A prediction analysis for the case of a Korean police dataset

Benjamin Aziz, Alaa Mohasseb, Jeyong Jung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the evolving landscape of law enforcement, predictive policing, which leverages data analysis and machine learning to anticipate crimes and optimise police responses, has emerged as a critical tool. This paper explores the application of machine learning techniques in predictive policing through a detailed analysis of a Korean police dataset. Focusing on predicting the patterns and duration of police responses, the study employs various algorithms such as RandomForest, Gaussian Naive Bayes, Decision Tree and K-Nearest Neighbors. These models are evaluated based on accuracy, precision, recall, and F-score to determine their efficacy in different response scenarios. Our findings indicate that RandomForest has a much better performance in forecasting response duration, whilst Decision Tree and K-Nearest Neighbour models are particularly effective in predicting the type of response for incidents. The study underscores the significance of specific features like incident severity and police response type in influencing prediction outcomes. Through this research, we contribute to understanding the potential and challenges of machine learning in enhancing the efficiency of police operations in Korea, providing a framework applicable to broader contexts.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Artificial Intelligence Applications and Innovations
PublisherSpringer
Publication statusAccepted for publication - 29 Mar 2024
EventAIAI 2024: 20th International Conference on Artificial Intelligence Applications and Innovations - Ionian University, Corfu, Greece
Duration: 27 Jun 202430 Jun 2024
https://ifipaiai.org/2024/

Conference

ConferenceAIAI 2024: 20th International Conference on Artificial Intelligence Applications and Innovations
Country/TerritoryGreece
CityCorfu
Period27/06/2430/06/24
Internet address

Keywords

  • data analysis
  • machine learning
  • police datasets
  • predictive law enforcement

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