Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks
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Date
2020-10
Profesor/a Guía
Facultad/escuela
Idioma
en
Journal Title
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Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Nombre de Curso
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Abstract
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).
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Keywords
CNN and LSTM neural networks; crime prediction; deep learning; spatial and temporal structure
Citation
IEEE AccessOpen AccessVolume 8, Pages 209101 - 2091122020 Article number 9252093