Abstract

Reported gang-related violent crimes in Los Angeles, California, from 1/1/14 to 12/31/17 are modeled using spatial–temporal marked Hawkes point processes with covariates. We propose an algorithm to estimate the spatial-temporally varying background rate nonparametrically as a function of demographic covariates. Kernel smoothing and generalized additive models are used in an attempt to model the background rate as closely as possible in an effort to differentiate inhomogeneity in the background rate from causal clustering or triggering of events. The models are fit to data from 2014 to 2016 and evaluated using data from 2017, based on log-likelihood and superthinned residuals. The impact of nonrandomized violence interruption performed by The City of Los Angeles Mayor’s Office of Gang Reduction Youth Development (GRYD) Incident Response (IR) Program is assessed by comparing the triggering associated with GRYD IR Program events to the triggering associated with sub-sampled non-GRYD events selected to have a similar spatial–temporal distribution. The results suggest that GRYD IR Program violence interruption yields a reduction of approximately 18.3% in the retaliation rate in locations more than 130 m from the original reported crimes, and a reduction of 14.2% in retaliations within 130 m.

摘要

从 2014 年 1 月 1 日到 2017 年 12 月 31 日，加利福尼亚州洛杉矶报告的与帮派相关的暴力犯罪是使用带有协变量的时空标记霍克斯点过程建模的。我们提出了一种算法来非参数地估计时空变化的背景率作为人口统计协变量的函数。核平滑和广义加性模型用于尝试尽可能接近地模拟背景速率，以努力区分背景速率的不均匀性与因果聚类或事件触发。这些模型适用于 2014 年至 2016 年的数据，并使用 2017 年的数据基于对数似然和超薄残差进行评估。洛杉矶市 M进行的非随机暴力中断的影响通过将与 GRYD IR 计划事件相关的触发与与选择具有相似时空分布的子采样非 GRYD 事件相关的触发进行比较，评估纽约市长办公室的帮派减少青年发展 (GRYD) 事件响应 (IR) 计划。结果表明，GRYD IR 计划暴力中断使 130 m 以上位置的报复率比最初报告的犯罪率降低了约 18.3%，130 m 内的报复率降低了 14.2%。