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A Bayesian Network Profiler for Wildfire Arsonists

  • Rosario DelgadoEmail author
  • José Luis González
  • Andrés Sotoca
  • Xavier-Andoni Tibau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

Arson-caused wildfires have a rate of clarification that is extremely low compared to other criminal activities. This fact made evident the importance of developing methodologies to assist investigators in the criminal profiling. For that we introduce Bayesian Networks (BN), which have only recently be applied to criminal profiling and never to arsonists. We learn a BN from data and expert knowledge and, after validation, we use it to predict the profile (characteristics) of the offender from the information about a particular arson-caused wildfire, including confidence levels that represent expected probabilities.

Keywords

Bayesian network Criminal profiling Expert system Wildfire arson 

Notes

Acknowledgments

The authors wish to thank the anonymous referees for careful reading and helpful comments that resulted in an overall improvement of the paper. They also would express their acknowledgment to the Prosecution Office of Environment and Urbanism of the Spanish state for providing data and promote research.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Rosario Delgado
    • 1
    Email author
  • José Luis González
    • 2
  • Andrés Sotoca
    • 3
  • Xavier-Andoni Tibau
    • 4
  1. 1.Departament de MatemàtiquesUniversitat Autònoma de BarcelonaCerdanyola del VallèsSpain
  2. 2.Gabinete de Coordinación y EstudiosSecretaría de Estado de SeguridadMadridSpain
  3. 3.Sección de Análisis del Comportamiento DelictivoUnidad Técnica de Policía JudicialMadridSpain
  4. 4.Facultat d’Economia i Empresa, Institut d’Economia de BarcelonaUniversitat de BarcelonaBarcelonaSpain

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