Authors:
Felipe A. L. Soares
;
Tiago B. Silveira
and
Henrique C. Freitas
Affiliation:
Graduate Program in Informatics, Pontifícia Universidade Católica de Minas Gerais (PUC Minas), Belo Horizonte, MG, Brazil
Keyword(s):
Crime Rate Prediction, Mathematical Models, Artificial Neural Networks, SARIMA, Time Series, Knowledge Discovery.
Abstract:
The fight against crime in Brazilian cities is an extremely important issue and has become a priority agenda in public, statutory or municipal discussions. Even so, reducing cases of violence is a complex task in large Brazilian cities, such as Rio de Janeiro and São Paulo, as these large cities have vast criminal points. Therefore, this paper presents the steps followed in the process of knowledge discovery applied to prediction of crime rate numbers in different regions of São Paulo city in order to better understand it and distribute the security forces more efficiently. Then, a hybrid model composed of an Artificial Neural Network and the SARIMA mathematical model was applied to databases related to different areas of the city. The average results showed assertiveness rates of 83.12% and 76.78% and root mean square deviation of 1.75 and 2.16 for two different tests.