Applied spatial statistics for public health data /
Lance A. Waller, Carol A. Gotway. --
- Hoboken: John Wiley & Sons, 2004.
- xviii, 494 p.
Inclui bibliografia
1. Introduction -- 1.1. Why Spatial Data in Public Health? -- 1.2. Why Statistical Methods for Spatial Data? -- 1.3. Intersection of Three Fields of Study -- 1.4. Organization of the Book 2. Analyzing Public Health Data -- 2.1. Observational vs. Esperimental Data -- 2.2. Risk and Rates -- 2.3. Making Rates Compareble: Standardized Rates -- 2.4. Basic Epidemiological Study Designs -- 2.5.Basic Analytic Tool: The Odds Ratio -- 2.6. Modeling Counts and Rates -- 2.7. Challenges in the Analysis of Observational Data -- 2.8. Additional Topics and Further Reading 3. Spatial Data -- 3.1. Components of Spatial Data -- 3.2. An Odyssey into Geodesy -- 3.3. Sources of Spatial Data -- 3.4. Geographic Information Systems -- 3.5. Problems with Spatial Data and GIS 4. Visualizing Spatial Data -- 4.1. Cartography: The Art and Science of Mapmaking -- 4.2. Types of Statistical Maps -- 4.3. Symbolization -- 4.4. Mapping Smoothed Rates and Probabilities -- 4.5. Madifiable Areal Unit Problem -- 4.6. Additional Topics and Further Reading 5. Analysis of Apatial Point Patterns -- 5.1. Types of Patterns -- 5.2. Spatial Point Processes -- 5.3. K Function -- 5.4. Other Spatial Point Processes -- 5.5. Additional Topics and Further Reading 6. Spatial Clusters of Health Events: Point Data for Cases and Controls -- 6.1. What Do We Have? Data Types and Related Issues -- 6.2. What Do We Want? Null and Alternative Hypotheses -- 6.3. Categorization Point Process Summaries -- 6.5. Scanning Local Rates -- 6.6. Nearest-Neighbor Statisties -- 6.7. Further Reading 7. Spatial Clustering of Health Events: Regional Count Data -- 7.1. What Do We Have and What Do We Want? -- 7.2. Categorization of Methods -- 7.3. Scanning Local Rates -- 7.4. Global Indexes of Spatial Autocorrelation -- 7.5. Local Indicators of Spatial Association -- 7.6. Goodness-of Fit Statistics -- 7.7. Statistical Power and Related Considerations -- 7.8. Additional Topics and Further Reading 8. Spatial Exposure Data -- 8.1. Random Fields and Stationarity -- 8.2. Semivariograms -- 8.3. Interpolation and Spatial Predection -- 8.4. Additional Topics and Further Reading 9. Linking Spatial Exposure Data to Health Events -- 9.1. Linear Regression Models for Independent Data -- 9.2. Linear Regression Models for Spatially Autocorrelated Data -- 9.3. Spatial Autoregressive Models -- 9.4. Generalized Linear Models -- 9.5. Bayesian Models for Disease Mapping -- 9.6. Parting Thoghts -- 9.7. Additional Topics and Further Reading
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Saúde Pública informações estatísticas sistema de informações geográficas; Região Metropolitana; análise espacial