Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN
Urbanization has been speeding up social and economic transformations in urban communities, the smallest social units in a city. However, urbanization brings challenges to urban management and security. Therefore, a system of risk prediction of crimes may be essential to crime prevention and control...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9276416/ |
| _version_ | 1798001882881327104 |
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| author | Xinge Han Xiaofeng Hu Huanggang Wu Bing Shen Jiansong Wu |
| author_facet | Xinge Han Xiaofeng Hu Huanggang Wu Bing Shen Jiansong Wu |
| author_sort | Xinge Han |
| collection | DOAJ |
| description | Urbanization has been speeding up social and economic transformations in urban communities, the smallest social units in a city. However, urbanization brings challenges to urban management and security. Therefore, a system of risk prediction of crimes may be essential to crime prevention and control in urban communities and its system improvement. To tackle crime-related problems in urban communities, this paper proposes a model of daily crime prediction by combining Long Short-Term Memory Network (LSTM) and Spatial-Temporal Graph Convolutional Network (ST-GCN) to automatically and effectively detect the high-risk areas in a city. Topological maps of urban communities carry the dataset in the model, which mainly includes two modules — spatial-temporal features extraction module and temporal feature extraction module — to extract the factors of theft crimes collectively. We have performed the experimental evaluation of the existing crime data from Chicago, America. The results show that the integrated model demonstrates positive performance in predicting the number of crimes within the sliding time range. |
| first_indexed | 2024-04-11T11:43:11Z |
| format | Article |
| id | doaj.art-520d94dfa52f44f3925141a383e97d8a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:43:11Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-520d94dfa52f44f3925141a383e97d8a2022-12-22T04:25:43ZengIEEEIEEE Access2169-35362020-01-01821722221723010.1109/ACCESS.2020.30419249276416Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCNXinge Han0https://orcid.org/0000-0002-3151-6590Xiaofeng Hu1https://orcid.org/0000-0003-3754-9913Huanggang Wu2Bing Shen3Jiansong Wu4School of Information Network Security, People’s Public Security University of China, Beijing, ChinaSchool of Information Network Security, People’s Public Security University of China, Beijing, ChinaSchool of International Police Studies, People’s Public Security University of China, Beijing, ChinaSchool of Information Network Security, People’s Public Security University of China, Beijing, ChinaSchool of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing, ChinaUrbanization has been speeding up social and economic transformations in urban communities, the smallest social units in a city. However, urbanization brings challenges to urban management and security. Therefore, a system of risk prediction of crimes may be essential to crime prevention and control in urban communities and its system improvement. To tackle crime-related problems in urban communities, this paper proposes a model of daily crime prediction by combining Long Short-Term Memory Network (LSTM) and Spatial-Temporal Graph Convolutional Network (ST-GCN) to automatically and effectively detect the high-risk areas in a city. Topological maps of urban communities carry the dataset in the model, which mainly includes two modules — spatial-temporal features extraction module and temporal feature extraction module — to extract the factors of theft crimes collectively. We have performed the experimental evaluation of the existing crime data from Chicago, America. The results show that the integrated model demonstrates positive performance in predicting the number of crimes within the sliding time range.https://ieeexplore.ieee.org/document/9276416/Crime predictioncrime ratesgraph convolutional networklong short-term memory networkspatial-temporal |
| spellingShingle | Xinge Han Xiaofeng Hu Huanggang Wu Bing Shen Jiansong Wu Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN IEEE Access Crime prediction crime rates graph convolutional network long short-term memory network spatial-temporal |
| title | Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN |
| title_full | Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN |
| title_fullStr | Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN |
| title_full_unstemmed | Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN |
| title_short | Risk Prediction of Theft Crimes in Urban Communities: An Integrated Model of LSTM and ST-GCN |
| title_sort | risk prediction of theft crimes in urban communities an integrated model of lstm and st gcn |
| topic | Crime prediction crime rates graph convolutional network long short-term memory network spatial-temporal |
| url | https://ieeexplore.ieee.org/document/9276416/ |
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