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A genetic-fuzzy algorithm for spatio-temporal crime prediction
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2021-01-16 , DOI: 10.1007/s12652-020-02858-3
Yaghoub Farjami , Khabat Abdi

Detecting spatio-temporal patterns as an interesting topic from different perspectives like detecting anomalies, unexpected and high-risk areas is a powerful means to manage future events by describing the current state and giving the possibility to predict the future. Criminology theories explain that the distribution of crime is not random. Although various specifications of crime are necessary for crime analysis, three types of most important information are location, time and environment data. Therefore, spatio-temporal crime patterns recognition plays an important role in crime analysis. This paper presents a new approach to detect the space and time areas which the crimes are most likely to happen. For this aim, a genetic-fuzzy system is developed to generate an interpretable fuzzy knowledge base that encompasses patterns for predicting future spatio-temporal crimes. It composed of three steps: fuzzy partitioning of the problem space, selecting meaningful features, and constructing the fuzzy knowledge base. We evaluate our proposed system using both a simulated dataset and a real-world dataset from Tehran, Iran. The results show that the proposed system is a suitable tool to find patterns and predict future crimes for environments with clustered crimes in space and time.



中文翻译:

时空犯罪预测的遗传模糊算法

从不同的角度将时空模式检测作为有趣的话题,例如检测异常,意外和高风险区域,是通过描述当前状态并提供预测未来的可能性来管理未来事件的有力手段。犯罪学理论解释说犯罪分布不是随机的。尽管犯罪分析需要各种犯罪规范,但是三种最重要的信息是位置,时间和环境数据。因此,时空犯罪模式识别在犯罪分析中起着重要的作用。本文提出了一种检测犯罪最有可能发生的时空区域的新方法。为了这个目的,开发了一种遗传模糊系统,以生成一个可解释的模糊知识库,其中包含用于预测未来时空犯罪的模式。它由三个步骤组成:问题空间的模糊划分,选择有意义的特征以及构建模糊知识库。我们使用来自伊朗德黑兰的模拟数据集和真实数据集来评估我们提出的系统。结果表明,所提出的系统是在空​​间和时间上具有集群犯罪的环境中寻找模式并预测未来犯罪的合适工具。

更新日期:2021-01-18
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