The association between alcohol outlets and violence has long been recognised, and is commonly used to inform policing and licensing policies (such as staggered closing times and zoning). Less investigated, however, is the association between violent crime and other urban points of interest, which while associated with the city centre alcohol consumption economy, are not explicitly alcohol outlets. Here, machine learning (specifically, LASSO regression) is used to model the distribution of violent crime for the central 9 km² of ten large UK cities. Densities of 620 different Point of Interest types (sourced from Ordnance Survey) are used as predictors, with the 10 most explanatory variables being automatically selected for each city. Cross validation is used to test generalisability of each model. Results show that the inclusion of additional point of interest types produces a more accurate model, with significant increases in performance over a baseline univariate alcohol-outlet only model. Analysis of chosen variables for city-specific models shows potential candidates for new strategies on a per-city basis, with combined-model variables showing the general trend in POI/violence association across the UK. Although alcohol outlets remain the best individual predictor of violence, other points of interest should also be considered when modelling the distribution of violence in city centres. The presented method could be used to develop targeted, city-specific initiatives that go beyond alcohol outlets and also consider other locations.

酒精饮料商店与暴力行为之间的关联早已得到认可，并且通常用于告知治安和许可政策（例如交错的关闭时间和分区）。然而，较少调查的是暴力犯罪与其他城市关注点之间的关联，这些关注点虽然与市中心的酒精消费经济相关，但并不是明确的酒精出口。在这里，使用机器学习（特别是LASSO回归）来模拟中心9 km ²的暴力犯罪分布英国十个大城市。620种不同的兴趣点类型（来自军械测量局）的密度用作预测因子，并为每个城市自动选择10个最具解释性的变量。交叉验证用于测试每个模型的通用性。结果表明，包含其他兴趣点类型可产生更准确的模型，与仅使用基线单变量酒精出口的模型相比，性能有了显着提高。针对特定于城市的模型选择的变量的分析显示了每个城市基于新策略的潜在候选者，组合模型变量显示了英国POI /暴力关联的总体趋势。尽管饮酒场所仍然是暴力行为的最佳个体预测者，在模拟城市中心的暴力分布时，还应考虑其他兴趣点。所提出的方法可用于制定针对目标的，针对特定城市的计划，这些计划不仅限于酒精店，还考虑了其​​他地点。