| Introduction: The Need for Spatial Statistics, D.A. Griffith |
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| Components of Geographic Information and Analysis |
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| Background: The Importance of Locational Information |
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| Background: Statistical Estimator Properties |
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| Organization of the Book |
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| Summary |
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| References |
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| Visualization of Spatial Dependence: An Elementary View of Spatial Autocorrelation, I.R. Vasiliev |
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| Editorial Note |
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| Introduction |
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| The Spatial Mean and Other Basic Concepts |
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| Spatial Autocorrelation |
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| Map Complexity |
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| Map Representations of Changes in Space and Time |
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| Summary: Rules-of-Thumb for Spatial Autocorrelation |
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| References |
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| Spatial Sampling, S.V. Stehman and W.S. Overton |
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| Introduction |
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| Spatial Universes and Populations |
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| Sampling Fundamentals |
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| Sampling a Continuous Universe |
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| Sampling Spatially Distributed Objects via Areal Samples of the Continuous Universe |
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| Inference in Spatial Sampling |
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| Applications of Spatial Sampling |
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| Empirical Evaluation of Sampling Strategies |
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| Summary |
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| References |
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| Some Guidelines for Specifying the Geopraphic Weights Matrix Contained in Spatial Statistical Models, D.A. Griffith |
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| Introduction |
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| Background |
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| Evaluation Criteria |
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| Rules-of-Thumb Implications |
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| References |
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| Aggregation Effects in Geo-Referenced Data, D.W.S. Wong |
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| Spatial Dependency of Spatial Data Analysis |
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| Source of the MAUP: Spatial Dependence and the Averaging Process |
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| General Impacts of the MAUP on Spatial Data |
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| Approaches to "Solving" the MAUP |
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| Guidelines for Analyzing Data From Different Scales |
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| Conclusions |
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| References |
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| Implementing Spatial Statistics on Parallel Computers, B. Li |
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| Introduction |
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| A Brief Introduction to Parallel Processing |
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| Software Models for Parallel Processing |
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| Parallel Implementations |
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| Performance |
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| Summary |
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| References |
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| Appendix I: Test Statistics for Spatial Autocorrelation Coefficients |
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| Appendix II: Source Code |
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| Spatial Statistics and GIS Applied to Internal Migration in Rwanda, Central Africa, D.G. Brown |
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| Introduction |
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| Study Area |
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| Database Description |
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| GIS Data Management |
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| Traditional Regression Analysis |
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| Mapping Residuals |
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| Spatial Statistical Model |
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| Conclusions |
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| References |
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| Spatial Statistical Modeling of Regional Fertility Rates: A Case Study of He-Nan Province, China, H.M. Feng |
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| Introduction |
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| Preliminary Considerations of the Spatial Statistical Application |
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| The Dataset and the Model Specification |
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| Explicit Variables |
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| A Classical Linear Regression Model of Explicit Variables |
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| In Search of a Spatial Pattern |
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| Interpretation and Conclusions |
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| References |
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| Appendix I: Description of Data Set |
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| Appendix II: Maps |
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| Appendix III: Scatter-Plots |
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| Spatial Statistical/Econometric Versions of Simple Urban Population Density Models, D.A. Griffith and A. Can |
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| Introduction and Background |
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| The Selected Metropolitan Landscapes |
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| Preliminaries for Estimating the Autoregressive Model |
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| The Estimated Population Density Models |
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| Implementation Findings |
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| References |
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| Spatial Statistics for Analysis of Variance of Agronomic Field Trials, D.S. Long |
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| The Example Data Set |
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| Goals of the Case Study |
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| The Autoregressive Response Model |
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| Calculating the Moran Coefficient |
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| Calculating the Necessary Eigenvalues |
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| Estimating the Jacobian Term |
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| Estimating an Autoregressive Response Model |
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| Comparison of AR-based ANOVA and Conventional ANOVA |
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| Conclusions |
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| Acknowledgments |
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| References |
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| Index |
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