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Spatial Statistics and Geostatistics

Spatial Statistics and Geostatistics
Theory and Applications for Geographic Information Science and Technology

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January 2013 | 200 pages | SAGE Publications Ltd
"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 ArcGIS, R, SAS and WinBUGS, 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.

About the Authors
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

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
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


Companion Website
Includes digital R code for implementing the analyses in specific chapters as well as relevant data sets to run the R codes.

Statistical software R is increasing its popularity in scientific community. Nowadays more and more researchers, geographers and GIS analysts are using R for spatial statistics. Therefore, it is important to provide geography and GIS students an opportunity to study also R applications as well. This textbook provides basic tools for applying R for spatial statistics. In addition, this book has brief examples and short strings of codes to apply. This book suites well to advanced level geography and GIS students which are interested in quantitative approach. Together, this book is handy also for PhD candidates utilizing spatial statistics in their work.

Dr Petteri Muukkonen
Department of Geography, University of Helsinki
December 8, 2015

An introduction to R software is very short and I guess students have to know the basics of R before. On the other hand it is great that R code and all the necessary datasets are available online. Still it is too advanced for our students, mainly because they are not used to work in R yet.

Mr David Fiedor
Geography , Palacky University in Olomouc
September 28, 2015

The book spatial statistics and geostatistics is an essential resource for undergraduate and postgraduate students as well as early researcher, because the authors provide a comprehensive overview of spatial statistics and geostatistics. The book provides an excellent overview, supported by excellent examples. A really useful textbook.

Dr Thomas Thaler
Institute of Mountain Risk Engineering, University of Life Sciences and Natural Resources, Vienna
July 3, 2015

The book is extremely well done, very clear and very helpful, but pretty much advanced. As we are currently developing R skills among students, we are likely to adopt the book in the near future, but for the moment it is too advanced.

Dr Luana Russo
Department of Political Science, Maastricht University
March 20, 2015

The text is a supplementary reading for social sciences students, since it develops the statistical foundations behind most of the techniques employed in social sciences.

Dr Rodrigo Rodrigues-Silveira
Lateinamerika-Institut, Free University of Berlin
February 6, 2014

Too technical for this level

Dr Richard Harris
School of Geographical Sciences, Bristol University
January 9, 2014

I quite liked this book and learned some useful things from it. However, it's also rather quirky (eclectic) and not always very clearly written. To me it lacked a clear sense of cohesion or logic to the chapters. Most annoyingly, as far as I can tell the data used in the book are not available anywhere - where to get it isn't mentioned - which renders the R code a bit pointless.

Dr Richard Harris
School of Geographical Sciences, Bristol University
January 9, 2014