Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology - Brossura

Yongwan Chun; Daniel Griffith

Brossura

ISBN 10: 1446201740 ISBN 13: 9781446201749

Casa editrice: SAGE Publications Ltd, 2013

Riassunto

Spatial Statistics and Geostatistics is the definitive text on spatial statistics. Its focus is on spatial statistics as a distinct form of statistical analysis and it includes computer components for ArcGIS, R, SAS, and WinBUGS. The teaching and learning objective of the text is to illustrate the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all the relevant programs and software. Fully explanatory, Spatial Statistics and Geostatistics uses boxed computer code, diagrams, illustrations; and includes further readings. Case study and exemplary materials and data sets are also included.

Su questo libro

Recensione:

This book is 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

This text is a remarkable roadmap to the methods of spatial statistics and in particular, the technique of spatial filtering. The included case studies and computer code make the book extraordinarily interactive and will benefit both students and applied researchers across many disciplines.
W. Ryan Davis
PhD Candidate in Economics, University of Texas at Dallas

This is a valuable and enjoyable addition to applied spatial statistics, particularly because the reader, or rather user, of the book can see exactly what the authors are doing, and so may reproduce all the analyses using the code provided.
Professor Roger S. Bivand
Norges Handelshøyskole Norwegian School of Economics

SAGE has a long tradition of publishing accessible texts explaining key concepts in statistics. This book is in my opinion very useful. I particularly like the choice of statistical problems, the focus on one region to explain a series of problems and the availability of R code, which makes it easy for the reader to reproduce the analysis.

(Sietse O Los 2013-09-17)

Contenuti:

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

Su questa edizione

• Editore: SAGE Publications Ltd
• Data di pubblicazione: 2013
• ISBN 10: 1446201740
• ISBN 13: 9781446201749
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 200