Deep Learning Based Crime Prediction Models: Experiments and Analysis

TL;DR

This paper provides a comprehensive evaluation of deep learning models for crime prediction, highlighting their strengths, weaknesses, and suitability for various real-world scenarios to guide future research and application.

Contribution

It offers the first unified comparison of major deep learning crime prediction models across different scenarios, with practical recommendations for model design.

Findings

Deep learning models outperform classical methods in crime prediction.

Model performance varies significantly across different scenarios.

Guidelines for selecting and designing models for specific applications.

Abstract

Crime prediction is a widely studied research problem due to its importance in ensuring safety of city dwellers. Starting from statistical and classical machine learning based crime prediction methods, in recent years researchers have focused on exploiting deep learning based models for crime prediction. Deep learning based crime prediction models use complex architectures to capture the latent features in the crime data, and outperform the statistical and classical machine learning based crime prediction methods. However, there is a significant research gap in existing research on the applicability of different models in different real-life scenarios as no longitudinal study exists comparing all these approaches in a unified setting. In this paper, we conduct a comprehensive experimental evaluation of all major state-of-the-art deep learning based crime prediction models. Our evaluation provides several key insights on the pros and cons of these models, which enables us to select the most suitable models for different application scenarios. Based on the findings, we further recommend certain design practices that should be taken into account while building future deep learning based crime prediction models.