| |
| |
| Preface | |
| |
| |
| Acknowledgments | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Why Spatial Data in Public Health? | |
| |
| |
| |
| Why Statistical Methods for Spatial Data? | |
| |
| |
| |
| Intersection of Three Fields of Study | |
| |
| |
| |
| Organization of the Book | |
| |
| |
| |
| Analyzing Public Health Data | |
| |
| |
| |
| Observational vs. Experimental Data | |
| |
| |
| |
| Risk and Rates | |
| |
| |
| |
| Incidence and Prevalence | |
| |
| |
| |
| Risk | |
| |
| |
| |
| Estimating Risk: Rates and Proportions | |
| |
| |
| |
| Relative and Attributable Risks | |
| |
| |
| |
| Making Rates Comparable: Standardized Rates | |
| |
| |
| |
| Direct Standardization | |
| |
| |
| |
| Indirect Standardization | |
| |
| |
| |
| Direct or Indirect? | |
| |
| |
| |
| Standardizing to What Standard? | |
| |
| |
| |
| Cautions with Standardized Rates | |
| |
| |
| |
| Basic Epidemiological Study Designs | |
| |
| |
| |
| Prospective Cohort Studies | |
| |
| |
| |
| Retrospective Case-Control Studies | |
| |
| |
| |
| Other Types of Epidemiological Studies | |
| |
| |
| |
| Basic Analytic Tool: The Odds Ratio | |
| |
| |
| |
| Modeling Counts and Rates | |
| |
| |
| |
| Generalized Linear Models | |
| |
| |
| |
| Logistic Regression | |
| |
| |
| |
| Poisson Regression | |
| |
| |
| |
| Challenges in the Analysis of Observational Data | |
| |
| |
| |
| Bias | |
| |
| |
| |
| Confounding | |
| |
| |
| |
| Effect Modification | |
| |
| |
| |
| Ecological Inference and the Ecological Fallacy | |
| |
| |
| |
| Additional Topics and Further Reading | |
| |
| |
| |
| Exercises | |
| |
| |
| |
| Spatial Data | |
| |
| |
| |
| Components of Spatial Data | |
| |
| |
| |
| An Odyssey into Geodesy | |
| |
| |
| |
| Measuring Location: Geographical Coordinates | |
| |
| |
| |
| Flattening the Globe: Map Projections and Coordinate Systems | |
| |
| |
| |
| Mathematics of Location: Vector and Polygon Geometry | |
| |
| |
| |
| Sources of Spatial Data | |
| |
| |
| |
| Health Data | |
| |
| |
| |
| Census-Related Data | |
| |
| |
| |
| Geocoding | |
| |
| |
| |
| Digital Cartographic Data | |
| |
| |
| |
| Environmental and Natural Resource Data | |
| |
| |
| |
| Remotely Sensed Data | |
| |
| |
| |
| Digitizing | |
| |
| |
| |
| Collect Your Own! | |
| |
| |
| |
| Geographic Information Systems | |
| |
| |
| |
| Vector and Raster GISs | |
| |
| |
| |
| Basic GIS Operations | |
| |
| |
| |
| Spatial Analysis within GIS | |
| |
| |
| |
| Problems with Spatial Data and GIS | |
| |
| |
| |
| Inaccurate and Incomplete Databases | |
| |
| |
| |
| Confidentiality | |
| |
| |
| |
| Use of ZIP Codes | |
| |
| |
| |
| Geocoding Issues | |
| |
| |
| |
| Location Uncertainty | |
| |
| |
| |
| Visualizing Spatial Data | |
| |
| |
| |
| Cartography: The Art and Science of Mapmaking | |
| |
| |
| |
| Types of Statistical Maps | |
| |
| |
| Map Study: Very Low Birth Weights in Georgia Health Care District 9 | |
| |
| |
| |
| Maps for Point Features | |
| |
| |
| |
| Maps for Areal Features | |
| |
| |
| |
| Symbolization | |
| |
| |
| |
| Map Generalization | |
| |
| |
| |
| Visual Variables | |
| |
| |
| |
| Color | |
| |
| |
| |
| Mapping Smoothed Rates and Probabilities | |
| |
| |
| |
| Locally Weighted Averages | |
| |
| |
| |
| Nonparametric Regression | |
| |
| |
| |
| Empirical Bayes Smoothing | |
| |
| |
| |
| Probability Mapping | |
| |
| |
| |
| Practical Notes and Recommendations | |
| |
| |
| Case Study: Smoothing New York Leukemia Data | |
| |
| |
| |
| Modifiable Areal Unit Problem | |
| |
| |
| |
| Additional Topics and Further Reading | |
| |
| |
| |
| Visualization | |
| |
| |
| |
| Additional Types of Maps | |
| |
| |
| |
| Exploratory Spatial Data Analysis | |
| |
| |
| |
| Other Smoothing Approaches | |
| |
