Title: Crime Prediction by Data-Driven Green's Function method,
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.