2019/02/01 by Cristina Kadar, Kadar, Cristina, Rudolf Maculan +3
Medicine · Psychology · Social Sciences · #Computers and Society (cs.CY) #Crime Patterns and Interventions #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Gambling Behavior and Treatments
paper · pdf · doi:10.48550/arxiv.1902.03237
openalex publication_date 2019/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Crime events are known to reveal spatio-temporal patterns, which can be used for predictive modeling and subsequent decision support. While the focus has hitherto been placed on areas with high population density, we address the challenging undertaking of predicting crime hotspots in regions with low population densities and highly unequally-distributed crime.This results in a severe sparsity (i.e., class imbalance) of the outcome variable, which impedes predictive modeling. To alleviate this, we develop machine learning models for spatio-temporal prediction that are specifically adjusted for an imbalanced distribution of the class labels and test them in an actual setting with state-of-the-art predictors (i.e., socio-economic, geographical, temporal, meteorological, and crime variables in fine resolution). The proposed imbalance-aware hyper-ensemble increases the hit ratio considerably from 18.1% to 24.6% when aiming for the top 5% of hotspots, and from 53.1% to 60.4% when aiming for the top 20% of hotspots.