  Spatiotemporal prediction of event data is a challenging task with a long
history of research. While recent work in spatiotemporal prediction has
leveraged deep sequential models that substantially improve over classical
approaches, these models are prone to overfitting when the observation is
extremely sparse, as in the task of crime event prediction. To overcome these
sparsity issues, we present Multi-axis Attentive Prediction for Sparse Event
Data (MAPSED). We propose a purely attentional approach to extract both
short-term dynamics and long-term semantics of event propagation through two
observation angles. Unlike existing temporal prediction models that propagate
latent information primarily along the temporal dimension, the MAPSED
simultaneously operates over all axes (time, 2D space, event type) of the
embedded data tensor. We additionally introduce a novel Frobenius norm-based
contrastive learning objective to improve latent representational
generalization.Empirically, we validate MAPSED on two publicly accessible urban
crime datasets for spatiotemporal sparse event prediction, where MAPSED
outperforms both classical and state-of-the-art deep learning models. The
proposed contrastive learning objective significantly enhances the MAPSED's
ability to capture the semantics and dynamics of the events, resulting in
better generalization ability to combat sparse observations.
