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预测犯罪及神经网络在警方决策中的其他应用

Predicting Crime and Other Uses of Neural Networks in Police Decision Making.

作者信息

Walczak Steven

机构信息

School of Information and Florida Center for Cybersecurity, University of South Florida, Tampa, FL, United States.

出版信息

Front Psychol. 2021 Oct 7;12:587943. doi: 10.3389/fpsyg.2021.587943. eCollection 2021.

Abstract

Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime's location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

摘要

神经网络是一种在解决分类和预测问题方面表现出色的机器学习方法。它们也被证明是处理诸如执法等面向大数据环境的有用工具。本文回顾并审视了关于利用神经网络进行犯罪预测及解决其他警察决策问题的现有研究。开发了利用位置和时间信息预测特定类型犯罪的神经网络模型,以及在已知犯罪和时间时预测犯罪地点的模型,以展示神经网络在警察决策中的应用。神经网络犯罪预测模型利用地理空间性来提供有关犯罪的即时信息,以加强执法决策。对于27种不同类型的犯罪,神经网络模型能够在16.4%的时间内预测正在发生的犯罪类型;当将相似犯罪归为七类犯罪时,能够在27.1%的时间内预测。位置预测神经网络能够在31.2%的时间内预测邮政编码位置或相邻位置。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d5c/8529125/cb3f7c1454f0/fpsyg-12-587943-g001.jpg

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