Mixed Spatio-Temporal Neural Networks on Real-time Prediction of Crimes

Forecasting the crime rate in real-time is always an important task to public safety. However, there are no known models that provide satisfactory approximation to this complex spatio-temporal problem until recently. The crime rate may be affected by various factors, such as local education, public events, weather, etc. Such factors make the prediction of crimes more complex and challenging than other problems that are less influenced by outer factors. In this paper, we propose a deep-learning-based approach, which combines various methods in neural networks to handle the spatial temporal prediction problem. Some optimization techniques, such as Bayesian optimization, are applied for finding the optimal hyper-parameters as well as dealing with noises in the dataset. The model is trained on a dataset about crime information in Los Angeles at a scale of hours in block-divided areas, released by the LA Police Department (LAPD). The results of experiments on this dataset demonstrates the proposed model's ability in predicting potential crimes in real time.