Clustering of Crimes Using Latent Representations Obtained via Autoencoders

This article presents the use of autoencoders as part of a dimensionality-reduction method in the task of crime clustering. The study was conducted on a real-world crime dataset from the city of Chicago, based on publicly available police records. The initial data processing involved selecting and extracting variables, aggregating the selected variables, and converting crime categories and incident locations into contextual embeddings. The data prepared in this way was used to train various autoencoder architectures, including Vanilla, convolutional, denoising and variational models. The representations obtained from the latent layer of the encoder were then used as input data for clustering methods, such as k-means, Gaussian mixture model, and spectral clustering. The experimental results showed that the use of autoencoders in the clustering process enabled the identification of distinct groups of offences, with the best results (measured using the ARI and NMI metrics) obtained for Vanilla autoencoders combined with k-means and GMM, particularly with intermediate latent-space dimensions. The results confirm the potential of autoencoders as effective tools for dimensionality reduction and feature extraction in crime data analysis, as well as their usefulness in the exploratory analysis of complex urban data.