Forecasting Crime with Deep Learning
This work addresses crime forecasting for urban planning and public safety, but it is incremental as it applies existing deep learning methods to crime data with external datasets.
The authors tackled the problem of next-day crime count predictions in fine-grained city partitions using deep neural networks, achieving 75.6% accuracy for Chicago and 65.3% for Portland.
The objective of this work is to take advantage of deep neural networks in order to make next day crime count predictions in a fine-grain city partition. We make predictions using Chicago and Portland crime data, which is augmented with additional datasets covering weather, census data, and public transportation. The crime counts are broken into 10 bins and our model predicts the most likely bin for a each spatial region at a daily level. We train this data using increasingly complex neural network structures, including variations that are suited to the spatial and temporal aspects of the crime prediction problem. With our best model we are able to predict the correct bin for overall crime count with 75.6% and 65.3% accuracy for Chicago and Portland, respectively. The results show the efficacy of neural networks for the prediction problem and the value of using external datasets in addition to standard crime data.