TY - GEN
T1 - Predicting Motor Vehicle Theft in Santiago de Chile using Graph-Convolutional LSTM
AU - Esquivel, Nicolas
AU - Nicolis, Orietta
AU - Marquez, Billy Peralta
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Vehicle theft represents one of the most frequent crimes in Chile and in the world. In this work, we propose an application of the GCLSTM (Graph-Convolutional Long Short Term Memory) neural network that combines a graph convolutional model with a LSTM for the prediction of vehicle thefts in the metropolitan region of Chile the graph architecture considers the characteristics found in the neighbors to an area, assuming that the thefts of vehicles in nearby municipalities have similar patterns. For implementing the GCLSTM, first a smoothing technique based on LOESS regression was used for denoising the number of theft events for day, then the smoothed series of the last 30 days was considered as the input of the GCLSTM neural network for predicting the number of thefts in the following day the results provided a better performance of the GCLSTM compared to a traditional LSTM, achieving an R2 of 0.86.
AB - Vehicle theft represents one of the most frequent crimes in Chile and in the world. In this work, we propose an application of the GCLSTM (Graph-Convolutional Long Short Term Memory) neural network that combines a graph convolutional model with a LSTM for the prediction of vehicle thefts in the metropolitan region of Chile the graph architecture considers the characteristics found in the neighbors to an area, assuming that the thefts of vehicles in nearby municipalities have similar patterns. For implementing the GCLSTM, first a smoothing technique based on LOESS regression was used for denoising the number of theft events for day, then the smoothed series of the last 30 days was considered as the input of the GCLSTM neural network for predicting the number of thefts in the following day the results provided a better performance of the GCLSTM compared to a traditional LSTM, achieving an R2 of 0.86.
KW - Crime Prediction
KW - Deep Learning
KW - Graph Convolutional Long Short Term Memory
UR - http://www.scopus.com/inward/record.url?scp=85098639915&partnerID=8YFLogxK
U2 - 10.1109/SCCC51225.2020.9281174
DO - 10.1109/SCCC51225.2020.9281174
M3 - Conference contribution
AN - SCOPUS:85098639915
T3 - Proceedings - International Conference of the Chilean Computer Science Society, SCCC
BT - 2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020
PB - IEEE Computer Society
T2 - 39th International Conference of the Chilean Computer Science Society, SCCC 2020
Y2 - 16 November 2020 through 20 November 2020
ER -
