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Space‐time cluster identification in point processes
Author(s) -
Assunçäo Renato,
Tavares Andréa,
Correa Thais,
Kulldorff Martin
Publication year - 2007
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550350105
Subject(s) - point process , scan statistic , poisson point process , cluster (spacecraft) , test statistic , statistic , point (geometry) , space (punctuation) , statistical hypothesis testing , population , computer science , identification (biology) , null hypothesis , separable space , monte carlo method , data mining , statistics , mathematics , algorithm , mathematical analysis , geometry , demography , botany , sociology , biology , programming language , operating system
The authors propose a new type of scan statistic to test for the presence of space‐time clusters in point processes data, when the goal is to identify and evaluate the statistical significance of localized clusters. Their method is based only on point patterns for cases; it does not require any specific knowledge of the underlying population. The authors propose to scan the three‐dimensional space with a score test statistic under the null hypothesis that the underlying point process is an inhomogeneous Poisson point process with space and time separable intensity. The alternative is that there are one or more localized space‐time clusters. Their method has been implemented in a computationally efficient way so that it can be applied routinely. They illustrate their method with space‐time crime data from Belo Horizonte, a Brazilian city, in addition to presenting a Monte Carlo study to analyze the power of their new test.