Statistical Detection and Surveillance of Geographic Clusters

摘要: Introduction and Overview Setting the Stage The Roles of Spatial Statistics in Public Health Other Fields Limitations Associated with Visualization Data Some Fundamental Concepts Distinctions Types Tests for Clustering Structure Book Software Resources Sample Introductory Statistics: Description Inference Mean Center Median Standard Distance Relative Inferential Statistical Central Tendency Dispersion Illustration Angular Characteristics Processes: First-Order Second-Order Variation Kernel Density Estimation K-Functions Differences Ratios Estimators Global Nearest Neighbor Statistic Quadrat Methods Dependence: Moran's I Geary's C A Comparison Oden's Ipop Tango's a Chi-Square Getis Ord's Case-Control Data: Cuzick-Edwards Test Modified Local Moran Score CF Getis' Gi Stone's Modeling around Point Sources Cumulative Maximum as Focused an to Multiple Testing via M-Test Detection Clustering, Including Scan Openshaw et al.'s Geographical Analysis Machine (GAM) Besag Newell's Clusters Fotheringham Zhan's Method Cluster Evaluation Permutation Procedure Exploratory Approach Rushton Lolonis Kulldorff's Variable Window Size Bonferroni Sidak Adjustments Improvements on Adjustment Rogerson's Geographic Retrospective Changing Patterns Knox Space-Time Interaction Change Series Normally Distributed Observations Multinomial Probabilities Process Control Nonspatial Sum Surveillance Shewhart Charts (Cusum) Monitoring Small Counts Sums Poisson Variables Cusum Exponential Useful Modifications More Choice Parameters Temporal Brief Development That Are Observed Periodically CUSUM Updated Summary Discussion Simultaneous Many Regions Predefined Location Surveillance: Separate Each Region Simultaneously Appendix Approaches Maxima Multivariate Summary:Associated Prospective Tests: Regional Quick References Author Index Subject