This book aims to provide a text directed primarily at introducing spatial data analysis to the undergraduate interested in applied statistical methods. It emphasizes the way in which these approaches complement and reinforce each other in the analysis of real data. The book does not present methods in their full generality; preferring to provide their basic forms, with guides to further reading at the end of each chapter. It explains clearly the rationale and principles behind the methods discussed. The second overall objective is to provide a text that is "data driven" rather than "theory led". The organization of material is therefore firmly based on the type of spatial problem and spatial data concerned. Following on from the introductory part 1, each of the subsequent four parts 2-5 of the book relates to a different class of spatial problem and type of spatial data. Each of these parts is as self contained as possible, consisting of two chapters (except for part 5), the first of which contains "core material", the second involving more specialized topics. This structure should allow the reader to "dip into" the book without necessarily having to read it from the beginning.
Readers are urged to motivate methods by reference to examples and case studies which involve real data sets and reflect a range of applications across disciplines. A case study section has been included in each part of the book, which introduces and describes a number of data sets, distributed with the book on diskette. These are used in each part of the book, as appropriate, to illustrate the methods discussed there. The third objective of the book is to make the example data sets come "alive" for the reader, rather than just providing a file of the data on disk. The aim is for readers to understand that statistical analysis is rarely "right", but perhaps at best "not wrong" and to encourage them to experiment with methods and emphasize the interpretation of results and the validity of the assumptions upon which they depend.
Table of Contents
A: Introduction 1. Spatial Data Analysis 2. Computers and Spatial Data Analysis B: The Analysis of Data Associated with Points 3. Methods Relating to Point Patterns 4. Methods Relating to Marked Point Patterns 5. Methods Relating to a Continuously Varying Attribute Sampled at Points C: The Analysis of Data Associated with Areas 6. Univariate Analysis of Area Data 7. Analysis of Relationships Between Attributes of Areas 8. Multivariate Methods of Area Data D: The Analysis of Data Associated with Lines 9. Network Analysis 10. Spatial Interaction Models