Includes bibliographical references (pages 255-265) and index.

Cover -- Contents -- Acknowledgements -- 1 Local Statistics and Local Models for Spatial Data -- 1.1 Introduction -- 1.2 Local Aspatial Statistical Methods -- 1.3 Local versus Global Spatial Statistics -- 1.4 Spatial Non-stationarity -- 1.5 Examples of Local Univariate Methods for Spatial Data Analysis -- 1.5.1 Local Forms of Point Pattern Analysis -- 1.5.2 Local Graphical Analysis -- 1.5.3 Local Filters -- 1.5.4 Local Measures of Spatial Dependency -- 1.6 Examples of Local Multivariate Methods for Spatial Data Analysis -- 1.6.1 The Spatial Expansion Method -- 1.6.2 Spatially Adaptive Filtering -- 1.6.3 Multilevel Modelling -- 1.6.4 Random Coefficient Models -- 1.6.5 Spatial Regression Models -- 1.7 Examples of Local Methods for Spatial Flow Modelling -- 1.8 Summary -- 2 Geographically Weighted Regression: The Basics -- 2.1 Introduction -- 2.2 An Empirical Example -- 2.2.1 The Data -- 2.2.2 A Global Regression Model -- 2.2.3 Global Regression Results -- 2.3 Borough-Specific Calibrations of the Global Model -- 2.4 Moving Window Regression -- 2.5 Geographically Weighted Regression with Fixed Spatial Kernels -- 2.6 Geographically Weighted Regression with Adaptive Spatial Kernels -- 2.7 The Mechanics of GWR in More Detail -- 2.7.1 The Basic Methodology -- 2.7.2 Local Standard Errors -- 2.7.3 Choice of Spatial Weighting Function -- 2.7.4 Calibrating the Spatial Weighting Function -- 2.7.5 Bias-Variance Trade-Off -- 2.8 Testing for Spatial Non-stationarity -- 2.9 Summary -- 3 Extensions to the Basic GWR Model -- 3.1 Introduction -- 3.2 Mixed GWR Models -- 3.3 An Example -- 3.4 Outliers and Robust GWR -- 3.5 Spatially Heteroskedastic Models -- 3.6 Summary -- 4 Statistical Inference and Geographically Weighted Regression -- 4.1 Introduction -- 4.2 What is Meant by Inference' and How Does it Relate to GWR? -- 4.2.1 How Likely is it that Some Fact is True on the Basis of the Data? -- 4.2.2 Within What Interval Does Some Model Coefficient Lie? -- 4.2.3 Which One of a Series of Potential Mathematical Models is Best'? -- 4.3 GWR as a Statistical Model -- 4.3.1 Local Likelihood -- 4.3.2 Using Classical Inference ... Working with p-values -- 4.3.3 Testing Individual Parameter Stationarity -- 4.4 Confidence Intervals -- 4.5 An Alternative Approach Using the AIC -- 4.6 Two Examples -- 4.6.1 Basic Estimates -- 4.6.2 Estimates of Pointwise Standard Errors -- 4.6.3 Working with the AIC -- 4.7 Summary -- 5 GWR and Spatial Autocorrelation -- 5.1 Introduction -- 5.2 The Empirical Setting -- 5.3 Local Measures of Spatial Autocorrelation using GWR -- 5.4 Residuals in Global Regression Models and in GWR -- 5.5 Local Parameter Estimates from Autoregressive and Non-Autoregressive Models -- 5.6 Spatial Regression Models and GWR -- 5.6.1 Overview -- 5.6.2 Conditional Autoregressive (CA) Models -- 5.6.3 Simultaneous Autoregressive (SA) Models -- 5.6.4 GWR, Conditional Autoregressive Models and Simultaneous Autoregressive Models -- 5.7 Summary -- 6 Scale Issues and Geographically Weighted Regression -- 6.1 Introduction -- 6.2 Bandwidth and Scale: The Example of School Performance Analysis -- 6.2.1 Intr.