Crime is an unlawful act that carries legal repercussions. Bangladesh has a high crime rate due to poverty, population growth, and many other socio-economic issues. For law enforcement agencies, understanding crime patterns is essential for preventing future criminal activity. For this purpose, these agencies need structured crime database. This paper introduces a novel crime dataset that contains temporal, geographic, weather, and demographic data about 6574 crime incidents of Bangladesh. We manually gather crime news articles of a seven year time span from a daily newspaper archive. We extract basic features from these raw text. Using these basic features, we then consult standard service-providers of geo-location and weather data in order to garner these information related to the collected crime incidents. Furthermore, we collect demographic information from Bangladesh National Census data. All these information are combined that results in a standard machine learning dataset. Together, 36 features are engineered for the crime prediction task. Five supervised machine learning classification algorithms are then evaluated on this newly built dataset and satisfactory results are achieved. We also conduct exploratory analysis on various aspects the dataset. This dataset is expected to serve as the foundation for crime incidence prediction systems for Bangladesh and other countries. The findings of this study will help law enforcement agencies to forecast and contain crime as well as to ensure optimal resource allocation for crime patrol and prevention.
翻译:犯罪是一种具有法律后果的非法行为。孟加拉国由于贫困、人口增长和许多其他社会经济问题,犯罪率很高。对于执法机构来说,了解犯罪模式对于预防未来犯罪活动至关重要。为此目的,这些机构需要结构化的犯罪数据库。本文件介绍了一个新的犯罪数据集,其中包含孟加拉国6574起犯罪事件的时空、地理、天气和人口数据。我们从日报档案中手动收集7年期的犯罪新闻文章。我们从这些原始文本中提取基本特征。利用这些基本特征,我们然后咨询地理定位和天气数据的标准服务提供者,以便获取所收集的犯罪事件相关信息。此外,我们从孟加拉国全国人口普查数据中收集人口信息,所有这些信息都汇集到一个标准的机器学习数据集中。加在一起,为犯罪预测任务设计了36个特征。然后,对这一新建立的数据集进行了5个受监督的机器学习分类算法评估,并取得了令人满意的结果。我们还对这些数据集的各个方面进行了探索性分析。该数据集预计将作为孟加拉国和其他国家犯罪发生率预测系统的基础。此外,我们收集的所有这些信息的信息都将汇集到一个标准的机器学习数据集,作为最佳的研究所。