Forecasting Crime with Deep Learning

TL;DR

This paper demonstrates that deep neural networks, combined with external datasets like weather and census data, can effectively predict daily crime counts in city regions with over 65% accuracy.

Contribution

The study introduces a neural network-based approach for fine-grain daily crime prediction, incorporating diverse external data sources to improve accuracy.

Findings

Achieved 75.6% accuracy for Chicago

Achieved 65.3% accuracy for Portland

External datasets enhance prediction performance

Abstract

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.