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

Steven Walczak

doi:10.3389/fpsyg.2021.587943

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.

Highlights

Crime is a global concern that impacts individuals and society on a daily basis and negatively affects society (Costa, 2010)
Traditional NN research in crime science focuses on future hot spot predictions, datamining large corpora of data to connect relevant information together regarding a crime or serial crime or criminals, determining the probability of criminal recidivism, and analysis of crime scene objects and data to assist with criminal investigations
The author confirms being the sole contributor of this work and has approved it for publication

Summary

INTRODUCTION

Crime is a global concern that impacts individuals and society on a daily basis and negatively affects society (Costa, 2010). Artificial intelligence and more precisely machine learning, provide mechanisms for improving police knowledge of current and potential crimes and facilitating complex anti-criminal decision making. A literature review is used to facilitate this knowledge sharing for law enforcement automated decision making and planning researchers and developers. Existing research as shown through the accompanying literature review focuses on prediction of large areas of generalized increases in criminal activity or the increase in activity for a specific crime. The other NN model will show that when a type of crime and the time of day is known, a specific area of the city may be immediately identified where that crime is most likely to have occurred, which improves on existing research that uses much larger time horizons. While the research demonstrates that NNs are capable of performing a wide range of crime analyses, they should be viewed as advisory tools or for secondary confirmatory analysis (Tastle, 2013)

BACKGROUND

RESULTS AND DISCUSSION

SUMMARY AND CONCLUSION

DATA AVAILABILITY STATEMENT