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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 15-20    DOI: 10.11925/infotech.2096-3467.2018.0741
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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.

Key wordsCrime Fighting      Deep Learning      Neural Networks      Location Prediction     
Received: 09 July 2018      Published: 12 November 2018
ZTFLH:  TP393  

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0741     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I10/15

方法 精确度
1阶马尔可夫模型 0.48
2阶马尔可夫模型 0.51
1阶变阶马尔可夫模型 0.49
2阶变阶马尔可夫模型 0.52
LSTM模型 0.71
本文方法 0.79
Embedding层
单元个数
精确度
LSTM 本文
128 0.70 0.77
256 0.71 0.79
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