RT Book
T1 Crime Prediction by Data-Driven Green's Function method
A1 Kajita, Mami
A2 Kajita, Seiji
LA English
YR 2019
UL https://krimdok.uni-tuebingen.de/Record/1865846422
AB 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.Comment: 22 pages, 3 figur
K1 Research
DO 10.1016/j.ijforecast.2019.06.005