  Researchers regard crime as a social phenomenon that is influenced by several
physical, social, and economic factors. Different types of crimes are said to
have different motivations. Theft, for instance, is a crime that is based on
opportunity, whereas murder is driven by emotion. In accordance with this, we
examine how well a model can perform with only spatiotemporal information at
hand when it comes to predicting a single crime. More specifically, we aim at
predicting theft, as this is a crime that should be predictable using
spatiotemporal information. We aim to answer the question: "How well can we
predict theft using spatial and temporal features?". To answer this question,
we examine the effectiveness of support vector machines, linear regression,
XGBoost, Random Forest, and k-nearest neighbours, using different imbalanced
techniques and hyperparameters. XGBoost showed the best results with an
F1-score of 0.86.
