Improving social harm indices with a modulated Hawkes process
Improving social harm indices with a modulated Hawkes process
复制标题
DOI：
10.1016/j.ijforecast.2018.01.006
复制
发表时间：
2018-07-01
影响因子：
7.9
通讯作者：
Raje, Rajeev
中科院分区：
文献类型：
--
作者：
Mohler, George;Carter, Jeremy;Raje, Rajeev
社区受到一系列社会危害事件的不利影响，如犯罪、交通事故、医疗紧急情况和吸毒。警察、消防、卫生和社会服务部门的任务是通过各种干预措施减轻这种社会危害。虽然已经提出了各种不同的社会危害指数，用于将资源分配给空间固定的热点，但社会危害事件的风险是动态的，需要能够快速识别风险并触发适当公共安全响应的新算法和软件系统。为此，我们提出了一种新的调制霍克斯过程，提供了灵活的方法（i）纳入空间协变量和领先指标的情况下，减少罕见的事件类别的方差，和（ii）通过自激捕获动态热点的形成。我们提出了一个有效的l1-惩罚EM算法估计模型，同时执行每个事件类型的空间协变量的特征选择。我们提供的模拟结果使用数据从印第安纳波利斯大都会警察局，以说明调制霍克斯过程模型的社会危害的优势，最近推出的各种社会危害指数和财产犯罪霍克斯过程。(C)2018年国际预测研究所。Elsevier B.V.出版，保留所有权利。
Communities are affected adversely by a range of social harm events, such as crime, traffic crashes, medical emergencies, and drug use. The police, fire, health and social service departments are tasked with mitigating such social harm through various types of interventions. While various different social harm indices have been proposed for allocating resources to spatially fixed hotspots, the risk of social harm events is dynamic, and new algorithms and software systems that are capable of quickly identifying risks and triggering appropriate public safety responses are needed. We propose a novel modulated Hawkes process for this purpose that offers flexible approaches to both (i) the incorporation of spatial covariates and leading indicators for variance reduction in the case of rarer event categories, and (ii) the capture of dynamic hotspot formation through self-excitation. We present an efficient l1-penalized EM algorithm for estimating the model that performs feature selection for the spatial covariates of each incident type simultaneously. We provide simulation results using data from the Indianapolis Metropolitan Police Department in order to illustrate the advantages of the modulated Hawkes process model of social harm over various recently introduced social harm indices and property crime Hawkes processes. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.