Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network
This work addresses the challenge of identifying serial offenders for law enforcement agencies, representing an incremental advancement by applying a known neural network architecture to a specific domain with practical preprocessing guidance.
The paper tackled the problem of crime linkage analysis by proposing a Siamese Autoencoder framework to handle high-dimensional, sparse, and heterogeneous crime data, resulting in up to a 9% improvement in AUC over traditional methods.
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.