Spatial cluster modelling:
Gespeichert in:
Format: | Buch |
---|---|
Sprache: | English |
Veröffentlicht: |
Boca Raton [u.a.]
Chapman & Hall/CRC
2002
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Schlagworte: | |
Online-Zugang: | Table of contents Inhaltsverzeichnis |
Beschreibung: | XIV, 287 S. Ill., graph. Darst., Kt. |
ISBN: | 1584882662 |
Internformat
MARC
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245 | 1 | 0 | |a Spatial cluster modelling |c ed. by Andrew B. Lawson ... |
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Datensatz im Suchindex
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adam_text | SPATIAL CLUSTER MODELLING EDITED BY ANDREW B. LAWSON DEPARTMENT OF
MATHEMATICAL SCIENCES UNIVERSITY OF ABERDEEN ABERDEEN, UK S DAVID G.T.
DENISON DEPARTMENT OF MATHEMATICS IMPERIAL COLLEGE OF SCIENCE,
TECHNOLOGY AND MEDICINE LONDON, UK CHAPMAN & HALL/CRC A CRC PRESS
COMPANY BOCA RATON LONDON NEW YORK WASHINGTON, D.C. CONTENTS LIST OF
CONTRIBUTORS XI PREFACE XIII 1 SPATIAL CLUSTER MODELLING: AN OVERVIEW 1
1.1 INTRODUCTION 1 1.2 HISTORICAL DEVELOPMENT 3 1.2.1 CONVENTIONAL
CLUSTERING 6 1.2.2 SPATIAL CLUSTERING 8 1.3 NOTATION AND MODEL
DEVELOPMENT 12 1.3.1 NONPARAMETRIC APPROACHES 13 1.3.2 POINT OR OBJECT
PROCESS MODELLING 15 1.3.3 RANDOM EFFECT MODELLING 16 1.3.4 PARTITION
MODELLING 18 1.3.5 SPATIO-TEMPORAL PROCESS MODELLING * 18 1 POINT
PROCESS CLUSTER MODELLING 21 2 SIGNIFICANCE IN SCALE-SPACE FOR
CLUSTERING 23 2.1 INTRODUCTION 23 2.2 OVERVIEW -* 24 2.3 NEW METHOD 29
2.4 FUTURE DIRECTIONS ., 35 3 STATISTICAL INFERENCE FOR COX PROCESSES 37
3.1 INTRODUCTION 37 3.2 POISSON PROCESSES 39 3.3 COX PROCESSES 41 3.4
SUMMARY STATISTICS 43 3.5 PARAMETRIC MODELS OF COX PROCESSES 45 3.5.1
NEYMAN-SCOTT PROCESSES AS COX PROCESSES 45 3.5.2 LOG GAUSSIAN COX
PROCESSES 48 3.5.3 SNOT NOISE G COX PROCESSES 49 3.6 ESTIMATION FOR
PARAMETRIC MODELS OF COX PROCESSES 51 CONTENTS 3.7 PREDICTION 54 3.7.1
CONDITIONAL SIMULATION FOR NEYMAN-SCOTT PROCESSES 55 3.7.2 CONDITIONAL
SIMULATION FOR LGCPS 55 3.7.3 CONDITIONAL SIMULATION FOR SHOT-NOISE G
COX PRO- CESSES 56 3.8 DISCUSSION 58 EXTRAPOLATING AND INTERPOLATING
SPATIAL PATTERNS 61 4.1 INTRODUCTION 61 4.2 FORMULATION AND NOTATION 62
4.2.1 GERM-GRAIN MODELS 63 4.2.2 PROBLEM STATEMENT 63 4.2.3 EDGE EFFECTS
AND SAMPLING BIAS 64 4.2.4 EXTRAPOLATION 65 4.3 SPATIAL CLUSTER
PROCESSES 66 4.3.1 INDEPENDENT CLUSTER PROCESSES 67 4.3.2 COX CLUSTER
PROCESSES 68 4.3.3 CLUSTER FORMATION DENSITIES 69 4.4 BAYESIAN CLUSTER
ANALYSIS 72 4.4.1 MARKOV POINT PROCESSES 72 4.4.2 SAMPLING BIAS FOR
INDEPENDENT CLUSTER PROCESSES 74 4.4.3 SPATIAL BIRTH-AND-DEATH PROCESSES
75 EXAMPLE: REDWOOD SEEDLINGS 76 4.4.4 PARAMETER ESTIMATION 78 EXAMPLE:
COX-MATERN CLUSTER PROCESS 80 4.4.5 ADAPTIVE COUPLING FROM THE PAST 80
4.4.6 EXAMPLE: COX-MATERN CLUSTER PROCESS 84 4.5 SUMMARY AND CONCLUSION
86 PERFECT SAMPLING FOR POINT PROCESS CLUSTER MODELLING 87 5.1
INTRODUCTION : 87 5.2 BAYESIAN CLUSTER MODEL 89 5.2.1 PRELIMINARIES 89
5.2.2 MODEL SPECIFICATION 90 5.3 SAMPLING FROM THE POSTERIOR 93 5.4
SPECIALIZED EXAMPLES 95 5.4.1 NEYMAN-SCOTT MODEL 95 5.4.2 PURE
