Abstract
Predicting the number of crimes that will be committed in a certain geographical area is important for the management of resources destined for crime prevention. This research develops two predictive models of time series for the geographic prediction of property crimes in two districts of Chile Talcahuano and Hualpén. The models investigated were Neural Networks and SARIMA. Both models were trained and tested with the information provided by the Regional Prosecutor's Office of BioBío, Chile. The information contains the crimes against property committed in both districts between 2009 and 2019. The models were compared using the MAPE, MAD, and MSE forecast error measures. The comparison of the results does not show statistically significant differences between the results of both models.
 Keywords: Predictive Model, Time Series, Neural Networks, Crime Analitycs.
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