SciELO - Scientific Electronic Library Online

 
vol.8 número1Valuation Methodology for Technology Developed at Academic R&D Groups índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Journal of applied research and technology

versão On-line ISSN 2448-6736versão impressa ISSN 1665-6423

Resumo

GODOY-CALDERON, S.; CALVO, H.; MARTINEZ-HERNANDEZ, V. M.  e  MORENO-ARMENDARIZ, M. A.. The CR-Ω+ Classification Algorithm for Spatio-Temporal Prediction of Criminal Activity. J. appl. res. technol [online]. 2010, vol.8, n.1, pp.5-23. ISSN 2448-6736.

We present a spatio-temporal prediction model that allows forecasting of the criminal activity behavior in a particular region by using supervised classification. The degree of membership of each pattern is interpreted as the forecasted increase or decrease in the criminal activity for the specified time and location. The proposed forecasting model (CR-Ω+) is based on the family of Kora-Q Logical-Combinatorial algorithms operating on large data volumes from several heterogeneous sources using an inductive learning process. We propose several modifications to the original algorithms by Bongard and Baskakova and Zhuravlëv which improve the prediction performance on the studied dataset of criminal activity. We perform two analyses: punctual prediction and tendency analysis, which show that it is possible to predict punctually one of four crimes to be perpetrated (crime family, in a specific space and time), and 66% of effectiveness in the prediction of the place of crime, despite of the noise of the dataset. The tendency analysis yielded an STRMSE (Spatio-Temporal RMSE) of less than 1.0.

Palavras-chave : Logical-combinatorial pattern recognition; forecasting models; supervised classification; crime analysis.

        · resumo em Espanhol     · texto em Inglês     · Inglês ( pdf )

 

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons