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Predicting Crime Locations Based on Long Short Term Memory and Convolutional Neural Networks |
Xiao Yanhui, Wang Xin, Feng Wen’gang, Tian Huawei(), Wu Shaozhong, Li Lihua |
School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100038, China Research Center for Public Security Intelligence, People’s Public Security University of China, Beijing 100038, China |
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Abstract [Objective] This paper tries to predict the locations of suspects based on historical activity trajectory data, aiming to locate, track, monitor or arrest the suspects. [Methods] First, we proposed long short term memory (LSTM) and convolutional neural networks (CNN) models to predict crime locations. Then, we used the CNN model to extrct location features of key suspects and analyze their spatial correlations. Finally, we utlized the LSTM model to maintain the temporal continuity and obtain the future locations. [Results] Compared with previous models, the proposed method increased the prediction accuracy from 0.71 to 0.79 with the trajectory GeoLife dataset. [Limitations] The model was only examined with the Geolife dataset. [Conclusions] The proposed method fully exploits the spatial correlation and temporal continuity of data, which improves the effectiveness of public security intelligence analysis.
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Received: 09 July 2018
Published: 12 November 2018
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