S. Godoy–Calderón2, H. Calvo*1,2, V. M. Martínez–Hernández2, M. A. Moreno–Armendáriz2
1 Nara Institute of Science and Technology, Takayama, Ikoma, Nara 630–0192, Japan sgodoyc@cic.ipn.mx, *hcalvo@cic.ipn.mx, *calvo@is.naist.jp mhernandezb07@sagitario.cic.ipn.mx, marco_moreno@cic.ipn.mx.
2 Centro de Investigación en Computación CIC–IPN. Av. Juan de Dios Bátiz s/n esq. Manuel Othón de Mendizábal, 07738, Mexico City, Mexico.
ABSTRACT
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.
]]> Keywords: Logical–combinatorial pattern recognition, forecasting models, supervised classification, crime analysis.
RESUMEN
Presentamos un modelo de predicción espacio–temporal que permite la predicción de la actividad criminal dentro de la región estudiada usando clasificación supervisada. El grado de pertenencia de cada patrón es interpretado como el incremento o decremento previsto de la actividad criminal para un tiempo y lugar específico. El modelo propuesto de predicción CR–Ω+ está basado en la familia de los algoritmos Lógico–Combinatorios Kora–Ω para clasificación supervisada. Estos operan sobre volúmenes grandes de datos obtenidos a partir de fuentes heterogéneas de información, con un proceso inductivo de aprendizaje. Proponemos diversas modificaciones al algoritmo original de Bongard, así como el de Baskakova y Zhuravlëv, las cuales mejoran el desempeño de la predicción en el conjunto de datos estudiados de actividad criminal. Realizamos dos análisis: predicción puntual y análisis de tendencias, los cuales muestran que es posible predecir puntualmente uno de cuatro crímenes a ser perpetrados (por familia del crimen) en un tiempo y espacio específicos, así como un 66% de predicción del lugar del crimen, a pesar del ruido en el conjunto de datos de entrada. El análisis de tendencias dio como resultado un RMSE Espacio–Temporal (STRMSE) menor a 1.0.
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Agradecimientos
We thank the support of the Mexican Government (SNI, SIP–IPN, COFAA–IPN, and PIFI–IPN), CONACYT, the Japanese Government and the President of the Municipality of Cuautitlan Izcalli, David Ulises Guzmán Palma, for their support and help in obtaining the data used in this research. The second author is currently a JSPS fellow.
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