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Spatial Data Analysis
Theory and Practice

This book, first published in 2003, is a comprehensive overview of the theory and practice of spatial data analysis for students and researchers.

Robert Haining (Author)

9780521774376, Cambridge University Press

Paperback, published 17 April 2003

454 pages, 88 b/w illus. 33 tables
24.8 x 17.5 x 3.6 cm, 0.92 kg

Spatial Data Analysis: Theory and Practice, first published in 2003, provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis.

Preface
Readership
Acknowledgements
Introduction
Part I. The Context for Spatial Data Analysis: 1. Spatial data analysis: scientific and policy context
2. The nature of spatial data
Part II. Spatial Data: Obtaining Data And Quality Issues: 3. Obtaining spatial data through sampling
4. Data quality: implications for spatial data analysis
Part III. The Exploratory Analysis of Spatial Data: 5. Exploratory analysis of spatial data
6. Exploratory spatial data analysis: visualisation methods
7. Exploratory spatial data analysis: numerical methods
Part IV. Hypothesis Testing in the Presence of Spatial Autocorrelation: 8. Hypothesis testing in the presence of spatial dependence
Part V. Modeling Spatial Data: 9. Models for the statistical analysis of spatial data
10. Statistical modeling of spatial variation: descriptive modeling
11. Statistical modeling of spatial variation: explanatory modeling
Appendices
References
Index.

Subject Areas: Environmental science, engineering & technology [TQ], Biology, life sciences [PS]

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