SILHOUETTE MODELS 98 5.5 LEUKEMIA INCIDENCE IN UPSTATE NEW YORK 100 5.6
REDWOOD SEEDLINGS DATA 106 BAYESIAN ESTIMATION AND SEGMENTATION OF
SPATIAL POINT PROCESSES USING VORONOI TILINGS 109 6.1 INTRODUCTION 109
CONTENTS VII 6.2 PROPOSED SOLUTION FRAMEWORK 110 6.2.1 FORMULATION 110
6.2.2 VORONOI TILINGS 111 6.2.3 MARKOV CHAIN MONTE CARLO USING DYNAMIC
VORONOI TILINGS 111 6.3 INTENSITY ESTIMATION 112 6.3.1 FORMULATION 112
6.3.2 MCMC IMPLEMENTATION 112 FIXED NUMBER OF TILES 113 VARIABLE NUMBER
OF TILES 114 6.4 INTENSITY SEGMENTATION 115 6.4.1 FORMULATION 115 FIXED
NUMBER OF TILES 115 VARIABLE NUMBER OF TILES 117 6.5 EXAMPLES 117 6.5.1
SIMULATED EXAMPLES 117 A SINE WAVE 117 A LINEAR FEATURE 118 6.5.2 NEW
MADRID SEISMIC REGION 118 6.6 DISCUSSION 119 II SPATIAL PROCESS CLUSTER
MODELLING 123 7 PARTITION MODELLING 125 7.1 INTRODUCTION 5 125 7.2
PARTITION MODELS 126 7.2.1 PARTITIONING FOR SPATIAL DATA 127 7.2.2
BAYESIAN INFERENCE 128 7.2.3 PREDICTIVE INFERENCE . 131 7.2.4 MARKOV
CHAIN MONTE CARLO SIMULATION ^ 132 7.2.5 PARTITION MODEL PRIOR 133
7.3 PIAZZA ROAD DATASET 135 7.4 SPATIAL COUNT DATA .. 135 7.4.1 THE
POISSON-GAMMA MODEL FOR DISEASE MAPPING 137 7.4.2 DISEASE MAPPING WITH
COVARIATES 138 7.4.3 EAST GERMAN LIP CANCER DATASET 141 7.5 DISCUSSION
144 7.6 FURTHER READING 144 8 CLUSTER MODELLING FOR DISEASE RATE MAPPING
147 8.1 INTRODUCTION 147 8.2 STATISTICAL MODEL 148 8.3 POSTERIOR
CALCULATION 150 8.4 EXAMPLE: U.S. CANCER MORTALITY ATLAS 153 8.4.1 8.4.2
8.4.3 8.4.4 8.4.5 BREAST CANCER CERVICAL CANCER COLORECTAL CANCER LUNG
CANCER STOMACH CANCER CONCLUSIONS CONTENTS 154 155 155 159 159 8.5
CONCLUSIONS 159 9 ANALYZING SPATIAL DATA USING SKEW-GAUSSIAN PROCESSES
163 9.1 INTRODUCTION 163 9.2 SKEW-GAUSSIAN PROCESSES 164 9.2.1 THE MODEL
165 9.2.2 BAYESIAN ANALYSIS 166 9.2.3 COMPUTATIONAL STRATEGY 167 9.3
REAL DATA ILLUSTRATION: SPATIAL POTENTIAL DATA PREDICTION 168 9.4
DISCUSSION 171 10 ACCOUNTING FOR ABSORPTION LINES IN IMAGES OBTAINED
WITH THE CHANDRA X-RAY OBSERVATORY 175 10.1 STATISTICAL CHALLENGES OF
THE CHANDRA X-RAY OBSERVATORY 175 10.2 MODELING THE IMAGE 179 10.2.1
MODEL-BASED SPATIAL ANALYSIS 179 10.2.2 MODEL-BASED SPECTRAL ANALYSIS
183 10.3 ABSORPTION LINES 184 10.3.1 SCIENTIFIC BACKGROUND 184 10.3.2
STATISTICAL MODELS ? 185 10.3.3 MODEL FITTING 188 10.3.4 A
SIMULATION-BASED EXAMPLE 190 10.4 SPECTRAL MODELS WITH ABSORPTION LINES
192 10.4.1 COMBINING MODELS AND ALGORITHMS 192 10.4.2 AN EXAMPLE . 195
10.5 DISCUSSION 196 11 SPATIAL MODELLING OF COUNT DATA: A CASE STUDY IN
MOD- ELLING BREEDING BIRD SURVEY DATA ON LARGE SPATIAL DO- MAINS 199
11.1 INTRODUCTION 199 11.2 THE POISSON RANDOM EFFECTS MODEL 200 11.2.1
SPECTRAL FORMULATION 202 11.2.2 MODEL IMPLEMENTATION AND PREDICTION 204
SELECTED FULL-CONDITIONAL DISTRIBUTIONS 205 PREDICTION 206
IMPLEMENTATION 206 11.3 RESULTS 206 11.4 CONCLUSION 207 CONTENTS IX
III SPATIO-TEMPORAL CLUSTER MODELLING 211 12 MODELLING STRATEGIES FOR
SPATIAL-TEMPORAL DATA 213 12.1 INTRODUCTION 213 12.2 MODELLING STRATEGY
214 12.3 D-D (DRIFT-DRIFT) MODELS 215 12.4 D-C (DRIFT-CORRELATION)
MODELS 220 12.5 C-C (CORRELATION-CORRELATION) MODELS 222 12.6 A UNIFIED
ANALYSIS ON THE CIRCLE 224 12.7 DISCUSSION 225 13 SPATIO-TEMPORAL