| |
| |
| Edge Effects | |
| |
| |
| |
| Exercises | |
| |
| |
| |
| Analysis of Spatial Point Patterns | |
| |
| |
| |
| Types of Patterns | |
| |
| |
| |
| Spatial Point Processes | |
| |
| |
| |
| Stationarity and Isotropy | |
| |
| |
| |
| Spatial Poisson Processes and CSR | |
| |
| |
| |
| Hypothesis Tests of CSR via Monte Carlo Methods | |
| |
| |
| |
| Heterogeneous Poisson Processes | |
| |
| |
| |
| Estimating Intensity Functions | |
| |
| |
| Data Break: Early Medieval Grave Sites | |
| |
| |
| |
| K Function | |
| |
| |
| |
| Estimating the K Function | |
| |
| |
| |
| Diagnostic Plots Based on the K Function | |
| |
| |
| |
| Monte Carlo Assessments of CSR Based on the K Function | |
| |
| |
| Data Break: Early Medieval Grave Sites | |
| |
| |
| |
| Roles of First- and Second-Order Properties | |
| |
| |
| |
| Other Spatial Point Processes | |
| |
| |
| |
| Poisson Cluster Processes | |
| |
| |
| |
| Contagion/Inhibition Processes | |
| |
| |
| |
| Cox Processes | |
| |
| |
| |
| Distinguishing Processes | |
| |
| |
| |
| Additional Topics and Further Reading | |
| |
| |
| |
| Exercises | |
| |
| |
| |
| Spatial Clusters of Health Events: Point Data for Cases and Controls | |
| |
| |
| |
| What Do We Have? Data Types and Related Issues | |
| |
| |
| |
| What Do We Want? Null and Alternative Hypotheses | |
| |
| |
| |
| Categorization of Methods | |
| |
| |
| |
| Comparing Point Process Summaries | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions and Typical Output | |
| |
| |
| |
| Method: Ratio of Kernel Intensity Estimates | |
| |
| |
| Data Break: Early Medieval Grave Sites | |
| |
| |
| |
| Method: Difference between K Functions | |
| |
| |
| Data Break: Early Medieval Grave Sites | |
| |
| |
| |
| Scanning Local Rates | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions and Typical Output | |
| |
| |
| |
| Method: Geographical Analysis Machine | |
| |
| |
| |
| Method: Overlapping Local Case Proportions | |
| |
| |
| Data Break: Early Medieval Grave Sites | |
| |
| |
| |
| Method: Spatial Scan Statistics | |
| |
| |
| Data Break: Early Medieval Grave Sites | |
| |
| |
| |
| Nearest-Neighbor Statistics | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions and Typical Output | |
| |
| |
| |
| Method: q Nearest Neighbors of Cases | |
| |
| |
| Case Study: San Diego Asthma | |
| |
| |
| |
| Further Reading | |
| |
| |
| |
| Exercises | |
| |
| |
| |
| Spatial Clustering of Health Events: Regional Count Data | |
| |
| |
| |
| What Do We Have and What Do We Want? | |
| |
| |
| |
| Data Structure | |
| |
| |
| |
| Null Hypotheses | |
| |
| |
| |
| Alternative Hypotheses | |
| |
| |
| |
| Categorization of Methods | |
| |
| |
| |
| Scanning Local Rates | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions | |
| |
| |
| |
| Method: Overlapping Local Rates | |
| |
| |
| Data Break: New York Leukemia Data | |
| |
| |
| |
| Method: Turnbull et al.'s CEPP | |
| |
| |
| |
| Method: Besag and Newell Approach | |
| |
| |
| |
| Method: Spatial Scan Statistics | |
| |
| |
| |
| Global Indexes of Spatial Autocorrelation | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions and Typical Output | |
| |
| |
| |
| Method: Moran's I | |
| |
| |
| |
| Method: Geary's c | |
| |
| |
| |
| Local Indicators of Spatial Association | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions and Typical Output | |
| |
| |
| |
| Method: Local Moran's I | |
| |
| |
| |
| Goodness-of-Fit Statistics | |
| |
| |
| |
| Goals | |
| |
| |
| |
| Assumptions and Typical Output | |
| |
| |
| |
| Method: Pearson's x[superscript 2] | |
| |
| |
| |
| Method: Tango's Index | |
| |
| |
| |
| Method: Focused Score Tests of Trend | |
| |
| |
| |
| Statistical Power and Related Considerations | |
| |
| |
| |
