Using Data Analytics to Forecast Violent Crime

Herious A. Cotton, T. Kwembe
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引用次数: 1

Abstract

In this paper, we used data analytics to analyze criminal data. Prophet model, LSTM recurrent neural network model, a linear regression model, and traditional neural network model were used to predict homicide and rape in the Southeastern Cities of Memphis Tennessee, Jackson Mississippi, and New Orleans Louisiana. LSTM recurrent neural network model and traditional neural network model have smaller RMSE. Thus, LSTM recurrent neural network model and traditional neural network model performed better than the prophet and linear regression models. These promising outcomes will be significant to scholars, policymakers, and law enforcement officers.
使用数据分析预测暴力犯罪
在本文中,我们使用数据分析来分析犯罪数据。采用先知模型、LSTM递归神经网络模型、线性回归模型和传统神经网络模型对田纳西州孟菲斯市、密西西比州杰克逊市和路易斯安那州新奥尔良市的杀人和强奸事件进行了预测。LSTM递归神经网络模型与传统神经网络模型的均方根误差较小。因此,LSTM递归神经网络模型和传统神经网络模型的性能优于先知模型和线性回归模型。这些有希望的成果将对学者、政策制定者和执法人员具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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