Deep Learning for Crime Forecasting of Multiple Regions, Considering Spatial–Temporal Correlations between Regions †

Abstract

Crime forecasting has gained popularity in recent years; however, the majority of studies have been conducted in the United States, which may result in a bias towards areas with a substantial population. In this study, we generated different models capable of forecasting the number of crimes in 83 regions of Costa Rica. These models include the spatial–temporal correlation between regions. The findings indicate that the architecture based on an LSTM encoder–decoder achieved superior performance. The best model achieved the best performance in regions where crimes occurred more frequently; however, in more secure regions, the performance decayed.