Relevant research has been highlighted in the computing community to develop machine learning models capable of predicting the occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crime, and analyzing crimes over time. However, models capable of predicting specific crimes, such as homicide, are not commonly found in the current literature. This research presents a machine learning model to predict homicide crimes, using a dataset that uses generic data (without study location dependencies) based on incident report records for 34 different types of crimes, along with time and space data from crime reports. Experimentally, data from the city of Bel\'em - Par\'a, Brazil was used. These data were transformed to make the problem generic, enabling the replication of this model to other locations. In the research, analyses were performed with simple and robust algorithms on the created dataset. With this, statistical tests were performed with 11 different classification methods and the results are related to the prediction's occurrence and non-occurrence of homicide crimes in the month subsequent to the occurrence of other registered crimes, with 76% assertiveness for both classes of the problem, using Random Forest. Results are considered as a baseline for the proposed problem.
翻译:计算界强调相关研究,以开发能够预测犯罪发生情况的机器学习模型,分析犯罪背景,提取与犯罪有关联的个人概况,并分析长期犯罪。然而,现有文献中并不常见能够预测特定犯罪(如杀人罪)的模型。这一研究提供了一种机器学习模型,用以预测杀人犯罪,使用基于34种不同类型犯罪事件报告记录以及犯罪报告的时间和空间数据的数据集,使用通用数据(不研究地点依赖数据),并使用其他已登记犯罪发生后的月份的预测发生和不发生杀人犯罪的结果,使用随机森林将这些数据转化成问题的一般问题,使这一模型复制到其他地方。在研究中,用所创建的数据集的简单和稳健的算法进行了分析。据此,用11种不同的分类方法进行了统计测试,结果与其他已登记犯罪的发生和未发生有关,使用随机森林将结果视为拟议问题的基线。