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E-raamat: Statistical Detection and Surveillance of Geographic Clusters

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Statistical Detection and Surveillance of Geographic ClustersSuurem pilt
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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.

Arvustused

Researchers in a surprisingly diverse range of disciplines find themselves faced with problems needing the methods that are described in this book: the detection of anomalous groupings of objects or events within geographical distributions. Chapters 710 [ explore] methods for the prospective detection of changes as they occur. This area is rapidly becoming increasingly important in applications such as detecting outbreaks of disease, fraud and even potential terrorist activity. The book is liberally illustrated with real examples and data a good introduction to the ideas for students encountering them for the first time. Journal of the Royal Statistical Society, Series A, 2010

Attractive features of this book are: (1) introduction of various statistical methods/tests for geographic clusters and surveillance, (2) many figures and tables to facilitate understanding, and (3) illustration of each method with real data. readers who are particularly interested in surveillance will find this book a helpful guide. Journal of the American Statistical Association, Sept. 2010, Vol. 105, No. 491

In contrast to most edited books, the authors text provides a comprehensive presentation that will provide a valuable reference source for years to come. I recommend that anyone working with geographic information systems (GIS) consult this volume, as there are many valuable nuggets of informationmany unexpected! International Statistical Review, 2009

List of Figures
xv
List of Tables
xix
Acknowledgments xxiii
Introduction and Overview
1(20)
Setting the Stage
1(1)
The Roles of Spatial Statistics in Public Health and Other Fields
2(1)
Limitations Associated with the Visualization of Spatial Data
3(6)
Visual Assessment of Clustering Tendency
3(2)
What to Map: Mapping Rates versus Mapping p-Values
5(1)
Example 1: Sudden Infant Death Syndrome in North Carolina
6(1)
Example 2: Breast Cancer in the Northeastern United States
7(2)
Some Fundamental Concepts and Distinctions
9(2)
Descriptive versus Inferential, and Exploratory versus Confirmatory, Spatial Statistics
9(1)
Types of Health Data
10(1)
Point Data
10(1)
Case-Control Data
10(1)
Areal Data
10(1)
Time-Subscripted Data
11(1)
Types of Tests for Clustering
11(1)
Structure of the Book
12(1)
Software Resources and Sample Data
13(8)
Software Resources
13(1)
GeoSurveillance
13(1)
GeoDa
13(1)
R
13(1)
SaTScan
13(1)
Cancer Atlas Viewer
14(1)
CrimeStat
14(1)
Sample Datasets
14(1)
Breast Cancer Mortality in the Northeastern United States
14(1)
Prostate Cancer Mortality in the United States
15(1)
Sudden Infant Death Syndrome in North Carolina
16(1)
Leukemia in Central New York State
16(1)
Leukemia and Lymphoma Case-Control Data in England
16(2)
Low Birthweight in California
18(3)
Introductory Spatial Statistics: Description and Inference
21(22)
Introduction
21(1)
Mean Center
22(1)
Median Center
23(1)
Standard Distance
23(2)
Relative Standard Distance
25(1)
Inferential Statistical Tests of Central Tendency and Dispersion
25(2)
Illustration
27(2)
Angular Data
29(2)
Characteristics of Spatial Processes: First-Order and Second-Order Variation
31(1)
Kernel Density Estimation
32(3)
K-Functions
35(2)
Differences and Ratios of Kernel Density Estimators
37(3)
Differences in K-Functions
40(3)
Global Statistics
43(42)
Introduction
43(1)
Nearest Neighbor Statistic
44(2)
Illustration
45(1)
Quadrat Methods
46(10)
Unconditional Approach
47(1)
Conditional Approach
48(1)
Example 1: Leukemia in Central New York State
49(1)
Example 2: Sudden Infant Death Syndrome in North Carolina
49(1)
Example 3: Lung Cancer in Cambridgeshire
50(1)
Minimum Expected Frequencies
51(1)
Issues Associated with Scale
51(1)
Testing with Multiple Quadrat Sizes
52(1)
Optimal Quadrat Size: Appropriate Spatial Scales for Cluster Detection
53(1)
A Comparison of Alternative Quadrat-Based Global Statistics
54(2)
Spatial Dependence: Moran's I
56(3)
Illustration
57(2)
Example: Low Birthweight Cases in California
59(1)
Geary's C
59(2)
Illustration
