Delinquencies are burden on a society, and minimizing the crime risk is essential. In this paper, we propose a multifaceted data-driven approach to long-term predictions of annual felony and misdemeanor rates and understanding their spatial patterns under various scenarios. Leveraging a suite of nonlinear statistical learning algorithms, we developed advanced models to predict county-level annual crime rates, and evaluate the associations of felony and misdemeanor rates with socioeconomic and demographic variables, infrastructure patterns in neighborhoods, and climatic variables. We implemented our proposed framework for the state of New York, the fourth most populous state in the U.S. For both felony and misdemeanor predictions, our results indicate that Random Forest outperforms all the other models achieving over 84% and 60% improvements in goodness-of-fit and predictive accuracy respectively, compared to the mean-only model. We identified population demography, socioeconomic condition, and infrastructure patterns to be key predictors of both felony and misdemeanor rates, with suburban crime rates being significantly higher than the urban and rural ones. Specifically, we observed that higher population count, higher poverty rate, and lower median family income are associated with elevated crime rates. Higher number of transport infrastructures, shopping malls and banks are also found to be positively correlated with increasing crime rates. In addition, we show how scenario-based sensitivity analysis can be leveraged to communicate crime risk to the stakeholders under various scenarios. Our proposed framework can help both policymakers and law enforcement in informed decision-making towards crime management, thereby minimizing risk of delinquencies in society at large.

犯罪是社会的负担，最大限度地减少犯罪风险至关重要。在本文中，我们提出了一种多方面的数据驱动方法来长期预测年度重罪和轻罪率，并了解其在各种情况下的空间格局。利用一套非线性统计学习算法，我们开发了先进的模型来预测县级年度犯罪率，并评估重罪和轻罪率与社会经济和人口统计学变量，邻里基础设施模式以及气候变量之间的关系。我们针对纽约州（美国人口第四大州）实施了我们提议的框架。对于重罪和轻罪的预测，我们的结果表明，与“均值”模型相比，“随机森林”优于其他所有模型，其拟合优度和预测准确性分别提高了84％和60％以上。我们确定人口人口统计学，社会经济状况和基础设施模式是重罪和轻罪率的关键预测因素，郊区犯罪率显着高于城市和农村。具体来说，我们观察到人口数量增加，贫困率提高和家庭收入中位数降低与犯罪率上升有关。还发现，交通基础设施，购物中心和银行数量增加与犯罪率上升呈正相关。此外，我们展示了在各种情况下如何利用基于情景的敏感性分析将犯罪风险传达给利益相关者。我们提出的框架可以帮助政策制定者和执法人员做出明智的犯罪管理决策，从而最大程度地降低整个社会的违法风险。