Cover Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Establishing the Boundaries -- 1.1 Setting the scene -- 1.2 What is quantitative geography? -- 1.3 Applications of quantitative geography -- 1.4 Recent developments in quantitative geography -- 1.5 Summary -- Notes -- 2 Spatial Data -- 2.1 Introduction -- 2.2 Spatial data capture -- 2.3 Spatial objects -- 2.4 Location on the globe -- location on a plane -- 2.5 Distance -- 2.6 Representing spatial data -- 2.7 Models for spatial data -- 2.7.1 The vector model -- 2.7.2 The raster model -- 2.8 Programming with spatial data -- 2.8.1 Point-in-polygon operations -- 2.8.2 The use of complex numbers to represent spatial data -- 2.9 Problems and opportunities -- 2.10 Summary -- Notes -- 3 The Role of Geographical Information Systems -- 3.1 Introduction -- 3.2 Simple GIS-based spatial analysis -- 3.2.1 Feature selection by attribute -- 3.2.2 Feature selection by geometric intersection -- 3.2.3 Buffering features -- 3.2.4 Geometric intersection: union -- 3.2.5 Geometric intersection: intersection -- 3.2.6 Proximity -- 3.2.7 Contiguity -- 3.2.8 Interpolation and fields -- 3.2.9 Density functions -- 3.2.10 Analysis on networks -- 3.2.11 Query -- 3.3 Advanced GIS-based spatial analysis -- 3.3.1 Data-integration and management -- 3.3.2 Exploration -- 3.3.3 Post-modelling visualization -- 3.4 Problems -- 3.4.1 Error modelling -- 3.4.2 Cross-area aggregation -- 3.5 Linking higher-order types of spatial analysis to GIS -- 3.6 A role for GIS? -- Notes -- 4 Exploring Spatial Data Visually -- 4.1 Introduction -- 4.2 Stem and leaf plots -- 4.3 Boxplots -- 4.4 Histograms -- 4.5 Density estimates -- 4.6 Maps -- 4.7 The scatterplot matrix -- 4.8 Linked plots -- 4.9 Parallel coordinate plots -- 4.10 RADVIZ -- 4.11 Projection pursuit -- 4.12 Summary -- Notes -- 5 Local Analysis -- 5.1 Introduction.

5.2 The nature of local variations in relationships -- 5.3 Measuring local relationships in univariate data -- 5.3.1 Local point pattern analysis -- 5.3.2 Other local measures of univariate spatial relationships -- 5.4 Measuring local relationships in multivariate data -- 5.4.1 Multilevel modelling -- 5.4.2 The spatial expansion method -- 5.4.3 Geographically weighted regression -- 5.5 An empirical comparison of the spatial expansion method and GWR -- 5.5.1 The data -- 5.5.2 Global regression model results -- 5.5.3 Spatial expansion method results -- 5.5.4 GWR results -- 5.6 Measuring local relationships in spatial interaction models -- 5.7 Summary -- Notes -- 6 Point Pattern Analysis -- 6.1 Introduction -- 6.2 Initial exploration -- 6.2.1 Scatterplots -- 6.2.2 Other exploratory plots -- 6.2.3 Non-graphical approaches to point pattern exploration -- 6.3 Modelling point patterns -- 6.4 First-order intensity analysis -- 6.4.1 Kernel density estimates -- 6.5 Second-order intensity analysis -- 6.6 Comparing distributions -- 6.6.1 Comparing kernel densities -- 6.6.2 Comparing K functions -- 6.6.3 Comparing a point pattern with a 'population at risk' -- 6.7 Conclusions -- Notes -- 7 Spatial Regression and Geostatistical Models -- 7.1 Introduction -- 7.2 Autoregressive models -- 7.2.1 Spatially autoregressive models -- 7.2.2 Spatial moving average models -- 7.3 Kriging -- 7.3.1 The statistical technique -- 7.3.2 A worked example -- 7.3.3 Trend surfaces from kriging residuals -- 7.4 Semi-parametric smoothing approaches -- 7.5 Conclusions -- 8 Statistical Inference for Spatial Data -- 8.1 Introduction -- 8.2 Informal inference -- 8.2.1 Exploratory data analysis (EDA) and visualization -- 8.2.2 Data mining -- 8.2.2.1 Cluster analysis -- 8.2.2.2 Neural networks -- 8.3 Formal inference -- 8.3.1 Bayesian inference -- 8.3.2 Classical inference.

8.3.3 Experimental and computational inference -- 8.3.3.1 Classical statistical inference and spatial autocorrelation -- 8.3.3.2 Experimental distributions and spatial autocorrelation -- 8.3.3.3 An empirical comparison of classical and experimental inference -- 8.3.4 Model building and model testing -- 8.4 Conclusions -- Notes -- 9 Spatial Modelling and the Evolution of Spatial Theory -- 9.1 Introduction -- 9.2 Spatial interaction as social physics (1860-1970) -- 9.3 Spatial interaction as statistical mechanics (1970-80) -- 9.4 Spatial interaction as aspatial information processing (1980-90) -- 9.5 Spatial interaction as spatial information processing (1990 onwards) -- 9.6 Summary -- Notes -- 10 Challenges in Spatial Data Analysis -- 10.1 Retrospect -- 10.2 Current challenges -- 10.2.1 The modifiable areal unit problem -- 10.2.2 Spatial non-stationarity -- 10.2.3 Alternative inferential frameworks (Bayes, MCMC) -- 10.2.4 Geometry -- 10.2.5 Basic spatial concepts: proximity and accessibility -- 10.2.6 Merging space and time -- 10.3 Training people to think spatially -- 10.3.1 Teaching quantitative geography -- 10.3.2 Software -- 10.4 Summary -- Bibliography -- Index.