Professor Andrew B. Lawson is a respected and well-known academic. He has published many papers in leading journals, and a number of books on spatial statistics, including five for Wiley.

Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling.
* Provides a comprehensive overview of the main statistical methods used in spatial epidemiology.
* Updated to include a new emphasis on bio-terrorism and disease surveillance.
* Emphasizes the importance of space-time modelling and outlines the practical application of the method.
* Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software.
* Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques.
This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

Preface and Acknowldegements to Second Edition.
Preface and Acknowldegements.
I: The Nature of Spatial Epidemiology.
1. Definitions, Terminolgy and Data Sets.
1.1 Map Hypotheses and Modelling Approaches.
1.2 Definitions and Data Examples.
1.3 Further definitions.
1.4 Some Data Examples.
2.Scales of Measurement and Data Availability.
2.1 Small Scale.
2.2 Large Scale.
2.3 Rate Dependence.
2.4 DataQuality and the Ecological Fallacy.
2.5 Edge E.ects.
3.Geographical Representation and Mapping.
3.1 Introduction and Definitions.
3.2 Maps and Mapping.
3.3 Statistical Accuracy.
3.4 Aggregation.
3.5 Mapping Issues related toAggregated Data.
3.6 Conclusions.
4.Basic Models.
4.1 Sampling Considerations.
4.2 Likelihood-based and Bayesian Approaches.
4.3 Point EventModels.
4.4 CountModels.
5.Exploratory Approaches, Parametric Estimation and Inference.
5.1 ExploratoryMethods.
5.2 Parameter Estimation.
5.3 Residual Diagnostics.
5.4 Hypothesis Testing.
5.5 Edge E.ects.
II:Important Problems in Spatial Epidemiology.
6.Small Scale: Disease Clustering.
6.1 Definition of Clusters and Clustering.
6.2 Modelling Issues.
6.3 Hypothesis Tests for Clustering.
6.4 Space-Time Clustering.
6.5 Clustering Examples.
6.6 OtherMethods related to clustering.
7.Small Scale: Putative Sources of Hazard.
7.1 Introduction.
7.2 StudyDesign.
7.3 Problems of Inference.
7.4 Modelling the Hazard Exposure Risk.
7.5 Models for Case Event Data.
7.6 ACase Event Example.
7.7 Models for CountData.
7.8 ACountData Example.
7.9 OtherDirections.
8. Large Scale: Disease Mapping.
8.1 Introduction.
8.2 Simple Statistical Representation.
8.3 BasicModels.
8.4 AdvancedMethods.
8.5 Model Variants and Extensions.
8.6 ApproximateMethods.
8.7 MultivariateMethods.
8.8 Evaluation ofModel Performance.
8.9 Hypothesis Testing in DiseaseMapping.
8.10 Space-Time DiseaseMapping.
8.11 Spatial Survival and longitudinal data.
8.12 DiseaseMapping: Case Studies.
9.Ecological Analysis and Scale Change.
9.1 Ecological Analysis: Introduction.
9.2 Small-ScaleModelling Issues.
9.3 Changes of Scale andMAUP.
9.4 A Simple Example: Sudden Infant Death in North Carolina.
9.5 ACase Study: Malaria and IDDM.
10.Infectious Disease Modelling.
10.1 Introduction.
10.2 GeneralModelDevelopment.
10.3 SpatialModelDevelopment.
10.4 Modelling Special Cases for Individual Level Data.
10.5 Survival Analysis with spatial dependence.
10.6 Individual level data example.
10.7 Underascertainment and Censoring.
10.8 Conclusions.
11.Large Scale: Surveillance.
11.1 Process ControlMethodology.
11.2 Spatio-Temporal Modelling.
11.3 Spatio-TemporalMonitoring.
11.4 Syndromic Surveillance.
11.5 Multivariate-Mulitfocus Surveillance.
11.6 Bayesian Approaches.
11.7 Computational Considerations.
11.8 Infectious Diseases.
11.9 Conclusions.
Appendix A:Monte Carlo Testing, Parametric Bootstrap and Simulation Envelopes.
A.1 Nuisance parameters and test statistics.
A.2 Monte Carlo Tests.
A.3 Null Hypothesis Simulation.
A.4 Parametric Bootstrap.
A.5 Simulation Envelopes.
Appendix B:Markov ChainMonte Carlo Methods.
B.1 Definitions.
B.2 Metropolis and Metropolis--Hastings
Algorithms.
Appendix C:Algorithms and Software.
C.1 Data Exploration.
C.2 Likelihood and BayesianModels.
C.3 LikelihoodModels.
C.4 Bayesian HierarchicalModels.
C.5 Space-Time Analysis.
Appendix D: Glossary of Estimators.
D.1 Case Event Estimators.
D.2 Tract Count Estimators.
Appendix E:Software.
E.1 Software.
E.1.2 Geographical Information Systems.
Bibliography.
Index.