SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia

Crimes have clearly had a detrimental impact on a nation’s development, prosperity, reputation, and economy. The issue of crime has become one of the most pressing concerns in societies, thus reducing the crime rate has become an increasingly critical task. Recently, several studies have been propos...

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Bibliographic Details
Published inElectronics (Basel) Vol. 11; no. 23; p. 3986
Main Authors Noor, Talal H., Almars, Abdulqader M., Alwateer, Majed, Almaliki, Malik, Gad, Ibrahim, Atlam, El-Sayed
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2022
Subjects
Accuracy
Algorithms
Autoregressive models
Clustering
Crime
Crime analysis
Crime prevention
Data mining
Datasets
Deep learning
Impact analysis
Machine learning
Methods
Technology application
Time series
Trends
Saudi Arabia
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Summary:Crimes have clearly had a detrimental impact on a nation’s development, prosperity, reputation, and economy. The issue of crime has become one of the most pressing concerns in societies, thus reducing the crime rate has become an increasingly critical task. Recently, several studies have been proposed to identify the causes and occurrences of crime in order to identify ways to reduce crime rates. However, few studies have been conducted in Saudi Arabia technological solutions based on crime analysis. The analysis of crime can help governments identify hotspots of crime and monitor crime distribution. This study aims to investigate which Saudi Arabian areas will experience increased crime rates in the coming years. This research helps law enforcement agencies to effectively utilize available resources in order to reduce crime rates. This paper proposes SARIMA model which focuses on identifying factors that affect crimes in Saudi Arabia, estimating a reasonable crime rate, and identifying the likelihood of crime distribution based on various locations. The dataset used in this study is obtained from Saudi Arabian official government channels. There is detailed information related to time and place along with crime statistics pertaining to different types of crimes. Furthermore, the new proposed method performs better than other traditional classifiers such as Linear Regression, XGB, and Random Forest. Finally, SARIMA model has an MAE score of 0.066559, which is higher than the other models.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11233986