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Predicting criminal behavior with Lévy flights using real data from Bogotá
Abstract

I use residential burglary data from Bogota, Colombia, to fit an agent-based model following truncated L´evy flights (Pan et al., 2018) elucidating criminal rational behavior and validating repeat/near-repeat victimization and broken windows effects. The estimated parameters suggest that if an average house or its neighbors have never been attacked, and it is suddenly burglarized, the probability of a new attack the next day increases, due to the crime event, in 79 percentage points. Moreover, the following day its neighbors will also face an increment in the probability of crime of 79 percentage points. This effect persists for a long time span. The model presents an area under the Cumulative Accuracy Profile (CAP) curve, of 0.8 performing similarly or better than state-of-the-art crime prediction models. Public policies seeking to reduce criminal activity and its negative consequences must take into account these mechanisms and the self-exciting nature of crime to effectively make criminal hotspots safer.

Autores:
Mateo Dulce Rubio
Palabras clave:
Criminal behavior, Crime prediction model, Machine learning, Agent-based model.
Archivo:
Año:
2019
Mes:
Febrero
Número:
11