An Integrated Graph Model for Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism

Bibliographic Data

ID22033015
AuthorsMiaomiao Hou (0000-0003-0783-4940, China People's Public Security University), Xiaofeng Hu (0000-0003-3754-9913, China People's Public Security University, corresponding author), Jitao Cai (0009-0001-6704-8872, China University of Mining and Technology), Xinge Han (0000-0002-3151-6590, China University of Mining and Technology), Shuaiqi Yuan (0000-0003-2758-546X, Delft University of Technology)
Year2022
Volume11
Issue5
Pages294
Publication date2022-04-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi11050294
OpenAlexW4225256012
LanguageEN
Citations received1
References cited43

Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial–temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an “online” integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial–temporal features, topological graphs, and external features. Then, the “online” integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial–temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation

Artificial neural network · Autoregressive integrated moving average · Convolutional neural network · Data mining · Gradient boosting · Graph · Machine learning · Random forest · Recurrent neural network · Residual · Support vector machine · Time series · Computer Science · Crime Patterns and Interventions · Traffic and Road Safety · Traffic Prediction and Management Techniques · Artificial Intelligence

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