Volume 10, Issue 8 (August 2023), Pages: 158-165

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 Original Research Paper

Forecasting the influx of crime cases using seasonal autoregressive integrated moving average model

 Author(s): 

 Cristine V. Redoblo ^1, *, Jose Leo G. Redoblo ¹, Rene A. Salmingo ², Charwin M. Padilla ¹, Jan Carlo T. Arroyo ^{3, 4}

 Affiliation(s):

 ¹College of Computer Studies, Carlos Hilado Memorial State University, Talisay, Negros Occidental, Philippines
 ²College of Engineering, Carlos Hilado Memorial State University, Talisay, Negros Occidental, Philippines
 ³College of Information and Computing Studies, Northern Iloilo State University, Estancia, Iloilo, Philippines
 ⁴College of Computing Education, University of Mindanao, Davao City, Davao del Sur, Philippines

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3291-7732

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.08.018

 Abstract:

Crime constitutes a profound challenge to the societal fabric of a nation and often finds its roots in factors such as avarice, destitution, and economic adversity. This study endeavors to proactively address the issue of crime through the employment of a crime forecasting model, aimed at uncovering latent correlations and underlying patterns. Specifically, it employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to project the future incidence of criminal cases. The research objectives encompass forecasting crime case numbers through time series analysis, appraising the statistical significance of monthly crime occurrences, and assessing the crime dataset utilizing the MATLAB Econometric Modeler. Leveraging historical crime data spanning from January 2018 to December 2021, sourced from nineteen municipalities in Negros Occidental, Philippines, forms the basis for crime case forecasting. An autoregressive test is applied to ascertain the acceptable confidence interval and goodness of fit for crime occurrences. Furthermore, MATLAB Econometric Modeler employs the Ljung-Box test to differentiate between stationary and non-stationary time series and residual crime cases. Notably, the study reveals a significant cyclic pattern in crime cases occurring every 20 months, underscoring the imperative for targeted crime prevention interventions. This study underscores the necessity for consistent and robust law enforcement measures by local government units across the nineteen municipalities in Negros Occidental, focusing on the five identified categories of criminal cases. It is recommended that these measures be implemented diligently to mitigate crime occurrences in the subsequent twenty-first month. Moreover, the study holds potential for extension to regions grappling with elevated crime rates due to inadequate control strategies in place.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Crime forecasting, SARIMA model, Time series analysis, MATLAB econometric modeler, Criminal case intervention

 Article History: Received 22 February 2023, Received in revised form 25 June 2023, Accepted 19 July 2023

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Redoblo CV, Redoblo JLG, Salmingo RA, Padilla CM, and Arroyo JCT (2023). Forecasting the influx of crime cases using seasonal autoregressive integrated moving average model. International Journal of Advanced and Applied Sciences, 10(8): 158-165

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 

 Tables

 Table 1 Table 2 Table 3 Table 4 

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