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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Detalles Bibliográficos
Autor principal: Wheeler, Andrew P. (Autor)
Otros Autores: Steenbeek, Wouter
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: [2021]
En: Journal of quantitative criminology
Año: 2021, Volumen: 37, Número: 2, Páginas: 445-480
Acceso en línea: Presumably Free Access
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Palabras clave:
Machine-learning
Micro-places
Random forests
Risk-terrain-models
Robbery
Descripción
Sumario: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