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.

城市犯罪率的急剧上升正在成为公共安全的最重要问题之一，影响到社会可持续性的许多方面，例如就业，生计，保健和教育。因此，至关重要的是要开发一种预测模型，该模型能够识别高犯罪强度的地区并检测此类地区的犯罪发生趋势，以分配稀缺资源并投资于预防和减少犯罪策略。本研究开发了一种基于K-means聚类，信号分解技术和神经网络的预测模型，以识别城市地区的犯罪分布，并准确预测每个地区的犯罪数量的变化趋势。我们发现，从长远来看，不同地区的犯罪数量的时间序列显示出相关性，但是这种长期影响无法在短期内体现出来。因此，我们认为短期联合执法没有理论依据，因为数据表明空间异质性和时滞不能在短期预测中及时反映出来。通过结合时间和空间影响，设计了一种高精度的反犯罪信息支持系统，可以帮助警察在微观层面实施更有针对性的犯罪预防策略。