PARTITION MODELLING: AN EXAMPLE FROM NEUROPHYSIOLOGY 227 13.1
INTRODUCTION 227 13.2 THE NEUROPHYSIOLOGICAL EXPERIMENT 227 13.3 THE
LINEAR INVERSE SOLUTION 228 13.4 THE MIXTURE MODEL 229 13.4.1 INITIAL
PREPARATION OF THE DATA 229 13.4.2 FORMULATION OF THE MIXTURE MODEL 230
13.5 CLASSIFICATION OF THE INVERSE SOLUTION 232 13.6 DISCUSSION 234 14
SPATIO-TEMPORAL CLUSTER MODELLING OF SMALL AREA HEALTH DATA 235 14.1
INTRODUCTION ? 235 14.2 BASIC CLUSTER MODELLING APPROACHES 235 14.2.1
CASE EVENT DATA MODELS 236 INTER-EVENT VERSUS HIDDEN PROCESS MODELLING
236 14.2.2 SMALL AREA COUNT DATA MODELS 239 14.2.3 SPATIO-TEMPORAL
EXTENSIONS TO CLUSTER MODELS 239 14.3 A SPATIO-TEMPORAL HIDDEN PROCESS
MODEL : 240 14.4 MODEL DEVELOPMENT 240 14.4.1 ESTIMATION OF G(S,T) ,I243
THE PRIOR DISTRIBUTION FOR CLUSTER CENTRES 244 CHOICE OF CLUSTER
DISTRIBUTION FUNCTION 245 OTHER PRIOR DISTRIBUTIONS AND THE POSTERIOR
DISTRI- BUTION 245 14.4.2 REGION COUNTS 246 INTEGRATED INTENSITY 247
14.5 THE POSTERIOR SAMPLING ALGORITHM 248 14.5.1 GOODNESS-OF-FIT
MEASURES FOR THE MODEL 249 14.6 DATA EXAMPLE: SCOTTISH BIRTH
ABNORMALITIES 249 14.6.1 INTRODUCTION 249 14.6.2 EXPLORATORY ANALYSIS
250 X CONTENTS 14.6.3 MODEL FITTING RESULTS 252 14.7 DISCUSSION 256
REFERENCES 259 INDEX 277 AUTHOR INDEX 281
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spelling | Spatial cluster modelling ed. by Andrew B. Lawson ... Boca Raton [u.a.] Chapman & Hall/CRC 2002 XIV, 287 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Clusteranalyse gtt Ruimtelijke analyse gtt Cluster analysis Spatial analysis Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Raumdaten (DE-588)4206012-6 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 s Raumdaten (DE-588)4206012-6 s DE-604 Lawson, Andrew 1959- Sonstige (DE-588)130212261 oth http://www.loc.gov/catdir/toc/fy032/2002019297.html Table of contents HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009771815&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | Spatial cluster modelling Clusteranalyse gtt Ruimtelijke analyse gtt Cluster analysis Spatial analysis Cluster-Analyse (DE-588)4070044-6 gnd Raumdaten (DE-588)4206012-6 gnd |
subject_GND | (DE-588)4070044-6 (DE-588)4206012-6 |
title | Spatial cluster modelling |
title_auth | Spatial cluster modelling |
title_exact_search | Spatial cluster modelling |
title_full | Spatial cluster modelling ed. by Andrew B. Lawson ... |
title_fullStr | Spatial cluster modelling ed. by Andrew B. Lawson ... |
title_full_unstemmed | Spatial cluster modelling ed. by Andrew B. Lawson ... |
title_short | Spatial cluster modelling |
title_sort | spatial cluster modelling |
topic | Clusteranalyse gtt Ruimtelijke analyse gtt Cluster analysis Spatial analysis Cluster-Analyse (DE-588)4070044-6 gnd Raumdaten (DE-588)4206012-6 gnd |
topic_facet | Clusteranalyse Ruimtelijke analyse Cluster analysis Spatial analysis Cluster-Analyse Raumdaten |
url | http://www.loc.gov/catdir/toc/fy032/2002019297.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009771815&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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