The CR-ω+ classification algorithm for spatio-temporal prediction of criminal activity

S. Godoy-Calderón, H. Calvo, V. M. Martínez-Hernández, M. A. Moreno-Armendáriz

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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-ω 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.
Original languageAmerican English
Pages (from-to)5-25
Number of pages2
JournalJournal of Applied Research and Technology
StatePublished - 1 Apr 2010

Fingerprint Dive into the research topics of 'The CR-ω+ classification algorithm for spatio-temporal prediction of criminal activity'. Together they form a unique fingerprint.

Cite this