Self-Exciting Point Process Modeling of Crime
Self-Exciting Point Process Modeling of Crime
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DOI：
10.1198/jasa.2011.ap09546
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发表时间：
2011-03-01
影响因子：
3.7
通讯作者：
Tita, G. E.
中科院分区：
文献类型：
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作者：
Mohler, G. O.;Short, M. B.;Tita, G. E.
由于犯罪行为的特定模式，在某些类型的犯罪数据中观察到高度聚集的事件序列，例如入室盗窃和帮派暴力。地震学家也观察到了类似的聚集模式，因为众所周知，地震会增加初始事件位置附近后续地震或余震的风险。空间时间聚类建模地震学的自激点过程和本文的重点是表明，这些方法非常适合犯罪学的应用。我们首先回顾自激点过程在地震学的背景下。接下来，使用住宅入室盗窃数据提供的洛杉矶警察局，我们说明了自激点过程模型的实施在城市犯罪的背景下。为了这个目的，我们使用一个完全非参数估计方法，以获得洞察的时空触发函数和时间趋势的背景入室盗窃率的形式。
Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space time clustering is modeled in seismology by self-exciting point processes and the focus of this article is to show that these methods are well suited for criminological applications. We first review self-exciting point processes in the context of seismology. Next, using residential burglary data provided by the Los Angeles Police Department, we illustrate the implementation of self-exciting point process models in the context of urban crime. For this purpose we use a fully nonparametric estimation methodology to gain insight into the form of the space time triggering function and temporal trends in the background rate of burglary.