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Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches

Vinicius Lima, Umit Karabiyik

TL;DR

The goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject and to support foundational knowledge for future data-driven methods.

Abstract

Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This domain has been extensively studied by researchers in sociology, psychology, and statistics. More recently, it has drawn interest from computer scientists, especially with advances in artificial intelligence. Despite this, the literature indicates that work in this latter discipline is still in its early stages. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods. To achieve this goal, we conducted a comprehensive survey of the main literature on the topic and developed a general framework for crime linkage processes, thoroughly describing each step. Our goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject.

Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches

TL;DR

The goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject and to support foundational knowledge for future data-driven methods.

Abstract

Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This domain has been extensively studied by researchers in sociology, psychology, and statistics. More recently, it has drawn interest from computer scientists, especially with advances in artificial intelligence. Despite this, the literature indicates that work in this latter discipline is still in its early stages. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods. To achieve this goal, we conducted a comprehensive survey of the main literature on the topic and developed a general framework for crime linkage processes, thoroughly describing each step. Our goal was to unify insights from diverse fields into a shared terminology to enhance the research landscape for those intrigued by this subject.

Paper Structure

This paper contains 20 sections, 5 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Modus Operandi in the Crime Linkage Context
  4. Data-driven studies
  5. Machine Learning studies
  6. Crime Linkage Framework
  7. MO variables
  8. Similarity measures
  9. Numerical attributes
  10. Categorical attributes
  11. Cosine Similarity
  12. Machine Learning Classification
  13. Evaluation Metrics
  14. Challenges in Crime Linkage
  15. Imbalanced Data
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Typical pipeline of Crime Linkage assessment.
  • Figure 2: 3 dimensional representation of the words words "gun", "firearm", and "knife". Similarity between 'gun' and 'knife': 0.5895; Similarity between 'gun' and 'firearm': 0.7925; Similarity between 'knife' and 'firearm': 0.5644.
  • Figure 3: Typical methodology used in Crime Linkage with Machine Learning algorithms.