Practical Handbook of Spatial Statistics

About this book

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.

Contents

Introduction: The Need for Spatial Statistics, D.A. GriffithComponents of Geographic Information and AnalysisBackground: The Importance of Locational InformationBackground: Statistical Estimator PropertiesOrganization of the BookSummaryReferencesVisualization of Spatial Dependence: An Elementary View of Spatial Autocorrelation, I.R. VasilievEditorial NoteIntroductionThe Spatial Mean and Other Basic ConceptsSpatial AutocorrelationMap ComplexityMap Representations of Changes in Space and TimeSummary: Rules-of-Thumb for Spatial AutocorrelationReferencesSpatial Sampling, S.V. Stehman and W.S. OvertonIntroductionSpatial Universes and PopulationsSampling FundamentalsSampling a Continuous UniverseSampling Spatially Distributed Objects via Areal Samples of the Continuous UniverseInference in Spatial SamplingApplications of Spatial SamplingEmpirical Evaluation of Sampling StrategiesSummaryReferencesSome Guidelines for Specifying the Geopraphic Weights Matrix Contained in Spatial Statistical Models, D.A. GriffithIntroductionBackgroundEvaluation CriteriaRules-of-Thumb ImplicationsReferencesAggregation Effects in Geo-Referenced Data, D.W.S. WongSpatial Dependency of Spatial Data AnalysisSource of the MAUP: Spatial Dependence and the Averaging ProcessGeneral Impacts of the MAUP on Spatial DataApproaches to "Solving" the MAUPGuidelines for Analyzing Data From Different ScalesConclusionsReferencesImplementing Spatial Statistics on Parallel Computers, B. LiIntroductionA Brief Introduction to Parallel ProcessingSoftware Models for Parallel ProcessingParallel ImplementationsPerformanceSummaryReferencesAppendix I: Test Statistics for Spatial Autocorrelation CoefficientsAppendix II: Source CodeSpatial Statistics and GIS Applied to Internal Migration in Rwanda, Central Africa, D.G. BrownIntroductionStudy AreaDatabase DescriptionGIS Data ManagementTraditional Regression AnalysisMapping ResidualsSpatial Statistical ModelConclusionsReferencesSpatial Statistical Modeling of Regional Fertility Rates: A Case Study of He-Nan Province, China, H.M. FengIntroductionPreliminary Considerations of the Spatial Statistical ApplicationThe Dataset and the Model SpecificationExplicit VariablesA Classical Linear Regression Model of Explicit VariablesIn Search of a Spatial PatternInterpretation and ConclusionsReferencesAppendix I: Description of Data SetAppendix II: MapsAppendix III: Scatter-PlotsSpatial Statistical/Econometric Versions of Simple Urban Population Density Models, D.A. Griffith and A. CanIntroduction and BackgroundThe Selected Metropolitan LandscapesPreliminaries for Estimating the Autoregressive ModelThe Estimated Population Density ModelsImplementation FindingsReferencesSpatial Statistics for Analysis of Variance of Agronomic Field Trials, D.S. LongThe Example Data SetGoals of the Case StudyThe Autoregressi

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