Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network

TL;DR

This paper introduces a Siamese Autoencoder framework that improves crime linkage analysis by effectively learning from complex, high-dimensional data, leading to significant accuracy enhancements over traditional methods.

Contribution

The study presents a novel Siamese Autoencoder approach that incorporates geographic-temporal features to better handle sparse, heterogeneous crime data, improving linkage accuracy.

Findings

AUC improved by up to 9% over traditional methods

Effective handling of high-dimensional, sparse data

Provides practical preprocessing guidance for crime data

Abstract

Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address limitations of traditional crime linkage methods in handling high-dimensional, sparse, and heterogeneous data, we propose a Siamese Autoencoder framework that learns meaningful latent representations and uncovers correlations in complex crime data. Using data from the Violent Crime Linkage Analysis System (ViCLAS), maintained by the Serious Crime Analysis Section of the UK's National Crime Agency, our approach mitigates signal dilution in sparse feature spaces by integrating geographic-temporal features at the decoder stage. This design amplifies behavioral representations rather than allowing them to be overshadowed at the input level, yielding consistent improvements across multiple evaluation metrics. We further analyze how different domain-informed data reduction strategies influence model performance, providing practical guidance for preprocessing in crime linkage contexts. Our results show that advanced machine learning approaches can substantially enhance linkage accuracy, improving AUC by up to 9% over traditional methods while offering interpretable insights to support investigative decision-making.