RT Article
T1 Mapping the Risk Terrain for Crime Using Machine Learning
JF Journal of quantitative criminology
VO 37
IS 2
SP 445
OP 480
A1 Wheeler, Andrew P.
A2 Steenbeek, Wouter
LA English
YR 2021
UL https://krimdok.uni-tuebingen.de/Record/1760917052
AB 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.
K1 Risk-terrain-models
K1 Micro-places
K1 Machine-learning
K1 Random forests
K1 Robbery
DO 10.1007/s10940-020-09457-7