Contributor to Uncertainty

R Code for Concept Implementations

References

Index