Crime Prediction by Data-Driven Green's Function method

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...

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Bibliographic Details
Main Author: Kajita, Mami (Author)
Contributors: Kajita, Seiji
Format: Electronic Book
Language:English
Published: 2019
In:Year: 2019
Online Access: Volltext (kostenfrei)
Volltext (kostenfrei)
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520 |a 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 
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