Practical Handbook of Spatial Statistics

Abstract

Multiple authors offer their views of spatial statistics: from the conceptual to real-world applications. | TABLE OF CONTENTS | Foreword: The Importance of Spatial Position, Sandra L. Arlinghaus and John D. Nystuen | Preface | Affiliations of Editors and Authors | Acknowledgments | Chapter 1, Introduction: The Need for Spatial Statistics, Daniel A. Griffith. Components of geographic information and analysis; Background: the importance of locational information; Background: statistical estimator properties; Organization of the book; Summary; References. | Chapter 2, Visualization of Spatial Dependence: An Elementary View of Spatial Autocorrelation, Irina Ren Vasiliev. Editorial note; Introduction; The spatial mean and other basic concepts; Spatial autocorrelation; Map complexity; Map representations of changes in space and time; Summary: rules of thumb for spatial autocorrelation; References. | Chapter 3, Spatial Sampling, Stephen V. Stehman and W. Scott Overton. Introduction; Spatial universes and populations; Sampling fundamentals; Sampling a continuous universe (Point sampling of a continuous population; Areal sampling of a continuous universe (Frames for areal sampling (Traditional areal sampling; A rigorous equal-probability areal sample); Support)); Sampling spatially distributed objects via areal samples of the continuous universe; Inference in spatial sampling; Applications of spatial sampling; Empirical evaluation of sampling strategies; Summary; References. | Chapter 4, Some Guidelines for Specifying the Geographic Weights Matrix Contained in Spatial Statistical Models, Daniel A. Griffith. Introduction; Background; Evaluation Criteria (Mean Response Estimation; Variance Estimation; Spatial Autoregressive Parameter 'rho' Estimation); Rules-of-thumb Implications; References. | Chapter 5, Aggregation Effects in Geo-referenced Data, David W. S. Wong. Spatial Dependency of Spatial Data Analysis; Source of the MAUP: Spatial Dependence and the Averaging Process; General impacts of the MAUP on spatial data; Approaches to 'solving' the MAUP (The Data Manipulation Approach; A Technique-oriented Approach; An Error Modeling Approach); Guidelines for analyzing data from different scales (Using Data from the Finest Scale; Reporting 'error' from aggregation; Using techniques insensitive to scale change); Conclusions; References. | Chapter 6, Implementing Spatial Statistics on Parallel Computers, Bin Li. Introduction; A brief introduction to parallel processing; Software models for parallel processing; Parallel implementations (Analysis of spatial autocorrelation; Estimating spatial autoregressive models); Performance (Analysis of spatial autocorrelation; Estimating spatial autoregressive models); Summary; References; Appendix I: Test Statistics for Spatial Autocorrelation Coefficients; Appendix II: Source Code. | Chapter 7, Spatial Statistics and GIS Applied to Internal Migration in Rwanda, Central Africa, Daniel G. Brown. Introduction; Study area; Database description (GIS database; Population and agricultural census data); GIS data management; Traditional regression analysis; Mapping residuals; Spatial statistical model; Conclusions; References. | Chapter 8, Spatial Statistical Modeling of Regional Fertility Rates: A Case Study of He-Nan Province, China, H. Michael Feng. Introduction; Preliminary considerations of the spatial statistical application; The dataset and the model specification; Explicit variables (Fertility rate; General living standard; Economic structure; Educational attainment; State population policies); A classical linear regression model of explicit variables; In search of spatial pattern; Interpretation and Conclusions; References; Appendix I: Description of Data Set; Appendix II: Maps; Appendix III: Scatter-plots. | Chapter 9, Spatial Statistical/Econometric Versions of Simple Urban Population Density Models, Daniel A. Griffith and Ayse Can. Introduction and Background (The Rudimentary Population Density Model; Spatial Autocorrelation and Population Density Models); The Selected Metropolitan Landscapes (The Toronto Metropolitan Area; The Ottawa-Hull Metropolitan Area; The Syracuse Metropolitan Area); Preliminaries for Estimating the Autoregressive Model (The Toronto Metropolitan Area; The Ottawa-Hull Metropolitan Area; The Syracuse Metropolitan Area); The Estimated Population Density Models; Implementation Findings; References. | Chapter 10, Spatial Statistics for Analysis of Variance of Agronomic Field Trials, D. S. Long. The Example Data Set; Goals of the Case Study; The Autoregressive Response Model; Calculating the Moran Coefficient; Calculating the Necessary Eigenvalues; Estimating the Jacobian Term; Estimating an Autoregressive Response Model; Comparison of AR-based ANOVA and Conventional ANOVA; Conclusions; Acknowledgments; References. | References, by Chapter | References, Alphabetically | Spatial Index, by Chapter |