  Crime forecasting is a critical component of urban analysis and essential for
stabilizing society today. Unlike other time series forecasting problems, crime
incidents are sparse, particularly in small regions and within specific time
periods. Traditional spatial-temporal deep learning models often struggle with
this sparsity, as they typically cannot effectively handle the non-Gaussian
nature of crime data, which is characterized by numerous zeros and
over-dispersed patterns. To address these challenges, we introduce a novel
approach termed Spatial Temporal Multivariate Zero-Inflated Negative Binomial
Graph Neural Networks (STMGNN-ZINB). This framework leverages diffusion and
convolution networks to analyze spatial, temporal, and multivariate
correlations, enabling the parameterization of probabilistic distributions of
crime incidents. By incorporating a Zero-Inflated Negative Binomial model,
STMGNN-ZINB effectively manages the sparse nature of crime data, enhancing
prediction accuracy and the precision of confidence intervals. Our evaluation
on real-world datasets confirms that STMGNN-ZINB outperforms existing models,
providing a more reliable tool for predicting and understanding crime dynamics.
