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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Bibliographic Details
Main Author: Wheeler, Andrew P. (Author)
Contributors: Steenbeek, Wouter
Format: Electronic Article
Language:English
Published: [2021]
In: Journal of quantitative criminology
Year: 2021, Volume: 37, Issue: 2, Pages: 445-480
Online Access: Presumably Free Access
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Keywords:
Machine-learning
Micro-places
Random forests
Risk-terrain-models
Robbery
Description
Summary: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