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

Aus der Reihe SAGE Advances in Geographic Information Science and Technology Series

207,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.02.2013

Verlag

Sage Publications

Seitenzahl

200

Maße (L/B/H)

25/17,5/1,5 cm

Gewicht

534 g

Sprache

Englisch

ISBN

978-1-4462-0173-2

Beschreibung

Zitat

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.

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.02.2013

Verlag

Sage Publications

Seitenzahl

200

Maße (L/B/H)

25/17,5/1,5 cm

Gewicht

534 g

Sprache

Englisch

ISBN

978-1-4462-0173-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Spatial Statistics and Geostatistics
  • Produktbild: Spatial Statistics and Geostatistics
  • 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