This paper introduces a method to find out the most robbery-prone time-slot of a place from the past records so as to provide alertness to the public and police. It cannot be predicted who all may be the victims of robbery but can predict the place and time that has high probability of its occurrence. Apart from finding the time-slot where robbery occurrence is more, visualization has been done to draw out the patterns for which race the criminals of robbery belongs. The factors resulting in robbery are also identified and their impact is calculated by developing a regression model so that this information helps to reduce the robbery rate in near future. For this purpose, dataset of Chicago is used as a secondary dataset and to fulfil the objective, the concept of projection is applied on m-Polar fuzzy context with respect to the object set for analysis which will provide the maximum membership-value of the object set depicting the time-slot where more robbery occurrence is there so that more and more security is implemented by the police department.

本文介绍了一种从过去的记录中找出最容易发生抢劫的时间段的方法，以使公众和警察保持警惕。无法预测谁可能都是抢劫的受害者，但可以预测发生劫案的地点和时间。除了找到更多发生抢劫的时间段外，还进行了可视化以绘制出抢劫罪犯所属种族的模式。还确定了导致抢劫的因素，并通过建立回归模型来计算其影响，以便该信息有助于在不久的将来降低抢劫率。为此，将Chicago数据集用作辅助数据集并实现了目标，