Authors: Yongwan Chun and Daniel A Griffith

Pub Date: January 2013

Pages: 200

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Table of Contents

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 Semivariogram 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

The 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 E-M Algorithm

Eigenvector Spatial Filtering: Another Spatial E-M 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