用深度学习预测城市分区犯罪数量，提升警务预警能力
Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models
- 融合CNN与LSTM构建时空序列预测模型
- 数据分组为10类时预测效果最佳，优于原始数据
- 适合城市安全规划与公安智能预警场景
本研究利用深度学习模型预测城市分区在特定日期的犯罪数量，助力警方加强监控、情报搜集和主动防范。将犯罪预测建模为时空序列问题，输入与目标均为时空序列数据。为提升预测精度，提出结合卷积神经网络（CNN）与长短期记忆网络（LSTM）的新模型。通过对比分析四种深度学习模型在不同数据序列下的表现，发现直接输入原始犯罪数据会导致高预测误差，不适用于实际应用。研究表明，当犯罪数据被划分为10组或5组时，所提CNN-LSTM模型性能最优。数据分箱可提升模型表现，但分组不合理会降低地图细节。相比5组分箱，10组分箱在保留数据特征的同时，显著优于原始数据，在预测建模中表现更优。
原文摘要 · Abstract (English)
This study uses deep-learning models to predict city partition crime counts on specific days. It helps police enhance surveillance, gather intelligence, and proactively prevent crimes. We formulate crime count prediction as a spatiotemporal sequence challenge, where both input data and prediction targets are spatiotemporal sequences. In order to improve the accuracy of crime forecasting, we introduce a new model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. We conducted a comparative analysis to access the effects of various data sequences, including raw and binned data, on the prediction errors of four deep learning forecasting models. Directly inputting raw crime data into the forecasting model causes high prediction errors, making the model unsuitable for real - world use. The findings indicate that the proposed CNN-LSTM model achieves optimal performance when crime data is categorized into 10 or 5 groups. Data binning can enhance forecasting model performance, but poorly defined intervals may reduce map granularity. Compared to dividing into 5 bins, binning into 10 intervals strikes an optimal balance, preserving data characteristics and surpassing raw data in predictive modelling efficacy.
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