Rogerson / Yamada Statistical Detection and Surveillance of Geographic Clusters

1. Auflage 2008
ISBN: 978-1-58488-936-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 324 Seiten

Reihe: Chapman & Hall/CRC Interdisciplinary Statistics

The widespread popularity of geographic information systems (GIS) has led to new insights in countless areas of application. It has facilitated not only the collection and storage of geographic data, but also the display of such data. Building on this progress by using an integrated approach, Statistical Detection and Monitoring of Geographic Clusters provides the statistical tools to identify whether data on a given map deviates significantly from expectations and to determine quickly whether new point patterns are emerging over time.
The book begins with a review of statistical methods for cluster detection, organized according to the different types of hypotheses and questions about clustering that can be investigated. It then delineates methods that allow for the quick detection of emergent geographic clusters.
The book delivers a cohesive presentation unlike that of most edited volumes. Drawing on the authors' extensive work in the field, the book delineates methods in such a way that they can be applied, almost instantly, to an array of disciplines. The readily applicable methods the book describes are useful for a multitude of problems in a variety of fields, particularly disease surveillance in the public health industry. Statistical Detection and Monitoring of Geographic Clusters is an essential volume for your reference shelf.

Zielgruppe

Students and practitioners in geography, statistics, and public health.

Autoren/Hrsg.

Rogerson, Peter

Yamada, Ikuho

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

Introduction and Overview
Setting the Stage
The Roles of Spatial Statistics in Public Health and Other Fields
Limitations Associated with the Visualization of Spatial Data
Some Fundamental Concepts and Distinctions
Types of Tests for Clustering
Structure of the Book
Software Resources and Sample Data

Introductory Spatial Statistics: Description and Inference
Introduction
Mean Center
Median Center
Standard Distance
Relative Standard Distance
Inferential Statistical Tests of Central Tendency and Dispersion
Illustration
Angular Data
Characteristics of Spatial Processes: First-Order and Second-Order Variation
Kernel Density Estimation
K-Functions
Differences and Ratios of Kernel Density Estimators
Differences in K-Functions

Global Statistics
Introduction
Nearest Neighbor Statistic
Quadrat Methods
Spatial Dependence: Moran’s I
Geary’s C
A Comparison of Moran’s I and Geary’s C
Oden’s Ipop Statistic
Tango’s Statistic and a Spatial Chi-Square Statistic
Getis and Ord’s Global Statistic
Case–Control Data: The Cuzick–Edwards Test
A Global Quadrat Test of Clustering for Case–Control Data
A Modified Cuzick–Edwards Test

Local Statistics
Introduction
Local Moran Statistic
Score Statistic
Tango’s CF Statistic
Getis’ Gi Statistic
Stone’s Test
Modeling around Point Sources with Case–Control Data
Cumulative and Maximum Chi-Square Tests as Focused Tests
The Local Quadrat Test and an Introduction to Multiple Testing via the M-Test

Tests for the Detection of Clustering, Including Scan Statistics
Introduction
Openshaw et al.’s Geographical Analysis Machine (GAM)
Besag and Newell’s Test for the Detection of Clusters
Fotheringham and Zhan’s Method
Cluster Evaluation Permutation Procedure
Exploratory Spatial Analysis Approach of Rushton and Lolonis
Kulldorff’s Spatial Scan Statistic with Variable Window Size
Bonferroni and Sidak Adjustments
Improvements on the Bonferroni Adjustment
Rogerson’s Statistical Method for the Detection of Geographic Clustering

Retrospective Detection of Changing Spatial Patterns
Introduction
The Knox Statistic for Space–Time Interaction
Test for a Change in Mean for a Series of Normally Distributed Observations
Retrospective Detection of Change in Multinomial Probabilities

Introduction to Statistical Process Control and Nonspatial Cumulative Sum Methods of Surveillance
Introduction
Shewhart Charts
Cumulative Sum (Cusum) Methods
Monitoring Small Counts
Cumulative Sums for Poisson Variables
Cusum Methods for Exponential Data
Other Useful Modifications for Cusum Charts
More on the Choice of Cusum Parameters
Other Methods for Temporal Surveillance

Spatial Surveillance and the Monitoring of Global Statistics
Brief Overview of the Development of Methods for Spatial Surveillance
Introduction to Monitoring Global Spatial Statistics
Cumulative Sum Methods and Global Spatial Statistics That Are Observed Periodically
CUSUM Methods and Global Spatial Statistics That Are Updated Periodically
Summary and Discussion

Cusum Charts for Local Statistics and for the Simultaneous Monitoring of Many Regions
Monitoring around a Predefined Location
Spatial Surveillance: Separate Charts for Each Region
Monitoring Many Local Statistics Simultaneously
Summary
Appendix

More Approaches to the Statistical Surveillance of Geographic Clustering
Introduction
Monitoring Spatial Maxima
Multivariate Cusum Approaches

Summary:Associated Tests for Cluster Detection and Surveillance
Introduction
Associated Retrospective Statistical Tests
Associated Prospective Statistical Tests: Regional Surveillance for Quick Detection of Change
References
Author Index
Subject Index