MAD-STEC: a method for multiple automatic detection of space-time emerging clusters

被引：0
作者：
Bráulio M. Veloso
Thais R. Correa
Marcos O. Prates
Gabriel F. Oliveira
Andréa I. Tavares
机构：
[1] Universidade Federal de Minas Gerais,Department of Computer Science
[2] Universidade Federal de Minas Gerais,Department of Statistics
[3] Universidade Federal de Minas Gerais,Department of Automation and Control Engineering
[4] Cadence Design Systems,undefined
来源：
Statistics and Computing | 2017年 / 27卷
关键词：
Surveillance; Point pattern; Prospective space-time surveillance; Space-time clustering;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.
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页码：1099 / 1110
页数：11
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