首页> 外文期刊>Kotuitui: New Zealand Journal of Social Sciences Online >Tavern proximity, tavern density and socio-economic status as predictors of assault occurrence within New Zealand: a temporal comparison
【24h】

Tavern proximity, tavern density and socio-economic status as predictors of assault occurrence within New Zealand: a temporal comparison

机译:小酒馆的接近程度,酒馆的密度和社会经济地位是新西兰境内发生袭击事件的指标:时间上的比较

获取原文
       

摘要

This paper investigates how the associations between tavern proximity, tavern density and area unit socio-economic status with assault occurrence vary in a temporal sense. Using New Zealand Police data specifying the day, time and location of assaults in 2016 and Ministry of Justice data specifying the location of on-licenced taverns, we construct logistic regression models to determine how well tavern proximity, tavern density and socio-economic status predict the occurrence of assaults at peak (Fri 22:00–Sat 03:00 and Sat 22:00–Sun 03:00) and off-peak times. An equal-sized sample of traffic generators (public venues whose primary function is not the sale of alcohol) is constructed and similar procedures applied. We find that tavern proximity and tavern density are stronger predictors of assault occurrence at peak, compared to off-peak, times. Conversely, socio-economic status is a better predictor of assault occurrence at off-peak times. We also find that whilst tavern proximity and density are stronger predictors of assault occurrence relative to traffic generator proximity and density at peak times, the opposite is true at off-peak times. These results suggest that in order to minimise alcohol-related harm, there is a need for policy-makers to take into account the temporal nature of these relationships.
机译:本文研究了小酒馆邻近度,小酒馆密度和面积单位社会经济状况与袭击发生之间的关联在时间意义上如何变化。我们使用新西兰警察局的数据指定了2016年袭击的日期,时间和地点,而司法部的数据则指定了持照小酒馆的位置,我们构建了逻辑回归模型,以确定小酒馆的邻近程度,小酒馆的密度和社会经济状况的预测能力在高峰时间(星期五22:00–星期六03:00和星期六22:00–星期日03:00)和非高峰时间发生袭击。构造了一个等量的流量生成器样本(主要功能不是出售酒精的公共场所),并应用了类似的程序。我们发现,与非高峰时间相比,小酒馆的接近度和小酒馆的密度更能预测高峰期的袭击发生。相反,社会经济状况可以更好地预测非高峰时间的袭击发生。我们还发现,相对于交通高峰时交通产生者的接近度和密度,酒馆的接近度和密度是发生袭击的更强预测指标,而在非高峰时间则相反。这些结果表明,为了最大程度地减少与酒精相关的伤害,决策者需要考虑这些关系的时间性质。

著录项

相似文献

  • 外文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号