The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level
The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level
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DOI：
10.1016/j.healthplace.2021.102736
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发表时间：
2021-12-24
期刊：
影响因子：
4.8
通讯作者：
Quigley, Brian M.
中科院分区：
文献类型：
--
作者：
Chang, Ting;Hu, Yingjie;Quigley, Brian M.
家庭暴力是一个严重的公共卫生问题，每年有三分之一的妇女和四分之一的男子经历某种形式的与伴侣有关的暴力。现有的研究表明，在个人层面上，饮酒与家庭暴力之间存在很强的关联。因此，酒精使用也可能是家庭暴力在社区一级的预测，帮助确定家庭暴力更有可能发生的社区。然而，这是困难和昂贵的收集数据，可以代表邻里水平的酒精使用，特别是对于一个大的地理区域。在这项研究中，我们建议从匿名的移动的电话位置数据中获得有关不同社区居民的酒精出口访问的信息，并调查所获得的访问是否可以帮助更好地预测DV在社区层面。我们使用SafeGraph公司的移动的电话数据，研究人员可以免费获得这些数据，其中包含人们如何访问各种兴趣点的信息，包括酒类销售点。在这样的数据中，基于移动的电话的GPS点位置和酒类出口的建筑物覆盖区（多边形）来识别对酒类出口的访问。我们提出了我们的方法，推导出街区级的酒精出口访问，并与四种不同的统计和机器学习模型的实验，以调查派生访问的作用，在增强DV预测的基础上，关于DV在芝加哥的经验数据集。我们的研究结果揭示了衍生的酒精出口访问的有效性，帮助确定更有可能遭受DV的社区，并可以告知有关DV干预和酒精出口许可的政策。
Domestic violence (DV) is a serious public health issue, with 1 in 3 women and 1 in 4 men experiencing some form of partner-related violence every year. Existing research has shown a strong association between alcohol use and DV at the individual level. Accordingly, alcohol use could also be a predictor for DV at the neighborhood level, helping identify the neighborhoods where DV is more likely to happen. However, it is difficult and costly to collect data that can represent neighborhood-level alcohol use especially for a large geographic area. In this study, we propose to derive information about the alcohol outlet visits of the residents of different neighborhoods from anonymized mobile phone location data, and investigate whether the derived visits can help better predict DV at the neighborhood level. We use mobile phone data from the company SafeGraph, which is freely available to researchers and which contains information about how people visit various points-of-interest including alcohol outlets. In such data, a visit to an alcohol outlet is identified based on the GPS point location of the mobile phone and the building footprint (a polygon) of the alcohol outlet. We present our method for deriving neighborhood-level alcohol outlet visits, and experiment with four different statistical and machine learning models to investigate the role of the derived visits in enhancing DV prediction based on an empirical dataset about DV in Chicago. Our results reveal the effectiveness of the derived alcohol outlets visits in helping identify neighborhoods that are more likely to suffer from DV, and can inform policies related to DV intervention and alcohol outlet licensing.