ABSTRACT
The methods outlined in this book have been developed in many different disciplines and with a diversity of problems in mind. Also, applications illustrated are wide ranging. Nonetheless, there are many common themes that link these approaches. In the following sections, the contents of the book are reviewed, some key issues are outlined and availability of software is discussed, before considering possible future developments in local modelling.
This book describes methods which can be applied to solve problems including characterising single properties and multivariate relationships locally for various applications, cluster detection, image compression, and spatial prediction. The substantive chapters concern analyses of single variables on grids (Chapter 3), spatial patterning in single variables represented as points or areas (Chapter 4), multiple variables (Chapter 5), deterministic and thin plate spline approaches to spatial prediction (Chapter 6), geostatistical prediction (Chapter 7), and point patterns (Chapter 8). Chapter 3 discusses methods for the analysis of gridded data (e.g., moving window statistics in general and wavelets in particular). Chapter 4 provides outlines of geographically weighted summary statistics and measures of spatial autocorrelation, while Chapter 5 considers recent developments in techniques such as geographically weighted regression for exploration of spatial relations. Spatial prediction is the subject of Chapters 6 and 7. In Chapter 8, widely used standard methods for point pattern analysis, both global and local, are outlined, in addition to some more specialised methods for the detection of spatial clusters or clustering. In the following section, some key issues raised in the book are summarised.
