预测短时间到犯罪的枪支:对加利福尼亚交易记录 (2010-2021) 的机器学习分析 (2010-2021)
Hannah S Laqueur1,2, Colette Smirniotis3,4, Christopher McCort3,4
1Violence Prevention Research Program, Department of Emergency Medicine, University of California, Davis, USA. hslaqueur@ucdavis.edu.
Journal of urban health : bulletin of the New York Academy of Medicine
|September 5, 2024
概括
机器学习模型可以预测转向犯罪的枪支,并使用交易数据识别高风险的销售. 购买者的年龄和口径是这种枪支转移风险的关键因素.
科学领域:
- 公共卫生 公共卫生
- 犯罪学 犯罪学
- 数据科学数据科学数据科学
背景情况:
- 与枪支有关的犯罪是美国城市的主要公共安全问题.
- 了解将枪支从合法市场转移到违法者手中至关重要.
研究的目的:
- 为了预测哪些枪支很可能在合法购买后不久被用于犯罪.
- 为了识别与犯罪枪支转移相关的买家,枪支和经销商的特征.
主要方法:
- 在加利福尼亚州的枪支交易和犯罪枪支恢复数据 (2010-2021) 上利用监督机器学习 (随机森林).
- 采用分层低样本分析,预测犯罪枪支在一年内被回收.
- 使用了夏普利添加剂扩展和特征重要性的平均精度下降.
主要成果:
- 模型实现了0.85的曲线下面积 (AUC),有效地区分了风险.
- 高风险交易 (得分≥0.98) 在一年内显示出74%的恢复率.
- 关键预测因素包括购买者的年龄和枪支口径大小.
结论:
- 应用于枪支交易数据的机器学习可以识别高风险转移的枪支.
- 这种方法为减少枪支暴力的积极干预提供了潜力.
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