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Practical Handbook of Spatial Statistics
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The guidance and special techniques provided in this handbook will allow you to understand and use complex spatial statistical techniques. You will learn how to apply proper spatial analysis techniques and why they are generally different from conventional statistical analyses. Clear and concise information on weighting, aggregation effects, sampling, spatial statistics and GIS, and visualization of spatial dependence is provided. Discussions on specific applications using actual data sets fill obvious gaps in the literature, and coverage of critical research frontiers allows readers to explore current areas of active research.
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Table of Contents

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

Reviews

"Practical Handbook of Spatial Statistics does achieve its objective of providing a handbook that practitioners can pick up and use immediatelymost people who work with environmental data will enjoy reading about these applications and seeing how spatial statistical models can be used in practice." -JASA, March 1999

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