The realization in the statistical and geographical sciences that relationships between explanatory variables and a response variable in a regression model may vary across a study area has lead to the development of regression models with spatially varying coefficients. Two such models are geographically weighted regression and Bayesian regression models with spatially varying coefficients. In the application of these models, inference on the regression coefficient spatial processes is typically of primary interest. The presence of collinearity necessitates the use of diagnostic tools in local regression model building to highlight areas in which the results are not reliable for statistical inference, in addition to presenting an opportunity for remedial methods. This book, therefore, provides diagnostic tools and remedial methods for spatially varying coefficient regression models and includes real-world and simulated examples demonstrating the utility of the new techniques. This book sheds light on the issues of implementing and interpreting results from these models and should prove especially useful to spatial data analysts in Geography, Statistics, and Public Health.

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