An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.