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Crime prediction by data-driven Green's function method
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DOI：10.1016/j.ijforecast.2019.06.005.png)
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摘要
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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. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Crime forecasting
Green's function
Near repeat victimization
Self-exciting point process
Expectation-maximization
Crime hotspot
Spatiotemporal forecasting
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