Crime Data Analysis using Machine Learning Models

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Introduction

The chapter “Crime Data Analysis using Machine Learning Models” is part of the book “Applied Technologies” edited by Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Sergio Montes León, Pablo Torres-Carrión, and Benjamin Durakovic. Written by Vivas Kumar, this chapter explores the application of machine learning techniques in analyzing crime data.

Machine Learning Techniques for Crime Data Analysis

Various studies have employed machine learning methods to analyze crime data, including:

• Classification Methods
  • Decision Trees
  • Random Forests
  • Support Vector Machines
• Regression-Based Approaches
  • Multiple Regression Analysis
  • Spatio-Temporal Data Approach
• Ensemble Methods
  • Bagging
  • Boosting

Specific Studies in Crime Data Analysis

Several studies have employed machine learning techniques to analyze crime data, including:

• Hossain et al. (2020) - Predicted crime rates using a spatio-temporal data approach.
• Singh et al. (2017) - Employed soft computing and multiple regression to predict geomechanical parameters.
• Farhadian and Katibeh (2017) - Developed an empirical model using multiple regression analysis to evaluate groundwater flow into circular tunnels.

Random Forest Algorithm in Crime Prediction

The random forest algorithm has been successfully applied in crime prediction, including:

• Yeşilkanat’s (2020) spatio-temporal estimation of daily COVID-19 cases worldwide
• Li et al.’s (2018) principle component analysis-based random forest with potential nearest neighbor method for automobile insurance fraud identification

Conclusion

This chapter provides an overview of various machine learning techniques that have been applied to crime data analysis, highlighting their potential in improving public safety and security.