Deep Learning for Real Time Crime Forecasting
Deep Learning for Real Time Crime Forecasting
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发表时间：
2017-07
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通讯作者：
Bao Wang;Duo Zhang;Duanhao Zhang;P. Brantingham;A. Bertozzi
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作者：
Bao Wang;Duo Zhang;Duanhao Zhang;P. Brantingham;A. Bertozzi
作者：王宝；张,两人;张,Duanhao;P·杰弗瑞；摘要：准确的实时犯罪预测是公共安全的基本问题，但对科学界来说仍然是一个具有挑战性的问题。犯罪的发生取决于许多复杂的因素。与许多可预测的事件相比，犯罪很少。在不同的时空尺度上，犯罪分布呈现出明显不同的格局。这些分布在空间和时间上的规律性都很低。在这项工作中，我们采用了最先进的深度学习时空预测器ST-ResNet [Zhang等人，AAAI, 2017]，来共同预测洛杉矶地区的犯罪分布。我们的模型分为两个阶段。首先，我们对原始犯罪数据进行预处理。这包括空间和时间上的正则化，以增强可预测的信号。其次，我们采用残差卷积单元的层次结构来训练多因素犯罪预测模型。在洛杉矶进行的为期半年的实验表明，我们的模型具有高度准确的预测能力。
Author(s): Wang, Bao; Zhang, Duo; Zhang, Duanhao; Brantingham, P Jeffery; Bertozzi, Andrea L | Abstract: Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.