ORCID Profile
0000-0002-6774-8510
Current Organisation
Wuhan University of Technology
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Publisher: Springer Nature Switzerland
Date: 2022
Publisher: ACM
Date: 26-10-2021
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 06-06-2021
Publisher: IEEE
Date: 29-10-2022
Publisher: Association for Computing Machinery (ACM)
Date: 05-01-2021
DOI: 10.1145/3432249
Abstract: Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public safety. However, urban crime data is a macro result of crime patterns overlapped by various irrelevant factors that cause inhomogeneous noises—local outliers and irregular waves. These noises might obstruct the learning process of crime prediction models and result in a deviation of performance. To tackle the problem, we propose a novel paradigm of underline Du /underline al- underline ro /underline bust Enhanced Spatial-temporal Learning underline Net /underline work (DuroNet), an encoder-decoder architecture that possesses an adaptive robustness for reducing the effect of outliers and waves. The robustness is mainly reflected on two aspects. One is a locality enhanced module that employs local temporal context information to smooth the deviation of outliers and dynamic spatial information to assist in understanding normal points. The other is a self-attention-based pattern representation module to weaken the effect of irregular waves by learning attentive weights. Finally, extensive experiments are conducted on two real-world crime datasets before and after adding Gaussian noises. The results demonstrate the superior performance of our DuroNet over the state-of-the-art methods.
Publisher: Elsevier BV
Date: 12-2023
No related grants have been discovered for Kaixi Hu.