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A spatial scan statistic for compound Poisson data
Author(s) -
Rosychuk Rhonda J.,
Chang HsingMing
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5891
Subject(s) - scan statistic , poisson distribution , cluster analysis , statistic , cluster (spacecraft) , spatial epidemiology , computer science , statistics , spatial analysis , data mining , poisson regression , count data , epidemiology , econometrics , medicine , artificial intelligence , mathematics , environmental health , pathology , population , programming language
The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease‐related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease‐related visits. Copyright © 2013 John Wiley & Sons, Ltd.