Different from a general density estimation,the crime density estimation usually has one important factor:the geographical constraint.In this paper,a new crime density estimation model is formulated,in which the regio...Different from a general density estimation,the crime density estimation usually has one important factor:the geographical constraint.In this paper,a new crime density estimation model is formulated,in which the regions where crime is impossible to happen,such as mountains and lakes,are excluded.To further optimize the estimation method,a learning-based algorithm,named Plug-and-Play,is implanted into the augmented Lagrangian scheme,which involves an off-the-shelf filtering operator.Different selections of the filtering operator make the algorithm correspond to several classical estimation models.Therefore,the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical methods.In the experiment part,synthetic examples with different invalid regions and samples of various distributions are first tested.Then under complex geographic constraints,we apply the proposed method with a real crime dataset to recover the density estimation.The state-of-the-art results show the feasibility of the proposed model.展开更多
基金the National Natural Science Foundation of China under Grant Nos.61772389 and 61871260the Open Project of National Engineering Laboratory for Forensic Science of China under Grant No.2017NELKFKT02the Key Scientific Research Projects in Henan Colleges and Universities of China under Grant No.19A110015.
文摘Different from a general density estimation,the crime density estimation usually has one important factor:the geographical constraint.In this paper,a new crime density estimation model is formulated,in which the regions where crime is impossible to happen,such as mountains and lakes,are excluded.To further optimize the estimation method,a learning-based algorithm,named Plug-and-Play,is implanted into the augmented Lagrangian scheme,which involves an off-the-shelf filtering operator.Different selections of the filtering operator make the algorithm correspond to several classical estimation models.Therefore,the proposed Plug-and-Play optimization based estimation algorithm can be regarded as the extended version and general form of several classical methods.In the experiment part,synthetic examples with different invalid regions and samples of various distributions are first tested.Then under complex geographic constraints,we apply the proposed method with a real crime dataset to recover the density estimation.The state-of-the-art results show the feasibility of the proposed model.