60(1)
Example: Low Birthweight Cases in California
60(1)
A Comparison of Moran's I and Geary's C
61(6)
Example: Spatial Variation in Handedness in the United States
62(2)
Statistical Power of I and C
64(3)
Oden's Ipop Statistic
67(2)
Illustration
68(1)
Tango's Statistic and a Spatial Chi-Square Statistic
69(4)
Illustration
71(1)
Example: Sudden Infant Death Syndrome in North Carolina
71(2)
Getis and Ord's Global Statistic
73(2)
Example: Low Birthweight Cases in California
74(1)
Case-Control Data: The Cuzick-Edwards Test
75(1)
Illustration
76(1)
A Global Quadrat Test of Clustering for Case-Control Data
76(4)
Example
78(2)
Spatial Scale
80(1)
A Modified Cuzick-Edwards Test
80(5)
Example: Leukemia and Lymphoma Case-Control Data in England
82(3)
Local Statistics
85(22)
Introduction
85(1)
Local Moran Statistic
86(3)
Illustration
87(1)
Example: Low Birthweight Cases in California
87(2)
Score Statistic
89(2)
Illustration
90(1)
Tango's CF Statistic
91(2)
Illustration
92(1)
Getis' Gi Statistic
93(2)
Illustration
94(1)
Example: Low Birthweight Cases in California
95(1)
Stone's Test
95(1)
Illustration
96(1)
Modeling around Point Sources with Case-Control Data
96(1)
Cumulative and Maximum Chi-Square Tests as Focused Tests
97(5)
Illustration
99(1)
Example: Leukemia and Lymphoma Case-Control Data in England
100(1)
Discreteness of the Maximum Chi-Square Statistic
101(1)
Relative Power of the Two Tests
101(1)
The Local Quadrat Test and an Introduction to Multiple Testing via the M-Test
102(5)
Fuchs and Kenett's M Test
103(2)
Example 1: Sudden Infant Death Syndrome in North Carolina
105(1)
Example 2: Lung Cancer in Cambridgeshire
105(2)
Tests for the Detection of Clustering, Including Scan Statistics
107(28)
Introduction
107(1)
Openshaw et al.'s Geographical Analysis Machine (Gam)
108(1)
Besag and Newell's Test for the Detection of Clusters
109(1)
Fotheringham and Zhan's Method
110(1)
Cluster Evaluation Permutation Procedure
111(1)
Exploratory Spatial Analysis Approach of Rushton and Lolonis
112(1)
Kulldorff's Spatial Scan Statistic with Variable Window Size
113(6)
Example 1: Low Birthweight Cases in California (Areal Data)
113(4)
Example 2: LBW Cases in California (Point Data)
117(2)
Bonferroni and Sidak Adjustments
119(3)
Power Loss with the Bonferroni Adjustment
121(1)
Improvements on the Bonferroni Adjustment
122(1)
Rogerson's Statistical Method for the Detection of Geographic Clustering
123(12)
The Geometry of Random Fields
125(1)
Illustration
125(1)
Approximation for Discreteness of Observations
126(1)
Approximations for the Exceedance Probability
127(1)
An Approach Based on the Effective Number of Independent Resels
128(2)
Example
130(3)
Discussion
133(2)
Retrospective Detection of Changing Spatial Patterns
135(22)
Introduction
135(1)
The Knox Statistic for Space-Time Interaction
135(2)
Illustration
137(1)
Test for a Change in Mean for a Series of Normally Distributed Observations
137(3)
Example
138(2)
Retrospective Detection of Change in Multinomial Probabilities
140(17)
Illustration
141(2)
Example 1: Breast Cancer Mortality in the Northeastern United States
143(2)
Example 2: Recent Changes in the Spatial Pattern of Prostate Cancer Mortality in the United States
145(1)
Introduction
145(1)
Geographic Variation in Incidence and Mortality Rates
146(1)
Data
146(1)
Descriptive Measures of Change
147(1)
Retrospective Detection of Change
148(5)
Discussion
153(3)
Example 3: Crime
156(1)
Introduction to Statistical Process Control and Nonspatial Cumulative Sum Methods of Surveillance
157(28)
Introduction
157(1)
Shewhart Charts
158(2)
Illustration
159(1)
Cumulative Sum (Cusum) Methods
160(5)
Illustration
163(2)
Monitoring Small Counts
165(2)
Transformations to Normality
166(1)
Cumulative Sums for Poisson Variables
167(4)
Cusum Charts for Poisson Data
167(1)
Example: Kidney Failure in Cats
168(1)
Poisson Cusums with Time-Varying Expectations
169(1)
Example: Lower Respiratory Infection Episodes
170(1)
Cusum Methods for Exponential Data
171(3)
Illustration
173(1)
Other Useful Modifications for Cusum Charts
174(2)
Fast Initial Response
174(1)
Unknown Process Parameters
175(1)
More on the Choice of Cusum Parameters
176(7)
Approximations for the Critical Threshold h for Given Choices of k and the In-Control ARL0
177(2)
Approximations for the Critical Threshold h for Given Choices of k and the Out-of-Control ARL1
