Deep Learning for Real Time Crime Forecasting
This work addresses crime prediction for public safety, but it is incremental as it applies an existing method to a new domain with preprocessing enhancements.
The authors tackled real-time crime forecasting by adapting an existing deep learning spatio-temporal predictor (ST-ResNet) to predict crime distributions in Los Angeles, achieving highly accurate results as demonstrated in experiments over a half-year period.
Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.