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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| Tipo de documento: | Electrónico Libro |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| En: | Año: 2019 |
| Acceso en línea: |
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| Verificar disponibilidad: | HBZ Gateway |
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| Sumario: | 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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| DOI: | 10.1016/j.ijforecast.2019.06.005 |