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Predicting criminal behavior with Lévy flights using real data from Bogotá

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  • Mateo Dulce Rubio

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.

Suggested Citation

  • Mateo Dulce Rubio, 2019. "Predicting criminal behavior with Lévy flights using real data from Bogotá," Documentos CEDE 17198, Universidad de los Andes, Facultad de Economía, CEDE.
    • Handle: RePEc:col:000089:017198
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    References listed on IDEAS

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    1. Santiago Gómez & Daniel Mejía & Santiago Tobón, 2021. "The Deterrent Effect Of Surveillance Cameras On Crime," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(2), pages 553-571, March.
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    3. Mohler, G. O. & Short, M. B. & Brantingham, P. J. & Schoenberg, F. P. & Tita, G. E., 2011. "Self-Exciting Point Process Modeling of Crime," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 100-108.
    4. Ehrlich, Isaac, 1973. "Participation in Illegitimate Activities: A Theoretical and Empirical Investigation," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 521-565, May-June.
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    More about this item

    Keywords

    Criminal behavior; Crime prediction model; Machine learning; Agent-based model.;
    All these keywords.

    JEL classification:

    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
    • H39 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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