| Power Depends on the Alternative Hypothesis | |
| |
| |
| |
| Power Depends on the Data Structure | |
| |
| |
| |
| Theoretical Assessment of Power | |
| |
| |
| |
| Monte Carlo Assessment of Power | |
| |
| |
| |
| Benchmark Data and Conditional Power Assessments | |
| |
| |
| |
| Additional Topics and Further Reading | |
| |
| |
| |
| Related Research Regarding Indexes of Spatial Association | |
| |
| |
| |
| Additional Approaches for Detecting Clusters and/or Clustering | |
| |
| |
| |
| Space-Time Clustering and Disease Surveillance | |
| |
| |
| |
| Exercises | |
| |
| |
| |
| Spatial Exposure Data | |
| |
| |
| |
| Random Fields and Stationarity | |
| |
| |
| |
| Semivariograms | |
| |
| |
| |
| Relationship to Covariance Function and Correlogram | |
| |
| |
| |
| Parametric Isotropic Semivariogram Models | |
| |
| |
| |
| Estimating the Semivariogram | |
| |
| |
| Data Break: Smoky Mountain pH Data | |
| |
| |
| |
| Fitting Semivariogram Models | |
| |
| |
| |
| Anisotropic Semivariogram Modeling | |
| |
| |
| |
| Interpolation and Spatial Prediction | |
| |
| |
| |
| Inverse-Distance Interpolation | |
| |
| |
| |
| Kriging | |
| |
| |
| Case Study: Hazardous Waste Site Remediation | |
| |
| |
| |
| Additional Topics and Further Reading | |
| |
| |
| |
| Erratic Experimental Semivariograms | |
| |
| |
| |
| Sampling Distribution of the Classical Semivariogram Estimator | |
| |
| |
| |
| Nonparametric Semivariogram Models | |
| |
| |
| |
| Kriging Non-Gaussian Data | |
| |
| |
| |
| Geostatistical Simulation | |
| |
| |
| |
| Use of Non-Euclidean Distances in Geostatistics | |
| |
| |
| |
| Spatial Sampling and Network Design | |
| |
| |
| |
| Exercises | |
| |
| |
| |
| Linking Spatial Exposure Data to Health Events | |
| |
| |
| |
| Linear Regression Models for Independent Data | |
| |
| |
| |
| Estimation and Inference | |
| |
| |
| |
| Interpretation and Use with Spatial Data | |
| |
| |
| Data Break: Raccoon Rabies in Connecticut | |
| |
| |
| |
| Linear Regression Models for Spatially Autocorrelated Data | |
| |
| |
| |
| Estimation and Inference | |
| |
| |
| |
| Interpretation and Use with Spatial Data | |
| |
| |
| |
| Predicting New Observations: Universal Kriging | |
| |
| |
| Data Break: New York Leukemia Data | |
| |
| |
| |
| Spatial Autoregressive Models | |
| |
| |
| |
| Simultaneous Autoregressive Models | |
| |
| |
| |
| Conditional Autoregressive Models | |
| |
| |
| |
| Concluding Remarks on Conditional Autoregressions | |
| |
| |
| |
| Concluding Remarks on Spatial Autoregressions | |
| |
| |
| |
| Generalized Linear Models | |
| |
| |
| |
| Fixed Effects and the Marginal Specification | |
| |
| |
| |
| Mixed Models and Conditional Specification | |
| |
| |
| |
| Estimation in Spatial GLMs and GLMMs | |
| |
| |
| Data Break: Modeling Lip Cancer Morbidity in Scotland | |
| |
| |
| |
| Additional Considerations in Spatial GLMs | |
| |
| |
| Case Study: Very Low Birth Weights in Georgia Health Care District 9 | |
| |
| |
| |
| Bayesian Models for Disease Mapping | |
| |
| |
| |
| Hierarchical Structure | |
| |
| |
| |
| Estimation and Inference | |
| |
| |
| |
| Interpretation and Use with Spatial Data | |
| |
| |
| |
| Parting Thoughts | |
| |
| |
| |
| Additional Topics and Further Reading | |
| |
| |
| |
| General References | |
| |
| |
| |
| Restricted Maximum Likelihood Estimation | |
| |
| |
| |
| Residual Analysis with Spatially Correlated Error Terms | |
| |
| |
| |
| Two-Parameter Autoregressive Models | |
| |
| |
| |
| Non-Gaussian Spatial Autoregressive Models | |
| |
| |
| |
| Classical/Bayesian GLMMs | |
| |
| |
| |
| Prediction with GLMs | |
| |
| |
| |
| Bayesian Hierarchical Models for Spatial Data | |
| |
| |
| |
| Exercises | |
| |
| |
| References | |
| |
| |
| Author Index | |
| |
| |
| Subject Index | |