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Preface Author
CHAPTER 1 Introduction
1.1 GRIDDED DATA
1.2 AREAL UNIT DATA
1.3 MAPPED POINT PATTERN DATA
1.4 PLAN OF THE BOOK
CHAPTER 2 Random field modelling and interpolation
2.1 RANDOM FIELDS
2.2 GAUSSIAN RANDOM FIELDS
2.3 STATIONARITY CONCEPTS
2.4 CONSTRUCTION OF COVARIANCE FUNCTIONS
2.5 PROOF OF BOCHNER'S THEOREM
2.6 THE SEMI-VARIOGRAM
2.7 SIMPLEKRIGING
2.8 BAYES ESTIMATOR
2.9 ORDINARY KRIGING
2.10 UNIVERSAL KRIGING
2.11 WORKED EXAMPLES WITH R
2.12 EXERCISES
2.13 POINTERS TO THE LITERATURE
CHAPTER 3 Models and inference for areal unit data
3.1 DISCRETE RANDOM FIELDS etnefno
3.2 GAUSSIAN AUTOREGRESSION MODELS
3.3 GIBBS STATES
3.4 MARKOV RANDOM FIELDS
3.5 INFERENCE FOR AREAL UNIT MODELS
3.6 MARKOV CHAIN MONTE CARLO SIMULATION
3.7 HIERARCHICAL MODELLING
3.7.1 Image segmentation
3.7.2 Disease mapping
3.7.3 Synthesis
3.8 WORKED EXAMPLES WITH R
3.9 EXERCISES
3.10 POINTERS TO THE LITERATURE
CHAPTER 4 Spatial point processes
4.1 POINT PROCESSES ON EUCLIDEAN SPACES
4.2 THE POISSON PROCESS
4.3 MOMENT MEASURES
4.4 STATIONARITY CONCEPTS AND PRODUCT DENSITIES
4.5 FINITE POINT PROCESSES
4.6 THE PAPANGELOU CONDITIONAL INTENSITY
4.7 MARKOV POINT PROCESSES
4.8 LIKELIHOOD INFERENCE FOR POISSON PROCESSES
4.9 INFERENCE FOR FINITE POINT PROCESSES
4.10 COX PROCESSES
4.10.1 Cluster processes
4.10.2 Log-Gaussian Cox processes
4.10.3 Minimum contrast estimation
4.11 HIERARCHICAL MODELLING
4.12 WORKED EXAMPLES WITH R
4.13 EXERCISES
4.14 POINTERS TO THE LITERATURE
Appendix£ºSolutions to theoretical exercises
Index
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