The Use Of Time-Series Methods For Both Crime Modeling And Prediction

Abstract

The goal of the research is to analyze contemporary time-series methods which are used to identify socio-economic reasons for generating crime in the society, to predict crime for the medium term. The literary analysis on the matters of both crime prediction and modeling is given in the article. The multiple regression model has been implemented which is based on the factors affecting the law enforcement system. The predictive model of registered crimes has been constructed. The source data of the research are official statistical figures taken from Russian and foreign statistics for the period of 1992–2020. The peculiarities of using time-series models are revealed in theoretical research. Various groups of factors are highlighted affecting crime. Both a multifactorial regression model and a predictive ARMA-model with three prediction variants have been constructed. The quality of the models and crime predictions depends on the choice of the optimal method and the prediction period, on the complete information database including social, economic, legal and other characteristics of social phenomena and processes that can affect criminal situations. Econometric analysis methods of predicting crimes can allow one to quantify complex interactions between variables characterizing crime. Practical realization of modeling tools will definitely predict crime for the future and develop some actions to fight crime.

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About this article

Publication Date

31 March 2022

eBook ISBN

978-1-80296-124-9

Publisher

European Publisher

Volume

125

Print ISBN (optional)

-

Edition Number

1st Edition

Pages

1-1329

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Cite this article as:

Terehov, A. M., Kuvychkov, S. I., Kolarkova, О. G., & Nametkin, D. V. (2022). The Use Of Time-Series Methods For Both Crime Modeling And Prediction. In & I. Savchenko (Ed.), Freedom and Responsibility in Pivotal Times, vol 125. European Proceedings of Social and Behavioural Sciences (pp. 96-102). European Publisher. https://doi.org/10.15405/epsbs.2022.03.12