论文标题
AIST：一种基于可解释的犯罪预测的基于注意力的深度学习模型
AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction
论文作者
论文摘要
准确性和解释性是犯罪预测模型的两个基本特性。由于犯罪可能对人类的生活，经济和安全产生不利影响，我们需要一个模型，可以尽可能准确地预测犯罪的未来发生，以便可以采取早期步骤来避免犯罪。另一方面，一个可解释的模型揭示了模型预测背后的原因，确保其透明度，并允许我们相应地计划预防犯罪的步骤。开发该模型的主要挑战是捕获特定犯罪类别的非线性空间依赖性和时间模式，同时保持模型的基础结构可解释。在本文中，我们开发了AIST，这是一个基于注意力的犯罪预测的可解释的时空时间网络。 AIST基于过去的犯罪事件，外部特征（例如，交通流量和兴趣点（POI）信息）和犯罪的反复趋势，模拟了犯罪类别的动态时空相关性。广泛的实验表明了使用真实数据集的精度和可解释性方面的优势。
Accuracy and interpretability are two essential properties for a crime prediction model. Because of the adverse effects that the crimes can have on human life, economy and safety, we need a model that can predict future occurrence of crime as accurately as possible so that early steps can be taken to avoid the crime. On the other hand, an interpretable model reveals the reason behind a model's prediction, ensures its transparency and allows us to plan the crime prevention steps accordingly. The key challenge in developing the model is to capture the non-linear spatial dependency and temporal patterns of a specific crime category while keeping the underlying structure of the model interpretable. In this paper, we develop AIST, an Attention-based Interpretable Spatio Temporal Network for crime prediction. AIST models the dynamic spatio-temporal correlations for a crime category based on past crime occurrences, external features (e.g., traffic flow and point of interest (POI) information) and recurring trends of crime. Extensive experiments show the superiority of our model in terms of both accuracy and interpretability using real datasets.
