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Applied Spatial Statistics for Public Health Data

ISBN-10: 0471387711
ISBN-13: 9780471387718
Edition: 2004
List price: $182.00 Buy it from $163.81
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Description: This text integrates spatial statistics with data management and the display capabilities of geographical information systems (GIS). It is designed to serve as an introduction for the novice and a reference for practitioners in the field.

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Book details

List price: $182.00
Copyright year: 2004
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/15/2004
Binding: Hardcover
Pages: 520
Size: 6.25" wide x 9.25" long x 1.00" tall
Weight: 2.134
Language: English

This text integrates spatial statistics with data management and the display capabilities of geographical information systems (GIS). It is designed to serve as an introduction for the novice and a reference for practitioners in the field.

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

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