# Crime Prediction by Data-Driven Green's Function method

## TL;DR

This paper introduces a Green's function-based algorithm for predicting cascading crime events, demonstrating superior accuracy and efficiency over standard methods using Chicago crime data.

## Contribution

The paper presents a novel Green's function approach for cascade event prediction, capturing long-term influences and reducing computational costs.

## Key findings

- Long-tail cascade influence observed in crime data
- Green's function method outperforms standard prediction methods
- Low computational cost for large datasets

## Abstract

We develop an algorithm that forecasts cascading events, by employing a Green's function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation-maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green's function method is the low computational cost in the case of high density of events and/or large amount of the training data.