Crime prediction by data-driven Green’s function method

Mami Kajita and Seiji Kajita

International Journal of Forecasting, 2020, vol. 36, issue 2, 480-488

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.

Keywords: Crime forecasting; Green’s function; Near repeat victimization; Self-exciting point process; Expectation–maximization; Crime hotspot; Spatiotemporal forecasting (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:480-488

DOI: 10.1016/j.ijforecast.2019.06.005

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