Neural Network based Continuous Conditional Random Field for Fine-grained
Crime Prediction
Abstract
Crime prediction has always been a crucial issue
for public safety, and recent works have shown
the effectiveness of taking spatial correlation, such
as region similarity or interaction, for fine-grained
crime modeling. In our work, we seek to reveal
the relationship across regions for crime prediction using Continuous Conditional Random Field
(CCRF). However, conventional CCRF would become impractical when facing a dense graph considering all relationship between regions. To deal
with it, in this paper, we propose a Neural Network
based CCRF (NN-CCRF) model that formulates
CCRF into an end-to-end neural network framework, which could reduce the complexity in model
training and improve the overall performance. We
integrate CCRF with NN by introducing a Long
Short-Term Memory (LSTM) component to learn
the non-linear mapping from inputs to outputs of
each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions. Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over
the state-of-the-art methods