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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.

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
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