179(2)
The Choice of k and h for Desired Values of ARL0 and ARL1.181
181(2)
Other Methods for Temporal Surveillance
183(2)
Spatial Surveillance and the Monitoring of Global Statistics
185(46)
Brief Overview of the Development of Methods for Spatial Surveillance
185(3)
Introduction to Monitoring Global Spatial Statistics
188(2)
Cumulative Sum Methods and Global Spatial Statistics That Are Observed Periodically
190(8)
Moran's I and Getis' G
190(1)
Example: Breast Cancer Mortality in the Northeastern United States
191(5)
Monitoring Chi-Square Statistics
196(1)
Illustration
197(1)
CUSUM Methods and Global Spatial Statistics That Are Updated Periodically
198(30)
Spatial Surveillance Using Tango's Test for General Clustering
199(1)
Illustration
200(3)
Example: Burkitt's Lymphoma in Uganda
203(3)
Discussion
206(1)
A Cusum Method Based upon the Knox Statistic: Monitoring Point Patterns for the Development of Space-Time Clusters
207(1)
A Local Knox Statistic
207(3)
A Method for Monitoring Changes in Space-Time Interaction
210(1)
Example: Burkitt's Lymphoma in Uganda
211(1)
Summary and Discussion
212(2)
Cusum Method Combined with Nearest-Neighbor Statistic
214(1)
Monitoring Changes in Point Patterns
214(1)
A Cusum Approach for the Nearest-Neighbor Statistic
215(2)
Simulations of Clustering in the Unit Square
217(1)
Example: Application to Crime Analysis and Data from the Buffalo Police Department
218(1)
Cusum Approach for Arson Data
218(4)
Surveillance Using a Moving Window of Observations
222(6)
Summary and Discussion
228(3)
Cusum Charts for Local Statistics and for the Simultaneous Monitoring of Many Regions
231(28)
Monitoring around a Predefined Location
231(12)
Introduction
231(1)
Raubertas' Approach to Monitoring Local Statistics
231(1)
Monitoring a Single Local Statistic: Autocorrelated Regional Variables
232(1)
An Approach Based on Score Statistics
233(1)
Spatial Surveillance around Foci: A Generalized Score Statistic, Tango's CF
233(2)
A Distance-Based Method
235(1)
Application to Data on Burkitt's Lymphoma
236(2)
Surveillance around Prespecified Locations Using Case-Control Data
238(1)
Introduction
238(1)
Prospective Monitoring around a Source, Using Case-Control Data
238(1)
Illustration
239(4)
Spatial Surveillance: Separate Charts for Each Region
243(9)
Illustration
245(4)
Example: Kidney Failure in Cats
249(1)
Example: Breast Cancer Mortality in the Northeastern United States
250(2)
Monitoring Many Local Statistics Simultaneously
252(5)
Example: Breast Cancer Mortality in the Northeastern United States
255(1)
Poisson Variables
256(1)
Summary
257(2)
Appendix
257(2)
More Approaches to the Statistical Surveillance of Geographic Clustering
259(30)
Introduction
259(1)
Monitoring Spatial Maxima
260(9)
Monitoring Spatial Maxima
261(1)
Type I Extreme Value (Gumbel) Distribution
262(1)
Cusum Surveillance of Gumbel Variates
263(1)
Example: Female Breast Cancer Mortality Rates in the Northeastern United States
264(2)
Example: Prostate Cancer Data in the United States
266(2)
Determination of Threshold Parameter
268(1)
Summary
268(1)
Multivariate Cusum Approaches
269(20)
Introduction
269(1)
Alternative Approaches to Monitoring Regional Change for More Than One Region
270(1)
Methods and Illustrations
271(1)
Multivariate Monitoring
271(1)
Hypothetical, Simulated Scenarios
272(4)
Spatial Autocorrelation
276(2)
Example: Breast Cancer Mortality in the Northeastern United States
278(2)
Multiple Univariate Results
280(3)
Multivariate Results
283(1)
Interpretation of Multivariate Results
283(2)
Estimation of Covariance and a Nonparametric Approach
285(2)
Discussion
287(2)
Summary: Associated Tests for Cluster Detection and Surveillance
289(14)
Introduction
289(1)
Associated Retrospective Statistical Tests
290(10)
Associated Retrospective Statistical Tests: Aspatial Case
291(1)
Associated Retrospective Statistical Tests: Spatial Case
292(4)
Maximum Local Statistic
296(1)
Illustration
297(1)
Example: Application to Leukemia Data for Central New York State
297(3)
Associated Prospective Statistical Tests: Regional Surveillance for Quick Detection of Change
300(3)
Prospective Methods: Aspatial Case
300(1)
Prospective Methods: Spatial Case
301(2)
References 303(10)
Author Index 313(4)
Subject Index 317
Peter Rogerson, Ikuho Yamada
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