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常规收集的关于袭击事件的救护车数据能否有助于减少社区暴力?

Can routinely collected ambulance data about assaults contribute to reduction in community violence?

作者信息

Ariel Barak, Weinborn Cristobal, Boyle Adrian

机构信息

Institute of Criminology, University of Cambridge, Cambridge, UK Institute of Criminology, Hebrew University, Jerusalem, Israel.

Institute of Criminology, University of Cambridge, Cambridge, UK.

出版信息

Emerg Med J. 2015 Apr;32(4):308-13. doi: 10.1136/emermed-2013-203133. Epub 2013 Dec 10.

Abstract

BACKGROUND

The 'law of spatiotemporal concentrations of events' introduced major preventative shifts in policing communities. 'Hotspots' are at the forefront of these developments yet somewhat understudied in emergency medicine. Furthermore, little is known about interagency 'data-crossover', despite some developments through the Cardiff Model. Can police-ED interagency data-sharing be used to reduce community-violence using a hotspots methodology?

METHODS

12-month (2012) descriptive study and analysis of spatiotemporal clusters of police and emergency calls for service using hotspots methodology and assessing the degree of incident overlap. 3775 violent crime incidents and 775 assault incidents analysed using spatiotemporal clustering with k-means++ algorithm and Spearman's rho.

RESULTS

Spatiotemporal location of calls for services to the police and the ambulance service are equally highly concentrated in a small number of geographical areas, primarily within intra-agency hotspots (33% and 53%, respectively) but across agencies' hotspots as well (25% and 15%, respectively). Datasets are statistically correlated with one another at the 0.57 and 0.34 levels, with 50% overlap when adjusted for the number of hotspots. At least one in every two police hotspots does not have an ambulance hotspot overlapping with it, suggesting half of assault spatiotemporal concentrations are unknown to the police. Data further suggest that more severely injured patients, as estimated by transfer to hospital, tend to be injured in the places with the highest number of police-recorded crimes.

CONCLUSIONS

A hotspots approach to sharing data circumvents the problem of disclosing person-identifiable data between different agencies. Practically, at least half of ambulance hotspots are unknown to the police; if causal, it suggests that data sharing leads to both reduced community violence by way of prevention (such as through anticipatory patrols or problem-oriented policing), particularly of more severe assaults, and improved efficiency of resource deployment.

摘要

背景

“事件时空集中定律”在社区治安方面引发了重大的预防性转变。“热点地区”处于这些发展的前沿,但在急诊医学领域的研究相对较少。此外,尽管通过卡迪夫模式有了一些进展,但对于跨部门的“数据交叉”却知之甚少。能否使用热点地区方法通过警方与急诊科的跨部门数据共享来减少社区暴力?

方法

采用热点地区方法对2012年为期12个月的警方和急救服务时空集群进行描述性研究和分析,并评估事件重叠程度。使用k均值++算法和斯皮尔曼等级相关系数对3775起暴力犯罪事件和775起袭击事件进行时空聚类分析。

结果

警方和救护车服务的求助电话时空位置高度集中在少数地理区域,主要集中在各部门内部的热点地区(分别为33%和53%),但跨部门热点地区也有分布(分别为25%和15%)。数据集在0.57和0.34水平上具有统计学相关性,调整热点地区数量后重叠率为50%。至少每两个警方热点地区中有一个没有与之重叠的救护车热点地区,这表明警方对一半的袭击时空集中情况并不知晓。数据还表明,根据转院情况估计,伤势较重的患者往往在警方记录犯罪数量最多的地方受伤。

结论

采用热点地区方法共享数据避免了在不同机构之间披露可识别个人的数据这一问题。实际上,警方对至少一半的救护车热点地区并不知晓;如果存在因果关系,这表明数据共享既能通过预防措施(如通过预期巡逻或问题导向型警务)减少社区暴力,尤其是更严重的袭击事件,又能提高资源部署效率。

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