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Inflation-Crime Nexus: A Predictive Analysis of Crime Rate Using Inflationary Indicator in Municipalities of North Cotabato, Philippines


Affiliations
1 College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
 

Predicting the occurrence of robberies poses significant challenges, requiring valuable data to assist law enforcement personnel in crime prevention and solving efforts. This research paper introduces Crata, a tool that leverages linear regression to predict the robbery frequency in North Cotabato, Philippines. The development of Crata was driven by the analysis of historical data encompassing robbery incidents and inflation rates from 2010 to 2022. The research emphasizes the importance of utilizing the inflation rate as a crucial predictor in determining the robbery frequency. By incorporating this variable into the predictive model, Crata enables more accurate predictions, thus empowering law enforcement agencies with vital insights to address and mitigate crime problems proactively. The robustness and reliability of the tool were assessed through comprehensive evaluations, demonstrating its practical utility and effectiveness. This paper significantly contributes to crime prevention by providing a reliable and data-driven approach to anticipating robberies, enhancing proactive efforts to safeguard the community, and combat crimes. Further research and development are warranted to refine the tool and expand its applicability to other regions for strengthening crime prevention strategies on a broader scale.

Keywords

crata; linear regression; predictors; crime analysis; probability
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  • Inflation-Crime Nexus: A Predictive Analysis of Crime Rate Using Inflationary Indicator in Municipalities of North Cotabato, Philippines

Abstract Views: 215  |  PDF Views: 91

Authors

Michaelangelo R. Serrano
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
Nero L. Hontiveros
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
EJ Ryle C. Mosquera
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
Aura Mae M. Celestino
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
John Louie D. Vilar
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
Junuel S. Baroy
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines
Mitziel C. Gelbolingo
College of Information Technology and Engineering, Notre Dame of Midsayap College, Midsayap, Cotabato, Philippines

Abstract


Predicting the occurrence of robberies poses significant challenges, requiring valuable data to assist law enforcement personnel in crime prevention and solving efforts. This research paper introduces Crata, a tool that leverages linear regression to predict the robbery frequency in North Cotabato, Philippines. The development of Crata was driven by the analysis of historical data encompassing robbery incidents and inflation rates from 2010 to 2022. The research emphasizes the importance of utilizing the inflation rate as a crucial predictor in determining the robbery frequency. By incorporating this variable into the predictive model, Crata enables more accurate predictions, thus empowering law enforcement agencies with vital insights to address and mitigate crime problems proactively. The robustness and reliability of the tool were assessed through comprehensive evaluations, demonstrating its practical utility and effectiveness. This paper significantly contributes to crime prevention by providing a reliable and data-driven approach to anticipating robberies, enhancing proactive efforts to safeguard the community, and combat crimes. Further research and development are warranted to refine the tool and expand its applicability to other regions for strengthening crime prevention strategies on a broader scale.

Keywords


crata; linear regression; predictors; crime analysis; probability

References