Mapping the Risk Terrain for Crime Using Machine Learning

We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the ‘black box’ of Random Forests, considerably improving the...

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Bibliographische Detailangaben
1. VerfasserIn: Wheeler, Andrew P. (VerfasserIn)
Beteiligte: Steenbeek, Wouter
Medienart: Elektronisch Aufsatz
Sprache:Englisch
Veröffentlicht: [2021]
In: Journal of quantitative criminology
Jahr: 2021, Band: 37, Heft: 2, Seiten: 445-480
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Schlagwörter:
Machine-learning
Micro-places
Random forests
Risk-terrain-models
Robbery
Beschreibung
Zusammenfassung:We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the ‘black box’ of Random Forests, considerably improving their interpretability.
ISSN:1573-7799
DOI:10.1007/s10940-020-09457-7