2019 IEEE International Conference on Big Data (Big Data) | 2019
Crime-GAN: A Context-based Sequence Generative Network for Crime Forecasting with Adversarial Loss
Abstract
Grasping the dynamics of crime situation is a long standing but significant problem and plays an instructive role in the field of security and protection. Traditional methods approach the crime forecasting via stochastic equations based on physics or statistics, which may be interpretable but less efficient in real applications. Recently, some data-driven models, especially sequence generative networks, seem to be promising in capturing spatio-temporal dynamics with massive dataset available. In this paper, we process some regional crime dataset of recent fifteen years in the crime situation awareness graphs and learn latent representations with variational auto-encoder. And then Crime Generative Adversarial Network (Crime-GAN) is formulated as a new crime forecasting model for four types of crime, integrating sequence to sequence structure and Wasserstein adversarial loss. In comparison to other typical algorithms, such as Conv-RNN, Crime-GAN shows superior forecasting performance for multi-type crime in spatio-temporal scale.