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
[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.
肖延辉, 王欣, 冯文刚, 田华伟, 吴绍忠, 李丽华. 基于长短记忆型卷积神经网络的犯罪地理位置预测方法*[J]. 数据分析与知识发现, 2018, 2(10): 15-20.
Xiao Yanhui,Wang Xin,Feng Wen’gang,Tian Huawei,Wu Shaozhong,Li Lihua. Predicting Crime Locations Based on Long Short Term Memory and Convolutional Neural Networks. Data Analysis and Knowledge Discovery, 2018, 2(10): 15-20.
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