Spatio-temporal prediction of Baltimore crime events using CLSTM neural networks
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Título
Spatio-temporal prediction of Baltimore crime events using CLSTM neural networksFecha de publicación
2020-11-09Editor
IEEEISSN
2169-3536Cita bibliográfica
N. Esquivel, O. Nicolis, B. Peralta and J. Mateu, "Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks," in IEEE Access, vol. 8, pp. 209101-209112, 2020, doi: 10.1109/ACCESS.2020.3036715.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/document/9252093Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of
crime events. ... [+]
Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of
crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of
police resources and surveillance. Deep learning techniques are considered efficient tools to predict future
events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: ‘‘street robbery’’ and ‘‘larceny’’. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR) [-]
Publicado en
IEEE Access, vol. 8 (2020)Entidad financiadora
Chilean Fondecyt | Ministerio de Ciencia e Innovación
Código del proyecto o subvención
ID1201478 | PID2019-107392RB-I00
Derechos de acceso
info:eu-repo/semantics/openAccess
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