| Introduction: The Need for Spatial Statistics, D.A. Griffith | |
| Components of Geographic Information and Analysis | |
| Background: The Importance of Locational Information | |
| Background: Statistical Estimator Properties | |
| Organization of the Book | |
| Summary | |
| References | |
| Visualization of Spatial Dependence: An Elementary View of Spatial Autocorrelation, I.R. 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 | |
| Spatial Sampling, S.V. Stehman and W.S. Overton | |
| Introduction | |
| Spatial Universes and Populations | |
| Sampling Fundamentals | |
| Sampling a Continuous Universe | |
| 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 | |
| Some Guidelines for Specifying the Geopraphic Weights Matrix Contained in Spatial Statistical Models, D.A. Griffith | |
| Introduction | |
| Background | |
| Evaluation Criteria | |
| Rules-of-Thumb Implications | |
| References | |
| Aggregation Effects in Geo-Referenced Data, D.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 | |
| Guidelines for Analyzing Data From Different Scales | |
| Conclusions | |
| References | |
| Implementing Spatial Statistics on Parallel Computers, B. Li | |
| Introduction | |
| A Brief Introduction to Parallel Processing | |
| Software Models for Parallel Processing | |
| Parallel Implementations | |
| Performance | |
| Summary | |
| References | |
| Appendix I: Test Statistics for Spatial Autocorrelation Coefficients | |
| Appendix II: Source Code | |
| Spatial Statistics and GIS Applied to Internal Migration in Rwanda, Central Africa, D.G. Brown | |
| Introduction | |
| Study Area | |
| Database Description | |
| GIS Data Management | |
| Traditional Regression Analysis | |
| Mapping Residuals | |
| Spatial Statistical Model | |
| Conclusions | |
| References | |
| Spatial Statistical Modeling of Regional Fertility Rates: A Case Study of He-Nan Province, China, H.M. Feng | |
| Introduction | |
| Preliminary Considerations of the Spatial Statistical Application | |
| The Dataset and the Model Specification | |
| Explicit Variables | |
| A Classical Linear Regression Model of Explicit Variables | |
| In Search of a Spatial Pattern | |
| Interpretation and Conclusions | |
| References | |
| Appendix I: Description of Data Set | |
| Appendix II: Maps | |
| Appendix III: Scatter-Plots | |
| Spatial Statistical/Econometric Versions of Simple Urban Population Density Models, D.A. Griffith and A. Can | |
| Introduction and Background | |
| The Selected Metropolitan Landscapes | |
| Preliminaries for Estimating the Autoregressive Model | |
| The Estimated Population Density Models | |
| Implementation Findings | |
| References | |
| 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 | |
| Index |
